MX347370B

Generating a suitable model for estimating patient radiation dose resulting from medical imaging scans.

Abstract

Techniques are disclosed for estimating patient radiation exposure during computerized tomography (CT) scans. More specifically, embodiments of the invention provide efficient approaches for generating a suitable patient model used to make such an estimate, to approaches for estimating patient dose by interpolating the results of multiple simulations, and to approaches for a service provider to host a dose estimation service made available to multiple CT scan providers.

MX347370B, drawing sheet 1 — Sheet 1 of 56

Term

6.7 yearsleft in the term

Expires 5 June 2033.

34 claims: 7 independent, 27 dependent

1
CLAIMS REIVINDICACIONES 1. A method implemented by computer to generate a model to create images that corresponds to a first individual, characterized in that it comprises the steps of:1. Un método implementado por computadora para generar un modelo para crear imágenes que corresponde a un primer individuo, caracterizado porque comprende los pasos de: seleccionar, con base en un análisis de una pluralidad de escaneos para crear imágenes que corresponden a uno o mas segundos individuos, una pluralidad de los escaneos para crear imágenes analizados para asociar con un fantasma inicial para crear imágenes como una pluralidad correspondiente de imágenes localizadoras de referencia, en donde la selección se basa en una similitud determinada de geometría, tamaño y posición de los escaneos para crear imágenes analizados con el fantasma inicial para crear imágenes;selecting, based on an analysis of a plurality of scans to create images corresponding to one or more second individuals, a plurality of the scans to create analyzed images to associate with an initial ghost to create images as a corresponding plurality of locator images of reference, where the selection is based on a certain similarity of geometry, size and position of scans to create images analyzed with the initial phantom to create images;seleccionar el fantasma inicial para crear imágenes para el primer individuo que recibe un escaneo para crear imágenes;selecting the initial imaging ghost for the first individual to receive an imaging scan;receive one or more scout images of the first individual;recibir una o más imágenes exploradoras del primer individuo;determinar una transformación entre al menos una de la pluralidad de imágenes localizadoras de referencia y al menos una de las imágenes exploradoras del primer individuo;y deformar el fantasma inicial para crear imágenes basado en la transformación determinada. determining a transformation between at least one of the plurality of reference locator images and at least one of the first individual's scanner images;and deform the initial ghost to create images based on the determined transformation.
10
A computer-implemented method to generate a model to create images that corresponds to an individual, characterized in that it comprises the steps of:10. Un método implementado por computadora para generar un modelo para crear imágenes que corresponde a un individuo, caracterizado porque comprende los pasos de: seleccionar un fantasma inicial para crear imágenes de un individuo que recibe un escaneo de tomografía computarizada (CT) realizado por un aparato de escaneo de CT;selecting an initial phantom to create images of an individual receiving a computed tomography (CT) scan performed by a CT scanning apparatus;segmentar un escaneo de CT de referencia asociado con el individuo para identificar un volumen tridimensional (3D) de una pluralidad de puntos de referencia anatómicos del individuo presente en el escaneo de CT de referencia;segmenting a reference CT scan associated with the individual to identify a three-dimensional (3D) volume from a plurality of anatomical landmarks of the individual present in the reference CT scan;for at least one of the plurality of anatomical landmarks, determining a centroid of the respective 3D volume;para al menos uno de la pluralidad de puntos de referencia anatómicos, determinar un centroide del respectivo volumen 3D;hacer coincidir uno o más de los puntos de referencia anatómicos identificados en el escaneo de CT de referencia segmentado con los puntos de referencia anatómicos correspondientes en el fantasma inicial para crear imágenes;y deformar el fantasma inicial para crear imágenes basado en los puntos de referencia anatómicos coincidentes, donde deformar el fantasma inicial para crear imágenes basado en los puntos de referencia anatómicos coincidentes comprende: matching one or more of the anatomical landmarks identified on the segmented landmark CT scan to the corresponding anatomical landmarks on the initial phantom to create images;and deforming the initial phantom to create images based on the matching anatomical landmarks, where deforming the initial phantom to create images based on the matching anatomical landmarks comprises: determinar un mapa de desplazamiento 3D que representa el desplazamiento del al menos un centroide de los puntos de referencia anatómicos identificados a los centroides de los correspondientes puntos de referencia anatómicos del fantasma inicial para crear imágenes;determining a 3D displacement map representing the displacement from the at least one centroid of the identified anatomical landmarks to the centroids of the corresponding anatomical landmarks of the initial phantom to create images;voxelizar el fantasma inicial para crear imágenes;voxelize the initial ghost to create images;IMPI IMPI ΙΝΓΠΤυΤΟ MEXICAN ΙΝΓΠΤυΤΟ MEXICANO DI LA INDUSTRIAL PROPERTY transform the initial ghost to create image & ^ QX £] j2a¿QjQg | ^ j¡ ^ match the 3D displacement map;and determining whether two or more of the corresponding anatomical landmarks of the initial imaging phantom occupy the same physical volume within the initial phantom to create transformed voxelized images. DI LA PROPIEDAD INDUSTRIAL transformar el fantasma inicial para crear imágane&^QX£]j2a¿QjQg|^j¡^ coincida con el mapa de desplazamiento 3D;y determinar si dos o más de los correspondientes puntos de referencia anatómicos del fantasma inicial para crear imágenes ocupan un mismo volumen físico dentro del fantasma inicial para crear imágenes voxelizado transformado.
14
A non-transient computer-readable storage medium, which contains a method, which, when executed by a processor performs a method to generate a model to create images corresponding to a first individual, the method comprises:14. Un medio de almacenaje legible por computadora no transitorio, que contiene un método, el cual, cuando es ejecutado por un procesador realiza un método para generar un modelo para crear imágenes que corresponde a un primer individuo, el método comprende: λ n nwrrrur »Mexican λ n nwrrrur» mexicano U DE LA KONEDAP O— U OF THE KONEDAP O— INDUSTRIA! TOfc M seleccionar, con base en un análisis de una pluralidad de escaneos para crear imágenes que corresponden a uno o mas segundos individuos, una pluralidad de los escaneos para crear imágenes analizados para asociar con un fantasma inicial para crear imágenes como una pluralidad correspondiente de imágenes localizadoras de referencia, en donde la selección se basa en una similitud determinada de geometría, tamaño y posición de los escaneos para crear imágenes analizados con el fantasma inicial para crear imágenes;INDUSTRY! TOfcM selecting, based on an analysis of a plurality of scans to create images corresponding to one or more second individuals, a plurality of the scans to create analyzed images to associate with an initial ghost to create images as a corresponding plurality of locator images of reference, where the selection is based on a certain similarity of geometry, size and position of scans to create images analyzed with the initial phantom to create images;seleccionar el fantasma inicial para crear imágenes para el primer individuo que recibe un escaneo para crear imágenes;selecting the initial imaging ghost for the first individual to receive an imaging scan;receive one or more scout images of the first individual;recibir una o más imágenes exploradoras del primer individuo;determinar una transformación entre al menos una de la pluralidad de imágenes localizadoras de referencia y al menos una de las imágenes exploradoras del primer individuo;y deformar el fantasma inicial para crear imágenes basado en la transformación determinada. determining a transformation between at least one of the plurality of reference locator images and at least one of the first individual's scanner images;and deform the initial ghost to create images based on the determined transformation.
22
The computer-readable storage medium in accordance with the 22. El medio de almacenaje legible por computadora de conformidad con la IMPI IMPI INSTITUW MEXICANO DE LA DEEDAD INDUmtlAL claim 14, further characterized in that the initial ghost to create images is selected based on an age and gender of the first individual. INSTITUW MEXICANO DE LA FROMEDAD INDUmtlAL reivindicación 14, caracterizado además porque el fantasma inicial para crear imágenes se selecciona basándose en una edad y un género del primer individuo.
23
2. 3. A non-transient computer-readable storage medium, which stores a method, which, when executed by a processor performs a method to generate a model to create images corresponding to an individual, the method comprises:23. Un medio de almacenaje legible por computadora no transitorio, que almacena un método, el cual, cuando es ejecutado por un procesador realiza un método para generar un modelo para crear imágenes que corresponde a un individuo, el método comprende: seleccionar un fantasma inicial para crear imágenes de un individuo que recibe un escaneo de tomografía computerizada (CT) realizado por un aparato de escaneo de CT;selecting an initial phantom to image an individual receiving a computed tomography (CT) scan performed by a CT scanning apparatus;segmentar un escaneo de CT de referencia asociado con el individuo para identificar un volumen tridimensional (3D) de una pluralidad de puntos de referencia anatómicos del individuo presente en el escaneo de CT de referencia;segmenting a reference CT scan associated with the individual to identify a three-dimensional (3D) volume from a plurality of anatomical landmarks of the individual present in the reference CT scan;For at least one of the plurality of anatomical landmarks, determine a centroid of the respective 3D volume, match one or more of the anatomical landmarks identified on the segmented landmark CT scan with the corresponding anatomical landmarks in the initial ghost to create images;and deforming the initial phantom to create images based on the matching anatomical landmarks, where deforming the initial phantom to create images based on the matching anatomical landmarks comprises: para al menos uno de la pluralidad de puntos de referencia anatómicos, determinar un centroide del respectivo volumen 3D¡ hacer coincidir uno o más de los puntos de referencia anatómicos identificados en el escaneo de CT de referencia segmentado con los puntos de referencia anatómicos correspondientes en el fantasma inicial para crear imágenes;y deformar el fantasma inicial para crear imágenes basado en los puntos de referencia anatómicos coincidentes, donde deformar el fantasma inicial para crear imágenes basado en los puntos de referencia anatómicos coincidentes comprende: determinar un mapa de desplazamiento 3D que representa el desplazamiento del al menos un centroide de los puntos de referencia anatómicos identificados a los centroides de los correspondientes puntos de referencia anatómicos del fantasma inicial para crear imágenes;determining a 3D displacement map representing the displacement of the at least one centroid of the identified anatomical landmarks to the centroids of the corresponding anatomical landmarks of the initial phantom to create images;voxelizar el fantasma inicial para crear imágenes;y transformar el fantasma inicial para crear imágenes voxelizado para que coincida con el mapa de desplazamiento 3D;voxelize the initial ghost to create images;and transform the initial ghost to create voxelized images to match the 3D displacement map;IMPIAS 'NSTTTUTOMBUCANo IMPIAS 'NSTTTUTOMBUCANo DE U FIOPIEDAD Λ iNDumtiAi determinar si dos o más de los correspondientes—pmtoe· gfe -referencia, anatómicos del fantasma inicial para crear imágenes ocupan un mismo volumen físico dentro del fantasma inicial para crear imágenes voxelizado transformado. DE U FIOPIEDAD Λ iNDumtiAi determine whether two or more of the corresponding — pmtoe · gfe -reference, anatomical of the initial phantom to create images occupy the same physical volume within the initial phantom to create transformed voxelized images.
27
A system, characterized in that it comprises:27. Un sistema, caracterizado porque comprende: a processor;and a memory that stores a method configured to perform a method to generate a model to create images corresponding to a first individual, the method comprises: un procesador;y una memoria que almacena un método configurado para realizar un método para generar un modelo para crear imágenes que corresponden a un primer individuo, el método comprende: 44 IMPI ^ • Ε THE nOHETY 44 IMPI^ •Ε LA nOHEDAD INDUSTRIAL seleccionar, con base en un análisis de una pluralidad de escaneos para crear imágenes que corresponden a uno o mas segundos individuos, una pluralidad de los escaneos para crear imágenes analizados para asociar con un fantasma inicial para crear imágenes como una pluralidad correspondiente de imágenes localizadores de referencia, en donde la selección se basa en una similitud determinada de geometría, tamaño y posición de los escaneos para crear imágenes analizados con el fantasma inicial para crear imágenes;INDUSTRIAL select, based on an analysis of a plurality of scans to create images corresponding to one or more second individuals, a plurality of the scanned images to create analyzed to associate with an initial ghost to create images as a corresponding plurality of locator images reference, where the selection is based on a certain similarity of geometry, size and position of scans to create images analyzed with the initial phantom to create images;seleccionar el fantasma inicial para crear imágenes para el primer individuo que recibe un escaneo para crear imágenes;selecting the initial imaging ghost for the first individual to receive an imaging scan;receive one or more scout images of the first individual;recibir una o más imágenes exploradoras del primer individuo;determinar una transformación entre al menos una de la pluralidad de imágenes localizadoras de referencia y al menos una de las imágenes exploradoras del primer individuo;y deformar el fantasma inicial para crear imágenes basado en la transformación determinada. determining a transformation between at least one of the plurality of reference locator images and at least one of the first individual's scanner images;and deform the initial ghost to create images based on the determined transformation.
28
A system, characterized in that it comprises:28. Un sistema, caracterizado porque comprende: a processor;and a memory that stores a method configured to perform a method to generate a model to create images corresponding to a first individual, the method comprises: un procesador;y una memoria que almacena un método configurado para realizar un método para generar un modelo para crear imágenes que corresponden a un primer individuo, el método comprende: seleccionar un fantasma inicial para crear imágenes de un individuo que recibe un escaneo de tomografía computarizada (CT);selecting an initial phantom to create images of an individual receiving a computed tomography (CT) scan;segmentar un escaneo de CT de referencia asociado con el individuo para identificar un volumen tridimensional (3D) de una pluralidad de puntos de referencia anatómicos del individuo presente en el escaneo de CT de referencia;segmenting a reference CT scan associated with the individual to identify a three-dimensional (3D) volume from a plurality of anatomical landmarks of the individual present in the reference CT scan;for at least one of the plurality of anatomical landmarks, determining a centroid of the respective 3D volume;para al menos uno de la pluralidad de puntos de referencia anatómicos, determinar un centroide del respectivo volumen 3D;IMPI IMPI MmurtMiBCANo MmurtMiBCANo DE LA nORIDAD INDUSTRIAL hacer coincidir uno o más de los puntos de referencia anatómicos identificados en el escaneo CT de referencia segmentado con los puntos de referencia anatómicos correspondientes en el fantasma inicial para crear imágenes;y deformar el fantasma inicial para crear imágenes basado en los puntos de referencia anatómicos coincidentes, donde deformar el fantasma inicial para crear imágenes basado en los puntos de referencia anatómicos coincidentes comprende: INDUSTRIAL NORITY to match one or more of the anatomical landmarks identified on the segmented landmark CT scan with the corresponding anatomical landmarks on the initial phantom to create images;and deforming the initial phantom to create images based on the matching anatomical landmarks, where deforming the initial phantom to create images based on the matching anatomical landmarks comprises: determinar un mapa de desplazamiento 3D que representa el desplazamiento del al menos un centroide de los puntos de referencia anatómicos identificados a los centroides de los correspondientes puntos de referencia anatómicos del fantasma inicial para crear imágenes;determining a 3D displacement map representing the displacement from the at least one centroid of the identified anatomical landmarks to the centroids of the corresponding anatomical landmarks of the initial phantom to create images;voxelizar el fantasma inicial para crear imágenes;y transformar el fantasma inicial para crear imágenes voxelizado para que coincida con el mapa de desplazamiento 3D;voxelize the initial ghost to create images;and transform the initial ghost to create voxelized images to match the 3D displacement map;determinar si dos o más de los correspondientes puntos de referencia anatómicos del fantasma inicial para crear imágenes ocupan un mismo volumen físico dentro del fantasma inicial para crear imágenes voxelizado transformado. determine whether two or more of the corresponding anatomical landmarks of the initial imaging phantom occupy the same physical volume within the initial phantom to create voxelized transformed images.

Independent claims7

209 paragraphs in 35 sections, as filed

(54) Title: GENERATE AN APPROPRIATE MODEL TO ESTIMATE THE RADIATION DOSE OF A PATIENT RESULTING FROM SCANNING TO CREATE MEDICAL IMAGES.

(54) Title: GENERATING A SUITABLE MODEL FOR ESTIMATING PATIENT RADIATION DOSE RESULTING FROM MEDICAL IMAGING SCANS.

(57) Summary

Techniques for estimating a patient's radiation exposure during computed tomography (CT) scans are described. More specifically, the embodiments of the invention provide efficient approaches for generating a suitable patient model used to make such an estimate, approaches for estimating a patient's dose by interpolation of multiple simulations, and approaches for a service provider to host a service of dose estimation that is available to multiple CT scan providers.

(57) Abstract

Techniques are disclosed for estimating patient radiation exposure during computerized tomography (CT) scans. More specifically, embodiments of the invention provide efficient approaches for generating a suitable patient model used to make such an estímate, to approaches for estimating patient dose by interpolating the results of multiple simulations, and to approaches for a Service provider to host a dose estimation Service made available to multiple CT providers.

Institute

Mexican Property

Industrial

PATENT TITLE NO. 347370

Owner (s): BAYER HEALTHCARE LLC

Address: 100 Bayer Boulevard, Whippany, New Jersey, 07981-0915, USA

Name: GENERATE AN APPROPRIATE MODEL TO ESTIMATE THE RADIATION DOSE OF A PATIENT RESULTING FROM SCANNING TO CREATE MEDICAL IMAGES.

Classification: lnt.CI.8: A61B6 / 03: A61B6 / 10; G06T3 / 00: G06T7 / 00

Inventor (s): GREGORY COUCH, JAMES COUCH

REQUEST

Number: International filing date:

MX / a / 2015/003173 December 8, 2011

Divisional Patent Number: 330442

PRIORITY

Country: Date: Number:

US December 8, 2010 61 / 420,834

Validity: Twenty years

Expiration Date: December 8, 2031

The reference patent is granted on the basis of articles 1, 2, section V, 6, section III, and 59 of the Industrial Property Law.

A% · <; : 'xAaaaB' í®

In accordance with article 23 of the Industrial Property Law, this patent is valid for twenty years, non-extendable, counted from the filing date of the international application and will be subject to the payment of the fee to keep the rights in force. .

Whoever signs this title does so based on the provisions of articles 5 · fractions lll and 7 bis 2 of the Industrial Property Law (Official Gazette of the Federation (DOF) 06/27/1991, amended on 02 / 08/1994, 10/25/1996, 12/26/1997, 05/17/1999, 01/26/2004, 06/16/2005, 01/25/2006, 05/06/2009, 06/01 / 2010, 18 / O6 / 201O, 08/28/2010, 01/27/2012 and 04/09/2012); Articles 1 * 3rd section V subsection a), 4or and 12th sections I and III of the Regulations of the Mexican Institute of Industrial Property (DOF 12/14/199, amended on 07/01/2002, 07/01/2004, 07/28/2004 and 09/07/2007) ; Articles 1 », 3 · 4» 5 · section V Subsection a), 18 sections I and III and 30 of the Organic Statute of the Mexican Institute of Industrial Property (DOF 12/27/1999, amended on 10/10/2002, 07/29/2004, 08/04/2004 and 09/13/2007); 1, 3 and 5 subsection a) of the Agreement that delegates powers to the Deputy General Directors, Coordinator, Divisional Directors, Heads of Regional Offices, Divisional Deputy Directors, Departmental Coordinators and other subordinates of the Mexican Institute of Industrial Property. (DOF 12/15/1999, amended on 02/04/2000, 07/29/2004, 08/04/2004 and 09/13/2007).

MX / 2017/34564

GENERATE AN APPROPRIATE MODEL TO ESTIMATE THE DF RADIATION DOSE ..... OF A PATIENT RESULTING FROM SCANNING TO CREATE IMAGES

MEDICAL

FIELD OF THE INVENTION

The embodiments of the invention are generally directed to approaches for estimating a patient's radiation exposure during computed tomography (CT) scans.

BACKGROUND OF THE INVENTION

As is known, a CT scanning system uses ionizing radiation (X-rays) to generate images of tissues, organs, and other structures within a body. X-ray data resulting from a CT scan can be converted to images on a computer screen. For example, CT scanning provides a collection of data used to create a three-dimensional (3D) volume corresponding to the scanned portion of a patient's body. The 3D volume is then sliced to create images of body tissue at small intervals along an axis of the patient's body. Such slices may include lateral and transverse slices (as well as other slices) depending on the tissues or structures that are imaged.

The use of CT scans and ionizing radiation to create medical images has grown exponentially over the past decade. And modern techniques such as CT scanning provide far more detailed and valuable diagnostic information than conventional X-ray imaging. Simultaneously however, patients are exposed to substantially larger doses of radiation. For example, a typical chest CT will expose a patient to 100-250 times the dose of a conventional chest X-ray depending on the voltage and current of the CT scanning system, the protocol followed to perform the procedure, and the size and the shape of the patient being scanned.

Despite the resulting increased use of radiation scans) the amount of radiation a patient is exposed to during one procedure, and more importantly, the cumulative dose during various procedures are not parameters that are regularly followed for a patient, and neither are These parameters are an easily accessible part of the patient's medical records. This occurs in part because the amount of radiation absorbed by internal organs and tissues cannot be measured in living patients directly as part of a CT scan, and the results obtained from cadavers, while more accurate, do not adequately match the dose. absorption in living tissues.

Similarly, currently used dose estimating approaches also provide inaccurate results. For example, one approach is to rely on a limited number of ghosts to create physical images to represent a given patient. However, the available imaging ghosts do not adequately represent the wide variation in size and weight of people in the population of individuals receiving CT scans. As a result, single-point surface measurements are currently made in most cases where the dose is estimated at all. However, this leads to poor and widely diverse results, depending on where the single point dose is measured. More generally, surface measurements of radiation exposure do not provide an accurate measure of actual absorption for internal tissues, organs, and structures.

BRIEF DESCRIPTION OF THE INVENTION

The modalities provide techniques for estimating a patient's radiation exposure during computed tomography (CT) scans. One modality includes a computer-implemented method of generating a model for creating images that corresponds to an individual. This method can generally include selecting an initial phantom to image an individual who receives an imaging scan, wherein the phantom for

IMPI *

iNfrmMJMixiomo create images has one or more locator images aooaiadew and iLuibulffWC plus individual explorer images. This method may further include determining a transformation between at least one of the locator images associated with the ghost to create images and deforming the initial ghost to create images based on the transformation.

In one particular modality, the imaging scan is a computed tomography (CT) scan, in other cases the imaging scan is a fluoroscopy scan, PET scan, angiography scan, etc. This method may further include receiving a set of parameters describing the scan to create images and the CT scanning apparatus that is used to perform the CT scan, simulate the scan to create images using the phantom to create deformed images, and the set parameters received, and estimate, based on simulation, the amounts of radiation absorbed by the individual as a result of scanning to create images. In one particular mode, the simulation is a Monte Carlo simulation.

Another embodiment includes a method of generating a model for creating images that corresponds to an individual. This method can generally include selecting an initial phantom to create images of an individual receiving a computed tomography (CT) scan and segmenting a baseline CT scan associated with the individual to identify a three-dimensional (3D) volume of a plurality of points. anatomical landmarks of the individual present on the baseline CT scan. This method may further include matching one or more of the anatomical landmarks identified on the segmented landmark CT scan with the corresponding anatomical landmarks on the initial phantom to create images and deforming the initial phantom to create images based on the images. coincident anatomical landmarks.

Additional modalities include a computer-readable storage medium for storing an application, which, when run on a processor, performs the above-mentioned method as well as a

system that has a processor and a memory that stores a program of a company information asset management application, which, when used

IMPI t JroSnt »'executes on the processor, performs the method mentioned above.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the manner in which the aforementioned aspects are achieved and can be understood in detail, a more particular description of the embodiments of the invention, briefly summarized above, may be had with reference to the attached drawings. Note however, that the accompanying drawings illustrate only typical embodiments of the invention and are therefore not limiting of its scope, so that the invention may admit other equally effective embodiments.

Figure 1 shows an example of a CT scanning system and related computer systems configured to provide estimates of patient radiation doses, in accordance with one embodiment of the invention.

Figure 2 shows an example of an imaging system used to obtain CT scan data, according to one modality.

Figure 3 shows an example of a dose estimation system used to estimate and track cumulative patient dose, according to one embodiment.

Figure 4 shows a method for generating a model suitable for estimating a patient's radiation dose resulting from CT scans, according to one modality.

Figure 5A shows an example of an image representing a deformable ghost, according to one embodiment.

Figure 5B shows an example of a two-dimensional (2D) reference image of a portion of a human body corresponding to the phantom shown in Figure 5A, according to one embodiment.

Figure 6 shows another method of generating an estimate of a patient's radiation dose resulting from CT scans, according to one modality.

Figure 7 shows an example of a phantom slice superimposed on a corresponding CT slice of a patient, according to one embodiment.

Figure 8 shows an example of a phantom cross section for creating images superimposed on a corresponding cross section CT of a patient, according to one embodiment.

Figure 9 shows an example of CT image segmentation and volume shift of phantom organs to create images, according to one modality.

Figure 10 shows a method for a dose estimation service to provide patient dose estimates to multiple CT scan providers, according to one modality.

Figure 11 shows an example of a computing infrastructure for a patient dose estimation service system configured to support multiple CT scan providers, according to one modality.

DETAILED DESCRIPTION OF THE INVENTION

The embodiments of the invention are generally directed to approaches for estimating a patient's radiation exposure during computed tomography (CT) scans. More specifically, the embodiments of the invention provide efficient approaches for generating a suitable patient model used to make such an estimate, approaches for estimating a patient's dose by interpolation of multiple simulations, and approaches for having a service provider host a service of dose estimation that is available to multiple CT scan providers. As described in detail below, the dose management system provides a simple system for tracking radiation dose across modalities and for presenting information to practitioners.

in a useful and easy to understand format. Routine consideration of cumulative dose when ordering 'pald UI3' | diagnostic imaging tests can lead to a more informed decision-making process and ultimately benefit patient care and safety.

In one embodiment, a virtual imaging phantom is generated to model a given patient receiving a CT scan. The virtual imaging phantom can be generated by deforming an existing mathematical phantom to better match the size, shape, and / or positions of the organs of a patient who is exposed to radiation on a CT scan. Initially, a math phantom can be selected based on, for example, the age and gender of the patient. Patient-specific geometry can be achieved by deforming the selected mathematical phantom using transformations obtained by locator analysis of scanning images of that patient. Note that in this context, as understood by one of ordinary skill in the art, a "locator" generally refers to a projection of a 2D image of a patient (typically an anterior / posterior X-ray image and / or a lateral X-ray). In such an approach, the chosen mathematical phantom may have its own reference set of locator images. The reference images for a given virtual phantom are selected to match the geometry, size, and position of that phantom (for example, arms up or to the side) and can be selected from images obtained from multiple individuals.

Image registration techniques are then used to map the points on the locator image of the patient to the points on the reference image (or images) associated with the virtual phantom. Doing this results in a set of transformations that can be used to warp the virtual phantom to better match the geometry of the patient. A similar approach involves using a 3D reference data set (selected CT scans) for the phantom and using 3D image registration techniques to map the points.

IMPI uemyiOMMUCANo DMAROHUAD in a CT scan of a given patient to the points? ΘΠ give Ul of reference associated with a given ghost.

Similarly, image segmentation can be used to identify a 3D volume within a CT scan corresponding to organs, tissues, or structures of interest in a CT scan of a patient. The 3D volume can be a bounding box, or a more accurate 3D volume considered to represent an organ, etc. Once identified, an offset can be determined between the organ's position in the phantom and the corresponding position on the patient's CT scan. Instead of working on individual image points (as in 2D / 3D image registration techniques) the image segmentation approach works by using larger 3D volumes of the CT image as data points to determine a transformation of a virtual ghost and a determined patient.

In each of these cases, the resulting hybrid phantom provides a much more accurate mathematical representation of a particular patient for use in a dose simulation than the unmodified phantoms alone. Once the transformations are determined, the hybrid virtual phantom can be used to simulate a given CT procedure for the patient. For example, the well-known Monte Carlo simulation techniques have been developed to estimate the dose absorbed by the organs for a virtual phantom. Such simulation techniques use the virtual phantom (transformed relative to a given patient), along with a number of adjustments related to the CT scanner model and the procedure to be performed in order to calculate accurate estimates of the absorbed doses by the organs. For example, a CT scanner can be modeled using kVp, i.e. peak kilovoltage, x-ray generator target angle, fan angle, collimation, slice thickness, focus to axis distance, flat filters (material and thickness) , and filters to shape the beam (material and geometry). Of course, these (and other parameters) can be selected as available or as necessary to meet the needs of a particular case.

However, estimate the dose absorbed by the organs using। ina_ ^ imuiaa¡én ri * r Monte Carlo can require significant amounts of computational time, much longer than that required to perform an actual CT scan. Given the high utilization of CT scanning systems in many imaging facilities, in cases where an estimate of a total cumulative dose should not exceed a prescribed maximum, this delay is simply not manageable. Even in cases where estimation is not used prior to performing a given procedure, unless the patient's dose estimates can be determined relatively in the same order of time as that required to perform a procedure, then keep an estimate record of Dosage for a given scan system becomes unmanageable - as the simulations will simply fall further and further behind the current scans being performed. This problem grows exponentially for a SaaS provider hosting a cloud dose estimating service for multiple imaging facilities.

Accordingly, in one embodiment, patient dose estimates determined by a given procedure can be generated by interpolating between two (or more) previously completed simulations. If no “close” simulations are available, then the hybrid virtual phantom, CT scanner, and procedural data can be added to a queue full of Monte Carlo simulations to be performed. Over time, a larger library of simulations allows dose estimates to be provided in real time as procedures are scheduled and performed. Doing this allows you to capture the cumulative dose amounts for a given patient, as well as the cumulative dose limits to be observed.

Additionally, in one embodiment, a software as a service (SaaS) or cloud provider model can be used to perform dose estimates, maintain a library of calculated simulations, as well as run Monte Carlo simulations. In such a case, a CT scanning provider can supply the SaaS provider with the parameters of a CT procedure

IMPI

Q INTTTTtrfn NAUGANO and "UHonipA" determined. For example, client software (or even a secure web-based portal) in an imaging center can be used to supply the SaaS provider with a selected virtual ghost, along with transformations used to create a hybrid ghost modeling an individual. in particular and the equipment and protocol that should be used in performing a CT procedure. Upon receipt, the service provider can select the appropriate simulations from the library to interpolate and return an estimate of the dose absorbed by the patient's organs to the imaging center.

Importantly, the SaaS provider does not need to receive any current identifying information about a given patient or individual receiving a CT scan. Instead, the SaaS provider receives only information related to a virtual phantom and a CT system / procedure. As a result, the operations of the service provider may not require compliance with a variety of laws and / or regulations related to the privacy of personal health information. Furthermore, by providing dose estimates for multiple imaging centers, the resulting simulation library becomes more diverse and candidates for interpolation are much more likely to be found than in a simulation library generated solely from scanning procedures performed by a single imaging center. Furthermore, centralizing the Monte Carlo simulation library and simulations grants ghost enhancements, a Monte Carlo simulation engine, and interpolation techniques that are shared between all imaging centers using the cloud-based service. . Ultimately, this approach leaves the imaging center to maintain information tied to cumulative doses for specific patients. Allowing actual patient data to remain with each individual provider. At the same time, the SaaS provider can, of course, communicate with the centers to create images using a variety of standardized protocols for exchanging images and data, including, for example, Communications and

MIXICAN

PBOHECMD

INDUTHUM.

Digital Imaging in Medicine (DICOM), Image Archive and Communication Systems (PACS), International Health Standards Level Seven (HL7), ICD-9, ICD-10 Diagnostic and Procedure Codes, etc.

Additionally, the following description refers to the embodiments of the invention. However, it should be understood that the invention is not limited to the specific embodiments described. Instead, any combination of the following elements and features, whether related to the different modalities or not, are contemplated to implement and practice the invention. Furthermore, although the embodiments of the invention may achieve advantages over other possible solutions and / or over the prior art, whether or not a particular advantage is achieved by a particular embodiment is not limiting of the invention. Thus, the following aspects, features, embodiments and advantages are merely illustrative and are not considered elements or limitations of the appended claims except where explicitly mentioned in one (s) claim (s). Similarly, the reference to "the invention" should not be construed as a generalization of any subject matter of the invention described herein and should not be considered as an element or limitation of the appended claims except where it is explicitly mentioned in a claim (s).

As will be appreciated by one of ordinary skill in the art, aspects of the present invention may be incorporated as a computer program system, method, or product. Accordingly, aspects of the present invention may take the form of a fully hardware mode, a fully software mode (including firmware, permanent software, microcode, etc.), or a mode that combines software and hardware aspects that can all generally be referred to herein as a "circuit," "module," or "system. In addition, aspects of the present invention may take the form of a computer program product embedded in one or more computer-readable media having computer-readable program code embedded therein.

Any combination of one or more computer-readable media can be used. Computer-readable medium can be either a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or system, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media would include the following: an electrical connection that has one or more wires, a portable computer floppy disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable read-only memory compact disc (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the context of this document, a computer-readable storage medium can be any tangible medium that may contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The flow and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of computer program systems, methods, and products in accordance with various embodiments of the present invention. In this regard, each block in the flow and block diagrams can represent a module, segment or piece of code, which comprise one or more executable instructions to implement the specific logic function (s) ( s). In some alternative implementations the functions mentioned in the block may occur out of the order mentioned in the figures. For example, two blocks displayed in succession may, in fact, run substantially at the same time, or the blocks may sometimes run in the reverse order, depending on the functionality involved. Each block in the block diagrams and / or the illustrations of the

IMPI

ΙΜΤΠΤυΜ MUICAN · 9t u noraiMD rNoumiAi flowcharts, and combinations of blocks in the block diagrams and / or illustrations of the flowcharts can be implemented by specific-purpose hardware-based systems that perform the specific actions or functions, or combinations of special purpose hardware and computer instructions.

The embodiments of the invention can be provided to end users through a cloud computing infrastructure. Cloud computing generally refers to the provision of scalable computing resources as a service over a network. More formally, cloud computing can be defined as a computing capability that provides an abstraction between the computing resource and its underlying technical architecture (e.g., servers, storage, networks), allowing convenient on-demand network access to a Shared set of configurable computing resources that can be rapidly provisioned and freed up with minimal management effort or service provider interaction. In this way, cloud computing allows a user to access virtual computing resources (eg, storage, data, applications, and even fully virtualized computing systems) in "the cloud", regardless of the underlying physical systems. (or the locations of those systems) used to provide the computing resources.

Typically, cloud computing resources are provided to a user on a pay-as-you-go basis, where users are charged only for actually used computing resources (for example, an amount of storage space consumed by a user or a number of virtualized systems instantiated by the user). A user can access any of the resources that reside in the cloud at any time and from anywhere via the Internet. In the context of the present invention, a service provider may provide centers for imaging patient dose estimates in both prediction and information perspectives. For example, a dose estimation interface can be used to submit the virtual phantom and CT data to the cloud-based provider.

IMPI

The flow and block diagrams in the figures .iluotran Ια «iquileULncT; 'functionality and operation of possible implementations of computer program systems, methods and products according to various embodiments of the present invention. In this regard, each block in the flow and block diagrams can represent a module, segment or piece of code, which comprise one or more executable instructions to implement the specific logic function (s) ( s). It should further be noted that, in some alternative implementations, the functions mentioned in the block may occur out of the order mentioned in the figures. For example, two blocks displayed in succession may, in fact, run substantially at the same time, or the blocks may sometimes run in the reverse order, depending on the functionality involved. It will further be noted that each block in the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be implemented by hardware-based systems. specific purpose that perform the specific actions or functions, or combinations of specific purpose hardware and computer instructions. Furthermore, the particular embodiments of the invention described below feature a particular example of a computed tomography CT scanning system that uses a client-server architecture to provide dose estimation to an imaging set. It should be understood, however, that the techniques described herein may be adapted for use with other medical imaging technology that relies on exposing individuals to limited radiation doses as part of the imaging procedure (e.g., PET scans, conventional X-ray imaging, and fluoroscopy and angiography, etc.).

Figure 1 illustrates an example of a CT scanning environment 100 and related computer systems configured to provide patient radiation dose estimates, in accordance with one embodiment of the invention. As shown, the CT 100 scanning environment includes a scanning system

IMPI

INSTITUTE ΜβυΟΛΙΒΟ

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of CT 105, an imaging system 125, and a dose estimating system 130. Additionally, the dose estimating system 130 includes a ghost database for creating images 132 and a simulation library 134.

As is known, CT scanner 105 provides a device that is used to bombard a subject 120 with X-rays from X-ray source 110. X-rays emitted from X-ray source 110 pass through the tissues, organs, and structures of the subject 120 at different speeds (some of which are absorbed by such tissues, organs and structures) depending on the density and type of matter through which the X-rays pass. Sensors arranged with a ring 115 detect the amount of radiation passing through subject 120. The resulting sensor information is passed to imaging system 125. Imaging system 125 provides a computing device configured to receive, store, and generating images of the sensor data obtained from the CT scanner.

The imaging system 125 enables an operator to perform a particular CT procedure as well as receive data obtained from performing CT scans. For example, the imaging system 125 can be configured to open various structures in the body, based on its ability to block X-rays emitted from source 110. CT scan images (often referred to as "slices") are typically made relative to an axial or transverse plane, perpendicular to the elongated axis of the body. However, the CT scanner 105 can allow imaging data to be reformatted in various planes or as representations of volumetric structures (3D). Once a CT scan is performed, the imaging data generated by the CT scanner 105 can be stored allowing the resulting scan images to be reviewed or evaluated in other ways. In one embodiment, the data for creating images can be formatted using the well-known DICOM standard and stored in a PACS repository. In one embodiment, the dose estimating system 130 provides a computer system and software applications configured to estimate the number of doses.

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CT determined. Note that such an estimate can be done in a predictive sense (that is, before performing a scan) but can still be done after the fact.

In the predictive case, the dose estimation system 130 may provide an estimate of the patient's dose prior to performing a CT scan. Furthermore, in one embodiment, the dose estimation system 130 can be configured to automatically generate alerts based on the configurable thresholds. The criteria for generating an alert can use a rules engine that can take into account age, gender, ICD9 / ICD10 encoding, and other information about a particular patient or procedure (for example, a specific cumulative dose limit). More generally, dose thresholds can be flexible enough to reflect any legislative, institutional, or treatment requirements to monitor dose. In one embodiment, the resulting dose estimates can be stored as part of a patient's medical record / history maintained by an imaging center, hospital, or other provider.

In addition, dose thresholds can optionally be used to create incidence reports sent to appropriate practitioners. Incidence reports can include a description of a procedure and any dose estimates that exceed a rule or threshold along with any supplemental information necessary to provide context for a practitioner intervention or decision making. In one embodiment, such a report can be printed / emailed using a customizable XML template.

Imaging ghosts 132 can provide accepted mathematical models of portions of human tissue, organs, structures, etc. For example, imaging ghosts 132 can provide a set of non-uniform rational base splines (NURBS) used to create a three-dimensional (3D) model of a human body (or a portion thereof). Alternatively, ghosts to create images can be represented using

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constructive solid geometry (CSG) or other mathematical representation. Different imaging ghosts 132 can be provided to generally model individuals based on age and gender. However, as mentioned above, the virtual geometry and body shape of a ghost to create selected images that is based solely on age and / or gender may (or may not) correspond to the size, shape and positions of the organs from a real person having a CT procedure. Accordingly, in one embodiment, the dose estimation system 130 can be configured to warp a virtual phantom to better model a particular patient. Examples of modalities for deforming a ghost to create virtual images 122 are discussed in more detail below.

Once an imaging ghost is deformed to model a particular individual, the dose estimating system 130 can perform a simulation to estimate an amount of a first past dose deposition resulting from a given CT scanning procedure. For example, in one embodiment, a Monte Carlo simulation can be performed using the CT scan parameters, the CT procedure parameters, and the warped phantom to arrive at a dose estimate. However, other simulation approaches could be used as well. The results of a given dose estimation simulation can be stored in simulation library 134.

For example, the CT scanner can be parameterized for a simulation based on the X-ray tube current and voltage, the CT scanner mode, the kVp, the X-ray generator target angle, the fan angle. , collimation, slice thickness, focus at axis distance, flat filters (material and thickness), filters for shaping the beam (material and geometry). Although a variety of approaches can be used in the simulation process, in one embodiment kVp, target angle, and filtration are used to model the X-ray phantom as described in "Computation of bremsstrahlung X-ray spectra over an energy range 15 KeV to 300 KeV, ”WJ lies, Regne Unit. National Radiological Protection Board, NRPB, 1987.

IMPI

Additionally, the distance from the focus to the axis determines the distance from the X-ray source to the axis of rotation and the aokine angle determines how widely the beam is spread in the cut plane. Of course, these (and other parameters) can be selected as available or as necessary to meet the needs of a particular case. However, typically, the energy deposition is stored per slice for each defined anatomical region in the phantom. A normalization simulation of a CTDIvol phantom can be performed for each CT model. This shear energy deposition information, combined with the masses for each anatomical region, is sufficient to calculate the absorbed dose in each region for a given scan region (using a subset of our full-body simulation).

However, performing a Monte Carlo simulation typically requires considerable processing time to complete - much longer than performing the CT scan itself. Accordingly, in one embodiment, the dose estimating system 130 estimates the dose by interpolating between two (or more) simulations in the simulation library 134. For example, a first patient dose can be calculated using multivariate scatter interpolation of existing simulation data. The patient dose information is refined as more applicable simulations are added. Similarly, new scanner models can be added to simulation library 134 as calibration measurements and specifications obtained from these scanners.

Simulation library 134 provides a database of Monte Carlo simulation results. In one embodiment, the simulation library 134 stores dose / energy deposition information for a set of phantoms, both as delivered and as deformed for individual patients, for a collection of scanners to create supported medical images, e.g., modalities. to create images of CT, RF, XA, among others. In one embodiment, the simulation library 134 is used to provide real-time search and / or acquisition parameter calculations.

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determined from dose distributions, patient description, and scan region.

As noted, simulation library 134 can be automatically enlarged over time when additional Monte Carlo simulations are completed. For example, simulations to be run can be added to a queue when CT scan analyzes occur. Priority can be given to simulations in an area with sparse existing data points. Doing this improves the probability of identifying simulations to interpolate, that is, it improves the simulation "space" covered by simulation library 134. Similarly, more simulations available in simulation library 134 allow more stringent thresholds for selecting simulations to interpolate in. a particular case leading to greater accuracy in dose estimation.

Note that although shown in Figure 1 as part of a CT 100 scanning environment, the dose estimation system 130 (and ghosts 132 and library 134) can be provided as a hosted service accessed by / from the CT 100 scan environment. For example, an imaging center may use a client interface on the imaging system 125 (eg, a secure web-based portal or a dedicated client application) to interact with a hosted dose estimation provider. An example of such an embodiment is discussed in more detail below with respect to Figures 11 and 12.

Figure 2 illustrates an example of an imaging system 125 used to obtain CT scan data and manage patient dose estimates, in accordance with one modality. As shown, the imaging system 125 includes, but is not limited to, a central processing unit (CPU) 205, a CT system interface 214, a network interface 215, an interconnector 217, a memory 225, and a storage. 230. The imaging system 125 may further include an I / O device interface 210 for connecting I / O devices 212 (eg, keyboard, display, and mouse) to the imaging system 125.

IMPI iwmuroMSKAN

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CPU 205 retrieves and executes programming instructions stored in memory 225. Similarly, CPU 205 stores and retrieves application data residing in memory 225. Interconnect 217 facilitates transmission of programming instructions and programming data. applications between CPU 2Ó5, I / O device interface 210, storage 230, a network interface 215, and memory 225. The CPU 205 is included to be representative of a single CPU, multiple CPUs, a single CPU having multiple processing cores, and the like. And memory 225 is generally included to be representative of random access memory. Storage 230 may be a disk drive storage device. Although shown as a single drive, storage 230 can be a combination of fixed and / or removable storage devices, such as disk drives, solid state storage devices (SSD), network attached storage (ÑAS), or a storage area network (SAN). Additionally, storage 230 (or connections to storage repositories) can conform to a variety of standards for storing data related to healthcare environments (eg, a PACS repository).

As shown, memory 220 includes a control component for creating images 222, an image storage component 224, and a dose estimation interface 226. And storage 235, protocols for creating images 232, and alarm thresholds. 234. The imaging control component 222 corresponds to software applications used to perform a particular CT scanning procedure - as specified by an imaging protocol 232. The protocols for creating images 232 generally specify the position, time, and duration to perform a specific CT procedure using a particular scanning modality. Image storage component 224 provides software configured to store images and derived CT data when performing a particular CT procedure or interacting with a suitable storage repository to store such images and data. For example, data from CT scans is

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can send over a TCP / IP connection (using a d? roH) h ^ cia / from a PACS repository.

Dose estimation interface 226 provides software components configured to interact with dose estimation system 130 to obtain an estimate of the patient's dose that may result from a particular CT procedure. As noted, in one embodiment, the dose estimation interface 226 can interact with systems that are local to the environment to create CT images. However, in an alternative embodiment, the dose estimation interface 226 may interact with a hosted service provider. In such a case, interface 226 may send requests for the estimation of the patient's dose to the hosted service provider. In addition, such a request may indicate a phantom to create images, transformations for that phantom, and the equipment and CT scanning protocols that are followed for a given imaging scan. In either case, when used in a predictive sense (i.e. before a procedure is performed), the patient's dose estimate can be compared against alarm thresholds and rules to determine if any alarms should be raised before that a particular procedure is performed (for example, an alarm indicating that a particular procedure will exceed (or be likely to be at) a cumulative dose limit for a patient, particular organ or body part, etc.

Figure 3 illustrates an example of a dose estimation system 130 used to estimate and track cumulative patient dose, in accordance with one embodiment. As shown, the dose estimating system 130 includes, but is not limited to, a central processing unit (CPU) 305, a network interface 315, an interconnector 320, a memory 325, and a storage 330. The dose estimating system 130 may further include an I / O device interface 310 for connecting I / O devices 312 (eg, keyboard, display, and mouse) to the dose estimating system 130.

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Like CPU 205, CPU 305 is included to be rAnrp ^ Antatiya of a single CPU, multiple CPUs, a single CPU that has multiple processing cores, etc., and memory 325 is generally included to be representative of one memory. random access. Interconnector 317 is used to transmit programming instructions and application data between CPU 305, I / O device interface 310, storage 330, a network interface 315, and memory 325. The network interface 315 is configured to transmit the data over the communications network, for example, to receive requests from a system to create images for dose estimation. Storage 330, such as a hard disk drive or solid state storage drive (SSD), can store non-volatile data.

As shown, memory 320 includes a dose estimation tool 321, which provides a set of software components. Illustratively, the dose estimation tool 321 includes a Monte Carlo simulation component 322, a simulation selection component 324, an image registration / segmentation component 326, and a dose interpolation component 328. And storage 330 contains the ghost data for imaging 332, protocols for creating CT imaging 334, and simulation library 336.

The Monte Carlo 322 simulation component is configured to estimate the radiation dose to the patient based on a simulation that uses ghost data to create images 322 and a particular set of equipment to create CT images and a protocol to create images specific 334. As noted, in one embodiment, the imaging ghost data 332 can be warped or otherwise transformed to better match the physical characteristics of a given patient.

The image registration / segmentation component 326 can be configured to determine a set of transformations to warp the ghost data to create images 332 prior to performing a Monte Carlo simulation using that ghost. For example, the Logging / Segmentation component of

IMPI

MACANO INSTITUTE

Dt THE FROF1EDAD

INDUSTRIAL image 326 can evaluate a reference or locator image associated with a phantom in conjunction with a scan locator image of a patient using image registration techniques. Image registration is the process of aligning two images within a common coordinate system. An image registration algorithm determines a set of transformations to establish a correspondence between the two images. Once the transformations between the patient's explorer image and a reference image of a ghost are determined, the same transformations can be used to warp the ghost. Such deformations can scale, translate, and rotate the geometry of the virtual phantom to match the patient.

In another embodiment, image segmentation is used to identify a relative size and position of the organs, tissues, and anatomical structures of a patient. In such a case, the CT scan data available to a patient can be segmented to identify geometric volumes that are believed to correspond to an organ (or other structure of interest). For example, in one embodiment, image segmentation can be used to identify a bounding box that is believed to contain a particular organ or structure. Other segmentation approaches can be used to provide a more definitive 3D volumetric region corresponding to an organ or structure. Once identified, this information is used to displace the geometry of the corresponding organ (or structure of interest) in the virtual phantom.

Note that although it is displayed as part of the dose estimation server 130, in one embodiment, the image logging / segmentation component 326 is part of the imaging system 125, or is otherwise part of the computing infrastructure in a facility to create images. Doing this allows a provider hosting a dose estimation service to receive transformations to warp a given virtual phantom, without further receiving any information that could be used to identify a patient receiving a CT scan at an imaging facility. This approach can

ΪΜΡΙ INSrnufttMUUCAW OF FNDUmiAL OWNERSHIP

simplify (or eliminate) certain legal or regulatory requirements associated with entities that process medical records or protected health information.

After completing a Monte Carlo simulation, the resulting estimates of the patient's dose, along with the parameters supplied to the simulation component 322 are stored in the simulation library 335. Instead, the dose interpolation component 328 is used to determining an estimate of patient dose from simulations in simulation library 335, without performing a full Monte Carlo simulation. To do this, the simulation selection component 324 can compare the parameters of a CT scan, the equipment used to perform the CT scan, and the ghost to create images deformed to represent a particular individual. This information is used to identify a set of two (or more) simulations to be interpolated. Although a variety of approaches can be used, in one embodiment, the selection component 324 may use a measured distance to compare the warped phantom, the CT procedure, and the CT equipment to one in the simulation library 335. In a modality, the top 2 (or top N) choices are selected for interpolation. Alternatively, any simulation with a general similarity measure within a specific threshold is selected for interpolation. In such a case, adjusting the thresholds plus or minus, simulations are used for interpolation.

Given the set of parameters that describe the scanner and the patient for an examination (kVp, target angle, gantry inclination, height, weight, etc.) the system allows to establish adaptable tolerances for each variable (for example , the actual kVp is within 10kV of the simulation). When looking for simulations, only those simulations within tolerance for all given parameters will be factored into the calculations. In one embodiment, the simulation results can be interpolated using the known Shepard method. The standard deviation across the set of simulation results is used as a measure of uncertainty (for example for the set of 5 simulations

IMPI

INtfHUf · M1XKANC) • In the industrial ItbOFtEDAO used, the dose absorbed through the chest has an SD of 0.2 mSv and the dose ........... o * y 1 »Absorbed by the liver has a SD of 0.15 mSv).

Figure 4 illustrates a method 400 for generating a model suitable for estimating radiation dose to a patient resulting from CT scans, according to one modality. More specifically, method 400 illustrates an example of an embodiment where image registration techniques are used to warp a virtual ghost. As shown, method 400 begins at step 405, where the dose estimation tool selects a virtual ghost with pre-assigned locator images. As noted, the virtual ghost can be selected based on the age and gender of an individual receiving the CT scanning procedure in question. At step 410, the dose estimation tool receives a scan image of the individual for whom the dose estimation is performed. The scan image provides a projection of a 2D image of the individual, such as an anterior / posterior and / or lateral scan image taken by the CT scanning system prior to performing a full CT procedure. Alternatively, the scan image could be a 3D volume of the individual obtained as part of a previous CT scanning procedure. In step 415, the pre-assigned locator images corresponding to the use to warp the selected virtual phantom are obtained. Pre-assigned images can be selected based on the relevant regions of the patient to be scanned. For example, for a patient who will receive (or who received) a chest CT scan, the selected reference image may represent this region of an individual with a body geometry that closely matches the virtual phantom.

Figure 5A illustrates an example of an image representing a deformable ghost, according to one embodiment. As shown, image 500 provides a front / back view 501 and a side view 502 of a virtual image ghost. As shown in views 501 and 502, the geometry of this phantom includes a bone structure representing ribs 505, spine 515, and legs 522. Additionally, views 501 and 502

IMPI iNmrunoMKiCAN *

OF INDUSTRIAL NONITY

include the geometry representing the organs, including a stomach 510 and a kidney 520. The virtual phantom (as depicted in views 51 and büzj provides a rough approximation of the size, shape, and position of the organs, human tissues and structures.

Although clearly a rough approximation of real human anatomy, virtual phantoms are generally accepted as they provide reasonably accurate estimates of dose absorption. Figure 5B illustrates an example of a 2D reference image of a portion of a human body corresponding to the ghost shown in Figure 5A, according to one embodiment. As shown, the relative positions, size, shape of bones, tissues, organs, in the reference image match well with the corresponding positions in the virtual phantom.

Referring again to method 400, at step 420, the dose estimation tool performs an image registration process to determine a transformation between the patient scan images and the reference images used to represent the virtual ghost. The result of the image registration is an assignment of the points in the 2D scan locator to the points in the reference image (or vice versa). Similarly, in cases of a 3D scan image of the patient (i.e., a current or previous CT scan), 3D image registration techniques can assign points between the 3D scan image of the patient and the points on a reference image corresponding to the ghost in a 3D coordinate space.

In step 425, this same transformation is used to deform the geometry representing the virtual ghost. By deforming the virtual phantom using the transformations obtained from the image registration process, the size, shape, and positions of the organs represented by the geometry of the virtual phantom match the geometry of the real patient much more accurately. For example, performing an image registration process using the reference image shown in Figure 5B and a scanning locator of a patient provides a transformation that can be used to warp the

IMPI mWwto MKDCaNO OI U PROPERTY INDUSTtMl virtual phantom shown in Figure 5A. The virtual phantom dp.fnrmarin is used to estimate the absorbed dose to the organs resulting from a given CT procedure (the same before, as after such a procedure is performed). Thus, dose estimates obtained from a Monte Carlo simulation are tailored to the patient, as well as more accurate and more consistent when used to estimate the patient dose from multiple scans.

Figure 6 illustrates another method of generating a model suitable for estimating the radiation dose of a patient resulting from CT scans, according to one modality. More specifically, method 600 illustrates an example of an embodiment where image segmentation techniques are used to warp a virtual ghost. Like method 400, method 600 begins where the dose estimation tool selects a ghost to create images to warp, for example, based on the age and gender of a patient (step 605). However, instead of retrieving the patient's 2D image locators, the dose estimation tool receives a 3D scan volume of a portion of the patient (at step 610), for example, a CT scan from from an anterior chest and abdomen CT. Once obtained, the image segmentation is used to identify tissues, organs, structures, or other landmarks in the image volume (step 615). Although a variety of available segmentation approaches can be used, in one embodiment, image segmentation provides a minimal bounding box surrounding each identified organ or structure.

At step 620, the dose estimation tool combines the organs and other anatomical landmarks (eg, bone position) identified in the CT scan segmentation with the corresponding landmarks in the virtual phantom. For example, Figure 7 illustrates an example of a slice of a CT scan superimposed on a corresponding slice of a virtual phantom, according to one embodiment. In this example, the virtual ghost 700 slice includes a line 702 representing the

volume delimited by the phantom along with cut portions of a heart 701, a lung 703, the spine 704, and a humerus bone 705. However, the location and position of the heart and lung in the virtual phantom do not correspond well with the position of these organs as depicted on CT. For example, the open space region of the lungs (at 706) does not match the size or position of the lungs 702 in the phantom. Similarly, the boundary line 702 of the phantom does not correspond well to the patient. Using this phantom to estimate the dose therefore results in a much higher dose absorption than would actually occur, since the phantom does not take into account the large amounts of adipose tissue in this patient.

At the same time, other landmarks of the phantom align well with the patient. For example, the spine and arms are generally placed on both the phantom (spine 704, humerus 705) and CT. Accordingly, at step 625, the dose estimation system determines a 3D displacement map based on the coincident anatomical or structural landmarks.

For example, in Figure 7, the phantom cutout 700 shows an unmodified or undeformed phantom and the phantom cutout 710 shows the same phantom cutout after being displaced using the method of Figure 6 (or before being deformed using an image registration technique according to the method of Figure 4).

As shown in the phantom 710 cutaway, after being deformed using the volumes and displacements of the identified organs of a particular patient the boundary line 702 'now more closely follows the contours of the patient's CT scan, and the lungs 703 'and the ghost heart 701' have been shifted to better reflect the position of these organs on the scan. At the same time, other anatomical landmarks such as the spine and humerus remain in the same general position. The imaging ghost shown in slice 700 is shown superimposed on the corresponding CT scan slice of a patient at slice 720.

IMPI

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INDUSTRIAL

Similarly, the deformed phantom shown in section 710 is shown superimposed on the corresponding CT scan section of a patient in section 730.

Referring again to Figure 6, at step 630, the dose estimation tool generates a raster 3D representation of the displaced organs, tissues, and structures of the virtual phantom. As mentioned above, the virtual phantom can be described as a series of non-uniform rational base splines (NURBS), although CT scan data is typically represented as a series of simple point values of 3D coordinates referred to as "voxels ”- short for“ volume element ”, a voxel extends the concept of a pixel within a third dimension, and a variety of known approaches are available to "voxelize" a collection of NURB or CSG data. Doing this converts the geometric or mathematical representation of the NURB or CSG data into a 3D array of voxel values. In one embodiment, step 630 (the voxelization step) is performed for the purpose of avoiding discontinuities that are often a problem with Monte Carlo simulations in math ghosts (both NURB and CSG based). Furthermore, voxel-based models are well suited for GPU-based computational methods to achieve improved speed.

Once the raster ghost is generated, it can be used to estimate the absorbed dose to the organs resulting from a given CT procedure (both before and after such a procedure is performed). Like image segmentation approaches, dose estimates made using the warped phantom using the segmentation approach are tailored to the patient, resulting in more accurate and consistent dose estimates, both for single and multiple scans.

Figure 8 illustrates an example of a cross-section of a phantom for creating images overlaid on a corresponding cross-sectional CT of a patient, according to one embodiment. In this example, a sectional view 800

corresponds to view 710 of Figure 7 and a cross-sectional view 850 corresponds to view 730 of Figure 7. The cross-sectional view is created by composing a linear section of individual slices to create a longitudinal image. As shown, cross-section views 800 and 805 provide a full-length view that

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WWW) ΜβΙΚΑΝΟ t »U CURRENCY nroqmuAE includes components that are not present in the patient's overlay CT image, for example brain 801 and kidney 802. As shown in view 800, a virtual ghost boundary 810 does not correspond well with the patient's silhouette (that is, with the body size defined by the patient's skin). However, in view 850, a boundary 815 of the phantom has been shifted to better match the baseline CT scan data for this patient. Similarly, internal organs, structures, and other tissues can be equally displaced.

Importantly, this example illustrates that drift can occur for elements of the virtual phantom that are not part of the patient's CT scan data. For example, kidney 802 could be displaced by the movement of other organs for which CT scan data is available, as shown by the displaced position of kidney 802 'in view 850. Additionally, this example illustrates that a virtual phantom is required to estimate patient dose even where CT scan data is available. This occurs because although the CT scan in this example was limited to the chest and abdomen, the scattering of the X-rays will result in some absorption by the brain, kidneys, and other organs and tissues of this patient. Stated differently, the virtual phantom is required to estimate the organ absorption dose for organs that are not imaged as part of a given CT scan or procedure.

Figure 9 illustrates another example of CT image segmentation and phantom organ volume shift for imaging, according to one embodiment. In this example, a CT 900 volume corresponding to an imaging includes a set of bounding boxes representing a segmented image position for a

variety of organs, for example, liver 905, vanilla h¡i¡ar Qin and right adrenal ear 915. Additionally, volume 900 shows arrows representing the displacement of these organs based on image segmentation of the data of CT scans. In this particular example, the liver 905 has moved down and to the right, while the gallbladder 910 has moved up and in front of the liver 905 and the right adrenal 915 has moved up and to the left. within the space formerly occupied by the liver 905. Also, in this example, the organs are represented by bounding boxes, and are moved based on a geometric centroid. However, in an alternative embodiment image segmentation (same for phantom or CT image data of a patient) can provide a more accurate geometric volume representing an element of an organ, tissue or body structure. In such a case, the displacement could be based on a centroid of mass of the organ, for example, where the centroid of the liver is located to one side based on mass or another approach that represents the topology of a volume of a given organ.

As illustrated in this example, displacing an organ (eg, liver 905) in a phantom based on its corresponding position on a baseline CT scan, may require displacing other organs (eg, gallbladder 910 and gallbladder right adrenal 915) as a result. This occurs when two organs clearly should not occupy the same physical volume when the phantom is used for dose estimation analysis. Consequently, in one embodiment, the dose estimating tool can shift organs, tissues, or structures until they reach a "steady state."

Note that the examples of the modalities illustrated in Figures 4 and 6 can be used separately or in conjunction with each other to warp a virtual ghost. The particular approach or combination of selected approaches can be tailored to meet the needs in a particular case based on ghosts to create available images, rendered 2D and / or 3D reference images, as well as the

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MUNOHlMt. industrial availability and type of scan locator images and / or data from previous CT scans for a given patient.

In one embodiment, a cloud provider models host systems used to perform dose estimates, maintain a library of calculated simulations, as well as run the Monte Carlo simulations to expand the simulation library with new cases. For example, Figure 10 illustrates a method 1000 for a dose estimation service to provide patient dose estimates to multiple CT scanning providers.

As shown, method 1000 starts at step 1005 where the dose estimation service receives a ghost of an image (or a reference to a ghost of an image) along with registration transformations of 2D or 3D images or a field. volumetric displacement in 3D and the voxelization of ghosts. In an alternative embodiment, the dose estimation service may receive data describing the deformed ghost such as transformed NURBS resulting from the 2D or 3D image registration process or CT field shift techniques described above.

At step 1010, the dose estimation service receives the parameters of a CT scanning system and a plan to create images for a CT scan performed (or performed) on a patient. Once the parameters are received from the patient, the scanning equipment, and the CT scanning provider, the dose estimation service can identify two (or more) simulations in the library that match the transformed phantom, the parameters of the CT scanning system and plan to create images (step 1015). The supplier can set customizable tolerances for each variable (for example, the actual kVp is within 10kV of the simulation). In addition to the evaluation simulations, only the simulations within tolerance for all (or a specific set) given parameters are factored into the calculations. In one embodiment, the simulation results can be interpolated using the known Shepard method. The standard deviation across the set of simulation results is used as a measure of uncertainty (for example for the set of 5

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INBUSTMAA simulations used, the dose absorbed by the chest has a SD of 0.2 mSv and the dose absorbed by the liver has a SD of 0.15 mSv). 1 ....... '

In step 1020, the dose estimation service determines whether the matching simulations identified in step 1015 are within a tolerance parameter (or meet other thresholds or criteria). If not, then the image ghost (and deformations / transformations) and received parameters are added to a queue of patient / scanner / image plan scenarios to simulate (step 1025). As noted, the simulation can use Monte Carlo simulation techniques to determine organ absorbed dose estimates tailored to the individual patient (based on the deformed phantom) and the facility to create particular images based on the CT scanner and calibration / adjustment data.

However, as a SaaS provider's simulation library grows, most requests should identify a set of simulations to be interpolated. In step 1030, the dose estimation service performs multivariate scatter interpolation using the matching simulations identified in step 1015 to estimate the dose absorbed by the organs for a particular patient and an associated CT scanning procedure. Note that such an analysis can be performed much faster than a full Monte Carlo simulation, allowing dose estimates to keep up with a sequence of procedures performed at a facility to create a given image (or facilities) as well as be provided. simultaneously with a given procedure (for example, to ensure that cumulative dose limits are not exceeded). In one embodiment, the currently used multivariate dispersion interpolation method is referred to as 'Shepard's method'. Examples of this method are described in Shepard, Donald (1968). A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 1968 ACM National Conference. P. 517-524.

In step 1035, once the interpolation process is complete, the dose estimates are returned to a request system (e.g., a

or.

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Dose estimation client program that runs in nfnrmátjcq in an imaging facility). At the client a dose management system tracks patient organ equivalent doses, effective doses, CTDI, DLP, DAP down to the examination level.This information is further added to provide cumulative tracking of the organ equivalent dose, dose effective, CTDI, DLP, DAP for the history of a given patient. In addition, the addition of this information is used to provide a presentation at the institutional level of the equivalent dose of the organs per capita, the effective dose of the patient, CTDI, DLP, DAP. In this way, the dose estimating service can provide a facility for creating images with a wide [incomplete sentence] variety. This same information is available for an imaging facility running a local instance of the dose estimation system.

Figure 11 illustrates an example of a computing infrastructure 1100 for a patient dose estimation service system configured to support multiple CT scan providers, according to one modality. As shown, a cloud-based provider 1125 hosting a dose estimation service 1130 receives requests for dose estimates over the network 1120 from the imaging facilities 1105i-2. In each imaging facility 1105, a CT system 1110 is used to provide imaging services for patients. An imaging / dosing client 1115 contacts the dose estimating service 1130 to request and receive patient dose estimates, where dose estimates are tailored based on the procedure and the patient. As noted, the application may include the parameters for a CT procedure, scanning equipment and modality, and a deformed phantom (or transformations used to deform a phantom) based on the morphology of the particular patient's body.

In the dose estimation service 1130, a simulation library 1135 is used to select simulations to interpolate a number of patient doses using the data in the application and the modules of a CT scanner and procedures (shown in Figure 11 data phantom / CT system

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1140). If there are no good candidate simulations available for interpolation, then the 1130 service can add the request to a queue of simulations to run. Monte Carlo simulations are then performed in response to the request, providing an estimate of the patient dose for a given patient and procedures for creating images as well as a new simulation data point to add to the 1125 library.

Advantageously, embodiments of the invention provide a variety of techniques for estimating radiation dose resulting from techniques for creating CT (and other) X-ray images. As described, image registration techniques and / or image segmentation techniques can be used to create a ghost to create hybrid images that more closely match an individual's body size and shape.Doing this improves accuracy. dose estimates determined from a simulation. Thus, the resulting hybrid phantom provides a much more accurate mathematical representation of a particular patient for use in a dose simulation than the unmodified phantoms alone.

Once the transformations are determined, the hybrid virtual phantom can be used to simulate a given CT procedure for the patient. For example, Monte Carlo simulation techniques can be used to estimate the dose absorbed by the organs for a virtual ghost. Such simulation techniques use the virtual phantom (transformed relative to a given patient), together with a number of parameters related to the CT scanner model and the procedure to be performed in order to calculate exact estimates of the absorbed doses by the organs. However, estimating the absorbed dose to organs using a Monte Caro simulation can require significant amounts of computational time, much longer than that required to perform an actual CT scan. Accordingly, in one embodiment, patient dose estimates determined by a given procedure can be generated by interpolating between two (or more) previously completed simulations. If no "close" simulations are available, then the ghost

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HYBRID virtual INDUSTRIAL, CT scanner and procedure data can be added to -T 111 '· · * · -> ——— rr—— a queue full of Monte Carlo simulations to be performed. Over time, a larger library of simulations allows dose estimates to be provided in real time when procedures are scheduled and performed. Doing this 5 allows you to capture the cumulative dose amounts for a given patient, as well as the cumulative dose limits to be observed. Furthermore, in one embodiment, a SaaS provider hosts a dose estimation service provided for multiple imaging facilities. In such a case, the service provider may have a robust simulation library to use in interpreting dose estimates for imaging providers.

Although the foregoing is directed to the embodiments of the present invention, still other embodiments of the invention are conceivable without departing from the basic scope thereof, and the scope thereof is determined by the following claims.

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ΙΝΪΤΤηΠΌ MEXICAN

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87 members in 16 offices

This recordMember, by publication datePriority claim6 more offices are in the list below

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Priority claims
Document	Office	Kind	Date
42083410	United States of America	P	2010-12-08
42083410	United States of America	P	2010-12-08
61420834	United States of America	–	2010-12-08
61420834	–	–	–
US20100420834P	–	–	–

Members87

Family members, oldest first
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CA2819331A1	Canada	A1	2012-06-14
CA2993848A1	Canada	A1	2012-06-14
CA3033428A1	Canada	A1	2012-06-14
US2012148131A1	United States of America	A1	2012-06-14
US2012148132A1	United States of America	A1	2012-06-14
US2012150505A1	United States of America	A1	2012-06-14
WO2012075577A1	World Intellectual Property Organization (WIPO)	A1	2012-06-14
AU2011340078A1	Australia	A1	2013-06-27
MX2013006336A	Mexico	A	2013-09-26
EP2648621A1	European Patent Office (EPO)	A1	2013-10-16
KR20130130772A	Republic of Korea	A	2013-12-02
CN103442644A	China	A	2013-12-11
JP2013544605A	Japan	A	2013-12-19
NZ611554A	New Zealand	A	2014-12-24
RU2013127784A	Russian Federation	A	2015-01-20
US8953861B2	United States of America	B2	2015-02-10
US8958617B2	United States of America	B2	2015-02-17
US2015139519A1	United States of America	A1	2015-05-21
US2015170363A1	United States of America	A1	2015-06-18
ZA201303796B	South Africa	B	2015-06-24
JP5917552B2	Japan	B2	2016-05-18
AU2011340078B2	Australia	B2	2016-06-30
AU2016203814A1	Australia	A1	2016-06-30
RU2592247C2	Russian Federation	C2	2016-07-20
JP2016129716A	Japan	A	2016-07-21
BR112013013804A2	Brazil	A2	2016-09-13
EP2648621A4	European Patent Office (EPO)	A4	2016-12-28
KR20170004029A	Republic of Korea	A	2017-01-10
US9547893B2	United States of America	B2	2017-01-17
EP3150126A1	European Patent Office (EPO)	A1	2017-04-05
MX347370BThis record	Mexico	B	2017-04-25
US2017123074A1	United States of America	A1	2017-05-04
CN103442644B	China	B	2017-05-24
US2017228860A1	United States of America	A1	2017-08-10
US2017243350A1	United States of America	A1	2017-08-24
CN107095689A	China	A	2017-08-29
JP6189468B2	Japan	B2	2017-08-30
ZA201409203B	South Africa	B	2017-09-27
US9792680B2	United States of America	B2	2017-10-17
JP2017192808A	Japan	A	2017-10-26
JP2017192809A	Japan	A	2017-10-26
US9805464B2	United States of America	B2	2017-10-31
AU2017251773A1	Australia	A1	2017-11-16
AU2017251775A1	Australia	A1	2017-11-16
AU2016203814B2	Australia	B2	2017-11-23
EP3281586A2	European Patent Office (EPO)	A2	2018-02-14
KR101845576B1	Republic of Korea	B1	2018-04-04
KR20180037288A	Republic of Korea	A	2018-04-11
EP3281586A3	European Patent Office (EPO)	A3	2018-05-16
EP2648621B1	European Patent Office (EPO)	B1	2018-07-04
HK1242946A1	Hong Kong, China	A1	2018-07-06
EP3150126B1	European Patent Office (EPO)	B1	2018-07-25
KR101889642B1	Republic of Korea	B1	2018-08-17
JP6395911B2	Japan	B2	2018-09-26
DK2648621T3	Denmark	T3	2018-10-22
JP6417000B2	Japan	B2	2018-10-31
ES2689751T3	Spain	T3	2018-11-15
DK3150126T3	Denmark	T3	2018-11-19
RU2016124149A	Russian Federation	A	2018-12-03
ES2692793T3	Spain	T3	2018-12-05
EP3281586B1	European Patent Office (EPO)	B1	2019-04-10
DK3281586T3	Denmark	T3	2019-06-24
KR20190075169A	Republic of Korea	A	2019-06-28
AU2017251773B2	Australia	B2	2019-08-08
EP3524159A1	European Patent Office (EPO)	A1	2019-08-14
AU2017251775B2	Australia	B2	2019-08-29
KR101994055B1	Republic of Korea	B1	2019-09-27
US10438348B2	United States of America	B2	2019-10-08
MX368991B	Mexico	B	2019-10-24
ES2733922T3	Spain	T3	2019-12-03
MX2019012639A	Mexico	A	2019-12-11
RU2016124149A3	Russian Federation	A3	2019-12-12
US10546375B2	United States of America	B2	2020-01-28
US10546376B2	United States of America	B2	2020-01-28
ZA201706370B	South Africa	B	2020-01-29
KR102124956B1	Republic of Korea	B1	2020-06-19
RU2725751C2	Russian Federation	C2	2020-07-03
CA2993848C	Canada	C	2020-09-22
RU2725751C9	Russian Federation	C9	2020-12-21
CA2819331C	Canada	C	2021-01-12
EP3524159B1	European Patent Office (EPO)	B1	2021-01-20
DK3524159T3	Denmark	T3	2021-04-06
ZA201606246B	South Africa	B	2021-07-28
ES2864040T3	Spain	T3	2021-10-13
CN113856066A	China	A	2021-12-31
CN107095689B	China	B	2022-02-25
CA3033428C	Canada	C	2022-03-22

Abstract

Term

PATENT TITLE NO. 347370

REQUEST

PRIORITY

MX / 2017/34564

MEDICAL

FIELD OF THE INVENTION

BACKGROUND OF THE INVENTION

BRIEF DESCRIPTION OF THE INVENTION

BRIEF DESCRIPTION OF THE DRAWINGS

DETAILED DESCRIPTION OF THE INVENTION

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MIXICAN

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OF NIGHTNESS

INDUSTRIAL

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MEXICAN IMWIUTO

INDUSTRIAL

INDUSTRIAL

INMSTUU

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MACANO INSTITUTE

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OF INDUSTRIAL NONITY

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INDUSTRIAL

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Priority claims5

Members87

Numbers

Titles2

Classification