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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on November 25, 2024, and April 08, 2025, are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
Applicant’s Amendment filed on September 24, 2024, has been entered and made of record.
Currently pending Claim(s) 1-13, 15-20, and 22
Independent Claim(s) 1, 12, and 22
Canceled Claim(s) 14 and 21
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are:
“Imaging device” in claims 1, 3, 5, 12, and 22.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in [0052-0054] of the specification as performing the claimed function, and equivalents thereof. This interpretation includes medical imaging devices for performing 2D, 3D, and/or 4D imaging. These devices include, for example, a CT device (e.g., a CT device), an X-ray imaging device, a DR device, a SPECT device, a PET-CT device, an X-ray-MRI device, a SPECT-MRI device, or a CT guided radiotherapy device.
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation recites sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 102
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 1-2, 5, 8, 10-13, 16, 18, 20, and 22 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Cao et al. (US 2021/0082161 A1), hereafter Cao.
Regarding claim 1, Cao teaches a method for medical image reconstruction ([0054] “An aspect of the present disclosure relates to systems and methods for image reconstruction.”), comprising:
determining a target temporal resolution of a target scan on a target subject within a scanning angle range, determining, based on the target temporal resolution, a plurality of reconstruction angle ranges each of which being within the scanning angle range (In [0113], Cao discusses selecting scanning angle ranges for acquiring a higher temporal resolution. For example, when imaging an organ which undergoes physiological motion (such as the heart), Cao teaches using a smaller angle range to improve the temporal resolution for images of the heart. In [0114], Cao teaches using a larger angle range with a longer scan time for capturing global features with sufficient temporal resolution. Thus, reconstruction angle ranges are selected based on a desired temporal resolution. Furthermore, [0054-0056] and Fig. 5 describe the process of selecting more than one reconstruction angle range to collect scan data in within a full scanning angle range.);
obtaining scan data acquired by causing an imaging device to perform the target scan on the target subject within the scanning angle range (Fig. 5 shows that scan data is acquired over each reconstruction angle range. At step 501, a first set of image data is acquired over a first reconstruction angle range; then at step 502, a second set of image data is acquired over a second reconstruction angle range.);
generating a plurality of reconstruction images corresponding to the plurality of reconstruction angle ranges based on the scan data (Fig. 5 shows generating images from the scan data. At step 503, a first image is generated from the first set of image data collected over the first reconstruction angle range; then at step 504, a second image is generated from the second set of image data collected over the second reconstruction angle range. At step 505, a new image can be generated using the first and second image. This new generated image may be an intermediate image or an image with improved temporal resolution [0114-0119, Fig. 6]).
Regarding claim 2, Cao teaches the method of claim 1, wherein at least one overlapping angle range exists between at least one pair of adjacent reconstruction angle ranges of the plurality of reconstruction angle ranges, one overlapping angle range corresponding to one of the at least one pair of adjacent reconstruction angle ranges ([0101] “In some embodiments, the first angle range may be a portion of the second angle range. Merely by way of example, the first angle range may include 90 to 240 degrees, and the second angle range may include 0 to 360 degrees. In some embodiments, at least a portion of the first angle range may be overlapped with the second angle range. Merely by way of example, the first angle range may include 90 to 240 degrees, and the second angle range may include 180 to 360 degrees.”).
Regarding claim 5, Cao teaches the method of claim 1, the determining a plurality of reconstruction angle ranges ([0102] discusses selecting reconstruction angle ranges.) including:
obtaining a rotation speed of the imaging device during the target scan (Cao teaches obtaining the rotational speed of the scanner as part of determining data acquisition angles. [0102] “…and then determine the data acquisition angle corresponding to the data acquisition time period based on the rotation speed of the scanner.”);
determining a reference value of a step based on the rotation speed and the target temporal resolution, the step being an angle interval between adjacent reconstruction angle ranges among the plurality of reconstruction angle ranges; and determining the plurality of reconstruction angle ranges based on the reference value (Based on the physiological characteristics of the patient and the rotational speed of the scanner, the doctor may determine the necessary reconstruction angle ranges for capturing scan data. [0102] “Merely by way of example, if the subject (e.g., a region of a patient (such as the heart, the lung, the rib, the enterocoelia, or the like)) undergoes a physiological motion, the processing device 140 may determine the data acquisition time period corresponding to a target phase of the physiological motion of the subject (e.g., when the cardiac motion is gentle), and then determine the data acquisition angle corresponding to the data acquisition time period based on the rotation speed of the scanner. The processing device 140 may determine the first angle range and/or the second angle range based on the data acquisition angle corresponding to the data acquisition time period. For example, the processing device 140 may determine the data acquisition angle as a middle angle between a starting angle and an ending angle of the first angle range or the second angle range.” These ranges may be non-overlapping and include a step interval between the angle ranges. [0101] “Merely by way of example, the first angle range may include 90 to 180 degrees, and the second angle range may include 181 to 360 degrees. It should be noted that the values of the first angle range and the second angle range described herein are merely provided for illustration, and are not limiting.”).
Regarding claim 8, Cao teaches the method of claim 1, further comprising: generating one or more predicted reconstruction images based on the plurality of reconstruction images, each of the one or more predicted reconstruction images corresponding to a reconstruction angle range that is between two adjacent reconstruction angle ranges among the plurality of reconstruction angle ranges (Fig. 5 shows generating images from the scan data. At step 503, a first image is generated from the first set of image data collected over the first reconstruction angle range; then at step 504, a second image is generated from the second set of image data collected over the second reconstruction angle range. At step 505, an intermediate image can be generated using the first and second image [0114-0119, Fig. 6]. [0116] “In some embodiments, the processing device 140 may generate a first intermediate image by performing image arithmetic between the first image and the second image.”).
Regarding claim 10, Cao teaches The method of claim 8, wherein the generating the one or more predicted reconstruction images based on the plurality of reconstruction images includes: obtaining at least one pair of adjacent reconstruction images among the plurality of reconstruction images; obtaining a second trained image prediction model; and for each of the at least one pair of adjacent reconstruction images, generating a predicted reconstruction image by inputting the pair of adjacent reconstruction images into the second trained image prediction model. (Cao teaches generating a new target image using the first and the second image. [0118] “In some embodiments, the processing device 140 may generate the target image based on the first image and the second image using a trained machine learning model.” These images are adjacent to one another.).
Regarding claim 11, Cao teaches the method of claim 1, wherein a temporal resolution of the plurality of reconstruction images is equal to or higher than the target temporal resolution (Cao teaches that reconstructed images created using a faster rotation speed and/or a smaller angle scanning range may have higher temporal resolution. When scanning the human heart, Cao teaches methods for obtaining reconstruction images with a higher temporal resolution. [0113] “A smaller angle range may require a shorter time period for a scanning operation designed to generate an image of the subject than a larger angle range, which may improve the time resolution of the image and reduce image noises (e.g., a motion artifact caused by a physiological motion that the subject undergoes).” [0114] “Since the second angle range may require a longer time period than the first angle range for a scanning operation, a time resolution corresponding to the second image may exceed a time resolution corresponding to the first image.”).
Regarding claim 12, Cao teaches a system for medical image reconstruction ([0054] “An aspect of the present disclosure relates to systems and methods for image reconstruction.”), comprising:
at least one storage device including a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein, when the instructions are executed, the at least one processor is configured to direct the system to perform operations ([0004] “The system may include at least one storage device including a set of instructions, and at least one processor configured to communicate with the at least one storage device.”), including:
determining a target temporal resolution of a target scan on a target subject within a scanning angle range; determining, based on the target temporal resolution, a plurality of reconstruction angle ranges each of which being within the scanning angle range (In [0113], Cao discusses selecting scanning angle ranges for acquiring a higher temporal resolution. For example, when imaging an organ which undergoes physiological motion (such as the heart), Cao teaches using a smaller angle range to improve the temporal resolution for images of the heart. In [0114], Cao teaches using a larger angle range with a longer scan time for capturing global features with sufficient temporal resolution. Thus, reconstruction angle ranges are selected based on a desired temporal resolution. Furthermore, [0054-0056] and Fig. 5 describe the process of selecting more than one reconstruction angle range to collect scan data in within a full scanning angle range.);
obtaining scan data acquired by causing an imaging device to perform the target scan on the target subject within the scanning angle range (Fig. 5 shows that scan data is acquired over each reconstruction angle range. At step 501, a first set of image data is acquired over a first reconstruction angle range; then at step 502, a second set of image data is acquired over a second reconstruction angle range.);
generating a plurality of reconstruction images corresponding to the plurality of reconstruction angle ranges based on the scan data (Fig. 5 shows generating images from the scan data. At step 503, a first image is generated from the first set of image data collected over the first reconstruction angle range; then at step 504, a second image is generated from the second set of image data collected over the second reconstruction angle range. At step 505, a new image can be generated using the first and second image. This new generated image may be an intermediate image or an image with improved temporal resolution [0114-0119, Fig. 6]).
Regarding claim 13, Cao teaches the system of claim 12, wherein at least one overlapping angle range exists between at least one pair of adjacent reconstruction angle ranges of the plurality of reconstruction angle ranges, one overlapping angle range corresponding to one of the at least one pair of adjacent reconstruction angle ranges ([101] “In some embodiments, the first angle range may be a portion of the second angle range. Merely by way of example, the first angle range may include 90 to 240 degrees, and the second angle range may include 0 to 360 degrees. In some embodiments, at least a portion of the first angle range may be overlapped with the second angle range. Merely by way of example, the first angle range may include 90 to 240 degrees, and the second angle range may include 180 to 360 degrees.”).
Regarding claim 16, Cao teaches the system of claim 12, the determining a plurality of reconstruction angle ranges ([0102] discusses selecting reconstruction angle ranges.) including:
obtaining a rotation speed of the imaging device during the target scan (Cao teaches obtaining the rotational speed of the scanner as part of determining data acquisition angles. [0102] “…and then determine the data acquisition angle corresponding to the data acquisition time period based on the rotation speed of the scanner.”);
determining a reference value of a step based on the rotation speed and the target temporal resolution, the step being an angle interval between adjacent reconstruction angle ranges among the plurality of reconstruction angle ranges; and determining the plurality of reconstruction angle ranges based on the reference value (Based on the physiological characteristics of the patient and the rotational speed of the scanner, the doctor may determine the necessary reconstruction angle ranges for capturing scan data. [0102] “Merely by way of example, if the subject (e.g., a region of a patient (such as the heart, the lung, the rib, the enterocoelia, or the like)) undergoes a physiological motion, the processing device 140 may determine the data acquisition time period corresponding to a target phase of the physiological motion of the subject (e.g., when the cardiac motion is gentle), and then determine the data acquisition angle corresponding to the data acquisition time period based on the rotation speed of the scanner. The processing device 140 may determine the first angle range and/or the second angle range based on the data acquisition angle corresponding to the data acquisition time period. For example, the processing device 140 may determine the data acquisition angle as a middle angle between a starting angle and an ending angle of the first angle range or the second angle range.” These ranges may be non-overlapping and include a step interval between the angle ranges. [0101] “Merely by way of example, the first angle range may include 90 to 180 degrees, and the second angle range may include 181 to 360 degrees. It should be noted that the values of the first angle range and the second angle range described herein are merely provided for illustration, and are not limiting.”).
Regarding claim 18, Cao teaches the system of claim 12, wherein the at least one processor is configured to direct the system to perform further operations including: generating one or more predicted reconstruction images based on the plurality of reconstruction images, each of the one or more predicted reconstruction images corresponding to a reconstruction angle range that is between two adjacent reconstruction angle ranges among the plurality of reconstruction angle ranges (Fig. 5 shows generating images from the scan data. At step 503, a first image is generated from the first set of image data collected over the first reconstruction angle range; then at step 504, a second image is generated from the second set of image data collected over the second reconstruction angle range. At step 505, an intermediate image can be generated using the first and second image [0114-0119, Fig. 6]. [0116] “In some embodiments, the processing device 140 may generate a first intermediate image by performing image arithmetic between the first image and the second image.”).
Regarding claim 20, Cao teaches the system of claim 18, wherein the generating the one or more predicted reconstruction images based on the plurality of reconstruction images includes: obtaining at least one pair of adjacent reconstruction images among the plurality of reconstruction images; obtaining a second trained image prediction model; and for each of the at least one pair of adjacent reconstruction images, generating a predicted reconstruction image by inputting the pair of adjacent reconstruction images into the second trained image prediction model (Cao teaches generating a new target image using the first and the second image. [0118] “In some embodiments, the processing device 140 may generate the target image based on the first image and the second image using a trained machine learning model.” These images are adjacent to one another.).
Regarding claim 22, Cao teaches a computer-readable non-transitory storage medium storing computer instructions ([0004] “The system may include at least one storage device including a set of instructions, and at least one processor configured to communicate with the at least one storage device.”), and when a computer reads the computer instructions in the non-transitory storage medium, the computer executes a method for medical image reconstruction ([0054] “An aspect of the present disclosure relates to systems and methods for image reconstruction.”), comprising:
determining a target temporal resolution of a target scan on a target subject within a scanning angle range; determining, based on the target temporal resolution, a plurality of reconstruction angle ranges each of which being within the scanning angle range (In [0113], Cao discusses selecting scanning angle ranges for acquiring a higher temporal resolution. For example, when imaging an organ which undergoes physiological motion (such as the heart), Cao teaches using a smaller angle range to improve the temporal resolution for images of the heart. In [0114], Cao teaches using a larger angle range with a longer scan time for capturing global features with sufficient temporal resolution. Thus, reconstruction angle ranges are selected based on a desired temporal resolution. Furthermore, [0054-0056] and Fig. 5 describe the process of selecting more than one reconstruction angle range to collect scan data in within a full scanning angle range.);
obtaining scan data acquired by causing an imaging device to perform the target scan on the target subject within the scanning angle range (Fig. 5 shows that scan data is acquired over each reconstruction angle range. At step 501, a first set of image data is acquired over a first reconstruction angle range; then at step 502, a second set of image data is acquired over a second reconstruction angle range.);
generating a plurality of reconstruction images corresponding to the plurality of reconstruction angle ranges based on the scan data (Fig. 5 shows generating images from the scan data. At step 503, a first image is generated from the first set of image data collected over the first reconstruction angle range; then at step 504, a second image is generated from the second set of image data collected over the second reconstruction angle range. At step 505, a new image can be generated using the first and second image. This new generated image may be an intermediate image or an image with improved temporal resolution [0114-0119, Fig. 6]).
Claim Rejections - 35 USC § 103
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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Cao (US 2021/0082161 A1) in view of Chelnokov (US 2022/0245867 A1).
Regarding claim 3, Cao teaches the method of claim 1, the obtaining scan data further including: obtaining real-time scan data collected by the imaging device during the target scan; and the generating a plurality of reconstruction images corresponding to the plurality of reconstruction angle ranges based on the scan data (Fig. 5 shows collecting scan data for two different reconstruction angle ranges; see steps 501 and 502. Steps 503 and 504 show performing reconstruction on the scan data collected during steps 501 and 502 to generate reconstructed images.). However, the image reconstruction of steps 503 and 504 occurs after the scanning of the reconstruction angle ranges in steps 501 and 502. Thus, Cao fails to teach for each of the plurality of reconstruction images, starting image reconstruction based on the real-time scan data after the imaging device rotates to a starting angle of the corresponding reconstruction angle range.
However, Chelnokov teaches further including: for each of the plurality of reconstruction images, starting image reconstruction based on the real-time scan data after the imaging device rotates to a starting angle of the corresponding reconstruction angle range (Chelnokov teaches performing CT scanning and performing image reconstruction during the scanning process. [0003] “A computer-implemented method for CT reconstruction can include receiving a plurality of detected radiographs from a CT scanner during CT scanning and beginning reconstruction of one or more of the detected radiographs before all of the detected radiographs are acquired.”).
Cao and Chelnokov are analogous in the art to the claimed invention because both teach methods for performing CT scans and performing image reconstruction. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cao’s invention by starting image reconstruction immediately as the scanning starts. This modification would increase the speed of the CT scanning system since reconstructed images would be available sooner, and this would improve upon CT systems which begin reconstruction only after a complete scan has been completed and saved to a storage medium ([Chelnokov 0002] “After the CT scan of the object is complete and all radiographs are acquired, software then loads the radiographs from the storage media and performs CT reconstruction on all of the acquired radiographs to generate the 3D representation as a volumetric density file. CT reconstruction thus does not begin until after CT scanning is complete, and after all of the radiographs have been acquired and stored in storage media. This can significantly delay reconstruction and generation of the final reconstructed volumetric image until after the CT scanning finishes acquiring all of the radiographs.”).
Claims 4 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Cao (US 2021/0082161 A1) in view of Giraldo et al. (US 2013/0251229 A1), hereafter Giraldo.
Regarding claim 4, Cao teaches the method of claim 1. Cao further teaches selecting reconstruction angle ranges based on the requirements of the system or according to physiological aspects of the patient ([0102] “As another example, the first angle range and/or the second angle range may be determined according to the subject for imaging. Merely by way of example, if the subject (e.g., a region of a patient (such as the heart, the lung, the rib, the enterocoelia, or the like)) undergoes a physiological motion, the processing device 140 may determine the data acquisition time period corresponding to a target phase of the physiological motion of the subject (e.g., when the cardiac motion is gentle), and then determine the data acquisition angle corresponding to the data acquisition time period based on the rotation speed of the scanner. The processing device 140 may determine the first angle range and/or the second angle range based on the data acquisition angle corresponding to the data acquisition time period.”).
Cao’s methods are applied to CT scans in general and are not specific to perfusion scans. Thus, Cao does not mention considering characteristics of contrast injection when determining reconstruction angle ranges and a desired temporal resolution. Therefore, Cao fails to teach wherein the target scan is a perfusion scan, and the determining a target temporal resolution includes: obtaining reference information relating to a contrast agent used in the perfusion scan, the reference information including at least one of a contrast agent concentration of the contrast agent, an injection volume, an injection speed, a development stage of the contrast agent, or accuracy requirement of perfusion parameters; and determining the target temporal resolution based on the reference information.
However, Giraldo teaches wherein the target scan is a perfusion scan ([0003] “The present invention relates generally to systems and methods for medical imaging and, particularly, to systems and methods for reducing partial scan reconstruction artifacts in computed tomography perfusion (CTP).”), and
the determining a target temporal resolution includes: obtaining reference information relating to a contrast agent used in the perfusion scan, the reference information including at least one of a contrast agent concentration of the contrast agent, an injection volume, an injection speed, a development stage of the contrast agent, or accuracy requirement of perfusion parameters; and determining the target temporal resolution based on the reference information (Giraldo teaches timing several consecutive partial CT scans based on the heart phases and movement of the contrast agent. [0023] “Referring to FIG. 2, in accordance with the present disclosure, a myocardial CT perfusion (MYOCARDIAL CTP) scan or other CT scan including interventional CT and fluoroscopic CT can be divided into several consecutive partial scans that can be used to track the transient arrival and washout of intravascular contrast agent. Specifically, as illustrated in FIG. 2, a graph 200 of amplitude 202 versus time 204 during an acquisition of CT imaging data from a subject undergoing motion, such as cardiac motion, provides phase information 206. A series of partial scans 208 may be used to acquire image data in view angle ranges of, for example, 180 degrees, 180+alpha degrees, and/or 180−alpha degrees; using either single, dual, or multiple x-ray source approach.” [0024] “Accordingly, when combined, an average full set of imaging data 218 is available. The partial scans 208 can improve temporal resolution…”).
Cao and Giraldo are analogous in the art because both teach methods of collecting partial scans of CT data and performing reconstruction on the CT data for the purpose of limiting artifacts and/or decreasing the amount of continuous radiation exposure to the patient being scanned. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve Cao’s invention by considering the movement and timing of a contrast agent through a patient’s blood when determining timings for partial scans. This modification would expand upon Cao’s invention by applying it to perfusion scanning with a contrast agent. Perfusion scans are traditionally timed and synchronized with the phases of the heart and/or the movement of a contrast agent ([Giraldo 0023] “a myocardial CT perfusion (MYOCARDIAL CTP) scan or other CT scan including interventional CT and fluoroscopic CT can be divided into several consecutive partial scans that can be used to track the transient arrival and washout of intravascular contrast agent.”).
Regarding claim 15, Cao teaches the system of claim 12, and Cao further teaches selecting reconstruction angle ranges based on the requirements of the system or according to physiological aspects of the patient [0102].
Cao’s methods are applied to CT scans in general and are not specific to perfusion scans. Thus, Cao does not mention considering characteristics of contrast injection when determining reconstruction angle ranges and a desired temporal resolution. Therefore, Cao fails to teach wherein the target scan is a perfusion scan, and the determining a target temporal resolution includes: obtaining reference information relating to a contrast agent used in the perfusion scan, the reference information including at least one of a contrast agent concentration of the contrast agent, an injection volume, an injection speed, a development stage of the contrast agent, or accuracy requirement of perfusion parameters; and determining the target temporal resolution based on the reference information.
However, Giraldo teaches wherein the target scan is a perfusion scan ([0003] “The present invention relates generally to systems and methods for medical imaging and, particularly, to systems and methods for reducing partial scan reconstruction artifacts in computed tomography perfusion (CTP).”), and
the determining a target temporal resolution includes: obtaining reference information relating to a contrast agent used in the perfusion scan, the reference information including at least one of a contrast agent concentration of the contrast agent, an injection volume, an injection speed, a development stage of the contrast agent, or accuracy requirement of perfusion parameters; and determining the target temporal resolution based on the reference information (Giraldo teaches timing several consecutive partial CT scans based on the heart phases and movement of the contrast agent. [0023] “Referring to FIG. 2, in accordance with the present disclosure, a myocardial CT perfusion (MYOCARDIAL CTP) scan or other CT scan including interventional CT and fluoroscopic CT can be divided into several consecutive partial scans that can be used to track the transient arrival and washout of intravascular contrast agent. Specifically, as illustrated in FIG. 2, a graph 200 of amplitude 202 versus time 204 during an acquisition of CT imaging data from a subject undergoing motion, such as cardiac motion, provides phase information 206. A series of partial scans 208 may be used to acquire image data in view angle ranges of, for example, 180 degrees, 180+alpha degrees, and/or 180−alpha degrees; using either single, dual, or multiple x-ray source approach.” [0024] “Accordingly, when combined, an average full set of imaging data 218 is available. The partial scans 208 can improve temporal resolution…”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to improve Cao’s invention by considering the movement and timing of a contrast agent through a patient’s blood when determining timings for partial scans. This modification would expand upon Cao’s invention by applying it to perfusion scanning with a contrast agent. Perfusion scans are traditionally timed and synchronized with the phases of the heart and/or the movement of a contrast agent ([Giraldo 0023] “a myocardial CT perfusion (MYOCARDIAL CTP) scan or other CT scan including interventional CT and fluoroscopic CT can be divided into several consecutive partial scans that can be used to track the transient arrival and washout of intravascular contrast agent.”).
Claims 9 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Cao (US 2021/0082161 A1) in view of Lee et al. (View-interpolation of sparsely sampled sinogram using convolutional neural network. Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 1013328.), hereafter Lee.
Regarding claim 9, Cao teaches the method of claim 8. Cao further teaches using arithmetic to interpolate an intermediate reconstructed image between two adjacent images, and Cao teaches using machine learning to generate a higher-quality image using two adjacent images ([0118] “In some embodiments, the processing device 140 may generate the target image based on the first image and the second image using a trained machine learning model.”). However, Cao fails to teach wherein the generating the one or more predicted reconstruction images based on the plurality of reconstruction images includes: obtaining a first trained image prediction model; and generating the one or more predicted reconstruction images by inputting the plurality of reconstruction images into the first trained image prediction model.
However, Lee teaches wherein the generating the one or more predicted reconstruction images based on the plurality of reconstruction images includes: obtaining a first trained image prediction model; and generating the one or more predicted reconstruction images by inputting the plurality of reconstruction images into the first trained image prediction model ([Section 2] teaches methods for utilizing a CNN for interpolation of a sparsely sampled sinogram.).
Cao and Lee are analogous in the art to the claimed invention, because both teach methods of interpolating CT scan data using limited views. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cao’s invention by utilizing machine learning to interpolate the missing views between the reconstruction angle ranges. This modification would allow for missing data to be interpolated, which is necessary for systems—such as taught by Cao and Lee—that do not capture all views of a 360-degree scan. Furthermore, Lee teaches that using a machine learning model for interpolation may provide better artifact removal than arithmetic-based methods (In Tables 1-2, Lee shows a lower RMSE produced by the CNN in comparison to arithmetic methods such as linear or directions interpolation.).
Regarding claim 19, Cao teaches the system of claim 18. Cao further teaches using arithmetic to interpolate an intermediate reconstructed image between two adjacent images, and Cao teaches using machine learning to generate a higher-quality image using two adjacent images ([0118] “In some embodiments, the processing device 140 may generate the target image based on the first image and the second image using a trained machine learning model.”). However, Cao fails to teach wherein the generating the one or more predicted reconstruction images based on the plurality of reconstruction images includes: obtaining a first trained image prediction model; and generating the one or more predicted reconstruction images by inputting the plurality of reconstruction images into the first trained image prediction model.
However, Lee teaches wherein the generating the one or more predicted reconstruction images based on the plurality of reconstruction images includes: obtaining a first trained image prediction model; and generating the one or more predicted reconstruction images by inputting the plurality of reconstruction images into the first trained image prediction model ([Section 2] teaches methods for utilizing a CNN for interpolation of a sparsely sampled sinogram.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Cao’s invention by utilizing machine learning to interpolate the missing views between the reconstruction angle ranges. This modification would allow for missing data to be interpolated, which is necessary for systems—such as taught by Cao and Lee—that do not capture all views of a 360-degree scan. Furthermore, Lee teaches that using a machine learning model for interpolation may provide better artifact removal than arithmetic-based methods (In Tables 1-2, Lee shows a lower RMSE produced by the CNN in comparison to arithmetic methods such as linear or directions interpolation.).
Allowable Subject Matter
Claims 6-7 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 6, the closest prior art of record, Cao (US 2021/0082161 A1), teaches the method of claim 5. However, Cao fails to teach wherein the reference value of the step includes a plurality of reference values, and the angle interval between adjacent reconstruction angle ranges among the plurality of reconstruction angle ranges is changing.
As shown in the rejection to claim 5, Cao teaches selecting reconstruction angle ranges based on the physiological characteristics of the patient and the rotational speed of the scanner [0102]. Furthermore, in [0101], Cao mentions that a step interval may occur between the selected reconstruction angle ranges when they do not overlap. However, Cao teaches methods focused on artifact removal, so Cao’s teachings are limited to only two reconstruction angle ranges which are used to generate a target image with reduced artifacts. Therefore, Cao teaches only a single interval and two adjacent reconstruction images.
Other cited prior art fails to make up for the deficiencies of Cao. Giraldo teaches a similar method to Cao which is applied to perfusion scanning. However, Giraldo’s timings for partial scans are also dependent upon the phases of the heart. Thus, Giraldo does not mention determining a plurality of reference values for intervals that are changing.
Regarding claim 7, although Cao teaches similar methods to the claimed invention regarding CT scanning and selecting reconstruction angle ranges, Cao is not specific to perfusion scanning using contrast agents. Thus, Cao fails to teach wherein the target scan is a perfusion scan, the target temporal resolution includes a plurality of target temporal resolutions corresponding to a plurality of development stages of a contrast agent used in the perfusion scan, and each of the plurality of reference values is determined based on one of the plurality of target temporal resolutions and the rotation speed.
Other cited prior art fails to make up for the deficiencies of Cao. Giraldo teaches a similar method of completing partial scans for perfusion scanning. The timings of the partial scans are dependent upon the phases of the heart; thus, the timings are directly related to the development stages of the contrast agent. However, Giraldo does not teach corresponding a plurality of target temporal resolutions to the plurality of development stages of the contrast agent.
Regarding claim 17, this claim includes the same limitations as claims 6-7; thus, claim 17 is allowable for the same reasons as claims 6-7 discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Strobel et al. (US 2005/0226392 A1) teaches systems and methods for performing a complete CT scan and image reconstruction using more than one partial scan.
Zheng et al. (CN 111710013 B) teaches methods for perfusion CT reconstruction using sparse-angles of scanning. The method involves obtaining a baseline image from a complete scan and utilizing that image in reconstruction of other images from sparse-angle scans. This way, subsequent scans can utilize a low scanning time to limit the amount of radiation exposure to the patient.
Cachovan et al. (US 2023/0162412 A1) teaches methods for utilizing machine learning to generate a reconstructed image volume from projection images of CT scan data. This method is performed on incomplete sets which may only include a subset of projection angles.
Tang et al. (On the data acquisition, image reconstruction, cone beam artifacts, and their suppression in axial MDCT and CBCT – A review. Med. Phys., 45: e761-e782.) teaches methods for performing CT scanning and image reconstruction including performing interpolation for more complete data and performing scans with a small angle range to increase the temporal resolution.
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/Eric Shoemaker/
Patent Examiner
/XIAO LIU/Primary Examiner, Art Unit 2664
Prosecution Insights