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 .
Response to Arguments
Applicant's arguments filed 12/11/2025 have been fully considered but they are not persuasive.
Applicant further argues on pages 8-10 with respect to the rejection of claims 1-7, and 21-33 under 35 U.S.C. 103, that Li (US 20200029918 A1) does not teach a plurality of PET images associated with a plurality of subjects, noting that none of the portions indicating a plurality of patients relates to the normalization process. However, [0039] states that “A total of 50 PET scans were analyzed, including Alzheimer's disease (AD) patients and normal control (NL) subjects, from two Tau PET tracers AV1451 and THK5351”
Therefore the claims stand rejected.
Withdrawn Rejections
Pursuant of Applicant’s responses filed 12/11/2025, the rejection of claims 1-7, and 21-33 under 35 U.S.C. 101 have been withdrawn.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-7 and 21-33 are rejected under 35 U.S.C. 103 as being unpatentable over Li, et al., US 20200029918 A1 in view of Chen, et al., US 20160100755 A1.
Regarding claim 1, Li teaches a method (abstract states “Systems and methods are for analyzing Positron Emission Tomography (PET) image data. The methods may include generating a set of standardized uptake values (SUVs) of global or localized PET data for voxels within a selected region of interest (ROI), normalizing the set of SUVs by generating a set of SUVPs where each corresponding SUVP for each SUV is obtained using the formula: SUVP=(SUV−M)/S, wherein M corresponds to a peak value for the set of SUVs, and S corresponds to a spread for the set of SUVs, and generating a normalized image based on the set of SUVPs for the ROI. The systems may include any suitable device for PET image analysis performing the methods”. Also see the steps of the method 100 of fig. 1), comprising:
receiving a plurality of positron emission tomography (PET) images associated with a plurality of subjects based on positron emission tomography ([0018] states that “In step 102, a PET image may be obtained from a patient to generate raw PET image data” and [0039] elaborates that “A total of 50 PET scans were analyzed, including Alzheimer's disease (AD) patients and normal control (NL) subjects”);
determining, based on the plurality of PET images, a plurality of calibration parameters ([0009] states that “The present invention provides a method for normalizing image intensity data corresponding voxels within a PET scan to improve quantitative PET data analysis. The description below are directed to analysis of a particular type of image intensity data, e.g., standardized uptake values (SUVs) to generate normalized values, e.g., standardized uptake value peak-alignment (SUVP)”)
determining at least one image associated with a patient ([0018] states that “In step 102, a PET image may be obtained from a patient to generate raw PET image data”);
applying, based on the plurality of calibration parameters, a transformation to modify one or more intensity values of the at least one image associated with the patient ([0015] states that “it is contemplated that the normalization methods described herein may be applied to any suitable image intensity data, e.g., raw PET intensity data”, and [0024] emphasizes the application of the normalization, stating that “The peak-alignment normalization method described above, in particular, the SUVP values, may be useful in: (1) diagnosing an abnormality, such as a disease or condition, e.g., diagnosis of Alzheimer's disease (AD) in a patient, based on a PET image of the patient; and/or (2) development and evaluation of tracers, in particular, new PET tracers, for use in quantitative PET imaging. As described further below, steps 112 and 116 relate to an exemplary use of the SUVPs in diagnosis of patients with AD.”); and
providing the transformed at least one image ([0009] discloses providing a normalized PET image as a result of the normalization method 100).
Li does not teach that the calibration parameters are indicative of standardized intensity values for corresponding percentiles of intensity values.
However, within the same field of endeavor, Chen teaches systems and methods for normalizing an image of an eye among multiple optical coherence tomography (OCT) devices (see abstract), and stating in [0035] that (“the system and methods described herein can calibrate the differences in intensity contrast when capturing images with different cameras, image acquisition equipment, settings, and different light sources. By shaping an input image histogram to a reference histogram, the systems and methods described herein can compensate for the differences in intensity and image contrast. While described primarily in relation to OCT techniques, the system and methods described herein can be used with other imaging modalities, such as, but not limited to, positron emission tomography (PET)”), meaning the steps described for OCT applies to PET images. Henceforth, any references to OCT or OCT image/data by the document applies to PET or PET image/data, for purposes of this office action. The calibration method comprises rescaling distribution of signal amplitudes, step 208 in fig. 2, and normalizing the distribution of the signal amplitudes, step 210 in fig. 2, [0058] stating that (“Referring back to FIG. 2 and also to FIG. 4, the method 200 continues with the rescaling of the distribution of signal amplitudes (step 208). The rescaling step can be performed on the filtered second pixmap by the amplitude normalization module described above… for an 8-bit gray scale image, much of dynamic range in the original A-scan image (and pixmap) may be devoted to noise or portions of the non-retinal signal. In one example, signal levels between the 66th percentile and 99th percentile on the histogram can be rescaled across the full dynamic range (or other percentiles as described herein). For example, in an 8-bit data gray scale level, the lower 66th percentile can be set to 0 and top 1st percentile can be set to 255, with the 66th percentile to 99th percentile distributed across the dynamic range (or other percentiles as described herein”). Of note, the signal amplitude refers to histogram of pixel intensities according to [0017].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the calibration parameters are indicative of standardized intensity values for corresponding percentiles of intensity values, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 2, Li in view of Chen teaches all the limitations of claim 1.
Li fails to teach wherein determining the plurality of calibration parameters comprises determining a standardized maximum percentile intensity value.
However, Chen further teaches wherein determining the plurality of calibration parameters comprises determining a standardized maximum percentile intensity value ([0017] states that “the one or more processors to rescale the distribution of signal amplitudes by (i) generating a histogram of pixel intensities of the second plurality of pixels; (ii) setting a portion of the second plurality of pixels below about a 66 percentile of the histogram to 0; and (iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”. The 99th percentile is the maximum percentile intensity value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein determining the plurality of calibration parameters comprises determining a standardized maximum percentile intensity value, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 3, Li in view of Chen teaches all the limitations of claim 2.
Li fails to teach wherein the standardized maximum percentile intensity value is determined based on intensity values above the standardized maximum percentile intensity value varying greater than a threshold amount among the plurality of PET images.
However, Chen further teaches wherein the standardized maximum percentile intensity value is determined based on intensity values above the standardized maximum percentile intensity value varying greater than a threshold amount among the plurality of PET images ([0062] states that “The minimum and maximum histogram levels can represent the lowest and the highest pixel values of the B-scan image. The noise level and the saturation level can represent the 66.sup.th and the 99.sup.th percentile, respectfully, of the pixel values of the B-scan image (or other percentiles as described herein). For each B-scan, the original OCT signal dataset was divided into three channels: low, medium, and high signal channels. The low signal channel (I.sub.Low) included pixel values between minimum and low levels, the high signal channel (I.sub.High) included pixel values between the high and saturation levels” and [0063] states that “Each channel is then processed to maximize the signal dynamic range by linearly rescaling the pixel values between the lowest and the highest values within each channel to the full 8-bit gray scale range (0 to 255) in each B-scan. Intensity values outside of the defined cutoff values (lower or higher) are forced to be either 0 or 255”. That is, a cutoff values for the high levels, as well as the mid and min values, are provided).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the standardized maximum percentile intensity value is determined based on intensity values above the standardized maximum percentile intensity value varying greater than a threshold amount among the plurality of PET images, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 4, Li in view of Chen teaches all the limitations of claim 2.
Li fails to teach wherein the standardized maximum percentile intensity value comprises a value in a range of one or more of 85 to 100, 90 to 100, 95 to 100, 95 to 95, or 96 to 97.
However, Chen further teaches wherein the standardized maximum percentile intensity value comprises a value in a range of one or more of 85 to 100, 90 to 100, 95 to 100, 95 to 95, or 96 to 97 ([0017] states that “the one or more processors to rescale the distribution of signal amplitudes by (i) generating a histogram of pixel intensities of the second plurality of pixels; (ii) setting a portion of the second plurality of pixels below about a 66 percentile of the histogram to 0; and (iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”. The 99th percentile is the maximum percentile intensity value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the standardized maximum percentile intensity value comprises a value in a range of one or more of 85 to 100, 90 to 100, 95 to 100, 95 to 95, or 96 to 97, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 5, Li in view of Chen teaches all the limitations of claim 2.
Li fails to teach wherein applying the transformation comprises transforming intensity values of the at least one image based on one or more ranges defined based on the plurality of calibration parameters.
However, Chen further teaches wherein applying the transformation comprises transforming intensity values of the at least one image based on one or more ranges defined based on the plurality of calibration parameters by stating in [0017], regarding the rescaling of the signal amplitudes, a step of “(iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein applying the transformation comprises transforming intensity values of the at least one image based on one or more ranges defined based on the plurality of calibration parameters, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 6, Li in view of Chen teaches all the limitations of claim 5.
Li fails to teach wherein intensity values of the at least one image that are above the standardized maximum percentile intensity value are transformed based on a range defined by a standardized median percentile value of the plurality of calibration parameters and the standardized maximum percentile intensity value.
However, Chen further teaches wherein intensity values of the at least one image that are above the standardized maximum percentile intensity value are transformed based on a range defined by a standardized median percentile value of the plurality of calibration parameters and the standardized maximum percentile intensity value ([0058] states that “For example, for an 8-bit gray scale image, much of dynamic range in the original A-scan image (and pixmap) may be devoted to noise or portions of the non-retinal signal. In one example, signal levels between the 66th percentile and 99th percentile on the histogram can be rescaled across the full dynamic range (or other percentiles as described herein”. In this case, the 66th percentile is tantamount to the median percentile while the 99th percentile belongs to the highest or maximum percentile, for application in the rescaling step, which is the transformation step in Chen).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein intensity values of the at least one image that are above the standardized maximum percentile intensity value are transformed based on a range defined by a standardized median percentile value of the plurality of calibration parameters and the standardized maximum percentile intensity value, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 7, Li in view of Chen teaches all the limitations of claim 2.
Li fails to teach wherein the plurality of calibration parameters comprises a standardized minimum percentile intensity value and a standardized median percentile intensity value.
However, Chen further teaches wherein the plurality of calibration parameters comprises a standardized minimum percentile intensity value and a standardized median percentile intensity value ([0017] states that “the one or more processors to rescale the distribution of signal amplitudes by (i) generating a histogram of pixel intensities of the second plurality of pixels; (ii) setting a portion of the second plurality of pixels below about a 66 percentile of the histogram to 0; and (iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”. The minimum percentile comprise plurality of pixels set to 0, the median percentile comprises pixels set to 66th percentile and the 99th percentile is the maximum percentile intensity value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the plurality of calibration parameters comprises a standardized minimum percentile intensity value and a standardized median percentile intensity value, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 21, Li in view of Chen teaches all the limitations of claim 1.
Li further teaches determining, based on an indication of an anatomic region and a model associated with the anatomic region, an indication of a disease burden associated with the transformed at least one image ([0008] states that “The method comprises localizing the PET data with at least one image mask to generate localized PET data. The PET data may be co-registered to correspond to anatomical structures represented by the image mask… A histogram of a set of image intensity values (Ps) within the ROI is generated, and a Gaussian curve is fit to the histogram” and [0029] states that “In step 118, the average whole brain probability density function PDF.sub.wb for the particular groups of subjects can be compared between diseased and normal groups, or between images generated using different tracers, to examine the different tracers' effectiveness of tissue binding and sensitivity to the disease (e.g., Alzheimer's disease (AD))”. The image mask is the indication of an anatomic region, the gaussian curve is the model and the two are used in the comparison step in [0029]).
Regarding claim 22, Li in view of Chen teaches all the limitations of claim 1.
Li further teaches determining, based on the transformed at least one image, a model associated with an anatomic region and causing output of one or more of the model or data based on the model ([0009] states that “The system further comprises a processing arrangement configured to localize the PET data with at least one image mask to generate localized PET data, the PET data be co-registered to correspond to anatomical structures represented by the image mask, the processing arrangement further configured to generate set of image intensity values (Ps) based on the localized PET data for voxels within a selected region of interest (ROI)”, an output of the model being the image intensity values).
Regarding claim 23, Li teaches a device (abstract states “Systems and methods are for analyzing Positron Emission Tomography (PET) image data. The methods may include generating a set of standardized uptake values (SUVs) of global or localized PET data for voxels within a selected region of interest (ROI), normalizing the set of SUVs by generating a set of SUVPs where each corresponding SUVP for each SUV is obtained using the formula: SUVP=(SUV−M)/S, wherein M corresponds to a peak value for the set of SUVs, and S corresponds to a spread for the set of SUVs, and generating a normalized image based on the set of SUVPs for the ROI. The systems may include any suitable device for PET image analysis performing the methods”. Also see the steps of the method 100 of fig. 1) comprising:
one or more processors ([0036] discloses a plurality of processor cores and [0037] discloses one or more microprocessors); and
memory storing instructions that, when executed by the one or more processors ([0037] states that “a computer-accessible medium 220 (e.g., as described herein, a storage device such as a hard disk, floppy disk, memory stick, CD-ROM, RAM, ROM, etc., or a collection thereof) can be provided (e.g., in communication with the processing arrangement 210)”), cause the device to:
receive a plurality of positron emission tomography (PET) images associated with a plurality of subjects based on positron emission tomography ([0018] states that “In step 102, a PET image may be obtained from a patient to generate raw PET image data” and [0039] elaborates that “A total of 50 PET scans were analyzed, including Alzheimer's disease (AD) patients and normal control (NL) subjects”);
determine, based on the plurality of PET images, a plurality of calibration parameters ([0009] states that “The present invention provides a method for normalizing image intensity data corresponding voxels within a PET scan to improve quantitative PET data analysis. The description below are directed to analysis of a particular type of image intensity data, e.g., standardized uptake values (SUVs) to generate normalized values, e.g., standardized uptake value peak-alignment (SUVP)”)
determine at least one image associated with a patient ([0018] states that “In step 102, a PET image may be obtained from a patient to generate raw PET image data”);
apply, based on the plurality of calibration parameters, a transformation to modify one or more intensity values of the at least one image associated with the patient ([0015] states that “it is contemplated that the normalization methods described herein may be applied to any suitable image intensity data, e.g., raw PET intensity data”, and [0024] emphasizes the application of the normalization, stating that “The peak-alignment normalization method described above, in particular, the SUVP values, may be useful in: (1) diagnosing an abnormality, such as a disease or condition, e.g., diagnosis of Alzheimer's disease (AD) in a patient, based on a PET image of the patient; and/or (2) development and evaluation of tracers, in particular, new PET tracers, for use in quantitative PET imaging. As described further below, steps 112 and 116 relate to an exemplary use of the SUVPs in diagnosis of patients with AD.”); and
provide the transformed at least one image ([0009] discloses providing a normalized PET image as a result of the normalization method 100).
Li does not teach that the calibration parameters are indicative of standardized intensity values for corresponding percentiles of intensity values.
However, within the same field of endeavor, Chen teaches systems and methods for normalizing an image of an eye among multiple optical coherence tomography (OCT) devices (see abstract), and stating in [0035] that (“the system and methods described herein can calibrate the differences in intensity contrast when capturing images with different cameras, image acquisition equipment, settings, and different light sources. By shaping an input image histogram to a reference histogram, the systems and methods described herein can compensate for the differences in intensity and image contrast. While described primarily in relation to OCT techniques, the system and methods described herein can be used with other imaging modalities, such as, but not limited to, positron emission tomography (PET)”), meaning the steps described for OCT applies to PET images. Henceforth, any references to OCT or OCT image/data by the document applies to PET or PET image/data, for purposes of this office action. The calibration method comprises rescaling distribution of signal amplitudes, step 208 in fig. 2, and normalizing the distribution of the signal amplitudes, step 210 in fig. 2, [0058] stating that (“Referring back to FIG. 2 and also to FIG. 4, the method 200 continues with the rescaling of the distribution of signal amplitudes (step 208). The rescaling step can be performed on the filtered second pixmap by the amplitude normalization module described above… for an 8-bit gray scale image, much of dynamic range in the original A-scan image (and pixmap) may be devoted to noise or portions of the non-retinal signal. In one example, signal levels between the 66th percentile and 99th percentile on the histogram can be rescaled across the full dynamic range (or other percentiles as described herein). For example, in an 8-bit data gray scale level, the lower 66th percentile can be set to 0 and top 1st percentile can be set to 255, with the 66th percentile to 99th percentile distributed across the dynamic range (or other percentiles as described herein”). Of note, the signal amplitude refers to histogram of pixel intensities according to [0017].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the calibration parameters are indicative of standardized intensity values for corresponding percentiles of intensity values, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 24, Li in view of Chen teaches all the limitations of claim 23.
Li fails to teach wherein the instructions that, when executed by the one or more processors, cause the device to determine the plurality of calibration parameters comprises instructions that, when executed by the one or more processors, cause the device to determine a standardized maximum percentile intensity value.
However, Chen further teaches wherein the instructions that, when executed by the one or more processors, cause the device to determine the plurality of calibration parameters comprises instructions that, when executed by the one or more processors, cause the device to determine a standardized maximum percentile intensity value ([0017] states that “the one or more processors to rescale the distribution of signal amplitudes by (i) generating a histogram of pixel intensities of the second plurality of pixels; (ii) setting a portion of the second plurality of pixels below about a 66 percentile of the histogram to 0; and (iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”. The 99th percentile is the maximum percentile intensity value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the instructions that, when executed by the one or more processors, cause the device to determine the plurality of calibration parameters comprises instructions that, when executed by the one or more processors, cause the device to determine a standardized maximum percentile intensity value, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 25, Li in view of Chen teaches all the limitations of claim 24.
Li fails to teach wherein the standardized maximum percentile intensity value is determined based on intensity values above the standardized maximum percentile intensity value varying greater than a threshold amount among the plurality of PET images.
However, Chen further teaches wherein the standardized maximum percentile intensity value is determined based on intensity values above the standardized maximum percentile intensity value varying greater than a threshold amount among the plurality of PET images ([0062] states that “The minimum and maximum histogram levels can represent the lowest and the highest pixel values of the B-scan image. The noise level and the saturation level can represent the 66.sup.th and the 99.sup.th percentile, respectfully, of the pixel values of the B-scan image (or other percentiles as described herein). For each B-scan, the original OCT signal dataset was divided into three channels: low, medium, and high signal channels. The low signal channel (I.sub.Low) included pixel values between minimum and low levels, the high signal channel (I.sub.High) included pixel values between the high and saturation levels” and [0063] states that “Each channel is then processed to maximize the signal dynamic range by linearly rescaling the pixel values between the lowest and the highest values within each channel to the full 8-bit gray scale range (0 to 255) in each B-scan. Intensity values outside of the defined cutoff values (lower or higher) are forced to be either 0 or 255”. That is, a cutoff values for the high levels, as well as the mid and min values, are provided).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the standardized maximum percentile intensity value is determined based on intensity values above the standardized maximum percentile intensity value varying greater than a threshold amount among the plurality of PET images, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 26, Li in view of Chen teaches all the limitations of claim 24.
Li fails to teach wherein the standardized maximum percentile intensity value comprises a value in a range of one or more of 85 to 100, 90 to 100, 95 to 100, 95 to 95, or 96 to 97.
However, Chen further teaches wherein the standardized maximum percentile intensity value comprises a value in a range of one or more of 85 to 100, 90 to 100, 95 to 100, 95 to 95, or 96 to 97 ([0017] states that “the one or more processors to rescale the distribution of signal amplitudes by (i) generating a histogram of pixel intensities of the second plurality of pixels; (ii) setting a portion of the second plurality of pixels below about a 66 percentile of the histogram to 0; and (iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”. The 99th percentile is the maximum percentile intensity value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the standardized maximum percentile intensity value comprises a value in a range of one or more of 85 to 100, 90 to 100, 95 to 100, 95 to 95, or 96 to 97, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 27, Li in view of Chen teaches all the limitations of claim 24.
Li fails to teach wherein the instructions that, when executed by the one or more processors, cause the device to apply the transformation comprises instructions that, when executed by the one or more processors, cause the device to transform intensity values of the at least one image based on one or more ranges defined based on the plurality of calibration parameters.
However, Chen further teaches wherein the instructions that, when executed by the one or more processors, cause the device to apply the transformation comprises instructions that, when executed by the one or more processors, cause the device to transform intensity values of the at least one image based on one or more ranges defined based on the plurality of calibration parameters by stating in [0017], regarding the rescaling of the signal amplitudes, a step of “(iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the instructions that, when executed by the one or more processors, cause the device to apply the transformation comprises instructions that, when executed by the one or more processors, cause the device to transform intensity values of the at least one image based on one or more ranges defined based on the plurality of calibration parameters, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 28, Li in view of Chen teaches all the limitations of claim 27.
Li fails to teach wherein intensity values of the at least one image that are above the standardized maximum percentile intensity value are transformed based on a range defined by a standardized median percentile value of the plurality of calibration parameters and the standardized maximum percentile intensity value.
However, Chen further teaches wherein intensity values of the at least one image that are above the standardized maximum percentile intensity value are transformed based on a range defined by a standardized median percentile value of the plurality of calibration parameters and the standardized maximum percentile intensity value ([0058] states that “For example, for an 8-bit gray scale image, much of dynamic range in the original A-scan image (and pixmap) may be devoted to noise or portions of the non-retinal signal. In one example, signal levels between the 66th percentile and 99th percentile on the histogram can be rescaled across the full dynamic range (or other percentiles as described herein”. In this case, the 66th percentile is tantamount to the median percentile while the 99th percentile belongs to the highest or maximum percentile, for application in the rescaling step, which is the transformation step in Chen).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein intensity values of the at least one image that are above the standardized maximum percentile intensity value are transformed based on a range defined by a standardized median percentile value of the plurality of calibration parameters and the standardized maximum percentile intensity value, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 29, Li in view of Chen teaches all the limitations of claim 24.
Li fails to teach wherein the plurality of calibration parameters comprises a standardized minimum percentile intensity value and a standardized median percentile intensity value.
However, Chen further teaches wherein the plurality of calibration parameters comprises a standardized minimum percentile intensity value and a standardized median percentile intensity value ([0017] states that “the one or more processors to rescale the distribution of signal amplitudes by (i) generating a histogram of pixel intensities of the second plurality of pixels; (ii) setting a portion of the second plurality of pixels below about a 66 percentile of the histogram to 0; and (iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”. The minimum percentile comprise plurality of pixels set to 0, the median percentile comprises pixels set to 66th percentile and the 99th percentile is the maximum percentile intensity value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the plurality of calibration parameters comprises a standardized minimum percentile intensity value and a standardized median percentile intensity value, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 30, Li in view of Chen teaches all the limitations of claim 23.
Li further teaches wherein the instructions, when executed by the one or more processors, further cause the device to determine, based on an indication of an anatomic region and a model associated with the anatomic region, an indication of a disease burden associated with the transformed at least one image ([0008] states that “The method comprises localizing the PET data with at least one image mask to generate localized PET data. The PET data may be co-registered to correspond to anatomical structures represented by the image mask… A histogram of a set of image intensity values (Ps) within the ROI is generated, and a Gaussian curve is fit to the histogram” and [0029] states that “In step 118, the average whole brain probability density function PDF.sub.wb for the particular groups of subjects can be compared between diseased and normal groups, or between images generated using different tracers, to examine the different tracers' effectiveness of tissue binding and sensitivity to the disease (e.g., Alzheimer's disease (AD))”. The image mask is the indication of an anatomic region, the gaussian curve is the model and the two are used in the comparison step in [0029])..
Regarding claim 31, Li in view of Chen teaches all the limitations of claim 23.
Li further teaches wherein the instructions, when executed by the one or more processors, further cause the device to determine, based on the transformed at least one image, a model associated with an anatomic region and causing output of one or more of the model or data based on the model([0009] states that “The system further comprises a processing arrangement configured to localize the PET data with at least one image mask to generate localized PET data, the PET data be co-registered to correspond to anatomical structures represented by the image mask, the processing arrangement further configured to generate set of image intensity values (Ps) based on the localized PET data for voxels within a selected region of interest (ROI)”, an output of the model being the image intensity values)..
Regarding claim 32, Li teaches a system method (abstract states “Systems and methods are for analyzing Positron Emission Tomography (PET) image data. The methods may include generating a set of standardized uptake values (SUVs) of global or localized PET data for voxels within a selected region of interest (ROI), normalizing the set of SUVs by generating a set of SUVPs where each corresponding SUVP for each SUV is obtained using the formula: SUVP=(SUV−M)/S, wherein M corresponds to a peak value for the set of SUVs, and S corresponds to a spread for the set of SUVs, and generating a normalized image based on the set of SUVPs for the ROI. The systems may include any suitable device for PET image analysis performing the methods”. Also see the steps of the method 100 of fig. 1) comprising:
an imaging device ([0037] discloses a PET imaging device and its associated microprocessors) configured to generate a plurality of positron emission tomography (PET) images associated with a plurality of subjects ([0009] discloses that the PET imaging device is “configured to generate PET data based on the detected radioactive emissions”, the PET data processed by the device to “generate a normalized PET image based on the set of normalized values (Ns)”); and
a computing device ([0036] discloses a plurality of processor cores and [0037] discloses one or more microprocessors) configured to:
receive a plurality of positron emission tomography (PET) images associated with a plurality of subjects based on positron emission tomography ([0018] states that “In step 102, a PET image may be obtained from a patient to generate raw PET image data” and [0039] elaborates that “A total of 50 PET scans were analyzed, including Alzheimer's disease (AD) patients and normal control (NL) subjects”);
determine, based on the plurality of PET images, a plurality of calibration parameters ([0009] states that “The present invention provides a method for normalizing image intensity data corresponding voxels within a PET scan to improve quantitative PET data analysis. The description below are directed to analysis of a particular type of image intensity data, e.g., standardized uptake values (SUVs) to generate normalized values, e.g., standardized uptake value peak-alignment (SUVP)”)
determine at least one image associated with a patient ([0018] states that “In step 102, a PET image may be obtained from a patient to generate raw PET image data”);
apply, based on the plurality of calibration parameters, a transformation to modify one or more intensity values of the at least one image associated with the patient ([0015] states that “it is contemplated that the normalization methods described herein may be applied to any suitable image intensity data, e.g., raw PET intensity data”, and [0024] emphasizes the application of the normalization, stating that “The peak-alignment normalization method described above, in particular, the SUVP values, may be useful in: (1) diagnosing an abnormality, such as a disease or condition, e.g., diagnosis of Alzheimer's disease (AD) in a patient, based on a PET image of the patient; and/or (2) development and evaluation of tracers, in particular, new PET tracers, for use in quantitative PET imaging. As described further below, steps 112 and 116 relate to an exemplary use of the SUVPs in diagnosis of patients with AD.”); and
provide the transformed at least one image ([0009] discloses providing a normalized PET image as a result of the normalization method 100).
Li does not teach that the calibration parameters are indicative of standardized intensity values for corresponding percentiles of intensity values.
However, within the same field of endeavor, Chen teaches systems and methods for normalizing an image of an eye among multiple optical coherence tomography (OCT) devices (see abstract), and stating in [0035] that (“the system and methods described herein can calibrate the differences in intensity contrast when capturing images with different cameras, image acquisition equipment, settings, and different light sources. By shaping an input image histogram to a reference histogram, the systems and methods described herein can compensate for the differences in intensity and image contrast. While described primarily in relation to OCT techniques, the system and methods described herein can be used with other imaging modalities, such as, but not limited to, positron emission tomography (PET)”), meaning the steps described for OCT applies to PET images. Henceforth, any references to OCT or OCT image/data by the document applies to PET or PET image/data, for purposes of this office action. The calibration method comprises rescaling distribution of signal amplitudes, step 208 in fig. 2, and normalizing the distribution of the signal amplitudes, step 210 in fig. 2, [0058] stating that (“Referring back to FIG. 2 and also to FIG. 4, the method 200 continues with the rescaling of the distribution of signal amplitudes (step 208). The rescaling step can be performed on the filtered second pixmap by the amplitude normalization module described above… for an 8-bit gray scale image, much of dynamic range in the original A-scan image (and pixmap) may be devoted to noise or portions of the non-retinal signal. In one example, signal levels between the 66th percentile and 99th percentile on the histogram can be rescaled across the full dynamic range (or other percentiles as described herein). For example, in an 8-bit data gray scale level, the lower 66th percentile can be set to 0 and top 1st percentile can be set to 255, with the 66th percentile to 99th percentile distributed across the dynamic range (or other percentiles as described herein”). Of note, the signal amplitude refers to histogram of pixel intensities according to [0017].
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the calibration parameters are indicative of standardized intensity values for corresponding percentiles of intensity values, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Regarding claim 33, Li in view of Chen teaches all the limitations of claim 32.
Li fails to teach wherein the computing device is configured to determine the plurality of calibration parameters based on determining a standardized maximum percentile intensity value.
However, Chen further teaches wherein the computing device is configured to determine the plurality of calibration parameters based on determining a standardized maximum percentile intensity value ([0017] states that “the one or more processors to rescale the distribution of signal amplitudes by (i) generating a histogram of pixel intensities of the second plurality of pixels; (ii) setting a portion of the second plurality of pixels below about a 66 percentile of the histogram to 0; and (iii) distributing a portion of the second plurality of pixels between about a 66 percentile and about a 99 percentile of the histogram across the substantial portion of the dynamic range”. The 99th percentile is the maximum percentile intensity value).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to Li wherein the computing device is configured to determine the plurality of calibration parameters based on determining a standardized maximum percentile intensity value, as taught by Chen, as such modification would improve the dynamic range within the meaningful signal representative of the region of interest ([0058]), and improve resolution of the signal ([0033], [0048]), with a reasonable expectation of success, since Li concerned with improving signal resolution ([0005]-[0006]).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/FAROUK A BRUCE/ Examiner, Art Unit 3793
/CHRISTOPHER KOHARSKI/ Supervisory Patent Examiner, Art Unit 3797