DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed 17 March 2026 have been fully considered but they are not persuasive.
Claims 1-14 and 16-21 are pending in this application and have been considered below. Claim 15 is canceled by the applicant.
Argument:
The applicant argues that claim 1 explicitly requires a multi-angle cone-beam projection generation along a rotational trajectory. Applicants argue that Mason, et al. is not relied upon for these CBCT- specific steps, and Kleinszig, et al. is directed to low-dose/preview image generation and does not disclose a volumetric CBCT simulation pipeline with rotational sampling and geometry-limited reconstruction.
Response:
US Patent Publication 2024 0245363 A1, (Mason et al.) shows the limitation wherein the forward-projecting comprises generating a plurality of cone-beam projection images corresponding to a rotational trajectory of the simulated CBCT scanner ("FIG. 2A and FIG. 2B, discussed below, generally illustrate examples of a radiation therapy device configured to provide radiotherapy treatment to a patient, including a configuration where a radiation therapy output can be rotated around a central axis ( e.g., an axis "A")," paragraph [0066]).
Priority
Receipt is acknowledged that application is a National Stage application of PCT EP2023/085985. Priority to EP22214132.7 with a priority date of 16 December 2022 is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDSs dated 1 August 2024 and 19 January 2024 that have been previously considered remain placed in the application file.
Specification
The specification has been amended, the objection to the specification is withdrawn.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. § 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claims 1, 5,16, 17 were rejected under 35 U.S.C. § 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 1 recites “projection image.” Applicants have stated that “the projection image comprises a CT image that is forward projected based on the scanner parameters of the simulated CBCT scanner.” This definition is acceptable and will be used. The rejection of claim 1, as indefinite over the definition of a “projection image” is withdrawn.
Claim 5, line 8 and 13 and claim 13 recite “and/or.” Applicants argue that “he plain language A and/or B means A or B, and both A and B. Since claims set out the legal boundaries of infringement, the examiner accepts the broadest interpretation as argued by applicants that “or” is the intended term. However, MPEP 2143.03 (I), “If a claim is subject to more than one interpretation, at least one of which would render the claim unpatentable over the prior art, the examiner should reject the claim as indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph (see MPEP § 2175) and should reject the claim over the prior art based on the interpretation of the claim that renders the prior art applicable. (Ex parte Ionescu, 222 USPQ 537 (Bd. Pat. App. & Inter. 1984)” and thus the rejection is maintained.
Claims 1, 5, 16 and 17 recite “based on.” Applicants argue that, using claim 5, that "generating a set of training data, the set of training data comprising the simulated CBCT image based on one of the first computed tomography image and the second computed tomography image" clearly means that training data are derived from first and second tomography images.” Examiner accepts that applicants intend for the terms “based on” to mean all methods of deriving one set of data from another set of data. However, MPEP 2143.03 (I), “If a claim is subject to more than one interpretation, at least one of which would render the claim unpatentable over the prior art, the examiner should reject the claim as indefinite under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph (see MPEP § 2175) and should reject the claim over the prior art based on the interpretation of the claim that renders the prior art applicable. (Ex parte Ionescu, 222 USPQ 537 (Bd. Pat. App. & Inter. 1984)” and thus the rejection is maintained.
Claim Interpretation
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification.
The following terms in the claims have been given the following interpretations in light of the specification:
creating raw data, Claim 17: Page 3, lines 32-35, “In some embodiments, the attenuation coefficients can be used for the forward projection to create the raw data. The representation of the raw data itself can be in different units (e.g., attenuation / line integral space). The units for the representation of the raw data depend on the type of noise that is desired to be added in subsequent step(s).”
Thus, creating raw data is creating new image data. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
attenuation coefficients, Claim 1: page 3, lines 25-27, “This attenuation coefficient may be given as a function of the energy of the X- ray radiation.”
Thus, attenuation coefficients are numerical values that relate received radiation to expected radiation. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
Scanner parameters, Claim 1 and 3: Page 3, Lines 30-34, “These scanner parameters may comprise a location of the scanner with respect to the patent, a rotation angle, scatter properties, beam-hardening effects, or detector deficiencies, for example.”
Thus, a Scanner parameters are physical characteristics of a scanner. This definition is used for purposes of searching for prior art, but cannot be incorporated into the claims.
Should applicant wish different definitions, Applicant should point to the portions of the specification that clearly show a different definition.
Claim Interpretation
Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
Claims 4, 8 and 14 state “or.” Claim 5 recites “one of the first computed tomography image and the second computed tomography image, the other one of the first computed tomography image and the second computed tomography image, and data representing the transformation.” Claim 5 further recites, “one of the first computed tomography image and the second computed tomography image according to the method of claim 1.” Since “one of” and “or” are disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-14 and 16-21are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2024 0245363 A1, (Mason et al.) in view of US Patent Publication 2016 0007946 A1, (Kleinszig et al.). The references are listed in a PTO-892 from the Office Action in which they are first used.
Claim 1
Regarding Claim 1, Mason et al. teach a computer-implemented method for generating a simulated cone-beam computed tomography (CBCT) image based on a computed tomography image ("the reference medical image is a 3D image provided from a computed tomography (CT) scan, and wherein the method further includes training of the regression model using a plurality of reference medical images from the CT scan," paragraph [0011]), the method comprising:
receiving data representing a CT image comprising a volume of a subject, wherein the volume is divided into voxels ("the software programs may register or associate a patient medical image (e.g., a CT image, an MR image, or a reconstructed CBCT image) with that patient's dose distribution of radiotherapy treatment ( e.g., also represented as an image) so that corresponding image voxels and dose voxels are appropriately associated," paragraph [0053]),
receiving scanner parameters of a simulated CBCT scanner ("Machine data information may include radiation beam size, arc placement, beam on and off time duration, machine parameters, segments, multi-leaf collimator (MLC) configuration, gantry speed, MRI pulse sequence, and the like," paragraph [0059]),
forward-projecting the CT image to a projection image based on the scanner parameters of the simulated CBCT scanner ("Projections corrected in this way could be used to reconstruct quantitative CBCT images in a variety of settings," paragraph [0104]);
adding artificial noise to the projection image, the artificial noise is a representation of noise detected by the simulated CBCT scanner ("noisy but statistically independent input and scatter corrected target pairs may be used, similar to an "nosie2inverse" model," paragraph [0126]);
back-projecting the projection image with a reconstruction algorithm, thereby generating a simulated CBCT image of the subject ("the input may be scatter contaminated and the target may be scatter corrected (for example with Monte Carlo simulation), whereby the network could infer how to correct for scatter," paragraph [0125]); and
providing the simulated CBCT image of the subject ("the model (e.g., neural network) is trained based on a single patient-specific patient dataset, such as a planning CT, to infer nonlinearity free projections from raw projections. Non-linearities may include, for example, beam hardening from polyenergetic source ad energy dependent x-ray attenuation; glare from scatter within the detector; lag from finite response rate of the detector; and afterglow corrupting subsequent measurements; variations in gain over detector area from different sensor banks or presence of objects in the beam path such as bow-tie filter or anti-scatter grid. The use case of this trained model may include, for each CBCT projection, to use the model to infer nonlinearity-free projections and then reconstruct a 3D CBCT image with these new projections," paragraph [0130]), wherein the forward-projecting comprises generating a plurality of cone-beam projection images corresponding to a rotational trajectory of the simulated CBCT scanner ("FIG. 2A and FIG. 2B, discussed below, generally illustrate examples of a radiation therapy device configured to provide radiotherapy treatment to a patient, including a configuration where a radiation therapy output can be rotated around a central axis ( e.g., an axis "A")," paragraph [0066]).
Mason et al. do not explicitly teach all of representing tissue in a Hounsfield unit.
However, Kleinszig et al. teach wherein the voxels comprise a representation of a tissue property of a tissue of the subject in a Hounsfield Unit ("Desirable properties of the CT volume Y cT include an untruncated reconstruction, high-resolution reconstruction (voxel size and reconstruction kernel), and low-noise images. For a CT volume UcT (in Hounsfield units HU), the volume is first segmented into material classes," paragraph [0063]); and
converting the Hounsfield Unit of the CT image into attenuation coefficients ("The LDP cBcTcT leverages a volumetric representation Y cT of the patient ( such as a preoperative CT) to simulate realistic CBCT projections. Desirable properties of the CT volume Y cT include an untruncated reconstruction, high-resolution reconstruction (voxel size and reconstruction kernel), and low-noise images. For a CT volume UcT (in Hounsfield units HU), the volume is first segmented into material classes," paragraph [0063] where the volumetric representation is the result of Hounsfield units converted into attenuation coefficients).
Therefore, taking the teachings of Mason et al. and Kleinszig et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Method, Data Processing System, Computer Program Product and computer Readable Medium for Determining Image Sharpness” as taught by Mason et al. to use “AI systems and Method to Enhance Images acquired through Random Medium” as taught by Kleinszig et al. The suggestion/motivation for doing so would have been that, “an artificial neural network is disclosed, along with the associated neural network system, that can learn or account for characteristics associated with spatial domain loss component and a frequency domain loss component, e.g., via Fourier space-loss function” as noted by the Kleinszig et al. disclosure in paragraph [0005], which also motivates combination because the combination would predictably have a higher productivity as there is a reasonable expectation that all images will have losses, some in the spatial domain and some in the frequency domain; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of machine readable medium claim 19 while noting that the rejection above cites to both device and method disclosures. Claims 19 is mapped below for clarity of the record and to specify any new limitations not included in claim 1.
Claim 2
Regarding claim 2, Mason et al. teach the method of claim 1, wherein the method further comprises the step of modifying the tissue property of the tissue of the subject ("Some specific positions of tumor and OARs (and their mutually related position as a function of treatment course) as well as unusual different conditions, such as obese patients or the presence of prosthetic implants, can result in the suboptimal use of CBCT. For example, consider scenarios involving of small tumors totally embedded in soft tissue whose contrast is not clearly distinguishable from the lesion itself, or hip implants which hinder the correct localization of prostate, rectum, and bladder due to metal imaging artifacts," paragraph [0076] and "an example workflow for capturing and processing imaging data from a CBCT imaging system, using trained imaging processing models. Here, the CBCT imaging processing includes the use of two models: a trained image reconstruction model 330, to reconstruct CBCT projections into a 3D image; and a trained artifact correction model 340 to remove artifacts from CBCT projections." paragraph [0077] where removing artifacts is modifying tissue properties).
Claim 3
Regarding claim 3, Mason et al. teach the method of claim 1, wherein the scanner parameters comprise at least two tube peak voltages of the simulated CBCT scanner ("The radiation therapy device 202 can be similar to the system described in relation to FIG. 2A, such as including a radiation therapy output 204, a gantry 206, a couch 216, and another imaging detector 214 (such as a flat panel detector). The X-ray source 218 can provide a comparatively-lower-energy X-ray diagnostic beam, for imaging," paragraph [0073]).
Claim 4
Regarding claim 4, Mason et al. teach the method of claims 1, wherein the computed tomography image is a fan-beam computed tomography image or a CBCT image ("the software programs may register or associate a patient medical image (e.g., a CT image, an MR image, or a reconstructed CBCT image) with that patient's dose distribution of radiotherapy treatment ( e.g., also represented as an image) so that corresponding image voxels and dose voxels are appropriately associated," paragraph [0053]).
Claim 5
Regarding claim 5, Mason et al. teach a computer-implemented method for generating training data for training of an artificial intelligence module to register a CBCT image to a computed tomography image, the method comprising:
receiving data representing a first computed tomography image of a subject ("the software programs may register or associate a patient medical image (e.g., a CT image, an MR image, or a reconstructed CBCT image) with that patient's dose distribution of radiotherapy treatment ( e.g., also represented as an image) so that corresponding image voxels and dose voxels are appropriately associated," paragraph [0053]);
generating data representing a second computed tomography image of the subject, wherein the second computed tomography image differs from the first computed tomography image in that a transformation is applied to the second computed tomography image, the transformation comprising a deformation, and/or distortion, and/or rotation, and/or translation of the subject, and/or a cropped field of view ("operation 1120: variation images, which provide variation of the representations of the anatomical area, are generated from the reference medical image. Such variation may include geometrical augmentations ( e.g., rotation) or changes (e.g., deformation) to the representations of the anatomical area," paragraph [0134]);
generating data representing the transformation ("At operation 1130: projection viewpoints are identified, in a CBCT projection space, for each of the variation images," paragraph [0135]);
generating a simulated CBCT image based on one of the first computed tomography image and the second computed tomography image according to the method of claim 1, wherein the data representing the computed tomography image comprises the first and/or the second computed tomography image ("the model (e.g., neural network) is trained based on a single patient-specific patient dataset, such as a planning CT, to infer nonlinearity free projections from raw projections. Non-linearities may include, for example, beam hardening from polyenergetic source ad energy dependent x-ray attenuation; glare from scatter within the detector; lag from finite response rate of the detector; and afterglow corrupting subsequent measurements; variations in gain over detector area from different sensor banks or presence of objects in the beam path such as bow-tie filter or anti-scatter grid. The use case of this trained model may include, for each CBCT projection, to use the model to infer nonlinearity-free projections and then reconstruct a 3D CBCT image with these new projections," paragraph [0130]);
generating a set of training data, the set of training data comprising the simulated CBCT image based on one of the first computed tomography image and the second computed tomography image, the other one of the first computed tomography image and the second computed tomography image, and data representing the transformation ("The approach depicted in FIG. 7B, for training an image processing model, also may be extended to model physical non-linearities of an imaging system using CBCT imaging data," paragraph [0104]); and
providing the set of training data ("Once all of the training data has been generated, paired Praw and Iscat information is used as patient-specific training data to train a regression model, at operation 741," paragraph [0099]).
Claim 6
Regarding claim 6, Mason et al. teach the method of claim 5, wherein the generating data representing a second computed tomography image of the subject comprises receiving data representing the second computed tomography image of the subject acquired by a computed tomography scanner ("Many of the following examples refer to the capture of CBCT projections in the image data 160, in a setting where the image acquisition device 170 is a CBCT scanner and produces cone beam CT image data. However, the image data 160 used as part of radiotherapy treatment may additionally include one or more MRI images ( e.g., 2D MRI, 3D MRI, 2D streaming MRI, 4D MRI, 4D volumetric MRI, 4D cine MRI, etc.), functional MRI images (e.g., fMRI, DCE-MRI, diffusion MRI), Computed Tomography (CT) images (e.g., 2D CT, 3D CT, 4D CT), ultrasound images (e.g., 2D ultrasound, 3D ultrasound, 4D ultrasound), Positron Emission Tomography (PET) images, X-ray images, fluoroscopic images, radiotherapy portal images, SinglePhoto Emission Computed Tomography (SPECT) images, computer generated synthetic images ( e.g., pseudo-CT images) and the like," paragraph [0057]).
Claim 7
Regarding claim 7, Mason et al. teach the method of claim 6, wherein the generating data representing the transformation comprises registering the first computed tomography image to the second computed tomography image ("the software programs may register or associate a patient medical image (e.g., a CT image, an MR image, or a reconstructed CBCT image) with that patient's dose distribution of radiotherapy treatment ( e.g., also represented as an image)," paragraph [0053]).
Claim 8
Regarding claim 8, Mason et al. teach the method of claim 7, wherein the registering of the first computed tomography image to the second computed tomography image is performed with a registering algorithm or an AI- based registering algorithm ("the particular machine learning algorithm used by the models may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of supervised learning algorithms include artificial neural networks, Bayesian networks, instance based learning, support vector machines, decision trees ( e.g., Iterative Dichotomiser 3, C4.5, Classification and Regression Tree (CART)," paragraph [0081]).
Claim 9
Regarding claim 9, Mason et al. teach the method of claim 5, wherein the generating data representing a second computed tomography image of the subject comprises applying an artificial transformation to the data representing the first computed tomography image of the subject thereby generating data representing the second computed tomography image of the subject ("the software programs operating on the image processing computing system 110 may convert or transform medical images of one format to another format, or may produce synthetic images," paragraph [0053]).
Claim 10
Regarding claim 10, Mason et al. teach the method of claim 9, wherein the step of generating data representing the transformation comprises receiving data representing the artificial transformation ("The storage device 116 and memory 114 may store and host data to perform these purposes, including the image data 160, patient data, and other data required to create and implement a radiation therapy treatment plan based on artifact-corrected imaging data," paragraph [0053]).
Claim 11
Regarding claim 11, Mason et al. teach a computer-implemented method for registering a computed tomography image to a CBCT image, the method comprising:
receiving data representing a computed tomography image of a subject("the software programs may register or associate a patient medical image (e.g., a CT image, an MR image, or a reconstructed CBCT image) with that patient's dose distribution of radiotherapy treatment ( e.g., also represented as an image) so that corresponding image voxels and dose voxels are appropriately associated," paragraph [0053]);
receiving data representing a CBCT image of the subject ("system 110 may obtain image data 160 (e.g., CBCT projections) from the image data source 150," paragraph [0053]);
determining a transformation necessary for registering the computed tomography image to the CBCT image using an artificial intelligence module, wherein the artificial intelligence module is trained with training data generated with the method of claim 5 ("the particular machine learning algorithm used by the models may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of supervised learning algorithms include artificial neural networks, Bayesian networks, instance based learning, support vector machines, decision trees ( e.g., Iterative Dichotomiser 3, C4.5, Classification and Regression Tree (CART)," paragraph [0081]);
registering the computed tomography image to the CBCT image according to the determined transformation ("the software programs may register or associate a patient medical image (e.g., a CT image, an MR image, or a reconstructed CBCT image) with that patient's dose distribution of radiotherapy treatment ( e.g., also represented as an image) so that corresponding image voxels and dose voxels are appropriately associated," paragraph [0053]); and
providing the computed tomography image registered to the CBCT ("the model (e.g., neural network) is trained based on a single patient-specific patient dataset, such as a planning CT, to infer nonlinearity free projections from raw projections. Non-linearities may include, for example, beam hardening from polyenergetic source ad energy dependent x-ray attenuation; glare from scatter within the detector; lag from finite response rate of the detector; and afterglow corrupting subsequent measurements; variations in gain over detector area from different sensor banks or presence of objects in the beam path such as bow-tie filter or anti-scatter grid. The use case of this trained model may include, for each CBCT projection, to use the model to infer nonlinearity-free projections and then reconstruct a 3D CBCT image with these new projections," paragraph [0130]).
Claim 12
Regarding claim 12, Mason et al. teach a computer-implemented method for segmenting a CBCT image, the method comprising the steps of:
receiving data representing a CBCT image of a subject acquired by a CBCT scanner("system 110 may obtain image data 160 (e.g., CBCT projections) from the image data source 150," paragraph [0053]);
wherein the artificial intelligence module is trained with training data comprising a plurality of simulated CBCT images generated with the method according to claim 1 ("Once all of the training data has been generated, paired Praw and Iscat information is used as patient-specific training data to train a regression model, at operation 741," paragraph [0099]); and
providing the segmented CBCT image ("the model (e.g., neural network) is trained based on a single patient-specific patient dataset, such as a planning CT, to infer nonlinearity free projections from raw projections. Non-linearities may include, for example, beam hardening from polyenergetic source ad energy dependent x-ray attenuation; glare from scatter within the detector; lag from finite response rate of the detector; and afterglow corrupting subsequent measurements; variations in gain over detector area from different sensor banks or presence of objects in the beam path such as bow-tie filter or anti-scatter grid. The use case of this trained model may include, for each CBCT projection, to use the model to infer nonlinearity-free projections and then reconstruct a 3D CBCT image with these new projections," paragraph [0130]).
Mason et al. do not explicitly teach all of segmenting the CBCT image using an artificial intelligence module.
However, Kleinszig et al. teach segmenting the CBCT image using an artificial intelligence module (" For a CT volume (in UcT Hounsfield units HU), the volume is first segmented into material classes," paragraph [0063]),
Mason et al. and Kleinszig et al. are combined as per claim 1.
Claim 13
Regarding claim 13, Mason et al. teach the method of claim 12, wherein the training data comprises a plurality of CT images acquired with a computed tomography scanner and/or a plurality of CBCT images acquired with a CBCT scanner ("the workflow for training begins with the capture of reference images within one or more 3D CT volumes 410, such as captured in ofl.line operations (e.g., from a planning CT before a radiotherapy treatment)," paragraph [0084]).
Claim 14
Regarding claim 14, Mason et al. teach the method of claim 11, wherein the artificial intelligence module is trained with the training data using a supervised or a semi-supervised training algorithm ("the particular machine learning algorithm used by the models may be selected from among many different potential supervised or unsupervised machine learning algorithms. Examples of supervised learning algorithms include artificial neural networks, Bayesian networks, instance based learning, support vector machines, decision trees ( e.g., Iterative Dichotomiser 3, C4.5, Classification and Regression Tree (CART)," paragraph [0081]).
Claim 16
Regarding claim 16, Mason et al. teach the method of claim 1, wherein the forward projecting is based on the attenuation coefficients ("The output then includes a linear projection of some quantity of interest: attenuation coefficient at a nominal energy, mass density, relative electron density, proton stopping power ratio, etc. Projections corrected in this way could be used to reconstruct quantitative CBCT images in a variety of settings," paragraph [0104]).
Claim 17
Regarding claim 17, Mason et al. teach the method of claim 1, wherein the forward projecting includes creating raw data based on the attenuation coefficients ("The output then includes a linear projection of some quantity of interest: attenuation coefficient at a nominal energy, mass density, relative electron density, proton stopping power ratio, etc. Projections corrected in this way could be used to reconstruct quantitative CBCT images in a variety of settings," paragraph [0104]).
Claim 18
Regarding claim 18, Mason et al. teach the method of claim 1, wherein the back-projecting comprises CBCT reconstruction with a cropped field of view corresponding to the cone-beam geometry of the simulated CBCT scanner ("For instance, a reconstructed 3D CBCT image may include spurious features caused by metal that are not present in the patient, resulting from the cumulative effect of combining 2D CBCT projections, each of which has been affected. With the present techniques, improved image quality-and a reduction in artifact-causing effects-is provided by the trained projection correction model 330 for individual 2D CBCT projections," paragraph [0080] which teaches reconstruction, which, as admitted in Applicants' specification on Page 4, 2nd paragraph, is "As the field of view of a CBCT is generally different, usually smaller, than that of the CT, the reconstruction algorithm may comprise a cropped field of view. Examples of reconstruction algorithms include, for example, distributed compressed sensing (DCS), Feldkamp-David-Kress (FDK), or other similar algorithms." emphasis added).
Claim 19
Regarding claim 19, Mason et al. teach a tangible, non-transitory computer readable medium that stores instructions for generating a simulated cone-beam computed tomography (CBCT) image based on a computed tomography image, wherein when executed by a processor("the reference medical image is a 3D image provided from a computed tomography (CT) scan, and wherein the method further includes training of the regression model using a plurality of reference medical images from the CT scan," paragraph [0011]), the instructions cause the processor to:
receive data representing a CT image comprising a volume of a subject, wherein the volume is divided into voxels ("the software programs may register or associate a patient medical image (e.g., a CT image, an MR image, or a reconstructed CBCT image) with that patient's dose distribution of radiotherapy treatment ( e.g., also represented as an image) so that corresponding image voxels and dose voxels are appropriately associated," paragraph [0053]),
receive scanner parameters of a simulated CBCT scanner ("Machine data information may include radiation beam size, arc placement, beam on and off time duration, machine parameters, segments, multi-leaf collimator (MLC) configuration, gantry speed, MRI pulse sequence, and the like," paragraph [0059]);
forward-project the CT image to a projection image based on the scanner parameters of the simulated CBCT scanner ("Projections corrected in this way could be used to reconstruct quantitative CBCT images in a variety of settings," paragraph [0104]);
add artificial noise to the projection image, the artificial noise is a representation of noise detected by the simulated CBCT scanner ("noisy but statistically independent input and scatter corrected target pairs may be used, similar to an "nosie2inverse" model," paragraph [0126]);
back-project the projection image with a reconstruction algorithm, thereby generating a simulated CBCT image of the subject ("the input may be scatter contaminated and the target may be scatter corrected (for example with Monte Carlo simulation), whereby the network could infer how to correct for scatter," paragraph [0125]); and
provide the simulated CBCT image of the subject ("the model (e.g., neural network) is trained based on a single patient-specific patient dataset, such as a planning CT, to infer nonlinearity free projections from raw projections. Non-linearities may include, for example, beam hardening from polyenergetic source ad energy dependent x-ray attenuation; glare from scatter within the detector; lag from finite response rate of the detector; and afterglow corrupting subsequent measurements; variations in gain over detector area from different sensor banks or presence of objects in the beam path such as bow-tie filter or anti-scatter grid. The use case of this trained model may include, for each CBCT projection, to use the model to infer nonlinearity-free projections and then reconstruct a 3D CBCT image with these new projections," paragraph [0130]), wherein the forward-projecting comprises generating a plurality of cone-beam projection images corresponding to a rotational trajectory of the simulated CBCT scanner ("FIG. 2A and FIG. 2B, discussed below, generally illustrate examples of a radiation therapy device configured to provide radiotherapy treatment to a patient, including a configuration where a radiation therapy output can be rotated around a central axis ( e.g., an axis "A")," paragraph [0066]).
Mason et al. do not explicitly teach all of representing the tissue of the subject in a Hounsfield unit.
However, Kleinszig et al. teach wherein the voxels comprise a representation of a tissue property of a tissue of the subject in a Hounsfield Unit ("Desirable properties of the CT volume Y cT include an untruncated reconstruction, high-resolution reconstruction (voxel size and reconstruction kernel), and low-noise images. For a CT volume UcT (in Hounsfield units HU), the volume is first segmented into material classes," paragraph [0063]); and
convert the Hounsfield Unit of the CT image into attenuation coefficients ("The LDP cBcTcT leverages a volumetric representation Y cT of the patient ( such as a preoperative CT) to simulate realistic CBCT projections. Desirable properties of the CT volume Y cT include an untruncated reconstruction, high-resolution reconstruction (voxel size and reconstruction kernel), and low-noise images. For a CT volume UcT (in Hounsfield units HU), the volume is first segmented into material classes," paragraph [0063] where the volumetric representation is the result of Hounsfield units converted into attenuation coefficients).
Mason et al. and Kleinszig et al. are combined as per claim 1.
Claim 20
Regarding claim 20, Mason et al. teach the computer readable medium of claim 19, wherein the instructions further cause the processor to modify the tissue property of the tissue of the subject ("Some specific positions of tumor and OARs (and their mutually related position as a function of treatment course) as well as unusual different conditions, such as obese patients or the presence of prosthetic implants, can result in the suboptimal use of CBCT. For example, consider scenarios involving of small tumors totally embedded in soft tissue whose contrast is not clearly distinguishable from the lesion itself, or hip implants which hinder the correct localization of prostate, rectum, and bladder due to metal imaging artifacts," paragraph [0076] and "an example workflow for capturing and processing imaging data from a CBCT imaging system, using trained imaging processing models. Here, the CBCT imaging processing includes the use of two models: a trained image reconstruction model 330, to reconstruct CBCT projections into a 3D image; and a trained artifact correction model 340 to remove artifacts from CBCT projections." paragraph [0077] where removing artifacts is modifying tissue properties).
Claim 21
Regarding claim 21, Mason et al. teach the computer readable medium of claim 19, wherein the back-projection comprises CBCT reconstruction with a cropped field of view corresponding to the cone- beam geometry of the simulated CBCT scanner ("For instance, a reconstructed 3D CBCT image may include spurious features caused by metal that are not present in the patient, resulting from the cumulative effect of combining 2D CBCT projections, each of which has been affected. With the present techniques, improved image quality-and a reduction in artifact-causing effects-is provided by the trained projection correction model 330 for individual 2D CBCT projections," paragraph [0080] which teaches reconstruction, which, as admitted in Applicants' specification on Page 4, 2nd paragraph, is "As the field of view of a CBCT is generally different, usually smaller, than that of the CT, the reconstruction algorithm may comprise a cropped field of view. Examples of reconstruction algorithms include, for example, distributed compressed sensing (DCS), Feldkamp-David-Kress (FDK), or other similar algorithms." emphasis added).
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2025 0086752 A1 to Patel et al. discloses process can include performing a depthwise convolution operation on image data and a depth-wise convolutional filter with pre-determined parameter values to obtain a plurality of color channels for the image data; performing a convolution operation on the plurality of color channels to obtain a processed plurality of color channels; arranging the processed plurality of color channels into a demosaiced image; and outputting the demosaiced image.
US Patent Publication 2024 0281921 A1 to Nossek et al. discloses leveraging neural networks (e.g., convolutional neural networks (CNNs)) for image restoration tasks (e.g., for demosaicing tasks) are described. In certain aspects, Mixture of Experts (MoE) techniques may be employed, where multiple different expert networks are used to divide a problem space (e.g., image reconstruction tasks) into homogenous regions. For example, each MoE module may reconstruct a certain problem in an image, and a gating component may activate certain MoE modules to provide a reconstructed image. In some aspects, training and optimization techniques are described for each expert of the MoE architecture, to increase individual performance (e.g., a sub-task for each expert of an image processing system may be imposed in a residual manner, a gating function may be trained, etc.).
Conclusion
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/H.E.W/Examiner, Art Unit 2664
Date: 23 April 2026
/JENNIFER MEHMOOD/Supervisory Patent Examiner, Art Unit 2664
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