DETAILED ACTION
Claims 1-6, 8-12, 15, and 17-20 are pending in this application and are being examined with the priority date of 7/10/2023 in accordance with the applicant’s claim for foreign priority. Claims 1, 3, 10, 15 and 17 have been amended, and claims 7, 13-14 and 16 have been canceled.
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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/17/2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
Amended claim 1
Applicant’s arguments (see Remarks filed 03/18/2026) regarding the rejections made under 35 U.S.C. 102 to amended claim 1 have been fully considered by the examiner and are not persuasive. The applicant argues that Lachaine fails to teach “the guidance image being an image generated based on an image-guided radiation therapy system”. The examiner disagrees, the generation of the guidance image as claimed in amended claim 1 is very broad, the broadest reasonable interpretation of this would be any image generated by a means of medical imaging that uses radiation, where the imager is part of a system for diagnosis of treatment determination. Lachaine teaches in paragraph [0032] that cone beam CT scans are obtained using an imager as part of a system which is used is to generate a radiation treatment plan for patient.
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(Lachaine, [0032])
Applicant further argues Lachaine fails to teach “performing image processing on the guidance image to generate a target image, the target image being an intensity projection image of a four-dimensional-computed tomography (4D-CT)”. The examiner disagrees, Lachaine teaches in [0034] that the CBCT, which is analogous to the guidance image, is transformed to generate a synthetic CT, or sCT. This sCT is generated using an iterative optimization process as disclosed in Lachaine paragraph [0036] to deform the CBCT (guidance image), because iterative optimization uses multiple projections to regularize the input image, this would mean the output sCT would be a projection of the CBCT image generated. One of ordinary skill in the art would understand the sCT which is generated using iterative optimization of a CBCT scan as being analogous to a projection of this CBCT scan.
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(Lachaine, [0034])
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(Lachaine, [0036])
Applicant further argues that Lachaine fails to teach “obtaining a planned image, wherein the planned image is a 4D-CT intensity projection image used to formulate a treatment plan” because the projections of Lachaine are not intensity projections. The examiner disagrees, Lachaine teaches in paragraph [0038] that projections can be generated from the CBCT images to generate 4D-CT from the projections to dynamically adapt the treatment to the patient. Given that an intensity projection is a method of condensing multiple images to visualize and enhance features of the images, one of ordinary skill in the art would understand that generating a 4D-CT projection from multiple binned (condensed) projections to better visualize margins would be analogous to a 4D-CT intensity projection.
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(Lachaine, [0038])
Applicant further argues that Lachaine fails to teach “performing registration on the target image and the planned image; and in response to a registration result, adjusting a position of a patient, stopping treatment, or adjusting the treatment plan” because the registration performed in Lachaine is different than the registration disclosed by the applicant. The examiner disagrees and notes that there is not a specified type of registration claimed in amended claim 1, therefore the broadest reasonable interpretation of this limitation means that any type of registration performed on two images is analogous. Lachaine teaches in paragraph [0129] that the 4D-CT projection of the respiratory phases, which is analogous to the planned image as discussed above, and the CT image data (target image) are registered to form a deformation vector field (DVF). One of ordinary skill in the art would under this as being analogous to the broadest reasonable interpretation of the claimed limitation. The applicant is encouraged to further narrow the scope of the claimed subject matter to distinguish the type of registration from the registration performed in Lachaine. Therefore, for at least the reasons above, the examiner is maintaining the rejections made under 35 U.S.C. 102 in view of Lachaine.
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(Lachaine, [0129])
Amended claim 10
Applicant’s arguments (see Remarks filed 03/18/2026) regarding the rejections made under 35 U.S.C. 103 to amended claim 10 have been fully considered by the examiner and are not persuasive. The applicant argues that no combination of Lachaine, Prokop or Paysan teaches the use of a single-phase planned CT image and therefore the combination fails to teach all limitations of amended claim 10. The examiner disagrees, applicant’s specification paragraphs [0091]-[0093] teach that a “single-phase planned CT image” is a CT image corresponding a single phase of the respiratory cycle. Paysan teaches in paragraph [0003] that forward projections of a planning CBCT are used to process volume changes of the patient during respiration, and in paragraphs [0045]-[0046] this is used to compute motion during breathing, therefore this would be analogous to the applicant’s disclosed definition of a single-phase planned CT image.
Applicant further argues that the combination fails to teach every aspect of the claim because Lachaine only teaches deformable registration between CT scans, not a deformation field as disclosed. The examiner disagrees, Lachaine teaches in paragraph [0036] that deformable vector fields may be generated when CT scans are registered or deformed to fit one another. Therefore for at least the reasons above the examiner maintains the rejections made under 35 U.S.C. 103 over Lachaine, in view of Prokop and Paysan.
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(Paysan, [0002]-[0003])
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(Paysan, [0045]-[0046])
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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-6, 8-9, 11-12, 15, and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Lachaine (US 20230302297 A1).
Regarding claim 1 Lachaine discloses; An image processing method, executed by a computing device, comprising:
obtaining a guidance image, the guidance image being an image generated based on an image-guided radiation therapy system (Lachaine, [0032] cone beam CT scans (guidance images) can be obtained for the radiotherapy system);
and performing image processing on the guidance image to generate a target image (Lachaine, [0034] the CBCT (guidance image) can be used to generate a sCT or synthetic CT (target image)),
the target image being a an intensity projection image of a four-dimensional-computed tomography (4D-CT) (Lachaine, [0034] the CBCT (guidance image) can be used to generate a sCT or synthetic CT (target image) which is a synthetic image or simulated image)
obtaining a planned image, wherein the planned image is a 4D-CT intensity projection image used to formulate a treatment plan (Lachaine, [0038] the 4D CT binned projections can be used for treatment and planning purposes);
performing registration on the target image and the planned image (Lachaine, [0129] for each respiratory phase, which per [0038] is a 4D CT projection used for treatment planning, the binned respiratory phase data/image is used to generate a DVF (which is a descriptor for how a patient’s organ is moving) using deformable registration between the 3D CT images (target images) and the respiratory phase data (planning images));
and in response to a registration result, adjusting a position of a patient, stopping treatment, or adjusting the treatment plan (Lachaine, [0037] after the DVF (Deformation field vector, which describes the movement in position of each point in the planning CT scan per [0035] of Lachaine, and is computed as part of the registration result) is computed the patients positioning may be changed accordingly).
Regarding claim 2 Lachaine discloses; The image processing method according to claim 1, wherein performing the image processing on the guidance image includes:
inputting the guidance image into an image conversion model trained based on deep learning (Lachaine, [0036] deep learning can be used to generate the sCT (target image), which per [0034] of Lachaine is generated using the original CBCT image (guidance image));
or performing deformable registration combined with forward and backward projection calculation on the guidance image (Lachaine, [0129] the deformable vectors generated during the processing of the CBCT image (guidance image) to generate the sCT (target image) can be generated by performing deformable registration, [0123]-[0124] the imaging system can acquire projections of the patient and bin them to be used in the processing).
Regarding claim 3 Lachaine discloses; The image processing method according to claim 2,
wherein the guidance image is a cone-beam computed tomography (CBCT) guidance image (Lachaine, [0034] the CBCT (guidance image) can be used to generate a synthetic CT or sCT (target image));
performing the image processing on the guidance image includes (Lachaine, [0048] the system has an image processing device which is connected to a database [0050] the system has the ability to process images using multiple models):
inputting the CBCT guidance image into the image conversion model trained based on deep learning to obtain the target image (Lachaine, [0050] the system may convert images such as CT into synthetic images using a trained model, [0183] deep learning may be used to generate the synthetic CT (target image) from the CBCT image (guidance image)),
wherein the target image is an intensity projection image of a 4D-CT simulation image corresponding to the CBCT guidance image (Lachaine, [0123] the system generates a 4D CT image from multiple projections of a 3D CT image).
Regarding claim 4 Lachaine discloses; The image processing method according to claim 3, wherein the image conversion model uses CBCT images generated based on the image-guided radiation therapy system as initial images (Lachaine, [0076] the system saves medical images as training images for training the models used in the system, where per [0050] the algorithm may be trained using medical images which can include the CT images (initial images) to convert the medical images to pseudo images or synthetic images),
and uses 4D-CT intensity projection images as training images (Lachaine, [0141]-[0145] a 4D-CT can be used to generate the synthetic image, which is done using a trained model, [0125] if validation of the output fails, the model can be trained with image data, such as CT images [0050] the system has image data to train a predictive conversion model, given that the model is able to take 4D-CT data as input to generate the synthetic image, the model would also need to be trained to perform this task using the aforementioned 4D-CT data);
wherein a CBCT image generated based on the image-guided radiation therapy system is a CBCT intensity projection image obtained by averaging at least one obtained initial CBCT image (Lachaine, [0183] the pixels of the CBCT are averaged to obtain a CBCT which is full field of view, since an intensity projection is obtained by averaging the pixel values of the image to create an averaged version of the initial image, this would be analogous to this step).
Regarding claim 5 Lachaine discloses; The image processing method according to claim 2,
wherein the guidance image is a four-dimensional-CBCT (4D-CBCT) guidance image (Lachaine, [0034] the CBCT (guidance image) can be used to generate a synthetic CT or sCT (target image), [0040] – [0044] the model may be based on inputting 4D CBCT images);
performing the image processing on the guidance image includes (Lachaine, [0040] the input images are processed):
inputting the 4D-CBCT guidance image into the image conversion model trained based on deep learning or performing the deformable registration combined with forward and backward projection calculation on the 4D-CBCT guidance image (Lachaine, [0040] – [0044] the model may be based on inputting 4D CBCT images, a model can be built/trained using 4D CBCT images which are input into the model to obtain a synthetic image, Further, per [0036] this process may be done via deep learning), so as to obtain the target image (Lachaine, [0034] the CBCT (guidance image) can be used to generate a synthetic CT or sCT (target image));
wherein the target image is an intensity projection image of a 4D-CT simulation image corresponding to the 4D-CBCT guidance image (Lachaine, [0183] the pixels of the CBCT are averaged to obtain a CBCT which is full field of view, since an intensity projection is obtained by averaging the pixel values of the image to create an averaged version of the initial image, this would be analogous to this step).
Regarding claim 6 Lachaine discloses; The image processing method according to claim 5, wherein the image conversion model uses 4D-CBCT images generated based on the image-guided radiation therapy system as initial images (Lachaine, [0040] – [0044] the model may be based on inputting 4D CBCT images, a model can be built/trained using 4D CBCT images which are input into the model to obtain a synthetic image, Further, per [0036] this process may be done via deep learning), and uses 4D-CT intensity projection images as training images (Lachaine, [0141]-[0145] a 4D-CT can be used to generate the synthetic image, which is done using a trained model, [0125] if validation of the output fails, the model can be trained with image data, such as CT images [0050] the system has image data to train a predictive conversion model, given that the model is able to take 4D-CT data as input to generate the synthetic image, the model would also need to be trained to perform this task using the aforementioned 4D-CT data);
wherein a 4D-CBCT image generated based on the image-guided radiation therapy system is a 4D-CBCT image generated by processing at least one obtained CBCT image (Lachaine, [0034] the CBCT (guidance image) can be used to generate a sCT or synthetic CT (target image) which is a synthetic image or simulated image which corresponds to the image input into the model),
and the 4D-CBCT image includes images of different phases of a respiratory cycle (Lachaine, [0038] for respiratory targets, the 4D images can be used where they correspond to a representative respiratory phased for planning and treatment, as well as respiratory motion).
Regarding claim 8 Lachaine discloses; The image processing method according to claim 2, wherein the guidance image is a 4D-CBCT guidance image (Lachaine, [0034] the CBCT (guidance image) can be used to generate a synthetic CT or sCT (target image), [0040] – [0044] the model may be based on inputting 4D CBCT images);
performing the image processing on the guidance image includes: inputting the 4D-CBCT guidance image into the image conversion model trained based on deep learning or performing the deformable registration combined with forward and backward projection calculation on the 4D-CBCT guidance image (Lachaine, [0040] – [0044] the model may be based on inputting 4D CBCT images, a model can be built/trained using 4D CBCT images which are input into the model to obtain a synthetic image, Further, per [0036] this process may be done via deep learning), so as to obtain a 4D-CT simulation image (Lachaine, [0141]-[0145] the 4D CT image can be transformed into a simulated 4D CBCT synthetic image);
and processing the 4D-CT simulation image to obtain the target image, the target image being an intensity projection image of the 4D-CT simulation image (Lachaine, [0183] the pixels of the CBCT are averaged to obtain a CBCT which is full field of view, since an intensity projection is obtained by averaging the pixel values of the image to create an averaged version of the initial image, this would be analogous to this step).
Regarding claim 9 Lachaine discloses; The image processing method according to claim 8, wherein the image conversion model uses 4D-CBCT images generated based on the image-guided radiation therapy system as initial images (Lachaine, [0040] – [0044] the model may be based on inputting 4D CBCT images, a model can be built/trained using 4D CBCT images which are input into the model to obtain a synthetic image, Further, per [0036] this process may be done via deep learning), and uses 4D-CT intensity projection images as training images (Lachaine, [0141]-[0145] a 4D-CT can be used to generate the synthetic image, which is done using a trained model, [0125] if validation of the output fails, the model can be trained with image data, such as CT images [0050] the system has image data to train a predictive conversion model, given that the model is able to take 4D-CT data as input to generate the synthetic image, the model would also need to be trained to perform this task using the aforementioned 4D-CT data);
wherein a 4D-CBCT image generated based on the image-guided radiation therapy system is a 4D-CBCT image generated by processing at least one obtained CBCT image (Lachaine, [0034] the CBCT (guidance image) can be used to generate a sCT or synthetic CT (target image) which is a synthetic image or simulated image which corresponds to the image input into the model),
and the 4D-CBCT image includes images of different phases of a respiratory cycle (Lachaine, [0038] for respiratory targets, the 4D images can be used where they correspond to a representative respiratory phased for planning and treatment, as well as respiratory motion).
Regarding claim 11 Lachaine discloses; The image processing method according to claim 1, further comprising: displaying the target image (Lachaine, [0048] system is equipped with a display coupled to an image acquisition device and a memory, [0066] results or other estimates and processed images are displayed, which would include the target image, [0093] the displayed device can display the medical images, projections, contours, targets, tracking of targets in images or other images).
Regarding claim 12 Lachaine discloses; The image processing method according to claim 11, wherein before displaying the target image, the image processing method further comprises (Lachaine, [0093] the user may input information into the interface which also displays the images, [0066] input may be requested during the iterative reconstruction which can trigger the termination of the reconstruction and display of the result, here the input would be analogous to the first information):
displaying first information, the first information being used to instruct display of the target image (Lachaine, [0066] the input terminates the process and instructs the display of a result, which can also signal the system to display the current structural estimate/reconstruction (target image));
wherein displaying the target image includes: displaying the target image in response to an operation on the first information (Lachaine, [0066] the input terminates the process and instructs the display of a result, which can also signal the system to display the current structural estimate/reconstruction (target image)).
Regarding claim 15 Lachaine discloses; The image processing method according to claim 1, wherein performing the registration on the target image and the planned image includes:
displaying the target image, wherein(Lachaine, [0087] multiple images are acquired which may contain a target tumor or organ, further [0089] the image may be processed (generating of a target image) to delineate a target which may be a tumor and generate contours, [0090] to delineate the tumor the images may be processing the 2D and 3D target images to overlay contours of the tumor on top the image, [0115] the processing may generate a tumor cross section or projection);
displaying the planned image, wherein the planned image includes tumor contours (Lachaine, [0086]-[0087] the images may be used to determine the locations of a tumor, and [0089] contours of the tumor may be computed, and overlaid on the image, this may be used to study and determine dosages and treatment planning, therefore this is analogous to a planning image);
and performing the registration on the tumor intensity projection of the target image and the tumor contours of the planned image (Lachaine, [0090] the contours of the tumor are overlaid on the images of the anatomical portion of interest (target image) further, [0091] 2D tumor contours (projections) may be layered with each 2D slice of the anatomical image to delineate the tumor on the image from the surrounding tissue).
Regarding claim 19 Lachaine discloses; A computing device, comprising: a processor (Lachaine, [0012] the system has a processor);
and a memory coupled to the processor (Lachaine, [0049] the processor is coupled to a memory), wherein the memory is used to store one or more programs, and the one or more programs include computer program instructions that, when executed by the processor, cause the computing device to perform the image processing method according to claim 1 (Lachaine, [0049] the processor is coupled to a memory, where the memory stores instruction to perform medical imaging and image processing methods to be executed by the processor).
Regarding claim 20 Lachaine discloses; A non-transitory computer-readable storage medium having stored computer program instructions that, when run on a computer, cause the computer to perform the image processing method according to claim 1 (Lachaine, [0012] the system has a processor that is coupled to non-transitory computer readable medium which executes a method as taught above).
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.
Claims 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Lachaine (US 20230302297 A1) in view of Paysan (US 20180229056).
Regarding claim 10 Lachaine discloses; The image processing method according to claim 8, wherein performing the deformable registration combined with forward and backward projection calculation on the 4D-CBCT guidance image includes (Lachaine, [0129] the 3D CT images can be processed to obtain each respiratory phase using deformable registration, [0038] the reconstruction algorithms use projections from different respiratory cycles coupled with the registration techniques, [0059]-[0060] the CBCT images undergo projection measurements to generate an X-ray measurement, which is a method of forward projection as forward projection in medical imaging is used to simulate X-ray data, [0038] the algorithms also reconstruct the CT images to generate the synthetic/simulated/target images which uses projections, since it is for the purposes of reconstruction this would be back projection.):
obtaining a planned image, (Lachaine, [0014] the system generates a treatment plan from multiple imaging sessions, where in the image is a CBCT image is obtained);
obtaining a CBCT image of each phase of the 4D-CBCT guidance image (Lachaine, [0038] 4D CBCT imaging can be used to obtain images of the respiratory phases to capture respiratory motion [0141]-[0145] each respiratory phased can have a corresponding CBCT image for it);
performing iteration on the CBCT image of each phase to generate a CT simulation image of each phase (Lachaine, [0038] CBCT and CT images can be generated from binned data for each phase of respiration in order to encompass respiratory motion, [0054] the system can iteratively reconstruct the images to get the x ray images to generate a 3D model of each region of interest, [0066]-[0067] the iterative x-ray reconstruction can be used in the CBCT reconstruction, which as described in [0038] of Lachaine is used for respiratory phase modeling);
and obtaining CT simulation images of a plurality of phases to generate the 4D-CT simulation image corresponding to the 4D-CBCT guidance image (Lachaine, [0038] CBCT and CT images can be generated from binned data for each phase of respiration in order to encompass respiratory motion, the output of this is a 4D CBCT or CT reconstruction based on respiratory cycle information [0054] the system can iteratively reconstruct the images to get the x ray images to generate a 3D model of each region of interest, [0066]-[0067] the iterative x-ray reconstruction can be used in the CBCT reconstruction, which as described in [0038] of Lachaine is used for respiratory phase modeling);
[wherein performing the iteration on the CBCT image of each phase includes:
performing forward projection on a single-phase planned CT deformation image to obtain a planned CT projection;
reconstructing a first image by subtracting a projection of the CBCT image from the planned CT projection, wherein a single-phase planned CT deformation image in a first iteration is the planned image;
obtaining a second image by subtracting the reconstructed first image from the single-phase planned CT deformation image;]
determining whether the current deformation field meets deformation requirements (Lachaine, [0035] A deformation vector field is generated between the simulated image and the Planning CT, [0036] regularization is performed to assure the deformation vector field is physically possible (deformation requirements));
and if it is determined that the current deformation field does not meet the deformation requirements, obtaining a current planned CT deformation image according to the current deformation field (Lachaine, [0175] a Deformation field vector can be determined such that it meets a set of criteria such as ensuring all voxels are mapped such that the CT and CBCT voxels correspond to one another, and all points are mapped to points on the CT external contour, [0181] when the data is incomplete(i.e. not meeting deformation requirements) the deformation field if limited to the points the system already knows, and the planning CT is deformed using the deformation field to generate the simulated CT),
and the current planned CT deformation image being used as a single-phase planned CT deformation image for a next iteration (Lachaine, [0175] a Deformation field vector can be determined such that it meets a set of criteria such as ensuring all voxels are mapped such that the CT and CBCT voxels correspond to one another, and all points are mapped to points on the CT external contour, [0181] when the data is incomplete(i.e. not meeting deformation requirements) the deformation field if limited to the points the system already knows, and the planning CT is deformed using the deformation field to generate the simulated CT, [0178] the deformation vector field can be applied iteratively to deform one surface to another), and until it is determined that the current deformation field meets the deformation requirements, ending the iteration (Lachaine, [0181] optimization is used to apply the deformation vector field to the images, so when the best result is reaches the iteration ends);
wherein a planned CT deformation image that is obtained according to a deformation field obtained in a last iteration is used as the CT simulation image (Lachaine, [0181] optimization is used to apply the deformation vector field to the images, so when the best result is reaches the iteration ends, the planning CT is deformed using the deformation field to generate the simulated CT).
Lachaine does not teach;
wherein performing the iteration on the CBCT image of each phase includes:
performing forward projection on a single-phase planned CT deformation image to obtain a planned CT projection;
reconstructing a first image by subtracting a projection of the CBCT image from the planned CT projection, wherein a single-phase planned CT deformation image in a first iteration is the planned image;
obtaining a second image by subtracting the reconstructed first image from the single-phase planned CT deformation image;
In the same field of endeavor, Paysan teaches;
wherein performing the iteration on the CBCT image of each phase includes (Paysan, [0015] and figure 6, where figure 6 shoes the iterative reconstruction, [0045]-[0046] motion compensation for breathing may be performed):
performing forward projection on a single-phase planned CT deformation image to obtain a planned CT projection (Paysan, [0003] the planning CT reconstructed volume is a forward projection of the planning CT);
reconstructing a first image by subtracting a projection of the CBCT image from the planned CT projection (Paysan, [0087] the reconstructed volume can be updated by subtracting the reconstructed volume from the previous iteration),
wherein a single-phase planned CT deformation image in a first iteration is the planned image (Paysan, [0087] the reconstructed volume can be updated by subtracting the reconstructed volume from the previous iteration, where [0111] the planning volume is compared to the reconstructed volume, and the difference is minimized, in this case the planned volume would be the comparison volume, and would need to be the input of the first iteration as understood by one of ordinary skill in the art);
obtaining a second image by subtracting the reconstructed first image from the single-phase planned CT deformation image (Paysan, [0087] the reconstructed volume may be updated by subtracting the previous iterations reconstruction);
performing deformable registration on the second image and the planned CT image to obtain a current deformation field (Paysan, [0111] deformable registration is used to alight the planned CT and the reconstructed volume);
The combination of Lachaine and Paysan would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The system of Lachaine teaches that iterative reconstruction is used with a deformation vector field to generate a simulated image. Paysan additionally teaches iterative reconstruction as well the use of subtraction of a volume to adjust a reconstruction. The addition of Paysan’s method would have been advantageous in that it teaches further methods of adjusting he reconstruction to improve the accuracy of the reconstruction image (Paysan [0087] and [0107]-[0116])
Regarding claim 17 Lachaine fails to teach; The image processing method according to claim 1
when a side of an intensity projection of the target image deviates from tumor contours of the planned image, adjusting the position of the patient;
and when two opposite sides of the intensity projection of the target image deviate from the tumor contours of the planned image, stopping the treatment or adjusting the treatment plan.
However, in the same field of endeavor, Paysan teaches;
wherein in response to a registration result, adjusting the position of the patient, stopping the treatment or adjusting the treatment plan includes (Paysan, [0045] the extracted volume of the tumor and the planned tumor margins can be compared during treatment so motion can be compensated for [0046] the patient’s posture and position can be adjusted during treatment in response to this to assure the tumor is in the right location for treatment [0118]-[0119] an alignment process such as registration is used to compared the tumor location in the CT is matched with the position in the planning image):
when a side of an intensity projection of the target image deviates from tumor contours of the planned image, adjusting the position of the patient (Paysan, [0123] patient can be moved based on a difference between the planning tumor volume and the CBCT reconstructed volume of the tumor [0124] when the contours of the planning CT do not match the tumor contours of the simulated CBCT, where [0022] the simulated CBCT and the planning CT are both projection images);
and when two opposite sides of the intensity projection of the target image deviate from the tumor contours of the planned image, stopping the treatment or adjusting the treatment plan (Paysan, [0073] the error between the planned tumor contours and the projection tumor contours are measure for each pixel or voxel, [0075]-[0076] this can be measured for multiple angle directions of each voxel in the reconstructed volume compared with the actual tumor volume, indicating that all sides of the contours are measured, [0110]-[0111] the differences between the planned and the reconstructed volumes (which is a projection) are computed iteratively, and then the patient is rotated and adjusted to minimize the error. [0114] the treatment plan is updated based on this adjustment, adjustment can include changing the positions, dosage or treatment plan parameters).
The combination of Lachaine and Paysan would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The system of Lachaine teaches that when the planned volume is different that the projection reconstructed volume that the treatment plan is adjusted, however it does not teach a directionality or thresholding of this difference. Paysan teaches this deficiency, the addition of this method to the method and system of Lachaine would reasonably improve the accuracy of the treatment and dosing plan for the patient. (Paysan, [0107]-[0116])
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Lachaine (US 20230302297 A1) in view of Prokop (US 12086979 B2).
Regarding claim 18 Lachaine fails to teach; The image processing method according to claim 3, wherein the intensity projection image of the 4D-CT simulation image includes a maximum intensity projection (MIP) image or an average intensity projection (AIP) image.
However, in the same field of endeavor of CT processing Prokop teaches; wherein the intensity projection image of the 4D-CT simulation image includes a maximum intensity projection (MIP) image or an average intensity projection (AIP) image (Prokop, Column 9 lines 4-16 the 4D to 3D projection image is generated using either a Maximum intensity projection or an average intensity projection).
The combination of Lachaine and Prokop would have been obvious to one of ordinary skill in the art prior to the effective filing date of the presently claimed invention. The system of Lachaine teaches reconstruction of 4D CBCT images and the generation of tumor treatment plans based upon the reconstructions, it however does not teach specifically using a maximum or average intensity projection to do this. However, in the same field of endeavor Prokop teaches this method. Using a maximum or average intensity projection to reconstruct CT images as taught in Prokop would reasonably improve the method of Lachaine because these types of projections are used for accurate representation of light and dark shades in medical images, which are used for distinguishing different structures and tissues. (Applicant’s specification paragraphs [0068]-[0071])
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. For a listing of analogous art as cited by the examined please see the attached PTO-892 Notice of References Cited form.
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/J.M.E./Examiner, Art Unit 2666 /Molly Wilburn/Primary Examiner, Art Unit 2666
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