DETAILED ACTION
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on June 9th, 2026 has been entered.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments, see “Remarks”, filed June 9th, 2026 with respect to the 35 U.S.C. 103 rejections of claim 1-2, 14-15, and 27 have been fully considered but they are not persuasive.
Applicant alleges that “Hsieh deliberately uses "noise images without subjects (object-free noisy images)" as the measurement target to verify the basic noise characteristics of the detector. Hsieh: Section II B, Paragraph 2. Therefore, Hsieh does not disclose, and could not perform, identification of a subject area within the analyzed images because no subject is present in the images.
Thus, the feature of "calculating a count number in a subject area in a reconstructed PET image of the subject," as recited in amended independent claim 1, is not disclosed or suggested by Hsieh.” Examiner respectfully disagrees.
Hsieh discusses that their study focuses on noise created from the visible light production (Hsieh Section IV “In this study, we supposed that most of the image noise was propagated from the Poisson statistic of visible light production, and the deviation of visible light production was combined from compound-Poisson statistic of x-ray energy spectrum.”) and also teaches that there are other sources which can create noise, such as the object which is being imaged (Hsieh Section IV “As introduced, noise contribution in digital radiography include physical effects on x-ray Poisson statistics, electronic noise, FPD gain mechanism, reabsorbed Compton-scattered photons or K x-rays, and the object randomness.”; “Randomness of spatial-spectral fluence created by object was neglected too; all FPD images for test were exposed without any object but air in our x-ray imaging system.”). This suggests that their study focuses on noise created from one aspect of the imaging, but does not discount that noise can come from other sources. So, although Hsieh does not teach that an object is present in their study’s images, they recognize that the noise created from the visible light production would still be present in an image with an object present. Hsieh’s disclosure could then be used in combination to teach eliminating noise created by visible light production in an object with an image, such as in the images found in Dutta, as the noise created by the visible light production would still be present. Therefore, the rejection is maintained.
Claim Interpretation
In the independent claims, the claim language of calculating “a count number” can be understood broadly to mean any count of any number. However, in this context, the count number refers to a count of particles (in this case, a pair of y-rays, also known as “Line of Response”) which were detected from a PET scan. The broadest reasonable interpretation of this term, in this context, is then understood to mean a count number of particles which are used to perform a scan. Evidence for this can be found in the specification, paragraph [0046] and [0051].
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 1-2, 14-15, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over “Non-Local Means Denoising of Dynamic PET Images” (herein after referred to by its primary author, Dutta) in view of “Compound Poisson Noise Verification for X-ray Flat Panel Imager” (herein after referred to by its primary author, Hsieh) and JP2016085064A (herein after referred to by its primary author, Kibo)
In regards to claim 1, Dutta teach an image processing method used for a PET apparatus comprising: a reconstruction step of reconstructing a PET image of a subject by performing reconstruction processing on emission data of the subject (Dutta Methods, Paragraph 1 “In order to generate a realistic simulation environment for testing the NLM denoising technique for dynamic PET images, we constructed a dynamic digital mouse phantom”); a standard deviation calculation step of calculating a noise standard deviation in the PET image of the subject Dutta Theory, Smoothing Parameter “A number of approaches have been proposed for choosing the smoothing parameter for NLM. Optimal parameter selection using Stein’s unbiased risk estimate has been shown to be particularly effective [20,24]. In many applications involving additive or multiplicative white noise, the smoothing parameter is set to a constant multiple of the noise standard deviation [15,19].”); and a noise reduction processing step of performing non-local means (NLM) filter processing on the PET image of the subject, using the noise standard deviation calculated in the standard deviation calculation step (Dutta Methods Conventional NLM denoising “As a fourth reference approach, we use the conventional NLM filter based on (1) and (2). The filter is applied to the spatiotemporal images slice by slice and time frame by time frame. The similarity metric is based on 2D spatial patches. A uniform smoothing parameter is used for all slices and time points.” Examiner note: This section teaches that NLM denoising is performed on a radiographic (PET) image using a smoothing parameter. Dutta teaches in their theory section cited above that the smoothing parameter is create based on a multiple of the noise standard deviation.).
Dutta does not teach a step of acquiring a plurality of function calculation PET images; a step of calculating a count number in each of the plurality of function calculation PET images and a noise standard deviation in each of the plurality of function calculation PET images; a step of preparing a basic noise deviation function that indicates a relationship between a count number and a noise standard deviation based on the count number in each of the plurality of function calculation PET images and the noise standard deviation in each of the plurality of function calculation PET images; a step of storing the basic noise deviation function; a count number calculation step of calculating a count number in a subject area in a reconstructed PET image of the subject by the reconstruction step; and a standard deviation calculation step of calculating a noise standard deviation in the PET image of the subject, by substituting the count number in the subject area into the basic noise deviation function, which is acquired in advance as a function associating a value of the count number and a value of the noise standard deviation based on the relationship between the count number and the noise standard deviation in each of the plurality of function calculation PET images acquired in advance.
However, Hsieh teaches a step of acquiring a plurality of function calculation Hsieh Figure 1; Section II B, Paragraph 2 “Object-free noisy images were then measured under the FPD radiography mode, with 427×427 mm2 full field-of-view, 1 frame-per-second (fps), 1×1 pixel binning and 3072×3072 image size”); a step of calculating a count number in each of the plurality of function calculation Hsieh Section II C, Paragraph 1 “To verify the compound-Poisson noise property in the FPD images, central 80% area of x-ray field-of-view in FPD images were selected as region-of-interest (ROI) for standard deviation measurements. Measured data were compared with entrance FPD air dose, x-ray photon counts and the Poisson std-index.”); a step of preparing a basic noise deviation function that indicates a relationship between a count number and a noise standard deviation based on the count number in each of the plurality of function calculation Hsieh Figure 3; Figure 3 description “Solid line with error bars in the right plot are the result and error of second-order polynomial fit, MSE of the fitting points is 21.9985.”); a reconstruction step of reconstructing a Hsieh Section II B, Paragraph 2 “Object-free noisy images were then measured under the FPD radiography mode, with 427×427 mm2 full field-of-view, 1 frame-per-second (fps), 1×1 pixel binning and 3072×3072 image size”); a count number calculation step of calculating a count number in a subject area in a reconstructed subject by the reconstruction step (Hsieh Section II C, Paragraph 1 “To verify the compound-Poisson noise property in the FPD images, central 80% area of x-ray field-of-view in FPD images were selected as region-of-interest (ROI) for standard deviation measurements. Measured data were compared with entrance FPD air dose, x-ray photon counts and the Poisson std-index.”); and a standard deviation calculation step of calculating a noise standard deviation in the , by substituting the count number in the subject area into the basic noise deviation function, which is acquired in advance as a function associating a value of the count number and a value of the noise standard deviation based on the relationship between the count number and the noise standard deviation in each of the plurality of function calculation (Hsieh Figure 3; Section II C, Paragraph 3 “Figure 3 shows image noise response to x-ray photon counts…The overall response is presented as a second-order polynomial fit, that the fitting line is better correlated to the measured data. Mean square error of the fitting points is decrease to 21.9985, or 4.82% error.” Examiner note: The count number and standard deviation calculations of Hsieh are applied to x-ray imagery instead of PET imagery. X-ray images are considered to be analogous to PET images because they are both medical imaging techniques which rely on measuring particles which pass through a subject. Furthermore, the claim language of “a count number” and “a noise standard deviation” in a PET image do not require any specifics that would not be found in a count number and a noise standard deviation of an x-ray image. The noise standard deviation relationship and calculation of Hsieh could be used to calculate the noise standard deviation for use as a smoothing parameter in Dutta.).
Hsieh is considered to be analogous to the claimed invention because they are both in the same field of medical imaging. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified they system of Dutta to include the teachings of Hsieh, to provide the advantage of a theoretical model which can accurately represent real world noise results (Hsieh Section V “We have verified the image noise statistics of an energy integrating scintillator-photodiode FPD based on compound-Poisson theory by using our laboratory x-ray imaging system. The result shows that the measures noise is highly correlated the theoretical model. Noise property of our FPD can be represented as a second-order polynomial function.”)
Furthermore, Kibo teaches a step of acquiring a plurality of function calculation PET images (Kibo Page 4, Paragraph 2 “In the noise model creating step, the noise model creating unit 21 may use tomographic images for a plurality of subjects as reference tomographic images for creating a noise model under each condition of data acquisition and reconstruction processing.”); a step of calculating a count number in each of the plurality of function calculation PET images (Kibo Page 2, Final paragraph “The radiation tomography apparatus 10 includes a detection unit having a large number of small radiation detectors arranged around a measurement space where a subject is placed. The radiation tomography apparatus 10 detects a photon pair of energy 511 keV generated along with the annihilation of an electron-positron pair in a subject into which a positron emission isotope (RI radiation source) is input, by a coincidence method using a detection unit, The coincidence count information is accumulated.”) and a noise standard deviation in each of the plurality of function calculation PET images (Kibo Page 3, Final paragraph “In the noise model creating step, the pixel value and the noise standard deviation σ in each of the plurality of partial regions having different pixel values in the reference tomographic image acquired”); a reconstruction step of reconstructing a PET image of a subject by performing reconstruction processing on emission data of the subject (Kibo Page 3, Paragraph 1 “Then, the PET apparatus reconstructs a tomographic image representing the spatial distribution of the occurrence frequency of photon pairs in the measurement space based on the accumulated many pieces of coincidence information”); and a count number calculation step of calculating a count number in a subject area in a reconstructed PET image of the subject by the reconstruction step (Kibo Page 3, Paragraph 1 “The coincidence count information is accumulated.” Examiner note: The disclosure of Kibo shows that PET imagery contains count information and noise standard deviation which could be used in place of the x-ray imagery count information and noise standard deviation of Hsieh. ).
Kibo is considered to be analogous to the claimed invention because they are both in the same field of PET imaging. Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified they system of Dutta in view of Hsieh to include the teachings of Kibo, to provide the advantage of smoothing parameter selection that accounts for the varying noise in an image (Kibo Page 3, Paragraphs 3-4 “In tomographic images reconstructed by these techniques, generally, the standard deviation σ of noise is large in a region where the pixel value is large, and the standard deviation σ of noise is small in a region where the pixel value is small. Therefore, using an image filter (non-local means filter, guided image filter), it is assumed that the standard deviation σ of the noise is constant over the entire tomographic image, and the smoothing parameter is made constant. Then, the noise removal effect is small in the region where the pixel value is large, and the noise removal effect is large in the region where the pixel value is small, and the result of the filter processing is not preferable. The image processing apparatus 20 is an apparatus that processes a tomographic image reconstructed by the radiation tomography apparatus 10. The image processing device 20 sets the smoothing parameter appropriately and performs filter processing on the tomographic image.”)
In regards to claim 2, Dutta in view of Hsieh and Kibo teaches the image processing method used for the PET apparatus as recited in claim 1, wherein a basic noise standard deviation σt in the basic noise deviation function is calculated by a following
σt = a1 * Na2 + a3
using the count number N in the subject area, and a first count model coefficient at, a second count model coefficient a2, and a third count model coefficient a3, the first count model coefficient at, the second count model coefficient a2, and the third count model coefficient a3 being obtained from the relationship between the count number in each of the plurality of function calculation PET images and the noise standard deviation in each of the plurality of function calculation PET images, and wherein the noise standard deviation σ in the noise reduction processing step satisfies a condition of σ = σt. (Hsieh Figure 3 Description “The left plot is measured standard deviations of incident x-ray photon count with kVp settings, line of the responses has slight differences on its slope. Solid line with error bars in the right plot are the result and error of second-order polynomial fit, MSE of the fitting points is 21.9985.” Examiner note: A second order polynomial fit uses the equation y = a3 + a4 * x + a1 * x2. In this scenario, a1 and a3 are found in the same form, and a2 is equal to 2.)
In regards to claim 14, Dutta in view of Hsieh and Kibo renders obvious the claim limitations as in the consideration of claim 1.
In regards to claim 15, Dutta in view of Hsieh and Kibo renders obvious the claim limitations as in the consideration of claim 2.
In regards to claim 27, Dutta in view of Hsieh and Kibo teaches a radiation detector configured to detect radiation transmitted through a subject and output radiation data (Hsieh Section II B “A laboratory x-ray imaging system consisting of an x-ray source, filter stage, and a FPD was constructed to verify the FPD image noise property in planar radiography.”) and renders obvious the remaining claim limitations as in the consideration of claim 14.
Allowable Subject Matter
Claims 3-13 and 16-26 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.
The following is a statement of reasons for the indication of allowable subject matter:
Dependent claims 3 and 16: The limitations of these claims require the standard deviation calculation step as recited in claim 1, and further requires that a standard deviation correction value be calculated from a value used in the reconstruction processing, this standard deviation correction value is then used to change the original standard deviation calculated in claim 1. While this claim is broad in that it merely requires the changing of the standard deviation by some value, it is also narrow in that the standard deviation must be calculated from a relation between a count number and that the value used to correct the standard deviation must be obtained from a relation between a value used in the reconstruction processing and the correction value, which is obtained from previously acquired images. The prior art of record does not teach these limitations alone or in combination.
Dependent claims 4-13 and 17-26: These claims are dependent upon a claim which contains allowable subject matter, and therefore contain allowable subject matter not taught by the prior art of record.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“NOISE DISTRIBUTION DUE TO EMISSION AND TRANSMISSION STATISTICS IN POSITRON EMISSION TOMOGRAPHY” teaches that the noise found in a PET image is concentrated in certain areas. Their final equation (10) also shows a relationship between the variance due to noise and the number of counts.
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/CALEB L ESQUINO/Examiner, Art Unit 2677
/ANDREW W BEE/Supervisory Patent Examiner, Art Unit 2677
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