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 .
Specification
The disclosure is objected to because of the following informalities: the specification contains multiple spelling, grammar, and machine translation errors that require corrections, for example: the terms “analysed”, “centre”, “Ionising”, “ionize”, “analyse”, “optimization”, “neighbouring”, etc.
Appropriate correction is required.
Claim Objections
Claims 1, 4, 7, 18, and 22 objected to because of the following informalities:
Claim 1 recites the limitation “A computer-implemented medical method of determining imaging control data for controlling medical imaging for localizing the position of an anatomical body part, the method comprising: acquiring image contrast data which describes a predetermined criterion to be fulfilled by the contrast of the synthesized two-dimensional medical image; determining localization imaging parameter data based on the synthetic imaging parameter data and the synthetic image data and the image contrast data” should read “A computer-implemented medical method of determining imaging control data for controlling medical imaging for localizing a position of an anatomical body part, the method comprising: acquiring image contrast data which describes a predetermined criterion to be fulfilled by a contrast of the digital synthesized two-dimensional medical image; determining localization imaging parameter data based on the synthetic imaging parameter data, the synthetic image data, and the image contrast data”.
Claim 4 recites the limitation “wherein the localization medical image is an X-ray image” should read “wherein the digital localization medical image is an X-ray image”.
Claim 7 recites the limitation “wherein the localization parameter data is determined if the contrast of the digital synthesized two-dimensional medical image fulfils the predetermined criterion”.
Claim 18 recites the limitation “a three-dimensional digital planning medical image of the anatomical body part; acquire image contrast data which describes a predetermined criterion to be fulfilled by the contrast of the synthesized two-dimensional medical image: determine localization imaging parameter data based on the synthetic imaging parameter data and the synthetic image data and the image contrast data, wherein the localization parameter data describes at least one localization imaging parameter to be used for generating a digital localization medical image of the anatomical body part for localizing the position of the anatomical body part” should read “a three-dimensional digital planning medical image of an anatomical body part; acquire image contrast data which describes a predetermined criterion to be fulfilled by a contrast of the digital synthesized two-dimensional medical image: determine localization imaging parameter data based on the synthetic imaging parameter data, the synthetic image data, and the image contrast data, wherein the localization parameter data describes at least one localization imaging parameter to be used for generating a digital localization medical image of the anatomical body part for localizing a position of the anatomical body part”.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-2, 4-16, and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception in the form of an abstract idea, specifically a mental process, without significantly more.
Regarding claim 1, the examiner notes that the claim is directed to a method for determining imaging control data for controlling medical imaging for localizing the position of an anatomical body part. Therefore, the claims fall within one of the statutory categories of invention.
The claim recites “determining synthetic image data, acquiring image contrast data, and determining localization imaging parameter data”. The limitations, under broadest reasonable interpretation, cover performance of the limitation in the mind, with aid of pen and paper, and/or read on analyzing data, drawing different synthetic images on a paper, comparing the contract of the synthetic images to a predetermined threshold, and determining the optimal imaging parameter that will optimize the localization image results. If a claim limitation under its broadest reasonable interpretation covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas.
The judicial exception is not integrated into a practical application because additional elements of:
- “computer implemented” are generically recited computer elements that do not add a meaningful limitation to the abstract idea because they amounts to simply implementing the abstract idea on a computer;
- “acquiring planning image data which describes a three-dimensional digital planning medical image of the anatomical body part; acquiring synthetic imaging parameter data which at least one synthetic imaging parameter usable for determining a digital synthetic medical image of the anatomical body part based on the planning image data” are generically recited insignificant extra-solution activity of data gathering.
Regarding claims 2, 4-16, and 18, The claims add additional limitations that append the judgement of claim 1 and/or do not include additional elements that are sufficient to amount to significantly more than the judicial exception, nor a practical application of the judicial exception because they disclose steps that can be practically performed within the mind.
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 10, 16, and 22 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 10 recites the limitation "the image representation" in lines 7-11. There is insufficient antecedent basis for this limitation in the claim.
Claim 16 recites the limitation "the parameters" in line 2. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 16, the phrase "for example" renders the claim indefinite because it is unclear whether the limitation(s) following the phrase are part of the claimed invention. See MPEP § 2173.05(d).
Claim 16 recites the limitation “acquiring the parameters of a trained learning algorithm trained by inputting, to the learning algorithm, a plurality of three-dimensional planning images of the anatomical body of different patients and at least one two-dimensional radiography of the anatomical body part associated with each of the three-dimensional planning images and generated using known imaging parameters and for example having a known imaging geometry relative to the associated three-dimensional planning image, wherein the learning algorithm is trained to establish a relation between the at least one two-dimensional radiography of the anatomical body part and the synthesized two-dimensional medical images generated from the associated three-dimensional planning image, wherein the synthetic image data is generated by the trained learning algorithm by inputting the planning image data to the trained learning algorithm” it is unclear what is being inputted into the trained learning algorithm and what is the output of the algorithm. The language of the claim is confusing and makes the examiner question whether the algorithm is trained using the three dimensional planning images and the two dimensional radiography or the trained algorithm is using these two types of images as an input to generate a synthesized image data. The examiner is interpreting the limitation as inputting a three dimensional planning image to a trained learning algorithm that outputs synthesize image data.
Claim 22 recites the limitation "the control data" in line 22. There is insufficient antecedent basis for this limitation in the claim.
Claim 22 recites the limitation “the radiation treatment apparatus for issuing a control signal to the radiation treatment apparatus for controlling the operation of the at least one medical imaging device on the basis of the control data”, based on the language of the limitation, it is unclear if the radiation treatment apparatus or the processor is issuing a control signal to the radiation treatment apparatus for controlling the operation of the at least one medical imaging device on the basis of the control data.
Claim Rejections - 35 USC § 102
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.
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4-14, 18, and 22 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Jordan et al. (US 2022/0305290).
Regarding claim 1, Jordan teaches a computer-implemented medical method of determining imaging control data for controlling medical imaging for localizing the position of an anatomical body part, the method comprising (para. 0054; methods of selecting angles for use during a treatment stage. The methods of FIGS. 2-6 may be performed prior to treatment and may be referred to as pre-treatment angle selection methods. The methods may be performed by a processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device to perform hardware simulation), or a combination thereof. The methods of FIGS. 2-6 may be performed by processing logic of a treatment planning system (e.g., treatment planning system 118 of FIG. 1) and/or by processing logic of an IGRT delivery system (e.g., IGRT delivery system 104 of FIG. 1) in embodiments. After the angles are selected, an imaging device that is a component of an IGRT delivery system may use a subset of the selected angles to generate tracking images and track a target during a treatment stage.):
acquiring planning image data which describes a three-dimensional digital planning medical image of the anatomical body part (fig. 2, para. 0055; Method 200 may begin by generating a three-dimensional treatment planning image of a patient at block 205. In one embodiment, as illustrated, the three-dimensional treatment planning image is a computer tomography (CT) scan of the patient.);
acquiring synthetic imaging parameter data which at least one synthetic imaging parameter usable for determining a digital synthetic medical image of the anatomical body part based on the planning image data (para. 0057; At block 215, processing logic determines a plurality of angles from which tracking images can be generated by an imaging device (e.g., by imaging device 110 of FIG. 1).);
determining synthetic image data based on the planning image data and the synthetic imaging parameter data, wherein the synthetic image data describes a digital synthesized two-dimensional medical image of at least part of the anatomical body part (para. 0058; At block 218, processing logic analyzes each of the determined angles. Analysis of the angles may include generating, at block 220, a plurality of projections of the CT scan of the patient. Each of the projections is generated for a different angle at which the imaging device may be positioned. In one embodiment, 360 projections are generated for angles 1 degree through 360 degrees. Thus, the projections may be generated for every 1 degree of angle separation. Alternatively, projections may be generated, for example, at every 5 degrees of angle separation (e.g., at 5 degrees, 10 degrees, 15 degrees, and so on), at every 10 degrees of angle separation, at every 0.5 degree of angle separate, and so on. Multiple different types of projections may be generated, as discussed below with reference to FIGS. 3A-3D. Some examples of projections that may be generated include digitally reconstructed radiographs (DRRs), geometric projections, ray traces of one or more rays, and so on. A DRR is a virtual x-ray image that is generated from a 3D CT image based on simulating the x-ray image formation process by casting rays through the CT image. Any of the projections may be projected onto a virtual detector plane.);
acquiring image contrast data which describes a predetermined criterion to be fulfilled by the contrast of the synthesized two-dimensional medical image (paras. 0072-0075; The selected angle corresponds to an angle of an imaging device that can be used to generate tracking images. At block 324, processing logic generates a DRR (e.g., a standard DRR) for the selected angle. At block 325, processing logic computes one or more quality metric values for the selected angle based on the DRR. Higher contrast values indicate an increased probability of finding the target during treatment. Accordingly, higher contrast values are preferable. In one embodiment, tracking quality metric criteria include a minimum acceptable contrast and/or a minimum acceptable contrast to noise ratio. The minimum acceptable contrast may be determined based on a combination (e.g., an average) of the contrasts computed for DRRs at multiple different angles. In one embodiment, an angle having a contrast value that is below the minimum acceptable contrast (and/or below the minimum acceptable contrast to noise ratio) fails to satisfy the one or more tracking quality metric criteria.);
determining localization imaging parameter data based on the synthetic imaging parameter data and the synthetic image data and the image contrast data, wherein the localization parameter data describes at least one localization imaging parameter to be used for generating a digital localization medical image of the anatomical body part for localizing the position of the anatomical body part (paras. 0060 and 0075; At block 228, processing logic selects a subset of the angles for which projections were generated. Angles may be selected for inclusion in the subset based on the tracking quality metric values associated with those angles. The angles that are selected for inclusion in the subset have a tracking quality metric value that satisfies a tracking quality metric criterion (or multiple tracking quality metric criteria). In one embodiment, the tracking quality metric criteria include a tracking quality metric threshold. Those angles associated with tracking quality metric values that meet or exceed the tracking quality metric threshold may be included in the subset, while those angles associated with tracking quality metric values below the threshold may not be included in the subset. Higher contrast values indicate an increased probability of finding the target during treatment. Accordingly, higher contrast values are preferable. In one embodiment, tracking quality metric criteria include a minimum acceptable contrast and/or a minimum acceptable contrast to noise ratio. The minimum acceptable contrast may be determined based on a combination (e.g., an average) of the contrasts computed for DRRs at multiple different angles. In one embodiment, an angle having a contrast value that is below the minimum acceptable contrast (and/or below the minimum acceptable contrast to noise ratio) fails to satisfy the one or more tracking quality metric criteria. The examiner notes that the target tracking image parameters is selected based on the set of angles, DDR data, and contract quality metric.);
wherein the synthetic imaging parameter and the localization imaging parameter describe at least one of an energy of an imaging beam used for imaging the anatomical body part and an exposure time of the imaging (para. 0064, 0080-0081, and 0162; DRR pixel values may be computed by summing CT values along each ray. At block 306, a standard DRR is generated for the selected angle. T The standard DRR differs from the target region DRR in that the rays are traced through all regions of the CT scan (or other treatment planning image) to generate the standard DRR. For both the standard DRR and the target region DRR the rays may be traced onto a virtual detector plane. Each pixel of the virtual detector plane may correspond to a ray traced through the CT scan data (or other treatment planning image data). The pixel value for a pixel may be based on an aggregation of the CT values of the associated ray. multiple rays are traced through the target at the selected angle, and effective depth values may be determined for each ray. For example, ray tracing may be performed for anywhere from two rays that pass through the target to all rays that pass through the target. The effective depth values may then be mathematically combined to determine a combined effective depth value. In one embodiment, the effective depth values of the multiple rays are averaged to compute an average effective depth value. In one embodiment, a median effective depth value is computed. The combined effective depth value, average effective depth value and/or median effective depth value may be used as the tracking quality metric value or as an input to the tracking quality metric value. The effective depth value (or values) and/or the tracking quality metric value may then be recorded for the angle. The examiner notes that the treatment plan includes control signals to control the localization imaging device. The control signals includes parameters such as the gantry rotation speed, dose rate, MLC shapes, and collimator angle could be varied during gantry rotation.).
Regarding claim 2, Jordan teaches the method according to claim 1, wherein the anatomical body part is subject to a changing vital state (para. 0040; Depending on the location of the target volume, the target volume can vary in position and orientation and/or can undergo volumetric deformations due to patient movement and/or physiological cycles such as respiration.).
Regarding claim 4, Jordan teaches the method according to claim 1, wherein the synthetic medical image is a synthetic X-ray image (para. 0058; Some examples of projections that may be generated include digitally reconstructed radiographs (DRRs), geometric projections, ray traces of one or more rays, and so on. A DRR is a virtual x-ray image that is generated from a 3D CT image based on simulating the x-ray image formation process by casting rays through the CT image.), and wherein the localization medical image is an X-ray image (para. 0065; the target in tracking images such as x-ray images).
Regarding claim 5, Jordan teaches the method according to claim 1, wherein the planning medical image is a tomographic image, a magnetic resonance tomography or an ultrasound tomography (para. 0055; In one embodiment, as illustrated, the three-dimensional treatment planning image is a computer tomography (CT) scan of the patient).
Regarding claim 6, Jordan teaches the method according to claim 1, wherein the anatomical body part comprises or consists of soft tissue (para. 0003; A non-invasive method to treat a pathological anatomy (e.g., tumor, lesion, vascular malformation, nerve disorder, etc.) is external beam radiation therapy, which typically uses a radiation treatment source (e.g., a linear accelerator (LINAC)) to generate radiation beams such as x-rays.).
Regarding claim 7, Jordan teaches the method according to claim 1, wherein the localization parameter data is determined if the contrast of the synthesized two-dimensional medical image fulfils the predetermined criterion (paras. 0072-0075; At block 322 of method 320 processing logic selects an angle. The selected angle corresponds to an angle of an imaging device that can be used to generate tracking images. At block 324, processing logic generates a DRR (e.g., a standard DRR) for the selected angle. At block 325, processing logic computes one or more quality metric values for the selected angle based on the DRR. Higher contrast values indicate an increased probability of finding the target during treatment. Accordingly, higher contrast values are preferable. In one embodiment, tracking quality metric criteria include a minimum acceptable contrast and/or a minimum acceptable contrast to noise ratio. The minimum acceptable contrast may be determined based on a combination (e.g., an average) of the contrasts computed for DRRs at multiple different angles. In one embodiment, an angle having a contrast value that is below the minimum acceptable contrast (and/or below the minimum acceptable contrast to noise ratio) fails to satisfy the one or more tracking quality metric criteria.).
Regarding claim 8, Jordan teaches the method according to claim 1, wherein the predetermined criterion is a threshold for the contrast of the synthesized two-dimensional digital medical image (paras. 0060 and 0075; At block 228, processing logic selects a subset of the angles for which projections were generated. Angles may be selected for inclusion in the subset based on the tracking quality metric values associated with those angles. The angles that are selected for inclusion in the subset have a tracking quality metric value that satisfies a tracking quality metric criterion (or multiple tracking quality metric criteria). In one embodiment, the tracking quality metric criteria include a tracking quality metric threshold. Those angles associated with tracking quality metric values that meet or exceed the tracking quality metric threshold may be included in the subset, while those angles associated with tracking quality metric values below the threshold may not be included in the subset.).
Regarding claim 9, Jordan teaches the method according to claim 7, wherein the synthetic image data is determined using the synthetic imaging parameter and the at least one localization imaging parameter corresponds to the synthetic imaging parameter (paras. 0057-0061; At block 215, processing logic determines a plurality of angles from which tracking images can be generated by an imaging device (e.g., by imaging device 110 of FIG. 1). At block 218, processing logic analyzes each of the determined angles. Analysis of the angles may include generating, at block 220, a plurality of projections of the CT scan of the patient. Some examples of projections that may be generated include digitally reconstructed radiographs (DRRs), geometric projections, ray traces of one or more rays, and so on. A DRR is a virtual x-ray image that is generated from a 3D CT image based on simulating the x-ray image formation process by casting rays through the CT image. At block 230, processing logic may order the angles based on their associated tracking quality metric values. The subset of angles to be used for tracking purposes may be those angles having highest tracking quality metric values. Accordingly, optimal angles may be determined for the purpose of generating images to track a target during a treatment stage of a patient.).
Regarding claim 10, Jordan teaches the method according to claim 1, wherein the three-dimensional digital planning medical image includes a plurality of three-dimensional digital planning medical images describing the anatomical body part in different vital states (fig. 5, para. 0097; a method 500 of selecting a set of angles for use by a rotational imaging device to be used during treatment, in accordance with one embodiment of the present invention. In certain embodiments method 500 is substantially similar to method 200. Specifically, embodiments of method 500 correspond to performing the operations of method 200 for multiple different times of a 4D CT scan (or other 4D treatment planning image) and selecting a subset of angles based on combined results of the tracking quality metric values for the angles at the multiple different time slices. The examiner notes that the planning data includes multiple images that are generated at different times), the digital synthesized two-dimensional medical image includes a plurality of synthesized two-dimensional medical images of at least part of the anatomical body part associated with the plurality of three-dimensional digital planning medical images (fig. 5, steps 518-535, paras. 0099-0103; At block 518, processing logic analyses the plurality of angles for a first time of the 4D CT scan (or other 4D treatment planning image). At block 420, analysis of the angles includes generating a first plurality of projections of the CT scan (or other 3D treatment planning image) of the patient for the first time. At block 528, processing logic analyzes the plurality of angles for a second time of the 4D CT scan (or other 4D treatment planning image). Some examples of projections that may be generated include digitally reconstructed radiographs (DRRs), geometric projections, ray traces of one or more rays, and so on. The examiner notes that different DDRs are generated for each of the plurality of CT images), and the at least one synthetic imaging parameter and the contrast of the image representation of the anatomical body part and anatomical structures surrounding the anatomical body part in the synthesized two-dimensional medical image are varied to optimize localization of the image representation the anatomical body part in each of the plurality of synthesized two-dimensional medical images (paras. 0061 and 0075; At block 230, processing logic may order the angles based on their associated tracking quality metric values. The subset of angles to be used for tracking purposes may be those angles having highest tracking quality metric values. Accordingly, optimal angles may be determined for the purpose of generating images to track a target during a treatment stage of a patient. Higher contrast values indicate an increased probability of finding the target during treatment. Accordingly, higher contrast values are preferable. In one embodiment, tracking quality metric criteria include a minimum acceptable contrast and/or a minimum acceptable contrast to noise ratio. The minimum acceptable contrast may be determined based on a combination (e.g., an average) of the contrasts computed for DRRs at multiple different angles. In one embodiment, an angle having a contrast value that is below the minimum acceptable contrast (and/or below the minimum acceptable contrast to noise ratio) fails to satisfy the one or more tracking quality metric criteria.).
Regarding claim 11, Jordan teaches the method according to claim 1, wherein the digital synthesized two-dimensional medical image includes at least two digital synthesized two-dimensional medical images of the at least part of the anatomical body part which are or have been generated using different values of the at least one synthetic imaging parameter and the at least two digital synthesized two-dimensional medical images are weighted and subtracted from each other and the result of the subtraction is used for determining the localization imaging parameter data (paras. 0064-0067 and 0072; At block 304, processing logic generates a target region DRR for the selected angle. A target region DRR is a DRR that is generated by casting or tracing rays through just the target in a CT scan or other treatment planning image (e.g., an MRI image). DRR pixel values may be computed by summing CT values along each ray. At block 306, a standard DRR is generated for the selected angle. T The standard DRR differs from the target region DRR in that the rays are traced through all regions of the CT scan (or other treatment planning image) to generate the standard DRR. For both the standard DRR and the target region DRR the rays may be traced onto a virtual detector plane. the target tracking algorithm performs pattern matching based on similarity values between the target region DRR and the standard DRR, as described in the example below and indicated in block 310. In such an example, the target tracking algorithm determines characteristics or patterns such as a shape of the target from the target region DRR. The target tracking algorithm computes similarity values between a first pattern of the target from the target region DRR and patterns for each of several candidate locations for the target in the standard DRR. The maximum of similarity values between the first pattern from the target region DRR and the additional patterns from the standard DRR indicates a location of the target in the standard DRR. The tracking quality metric value may be proportional to the degree of similarity between the first pattern and the closest pattern from the standard DRR in some embodiments. The selected angle corresponds to an angle of an imaging device that can be used to generate tracking images. At block 324, processing logic generates a DRR (e.g., a standard DRR) for the selected angle. At block 325, processing logic computes one or more quality metric values for the selected angle based on the DRR. Multiple different techniques may be used to compute the quality metric values, some of which are described herein. If multiple quality metric values are determined, these values may be combined into a combined quality metric value. The combined quality metric value may be based on a weighted or non-weighted combination of the different quality metric values. The examiner notes that the system generates a standard DDR and target region DDR. The two DDRs are weighted based on their quality and similarity between them is determined to generate quality metric for the best parameter to be used to generate the target tracking image).
Regarding claim 12, Jordan teaches the method according to claim 1, comprising determining, based on the localization imaging parameter data, control data for controlling a medical imaging device to generate an image of the anatomical body part (paras. 0042 and 0061; System controller 114 may be a computing device that includes a processing device such as discussed above with reference to processing device 170. System controller 114 may also include a system memory and a storage device, similar to system memory 177 and storage device 180. System controller 114 further a detector controller 122, a couch position controller 124, and an imaging device controller 126, each programmed and configured to achieve one or more of the functionalities described further herein. One or more imaging devices 110 selectively emit relatively low-energy (e.g., kV level) x-ray imaging radiation under the control of imaging device controller 126, the imaging radiation being captured by one or more imaging detectors 112. At block 230, processing logic may order the angles based on their associated tracking quality metric values. The subset of angles to be used for tracking purposes may be those angles having highest tracking quality metric values. Accordingly, optimal angles may be determined for the purpose of generating images to track a target during a treatment stage of a patient.).
Regarding claim 13, Jordan teaches the method according to claim 12, wherein the control data describes a control signal to be issued to the medical imaging device to generate an image of the anatomical body part using the localization imaging parameter (paras. 0042 and 0061; System controller 114 may be a computing device that includes a processing device such as discussed above with reference to processing device 170. System controller 114 may also include a system memory and a storage device, similar to system memory 177 and storage device 180. System controller 114 further a detector controller 122, a couch position controller 124, and an imaging device controller 126, each programmed and configured to achieve one or more of the functionalities described further herein. One or more imaging devices 110 selectively emit relatively low-energy (e.g., kV level) x-ray imaging radiation under the control of imaging device controller 126, the imaging radiation being captured by one or more imaging detectors 112. At block 230, processing logic may order the angles based on their associated tracking quality metric values. The subset of angles to be used for tracking purposes may be those angles having highest tracking quality metric values. Accordingly, optimal angles may be determined for the purpose of generating images to track a target during a treatment stage of a patient.).
Regarding claim 14, Jordan teaches the method according to claim 13, comprising execution of the control data (paras. 0042 and 0061; System controller 114 may be a computing device that includes a processing device such as discussed above with reference to processing device 170. System controller 114 may also include a system memory and a storage device, similar to system memory 177 and storage device 180. System controller 114 further a detector controller 122, a couch position controller 124, and an imaging device controller 126, each programmed and configured to achieve one or more of the functionalities described further herein. One or more imaging devices 110 selectively emit relatively low-energy (e.g., kV level) x-ray imaging radiation under the control of imaging device controller 126, the imaging radiation being captured by one or more imaging detectors 112. At block 230, processing logic may order the angles based on their associated tracking quality metric values. The subset of angles to be used for tracking purposes may be those angles having highest tracking quality metric values. Accordingly, optimal angles may be determined for the purpose of generating images to track a target during a treatment stage of a patient.).
Regarding claim 18, Jordan teaches a non-volatile computer-readable storage medium comprising instructions which, when executed by at least on processor, cause the at least one processor to (para. 0054; methods of selecting angles for use during a treatment stage. The methods of FIGS. 2-6 may be performed prior to treatment and may be referred to as pre-treatment angle selection methods. The methods may be performed by a processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device to perform hardware simulation), or a combination thereof. The methods of FIGS. 2-6 may be performed by processing logic of a treatment planning system (e.g., treatment planning system 118 of FIG. 1) and/or by processing logic of an IGRT delivery system (e.g., IGRT delivery system 104 of FIG. 1) in embodiments. After the angles are selected, an imaging device that is a component of an IGRT delivery system may use a subset of the selected angles to generate tracking images and track a target during a treatment stage.):
acquire planning image data which describes a three-dimensional digital planning medical image of the anatomical body part (fig. 2, para. 0055; Method 200 may begin by generating a three-dimensional treatment planning image of a patient at block 205. In one embodiment, as illustrated, the three-dimensional treatment planning image is a computer tomography (CT) scan of the patient.);
acquire synthetic imaging parameter data which at least one synthetic imaging parameter usable for determining a digital synthetic medical image of the anatomical body part based on the planning image data (para. 0057; At block 215, processing logic determines a plurality of angles from which tracking images can be generated by an imaging device (e.g., by imaging device 110 of FIG. 1).);
determine synthetic image data based on the planning image data and the synthetic imaging parameter data, wherein the synthetic image data describes a digital synthesized two-dimensional medical image of at least part of the anatomical body part (para. 0058; At block 218, processing logic analyzes each of the determined angles. Analysis of the angles may include generating, at block 220, a plurality of projections of the CT scan of the patient. Each of the projections is generated for a different angle at which the imaging device may be positioned. In one embodiment, 360 projections are generated for angles 1 degree through 360 degrees. Thus, the projections may be generated for every 1 degree of angle separation. Alternatively, projections may be generated, for example, at every 5 degrees of angle separation (e.g., at 5 degrees, 10 degrees, 15 degrees, and so on), at every 10 degrees of angle separation, at every 0.5 degree of angle separate, and so on. Multiple different types of projections may be generated, as discussed below with reference to FIGS. 3A-3D. Some examples of projections that may be generated include digitally reconstructed radiographs (DRRs), geometric projections, ray traces of one or more rays, and so on. A DRR is a virtual x-ray image that is generated from a 3D CT image based on simulating the x-ray image formation process by casting rays through the CT image. Any of the projections may be projected onto a virtual detector plane.);
acquire image contrast data which describes a predetermined criterion to be fulfilled by the contrast of the synthesized two-dimensional medical image (paras. 0072-0075; The selected angle corresponds to an angle of an imaging device that can be used to generate tracking images. At block 324, processing logic generates a DRR (e.g., a standard DRR) for the selected angle. At block 325, processing logic computes one or more quality metric values for the selected angle based on the DRR. Higher contrast values indicate an increased probability of finding the target during treatment. Accordingly, higher contrast values are preferable. In one embodiment, tracking quality metric criteria include a minimum acceptable contrast and/or a minimum acceptable contrast to noise ratio. The minimum acceptable contrast may be determined based on a combination (e.g., an average) of the contrasts computed for DRRs at multiple different angles. In one embodiment, an angle having a contrast value that is below the minimum acceptable contrast (and/or below the minimum acceptable contrast to noise ratio) fails to satisfy the one or more tracking quality metric criteria.);
determine localization imaging parameter data based on the synthetic imaging parameter data and the synthetic image data and the image contrast data, wherein the localization parameter data describes at least one localization imaging parameter to be used for generating a digital localization medical image of the anatomical body part for localizing the position of the anatomical body part (paras. 0060 and 0075; At block 228, processing logic selects a subset of the angles for which projections were generated. Angles may be selected for inclusion in the subset based on the tracking quality metric values associated with those angles. The angles that are selected for inclusion in the subset have a tracking quality metric value that satisfies a tracking quality metric criterion (or multiple tracking quality metric criteria). In one embodiment, the tracking quality metric criteria include a tracking quality metric threshold. Those angles associated with tracking quality metric values that meet or exceed the tracking quality metric threshold may be included in the subset, while those angles associated with tracking quality metric values below the threshold may not be included in the subset. Higher contrast values indicate an increased probability of finding the target during treatment. Accordingly, higher contrast values are preferable. In one embodiment, tracking quality metric criteria include a minimum acceptable contrast and/or a minimum acceptable contrast to noise ratio. The minimum acceptable contrast may be determined based on a combination (e.g., an average) of the contrasts computed for DRRs at multiple different angles. In one embodiment, an angle having a contrast value that is below the minimum acceptable contrast (and/or below the minimum acceptable contrast to noise ratio) fails to satisfy the one or more tracking quality metric criteria. The examiner notes that the target tracking image parameters is selected based on the set of angles, DDR data, and contract quality metric.);
wherein the synthetic imaging parameter and the localization imaging parameter describe at least one of an energy of an imaging beam used for imaging the anatomical body part and an exposure time of the imaging (para. 0064, 0080-0081, and 0162; DRR pixel values may be computed by summing CT values along each ray. At block 306, a standard DRR is generated for the selected angle. T The standard DRR differs from the target region DRR in that the rays are traced through all regions of the CT scan (or other treatment planning image) to generate the standard DRR. For both the standard DRR and the target region DRR the rays may be traced onto a virtual detector plane. Each pixel of the virtual detector plane may correspond to a ray traced through the CT scan data (or other treatment planning image data). The pixel value for a pixel may be based on an aggregation of the CT values of the associated ray. multiple rays are traced through the target at the selected angle, and effective depth values may be determined for each ray. For example, ray tracing may be performed for anywhere from two rays that pass through the target to all rays that pass through the target. The effective depth values may then be mathematically combined to determine a combined effective depth value. In one embodiment, the effective depth values of the multiple rays are averaged to compute an average effective depth value. In one embodiment, a median effective depth value is computed. The combined effective depth value, average effective depth value and/or median effective depth value may be used as the tracking quality metric value or as an input to the tracking quality metric value. The effective depth value (or values) and/or the tracking quality metric value may then be recorded for the angle. The examiner notes that the treatment plan includes control signals to control the localization imaging device. The control signals includes parameters such as the gantry rotation speed, dose rate, MLC shapes, and collimator angle could be varied during gantry rotation.).
Regarding claim 22, Jordan teaches a medical system, comprising: at least one processor with associated memory storing instructions wherein execution of the instructions by the at least one processor cause the processor to(para. 0054; methods of selecting angles for use during a treatment stage. The methods of FIGS. 2-6 may be performed prior to treatment and may be referred to as pre-treatment angle selection methods. The methods may be performed by a processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device to perform hardware simulation), or a combination thereof. The methods of FIGS. 2-6 may be performed by processing logic of a treatment planning system (e.g., treatment planning system 118 of FIG. 1) and/or by processing logic of an IGRT delivery system (e.g., IGRT delivery system 104 of FIG. 1) in embodiments. After the angles are selected, an imaging device that is a component of an IGRT delivery system may use a subset of the selected angles to generate tracking images and track a target during a treatment stage.):
acquire planning image data which describes a three-dimensional digital planning medical image of the anatomical body part (fig. 2, para. 0055; Method 200 may begin by generating a three-dimensional treatment planning image of a patient at block 205. In one embodiment, as illustrated, the three-dimensional treatment planning image is a computer tomography (CT) scan of the patient.);
acquire synthetic imaging parameter data which at least one synthetic imaging parameter usable for determining a digital synthetic medical image of the anatomical body part based on the planning image data (para. 0057; At block 215, processing logic determines a plurality of angles from which tracking images can be generated by an imaging device (e.g., by imaging device 110 of FIG. 1).);
determine synthetic image data based on the planning image data and the synthetic imaging parameter data, wherein the synthetic image data describes a digital synthesized two-dimensional medical image of at least part of the anatomical body part (para. 0058; At block 218, processing logic analyzes each of the determined angles. Analysis of the angles may include generating, at block 220, a plurality of projections of the CT scan of the patient. Each of the projections is generated for a different angle at which the imaging device may be positioned. In one embodiment, 360 projections are generated for angles 1 degree through 360 degrees. Thus, the projections may be generated for every 1 degree of angle separation. Alternatively, projections may be generated, for example, at every 5 degrees of angle separation (e.g., at 5 degrees, 10 degrees, 15 degrees, and so on), at every 10 degrees of angle separation, at every 0.5 degree of angle separate, and so on. Multiple different types of projections may be generated, as discussed below with reference to FIGS. 3A-3D. Some examples of projections that may be generated include digitally reconstructed radiographs (DRRs), geometric projections, ray traces of one or more rays, and so on. A DRR is a virtual x-ray image that is generated from a 3D CT image based on simulating the x-ray image formation process by casting rays through the CT image. Any of the projections may be projected onto a virtual detector plane.);
acquire image contrast data which describes a predetermined criterion to be fulfilled by the contrast of the synthesized two-dimensional medical image (paras. 0072-0075; The selected angle corresponds to an angle of an imaging device that can be used to generate tracking images. At block 324, processing logic generates a DRR (e.g., a standard DRR) for the selected angle. At block 325, processing logic computes one or more quality metric values for the selected angle based on the DRR. Higher contrast values indicate an increased probability of finding the target during treatment. Accordingly, higher contrast values are preferable. In one embodiment, tracking quality metric criteria include a minimum acceptable contrast and/or a minimum acceptable contrast to noise ratio. The minimum acceptable contrast may be determined based on a combination (e.g., an average) of the contrasts computed for DRRs at multiple different angles. In one embodiment, an angle having a contrast value that is below the minimum acceptable contrast (and/or below the minimum acceptable contrast to noise ratio) fails to satisfy the one or more tracking quality metric criteria.);
determine localization imaging parameter data based on the synthetic imaging parameter data and the synthetic image data and the image contrast data, wherein the localization parameter data describes at least one localization imaging parameter to be used for generating a digital localization medical image of the anatomical body part for localizing the position of the anatomical body part (paras. 0060 and 0075; At block 228, processing logic selects a subset of the angles for which projections were generated. Angles may be selected for inclusion in the subset based on the tracking quality metric values associated with those angles. The angles that are selected for inclusion in the subset have a tracking quality metric value that satisfies a tracking quality metric criterion (or multiple tracking quality metric criteria). In one embodiment, the tracking quality metric criteria include a tracking quality metric threshold. Those angles associated with tracking quality metric values that meet or exceed the tracking quality metric threshold may be included in the subset, while those angles associated with tracking quality metric values below the threshold may not be included in the subset. Higher contrast values indicate an increased probability of finding the target during treatment. Accordingly, higher contrast values are preferable. In one embodiment, tracking quality metric criteria include a minimum acceptable contrast and/or a minimum acceptable contrast to noise ratio. The minimum acceptable contrast may be determined based on a combination (e.g., an average) of the contrasts computed for DRRs at multiple different angles. In one embodiment, an angle having a contrast value that is below the minimum acceptable contrast (and/or below the minimum acceptable contrast to noise ratio) fails to satisfy the one or more tracking quality metric criteria. The examiner notes that the target tracking image parameters is selected based on the set of angles, DDR data, and contract quality metric.);
wherein the synthetic imaging parameter and the localization imaging parameter describe at least one of an energy of an imaging beam used for imaging the anatomical body part and an exposure time of the imaging (para. 0064, 0080-0081, and 0162; DRR pixel values may be computed by summing CT values along each ray. At block 306, a standard DRR is generated for the selected angle. T The standard DRR differs from the target region DRR in that the rays are traced through all regions of the CT scan (or other treatment planning image) to generate the standard DRR. For both the standard DRR and the target region DRR the rays may be traced onto a virtual detector plane. Each pixel of the virtual detector plane may correspond to a ray traced through the CT scan data (or other treatment planning image data). The pixel value for a pixel may be based on an aggregation of the CT values of the associated ray. multiple rays are traced through the target at the selected angle, and effective depth values may be determined for each ray. For example, ray tracing may be performed for anywhere from two rays that pass through the target to all rays that pass through the target. The effective depth values may then be mathematically combined to determine a combined effective depth value. In one embodiment, the effective depth values of the multiple rays are averaged to compute an average effective depth value. In one embodiment, a median effective depth value is computed. The combined effective depth value, average effective depth value and/or median effective depth value may be used as the tracking quality metric value or as an input to the tracking quality metric value. The effective depth value (or values) and/or the tracking quality metric value may then be recorded for the angle. The examiner notes that the treatment plan includes control signals to control the localization imaging device. The control signals includes parameters such as the gantry rotation speed, dose rate, MLC shapes, and collimator angle could be varied during gantry rotation.);
at least one electronic data storage device storing the control data (paras. 0035 and 0042; Treatment planning system 118 may also include A storage device 180, representing one or more storage devices (e.g., a magnetic disk drive, optical disk drive, solid state drive, etc.) coupled to the bus for storing information and instructions. Storage device 180 may be used for storing instructions for performing the treatment planning steps discussed herein, such as treatment planning operations to select a set of imaging angles. System controller 114 may be a computing device that includes a processing device such as discussed above with reference to processing device 170. System controller 114 may also include a system memory and a storage device, similar to system memory 177 and storage device 180. System controller 114 further a detector controller 122, a couch position controller 124, and an imaging device controller 126, each programmed and configured to achieve one or more of the functionalities described further herein. One or more imaging devices 110 selectively emit relatively low-energy (e.g., kV level) x-ray imaging radiation under the control of imaging device controller 126, the imaging radiation being captured by one or more imaging detectors 112.); and
a radiation treatment apparatus comprising at least one medical imaging device, wherein the at least one processor is operably coupled to (fig. 1, para. 0044; An imaging system of the IGRT delivery system 104 comprises one or more independent imaging devices 110 that produce relatively low intensity lower energy imaging radiation (each of which can be termed a “kV source”).):
the at least one electronic data storage device for acquiring, from the at least one data storage device, the control data, and the radiation treatment apparatus for issuing a control signal to the radiation treatment apparatus for controlling the operation of the at least one medical imaging device on the basis of the control data (para. 0042; System controller 114 may be a computing device that includes a processing device such as discussed above with reference to processing device 170. System controller 114 may also include a system memory and a storage device, similar to system memory 177 and storage device 180. System controller 114 further a detector controller 122, a couch position controller 124, and an imaging device controller 126, each programmed and configured to achieve one or more of the functionalities described further herein. One or more imaging devices 110 selectively emit relatively low-energy (e.g., kV level) x-ray imaging radiation under the control of imaging device controller 126, the imaging radiation being captured by one or more imaging detectors 112.).
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.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Jordan et al. (US 2022/0305290) in the view of Mailhe et al. (US 2022/0292742).
Regarding claim 15, Jordan teaches the method according to claim 1, however, fails to explicitly teach wherein the synthetic image data is determined by applying the Beer-Lambert law to the planning image data.
Mailhe, in the same field of endeavor, teaches synthetic image data is determined by applying the Beer-Lambert law to the planning image data (para. 0027; to generate the DRR, an input CT medical image is (optionally) normalized at block 102 and a body (or any other anatomical object of interest) of the patient is extracted from the normalized input CT medical image at block 104. The body extraction is performed to remove foreign objections in the normalized input CT medical image to be consistent with x-ray images. For example, a table that the patient is lying on, which is not shown in a typical x-ray image, may be removed from the normalized input CT medical image. The body of the patient may be extracted from the normalized input CT medical image by segmenting the body from the normalized input CT medical image, e.g., using a pre-trained machine learning based segmentation model. The regions of the normalized input CT medical image outside of the segmented body are then masked. The DRR may be generated by projecting the masked normalized input CT medical image using Beer's law to simulate the physical x-ray travelling through tissue. ).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the generation of digital reconstructed radiograph of Jordan to incorporate the teaching of Mailhe to include generating digital reconstructed radiograph by applying beer’s law to CT images. Doing so would accurately simulate the physical x-ray travelling through tissue as disclosed within Mailhe in para. 0027. The simulated physical x-ray travelling through tissue describes directly how x-rays are attenuated as they pass through the tissue.
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Jordan et al. (US 2022/0305290) in the view of Liyue et al. (NPL: “Novel-view X-ray projection synthesis through geometry-integrated deep learning”).
Regarding claim 16, Jordan teaches the method according to claim 1, however, fails to explicitly teach further comprising acquiring the parameters of a trained learning algorithm trained by inputting, to the learning algorithm, a plurality of three-dimensional planning images of the anatomical body of different patients and at least one two-dimensional radiography of the anatomical body part associated with each of the three-dimensional planning images and generated using known imaging parameters and for example having a known imaging geometry relative to the associated three-dimensional planning image, wherein the learning algorithm is trained to establish a relation between the at least one two-dimensional radiography of the anatomical body part and the synthesized two-dimensional medical images generated from the associated three-dimensional planning image, wherein the synthetic image data is generated by the trained learning algorithm by inputting the planning image data to the trained learning algorithm.
Liyue, in the same field of endeavor, teaches acquiring the parameters of a trained learning algorithm trained by inputting, to the learning algorithm, a plurality of three-dimensional planning images of the anatomical body of different patients and at least one two-dimensional radiography of the anatomical body part associated with each of the three-dimensional planning images and generated using known imaging parameters and for example having a known imaging geometry relative to the associated three-dimensional planning image, wherein the learning algorithm is trained to establish a relation between the at least one two-dimensional radiography of the anatomical body part and the synthesized two-dimensional medical images generated from the associated three-dimensional planning image (page 6, section 4.1; The experiments were conducted on a public dataset of The Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) ( Armato et al., 2011 , 2015 ; Clark et al., 2013 ). This dataset contains 1018 thoracic 3D CT images from different patients. We regarded each CT image as an independent data sample for model training. In data pre-processing, all the CT images were resampled with the same resolution of 1 mm in the z-axis, and were resized to the same image size of 128 × 128 in the xy-plane. In order to obtain the X-ray projections at different angles, we projected the 3D CT image to get the digitally reconstructed radio- graphs (DRRs) in different view angles. The cone-beam geometry of the projection operation was defined according to the clinical on-board cone-beam CT system for radiation therapy. Each 2D X- ray projection was of the size 180 × 300 . Following the image processing of model training, the intensity values of the all the 2D X-ray projection images were normalized to the data range of [0, 1]. In experiments, we randomly selected 80% of the dataset for training and validation (815 samples) while 20% of the data were held out for testing (203 samples).), wherein the synthetic image data is generated by the trained learning algorithm by inputting the planning image data to the trained learning algorithm (page 10, section 6; In this work, we tackle the problem of novel-view synthesis for X-ray projections and propose a deep learning-based DL-GIPS network. It is shown that the proposed model is able to generate the X-ray projection at the target-view angle with the given source-view projection. The synthesized X-ray projections can be utilized for numerous practical applications, such as gaining com- prehensive perspectives of the patient anatomy ( Ge et al., 2019 ), tumor target localization and patient setup in image guided radi- ation therapy ( Zhao et al., 2021 ), to ultra-sparse CT image recon- struction ( Shen et al., 2021 ), and so forth.).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the generation of digital reconstructed radiograph of Jordan to incorporate the teaching of Liyue to include using trained learning algorithm for generating digital reconstructed radiograph. Doing so would help to construct a robust, reliable, and interpretable projection synthesis model as disclosed within Liyue in page 2.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZAINAB M ALDARRAJI whose telephone number is (571)272-8726. The examiner can normally be reached Monday-Thursday7AM-5PM EST.
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/ZAINAB MOHAMMED ALDARRAJI/Patent Examiner, Art Unit 3797
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