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 .
Claim Objections
Claims 11-18 are objected to under 37 CFR 1.75(c) as being in improper form because a multiple dependent claim cannot depend from any other multiple dependent claim. See MPEP § 608.01(n). Accordingly, the claims 11-18 have not been further treated on the merits.
Claim 6 is objected to because of the following informalities:
Claim 6 is objected to for not being in the proper form. See MPEP 608.01(m) which specifically states: “Each claim begins with a capital letter and ends with a period.” Claim 6 does not begin with a capital letter.
Appropriate correction is required.
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.
Claim 19 is 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.
Regarding claim 19, Chen discloses “A computer readable storage device having stored thereon instructions that when executed by one or more processors result in the following operations comprising the method according to claim 1” (emphasis added). It is unclear from the claim language if there are additional “operations” in addition to the operations detailed in the method described in claim 1. Therefore, applicant has failed to particularly point out and distinctly claim the subject matter which the inventor regards as the invention.
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(s) 1-5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Grass et al. (U.S. Publ. No. 2021/0295574) in view of Chen et al. (“General rigid motion correcton for computer tomography imaging based on locally linear embedding”; SPIE Digital Library; 2018).
Regarding claim 1, Grass discloses a system and method for motion compensated reconstruction for helical computer tomography. More specifically and as it relates to the applicant’s claims, Grass discloses a method of motion correction image reconstruction (see paragraph 0025 where a motion compensated image reconstruction is generated) for photon-counting computed tomography (CT) images (see paragraph 0075 where a photon counting CT sensor may be used), the method comprising:
scanning a subject via a photon-counting CT scanner device (See Figure 1 and paragraphs 0027-0028 where a CT scanner is used to capturing projection data; also see paragraph 0075 where a photon counting CT sensor may be used) to obtain measured projection data (see paragraph 0028 where projection data is obtained);
transmitting the measured projection data to a motion correction system (see Figure 1 and paragraphs 0031 where the measured projection data may be transferred to the reconstructor, 112 (i.e. a motion correction system));
performing, via motion correction circuitry (paragraph 0031 describes the reconstructor as being implemented by a processor, which inherently includes circuitry) of the motion correction system, a (motion compensated reconstruction algorithm, 114; see Figure 2 and paragraph 0034) on the measured projection data to obtain motion correction data (see paragraph 0034 where the motion state reconstruction module, 202, and the distortion vector field determiner module, 204, operate together to generate motion correction data);
generating, via reconstruction circuitry of the motion correction system, reconstructed image data from the motion correction data (see paragraphs 0034 and 0044, where the motion compensated reconstruction processor, 206, generates reconstructed image data from the motion correction data); and
outputting corrected image data based, at least in part, on the reconstructed image data (see paragraph 0032 where the corrected, reconstructed imaged data is output to console, 120).
Grass, however, fails to disclose that the motion correction algorithm is a locally linear embedding motion correction algorithm. Chen, on the other hand, discloses that it is well known in the art to correct for motion in tomography image using a locally linear embedding motion correction algorithm. See the abstract and section 2.2 of the provided document where motion is corrected using a locally linear embedding algorithm. Chen discloses that this algorithm allows for a more robust motion correction process, reduces the number of sampling points, increases the accuracy, and is less computationally expensive. See section 4 of the provided document. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Grass to replace the motion correcton of Grass with a locally linear embedding motion correction algorithm for the reasons identified above.
As for claim 2, Chen discloses that the LLE motion correction algorithm comprises:
estimating motion parameters for each of six degrees of freedom of the measured projection data to form six sub-problems (see section 2.2 of Chen, “Step 1: Initialize a parameter vector P containing the six rigid motion parameters described in Sec. 2.1, calculate the system matrix, and perform the image reconstruction.”);
solving and updating the six sub-problems, wherein for each sub-problem the solving and updating comprises:
generating a dense sample grid (see section 2.2 of Chen, “Step 2: Sample the parameter vector densely in the para-metric ranges for each projection as
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calculating a reprojected projection grid (see section 2.2. of Chen, “With the sampled parameters, calculate the coordinates of the X-ray source and detector elements, calculate the corresponding system matrices, and reproject for projections b˜m” ;
finding the K nearest neighbors from the projection grid in terms of Euclidean distance (see section 2.2 of Chen, “Step 3: Find the K nearest neighbors of the original projection vector b in the reprojected projections ˜bm according to the Euclidean distance
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optimizing the weights for the K neighbors (See section 2.2 of Chen,
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updating the estimated motion parameters (see section 2.2. of Chen
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iterating the above solving and updating step until a convergence is reached (see section 2.2 of Chen, “Step 5: Generate the system matrix with the updated motion parameters and perform image reconstruction again. Steps 2 to 6 need to be repeated until the image quality is sufficiently good by some criteria.”.
With respect to claim 3, Chen discloses that for each iteration a sampling space for the sample grid is reduced while maintaining the same number of samples to generate a finer sample grid having improved searching accuracy. See section 4 of Chen, “the LLE-based optimization helps reduce the number of sampling points and increase the accuracy of motion correction”, “our method updates the motion parameters view-by-view, greatly compressing the search space.”)
Regarding claim 4, Chen discloses that the six sub-problems are solve and updated in parallel, not sequentially (See the abstract, “capable of calibrating the six parameters of the patient motion simultaneously”). Thus, Chen fails to specifically disclose that the six sub-problems are solved and updated sequentially. Official Notice is taken as to the fact that mathematical operations (i.e. problems) can be performed either sequentially or in parallel-fashion. One of ordinary skill in the art would recognize the benefits of performing the processing of the sub-problems sequentially as opposed to in-parallel – including the ability to perform the operations in a less computationally expensive fashion, resulting in the use of less powerful processors, and where speed of processing isn’t a concern. Therefore, it would have been obvious to one of ordinary skill in the art at the time the invention was filed to modify the combination of Grass and Chen to perform the solving and updating of the six sub-problems in sequential fashion.
As for claim 5, Chen discloses that the six sub-problems are solved and updated in parallel. See the abstract, “capable of calibrating the six parameters of the patient motion simultaneously”.
With respect to claim 19, Grass discloses a computer readable storage device having stored thereon instructions (see paragraph 0030 where computer executable insturctions may be stored on a computer readable medium) that when executed by one or more processors (see paragraph 0030 where a processor implements those instructions) result in the following operations comprising the method according to claim 1. See the discussion of claim 1 above, for a discussion of how the combination of Grass and Chen read on the claim limitations of claim 1.
Claim(s) 6-10 are rejected under 35 U.S.C. 103 as being unpatentable over Grass et al. (U.S. Publ. No. 2021/0295574) in view of Chen et al. (“General rigid motion correcton for computer tomography imaging based on locally linear embedding”; SPIE Digital Library; 2018) and further in view of Zhan et al. (U.S. Publ. No. 2022/0342098).
Regarding claim 6, as mentioned above in the discussion of claims 1-5, the combination of Grass and Chen disclose all of the limitations of the parent claim. The aforementioned references however fail to explicitly disclose detecting, via bad pixel masking circuitry of the motion correction system, bad pixels of the photon-counting CT scanner; and applying a binary bad pixel mask to exclude contributions from the bad pixels to the measured projection data. Zhan, on the other hand discloses that it is well known in the art to correct for defective pixels in projection data obtained in a CT imaging system. More specifically Zhan discloses detecting, via bad pixel masking circuitry of the motion correction system, bad pixels of the photon-counting CT scanner (see Figure 3 and paragraphs 0033-0038 where bad pixels are detected and stored in a bad pixel table; also see paragraph 0059 where the bad pixels may be stored in a map; one of ordinary skill in the art would understand a “map” that only stores the defective vs. not defective pixels to be a binary mask (i.e. defective vs. non-defective); and
applying a binary bad pixel mask to exclude contributions from the bad pixels to the measured projection data (see paragraph 0061 where the map is used in the object scanning process to determine the pixels to ignore in the scanning process).
As for claim 7, Zhan discloses that the bad pixels are detected via open beam projection data and based, at least in part, on at least one detection criteria. See paragraphs 0045-0055 where at least one detection criteria are used to detect the bad pixels.
With respect to claim 8, Zhan discloses the at least one detection criteria comprises a temporal mean of a pixel value is a statistical outlier in a group of all pixels. See paragraph 0050 and 0051 where a temporal variance of a pixel is used to determine a statistical outlier.
Regarding claim 9, Zhan discloses that the at least one detection criteria comprises a temporal variance of a pixel value is a statistical outlier in a group of all pixels. See paragraph 0050 and 0051 where a temporal mean of a pixel is used to determine a statistical outlier
As for claim 10, Zhan discloses that the at least one detection criteria comprises a temporal mean of a pixel value is a statistical outlier in a group of all pixels, and a temporal variance of the pixel value is a statistical outlier in the group of all pixels. See paragraph 0050 and 0051 where a temporal mean and a temporal variance of a pixel is used to determine a statistical outlier
Conclusion
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/JOHN VILLECCO/Supervisory Patent Examiner, Art Unit 2661
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