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 statement (IDS) submitted on 03/26/2026 has been considered by the examiner.
Allowable Subject Matter
The indicated allowability of claims 10-12 and 23 is withdrawn in view of the newly discovered reference(s) to Thierry and Norton. Rejections based on the newly cited reference(s) follow.
Response to Amendment
Claims 24-27 are added, and claims 1-27 remain pending in the application in response to the applicant’s amendments to the rejections previously set forth in the Non-Final Office Action mailed 01/07/2026.
Response to Arguments
Applicant’s arguments, see pg. 8-9, filed 03/26/2026, with respect to the rejection(s) of claim(s) 1 and 14 under 35 U.S.C. 102(a) (Zhang) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Yin in view of Nomura, as shown below.
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.
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, 14, and 24-27 are rejected under 35 U.S.C. 103 as being unpatentable over B. Yin et al, “Fast photoacoustic imaging system based on 320-element linear transducer array”, Physics in Medicine and Biology, vol. 49, pp. 1339-1346, Nov. 2003 in view of Nomura (US 7250984 B2, July 31, 2007), hereinafter referred to as Yin and Nomura, respectively.
Regarding claim 1, and similarly for claim 14, Yin teaches a photoacoustic computed tomography method, comprising:
acquiring photoacoustic data recorded by one or more data acquisition devices of a photoacoustic computed tomography system, the photoacoustic data based at least in part on photoacoustic signals obtained by transducer elements of the photoacoustic computed tomography system (Fig. 3; see pg. 1342, para. 3 – “For spatial focusing, time-domain PA signals detected by 11 sub-groups (corresponding to the N in the theory described above) were converted into a one-dimensional image, along the acoustic axis, after pre-amplification and phase adjustment. The procedure was repeated by shifting the 11 sub-groups, one detector position per step, down the linear array for 64 times, to form a 2D cross-sectional image of the sample.”).
Yin teaches acquiring photoacoustic data to reconstruct a 2D image, but does not explicitly teach applying location-based temporal filtering to the photoacoustic data acquired.
Whereas, Nomura, in an analogous field of endeavor, teaches applying location-based temporal filtering to the data acquired, wherein applying the location-based temporal filtering is on a sub-domain by sub-domain basis for a plurality of imaging sub-domains, the plurality of imaging sub-domains when aggregated forming an entire two-dimensional imaging domain (Fig. 13; col. 21, lines 32-35 – “The image data D22 obtained by performing a temporal filtering process on the image data D11 of each sub-field [sub-domain] in this way is transferred to the field synthesizing section 25.); and
reconstructing an image from the filtered data of the two-dimensional imaging domain (see col. 21, lines 39-44 – “That is, the original field image data is reconstructed by combining together every second pixel on each line from the image data of the two sub-fields, i.e. the odd number column and the even number column, forming the same field sequentially output from the filter section 20.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified acquiring photoacoustic data to reconstruct a 2D image, as disclosed in Yin, by also applying location-based temporal filtering to data acquired, as disclosed in Nomura. One of ordinary skill in the art would have been motivated to make this modification in order to reduce the size of the buffer memory for image data of each low pass filter, as taught in Nomura (see col. 22, lines 1-2).
Furthermore, regarding claims 24 and 26, Nomura further teaches wherein reconstructing the photoacoustic image from the filtered photoacoustic data of the two-dimensional imaging domain comprises: performing image reconstruction for each sub-domain of the plurality of sub-domains of the two-dimensional imaging domain to form a plurality of sub-domain images; and aggregating the plurality of sub-domain images to form the photoacoustic image (Fig. 13; see col. 21, lines 39-44 – “That is, the original field image data is reconstructed by combining together every second pixel on each line from the image data of the two sub-fields, i.e. the odd number column and the even number column, forming the same field sequentially output from the filter section 20.”).
Furthermore, regarding claims 25 and 27, Nomura further teaches wherein applying location-based temporal filtering to the photoacoustic data is applied on a sub-domain by sub-domain basis for one or more additional pluralities of imaging sub-domains, wherein each of the one or more additional pluralities of imaging sub-domains in aggregate form an additional two-dimensional imaging domain; and further comprising reconstructing one or more additional photoacoustic images from the filtered photoacoustic data of the one or more additional pluralities of two-dimensional imaging domains (Fig. 13; see col. 21, lines 39-44 – “That is, the original field image data is reconstructed by combining together every second pixel on each line from the image data of the two sub-fields, i.e. the odd number column and the even number column, forming the same field sequentially output from the filter section 20.”).
The motivation for claims 24-27 was shown previously in claim 1 and 14.
Claims 2-4 and 15-17 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Nomura, as applied to claim 1 and 14 above, and in further view of Naimi et al. (US 20110243409 A1, published October 6, 2011), hereinafter referred to as Naimi.
Regarding claims 2 and 15, Yin in view of Nomura teaches all of the elements disclosed in claim 1 and 14 above.
Yin in view of Nomura teaches apply location-based temporal filtering, but does not explicitly teach applying spatial interpolation after applying the location-based temporal filtering.
Whereas, Naimi, in an analogous field of endeavor, teaches applying spatial interpolation after applying the location-based temporal filtering (Fig. 14; see para. 0196 "The method begins at 310 and continues to 320 at which the method performs convolution of intensity data representing the thermal image (e.g., intensity data, variance, changes over time, etc.) with a predetermined vessel shapes filter thereby providing filtered data."; see para. 0226 "At 350 the method applies an interpolation procedure for generating contours between at least a few of the local intensity extrema.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified applying location-based temporal filtering to photoacoustic data, as disclosed in Yin in view of Nomura, by also applying spatial interpolation after applying the location-based temporal filtering, as disclosed in Naimi. One of ordinary skill in the art would have been motivated to make this modification in order to generate contours between at least a few of the local intensity extrema in the image, and identify the contours as blood vessels in the image, as taught in Naimi (see para. 0231).
Furthermore, regarding claims 3 and 16, Naimi further teaches wherein the spatial interpolation is performed on the sub-domain by sub-domain basis for the plurality of imaging sub-domains (Fig. 14; see para. 0196 "The method begins at 310 and continues to 320 at which the method performs convolution of intensity data representing the thermal image (e.g., intensity data, variance, changes over time, etc.) with a predetermined vessel shapes filter thereby providing filtered data."; see para. 0226 – "At 350 the method applies an interpolation procedure for generating contours between at least a few of the local intensity extrema.").
Furthermore, regarding claims 4 and 17, Naimi further teaches wherein applying location-based temporal filtering mitigates aliasing prior to the spatial interpolation (Fig. 14; see para. 0196 "The method begins at 310 and continues to 320 at which the method performs convolution of intensity data representing the thermal image (e.g., intensity data, variance, changes over time, etc.) with a predetermined vessel shapes filter thereby providing filtered data."; see para. 0226 "At 350 the method applies an interpolation procedure for generating contours between at least a few of the local intensity extrema.").
The motivation for claims 3-4 and 16-17 was shown previously in claims 2 and 15.
Claims 5-6, 8-9, 18-19, and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Nomura, as applied to claims 1 and 14 above, and in further view of H. Huang et al, "An adaptive filtered back-projection for photoacoustic image reconstruction", Medical Physics, vol. 42, no. 5, pp. 2169-2178, May 2015, hereinafter referred to as Huang.
Regarding claims 5 and 18, Yin in view of Nomura teaches all of the elements disclosed in claims 1 and 14 above.
Yin in view of Nomura teaches applying location-based temporal filtering, but does not explicitly teach applying one or more lowpass filters associated with the upper cutoff frequency to each imaging sub-domain.
Whereas, Huang, in the same field of endeavor, teaches wherein applying the location-based temporal filtering comprises: determining an upper cutoff frequency for each imaging sub-domain; and applying one or more lowpass filters associated with the upper cutoff frequency (see pg. 2171, col. 1, para. 3 "The weighting function has a series of singular points when ckt = +(n+1/2), therefore, in Fourier domain for a sampling period (T), one should, naturally, choose the cutoff frequency fcutoff using ckt < π/2 or π fcutoffT< π/2to avoid singular points. This allows usto obtain an objective upper limit for setting the cutoff frequency fcutofffor a low pass filter. We obtain that fcutoff< (fsampling/4), where fsampling is the sampling rate.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified applying location-based temporal filtering, as disclosed in Yin in view of Nomura, by applying one or more lowpass filters associated with the upper cutoff frequency to each imaging sub domain, as disclosed in Huang. One of ordinary skill in the art would have been motivated to make this modification in order to obtain a sharper image, as taught in Huang (see pg. 2176, col. 2, para. 2).
Furthermore, regarding claims 6 and 19, Huang further teaches wherein the one or more lowpass filters associated with the upper cutoff frequency comprise a plurality of lowpass filters each having a different cutoff frequency less than the upper cutoff frequency, and wherein applying the location-based temporal filtering comprises: applying the plurality of lowpass filters to the photoacoustic data (see pg. 2172, col. 2, para. 3 "Based on the discussion in Sec. 2, our cutoff frequency will be set adaptively; in this case, the cutoff frequency for the low-pass filter is set to 5.0 MHz."); and
temporal recentering the filtered photoacoustic data for each imaging subdomain of the plurality of imaging sub-domains based on distances between the transducer elements and a center of each imaging subdomain (see pg. 2172, col. 1, para. 5-"For a uniform sample spherical absorber, one can adopt the approximate model suggested in p(r,t) = [Equation (18)] where AO is a constant, U(a |R-ct|) is Heaviside function, a is radius of sphere, and R denotes the distance between the center of absorber and detector.").
Furthermore, regarding claims 8 and 21, Huang further teaches wherein the upper cutoff frequency for a given imaging sub-domain is determined based on locations of the transducer elements, a center of the given imaging sub-domain, and locations of points on a boundary of the given imaging sub domain (see pg. 2174, col. 1, para. 2 "The second measurement configuration is an arc-shaped detector array composed of 50-point detectors, evenly spaced over a span of 160°, with a radius of 65 mm. The detector array rotates in 2° steps over 360°, giving 9000 (50x180) detection points (see Fig. 3).").
Furthermore, regarding claims 9 and 22, Huang further teaches wherein a point source is outside ofan imaging sub-domain, and wherein the upper cutoff frequency for the imaging sub-domain is further based on locations of the transducer elements, a center of the imaging sub-domain, and location of the point source (see pg. 2174, col. 1, para. "The second measurement configuration is an arc-shaped detector array composed of 50-point detectors, evenly spaced over a span of 160°, with a radius of 65mm. The detector array rotates in 2° steps over 360°, giving 9000 (50x180) detection points (see Fig. 3).").
The motivation for claims 6, 8-9, 19, and 21-22 was shown previously in claims 5 and 18.
Claims 7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Nomura and Huang, as applied to claim 5 and 18 above, and in further view of Irisawa et al. (US 20140257079 A1, published September 11, 2014), hereinafter referred to as Irisawa.
Regarding claims 7 and 20, Yin in view of Nomura and Huang teaches all of the elements disclosed in claim 5 and 18 above.
Yin in view of Nomura and Huang teaches selecting an upper cutoff frequency, but does not explicitly teach where the upper cutoff frequency is selected such that a Nyquist criterion is satisfied for each imaging sub-domain.
Whereas, Irisawa, in the same field of endeavor, teaches wherein the upper cutoff frequency is selected such that a Nyquist criterion is satisfied for each imaging sub-domain (see para. 0091 "The resampling means 46 achieves the upsampling by, for example, adding zero between sample points of the photoacoustic signal sampled at a low sampling rate and applying a low-pass filter with a cut off frequency equal to a Nyquist frequency before the upsampling.").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified selecting an upper cutoff frequency, as disclosed in Yin in view of Nomura and Huang, by having the upper cutoff frequency selected such that a Nyquist criterion is satisfied for each sub-domain, as disclosed in Irisawa. One of ordinary skill in the art would have been motivated to make this modification in order to fill a difference of the band between the signals in the frequency domain, in place of the time domain, accurate deconvolution of the light pulse differential term can be achieved while using low speed sampling for the detection and the reconstruction of the photoacoustic signal, as taught in Irisawa (see para. 0103).
Claims 10-13 and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Yin in view of Nomura, as applied to claim 1 and 14 above, and in further view of Thierry et al. (EP 2009592 B1, published March 6, 2013) and S. Norton et al, “Backprojection reconstruction of random source distributions”, Journal of the Acoustical Society of America, vol. 81, no. 4, pp. 977-985, Jan. 1986, hereinafter referred to as Thierry and Norton, respectively.
Regarding claims 10 and 23, Yin in view of Nomura teaches all of the elements disclosed in claim 1 and 14 above, and
Yin further teaches wherein a general source causes reflections indicated in the photoacoustic data (Fig. 1, Acoustic source m as a general source; see pg. 1341, para. 1 – “When an acoustic source in the target is outside the focal region, the signals collected by the detectors are incoherent. The amplitude of the signal after the synthesizer is reduced due to phase cancellation, compared to that from the focal region. Therefore, the signals captured by the transducer array are mostly from the acoustic source within the focal region.”).
Yin in view of Nomura teaches a source causing reflections, but does not explicitly teach performing an initial reconstruction of a reconstructed image.
Whereas, Thierry, in an analogous field of endeavor, teaches wherein the photoacoustic computed tomography method further comprises:
performing an initial reconstruction without anti-aliasing to generate a reconstructed image (see pg. 11, para. 5 – “…a) reconstructing an initial 3D image of an object from a set of projection data (I.sub.mes) measured by x-ray tomography…”);
obtaining a set of point source candidates based on the reconstructed image, wherein the general source is different than the set of point source candidates (see pg. 12, para. 1 – “…- carrying out at least a first deterministic simulation of the single scattering over all voxels, wherein for each voxel only the instantly associated pixels are considered [point source candidates];…”);
dividing the entire two-dimensional imaging domain into a set of squares (see pg. 12, para. 2 – “- after the first deterministic simulation of the single scattering, subjecting the optionally filtered simulation result to an adaptive rasterization, whereby the detector surface D is divided into a plurality of raster elements {D.sub.mn}…”);
randomly selecting one point source in each square of the set of squares as a point source (see pg. 12, para. 1 – “…- associating, to each voxel V.sub.ijk.sup.(2) of the second voxelized representation {V¡.sub.jk.sup.(2)}, a set of independent randomly selected pixels {P.sub.mn} of the detector surface D;…”); and
applying the location-based temporal filtering for each imaging sub-domain based on the randomly selected point sources to generate an image (see pg. 12, para. 1 – “…- subsequently optionally carrying out a filtering of the simulation result thus obtained;…”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified a source causing reflections, as disclosed in Yin in view of Nomura, by also performing an initial reconstruction of a reconstructed image, as disclosed in Thierry. One of ordinary skill in the art would have been motivated to make this modification in order to provide a scatter correction of X-ray tomographic data, as taught in Thierry (see pg. 1, para. 1).
Yin in view of Nomura and Thierry teaches randomly selecting point sources, but does not explicitly teach generating a final reconstructed image based on an average of the multiple images.
Whereas, Norton, in an analogous field of endeavor, teaches
repeating the random selection of one point source in each square and applying the location- based temporally filtering for each imaging sub-domain at least one other time to obtain multiple images (see pg. 979, col. 1, para. 1 – “Our objective is to reconstruct the statistical structure of the random process f(r,t), as a function of r, on the basis of the field measurements u(R,t), where R ranges over the circumference of the enclosing circular array.”); and
generating a final reconstructed image based on an average of the multiple images (see pg. 985, col. 1, para. 2 – “If the source distribution is changing dynamically, the resultant image will represent a temporal average of the source intensity that existed during the time of observation.”).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have modified randomly selecting point sources, as disclosed in Yin in view of Nomura and Thierry, by also generating a final reconstructed image based on an average of the multiple images, as disclosed in Norton. One of ordinary skill in the art would have been motivated to make this modification in order for source images can be recovered under much lower signal-to-noise conditions than those required by traditional source location schemes, as taught in Norton (see Abstract).
Furthermore, regarding claim 11, Norton further teaches wherein the initial reconstruction is performed using universal back projection (see Abstract – “The technique requires multiple detecting transducers surrounding the source region, and employs a filtered-backprojection algorithm…”).
Furthermore, regarding claim 12, Thierry further teaches wherein the set of point source candidates is obtained by applying a threshold to the reconstructed image (see pg. 12, para. 1 – “- carrying out at least a first deterministic simulation of the single scattering over all voxels, wherein for each voxel only the instantly associated pixels are considered [thresholding to obtain point candiates]; - subsequently optionally carrying out a filtering of the simulation result thus obtained; and - taking over as single scattering contribution the - optionally filtered simulation result.”).
Furthermore, regarding claim 13, Norton further teaches wherein universal back projection is used to reconstruct the one or more photoacoustic images (see Abstract – “The technique requires multiple detecting transducers surrounding the source region, and employs a filtered-backprojection algorithm…”).
The motivation for claims 11-13 was shown previously in claim 10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Wang et al. (US 20150215529 A1, published July 30, 2015) discloses the filter has filter portions associated with the plurality of distinct qualities of light which are spatially pseudo-randomly ordered relative to each other, and the image processing system also comprises an image reconstruction algorithm specifically designed to operate with the filter.
Liu et al. (US 20190246999 A1, published August 15, 2019) discloses adding together the pixel values for each acquired image to form a final image.
Lienard et al. (US 20070058780 A1, published March 15, 2007) discloses a processing wherein a time filtering operation of the fixed pixels. For each pixel of the image, the differentiation between a pixel in motion and a fixed pixel is performed as a function of the result of a detection of motion during which, in a Zone of the image covering a set of pixels in the vicinity of the pixel, the existence or respectively the absence of motion of the object relative to a preceding image is detected.
Kervec et al. (US 20110285815 A1, published November 24, 2011) discloses interpolation carried out by spatio-temporal filtering on the critical areas of the image (that is to say the areas for which the confidence of the motion vector is low).
Tsunomori et al. (US 20170329501 A1, published November 16, 2017) discloses the obtained dynamic image is analyzed for each of a plurality of sub regions, the analysis result images each indicating analysis results at positions of the respective sub-regions in the medical image are generated.
Y. Zhao et al, “A Universal Fast Back Projection Method for Nonuniform Sampling Based on Nonuniform FFT”, 2018 Progress in Electromagnetics Research Symposium, pp. 1932-1937, August 2018 discloses a universal fast BP method for nonuniform sampling. The “universal” means the proposed method is suitable for arbitrary displacement of the transmitters and the receivers, for arbitrary kind of T/R antennas, for far field and near field imaging, and for the freespace and multilayered environment.
M. Haltmeier et al, “Compressed sensing and sparsity in photoacoustic tomography”, Journal of Optics, vol. 18, pp. 1-12, Sept. 2016 discloses applying temporal filtering, then applying back projection for sparse data whose locations are randomly selected.
M. Xu et al, “Universal back-projection algorithm for photoacoustic computed tomography”, Physical Review, vol. 71, pp. 1-7, July 2004 discloses the back projection formula can serve as a basis for time-domain photoacoustic reconstruction in three-dimensional space.
K. Kostli et al, “Two-dimensional photoacoustic imaging by use of Fourier-transform image reconstruction and a detector with an anisotropic response”, vol. 42, no. 10, pp. 1899-1908, April 2003 discloses for the practical implementation of 2D photoacoustic imaging, a rectangular detector geometry was used to obtain an anisotropic detection sensitivity in order to reject out-of-plane signals, thereby permitting a tomographic image slice to be reconstructed.
J. Yao et al, “Photoacoustic brain imaging: from microscopic to macroscopic scales”, Neurophotonics, vol. 1, no. 1, pp. 1-14, Feb. 2014 discloses photoacoustic computed tomography utilizes a state-of the-art transducer array to simultaneously detect the PA waves from the entire region of interest excited by an expanded laser beam.
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/N.C./Examiner, Art Unit 3798
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