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 .
Election/Restrictions
Applicant's election with traverse of Species I (claims 1-7 and 9-20) in the reply filed on 11/26/2025 is acknowledged. The traversal is on the grounds that the species are related and can be used together, and that one process corresponds to a more detailed description of one step in another process. This is not found persuasive because Applicant’s admission corresponds to combination and part (subcombination), which according to MPEP 802.01(II) amount to related by distinct inventions. In any of the examples provided by the Applicant, each of the processes 600, 700, 1000, 1200, and 1300, which usable together, are capable of being performed independently of the other processes. That is, as emphasized in MPEP 802.01(II), as long as related inventions as claimed can be made by, or used in, materially different process, the inventions are distinct.
The requirement is still deemed proper and is therefore made FINAL.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-4, 6-7, 10-11, and 14-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 5, 7-8, and 12 of U.S. Patent No. 11596366 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations recited in the claims mentioned above of the instant application are also recited in the claims mentioned above of the copending application.
Instant Application
US Patent US 11596366 B2
1. A method implemented on at least one machine each of which has at least one processor and a storage device, the method comprising:
obtaining a motion curve of a portion of a subject in at least one scan region of the subject; determining a spectrum corresponding to the motion curve;
determining at least one spectrum segment in the spectrum based on a target frequency,
wherein the target frequency corresponds to a maximum signal intensity in the spectrum;
determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment;
determining a scan mode for scanning the at least one scan region based on whether there is the physiological motion in the at least one scan region; and
generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode.
1. A method implemented on at least one machine each of which has at least one processor and a storage device, the method comprising:
for each scan region of the one or more scan regions, obtaining a motion curve of a center of mass of a portion of the subject in the scan region;
determining a spectrogram corresponding to the motion curve;
wherein the first spectrum segment includes a union of a spectrum range where a target frequency is located and a spectrum range where 2 times the target frequency is located, the second spectrum segment includes a spectrum range where noise is located, and the target frequency corresponds to a maximum signal intensity in the spectrogram;
determining whether there is a physiological motion in the scan region based on the ratio;
determining a scan mode for scanning the scan region based on whether there is the physiological motion in the scan region; and
generating a PET sub-image of the scan region based on the PET data of the scan region; and generating a PET image of the scan volume based on one or more PET sub-images.
2. The method of claim 1, wherein the at least one spectrum segment includes a first spectrum segment, the first spectrum segment is a spectrum range where a target frequency is located or a union of the spectrum range where the target frequency is located and a spectrum range where 2 times the target frequency is located.
1. …dividing the spectrogram into a first spectrum segment and a second spectrum segment, wherein the first spectrum segment includes a union of a spectrum range where a target frequency is located and a spectrum range where 2 times the target frequency is located, the second spectrum segment includes a spectrum range where noise is located…
3. The method of claim 2, wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum; and determining whether there is a physiological motion in the at least one scan region based on the ratio.
1. … obtaining a first intensity value of the first spectrum segment and a second intensity value of the second spectrum segment; obtaining a ratio of the first intensity value of the first spectrum… segment to the second intensity value of the second spectrum segment; determining whether there is a physiological motion in the scan region based on the ratio…
4. The method of claim 2, wherein the at least one spectrum segment further includes a second spectrum segment, the second spectrum segment includes a spectrum range where a noise is located.
1. … a second spectrum segment, wherein the first spectrum segment includes a union of a spectrum range where a target frequency is located and a spectrum range where 2 times the target frequency is located, the second spectrum segment includes a spectrum range where noise is located
6. The method of claim 1, wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region.
1. …wherein the scan mode includes a first mode or a second mode, the first mode is at least a static mode or a transmission mode for scanning the scan region, the second mode is a gating mode for scanning the scan region, the scan mode includes the first mode if it is determined that there is no physiological motion in the scan region, and the scan mode includes the second mode if it is determined that there is the physiological motion in the scan region…
7. The method of claim 1, wherein the scan mode includes a second mode if it is determined that there is the physiological motion in the at least one scan region, and the second mode includes a gating mode for scanning the at least one scan region.
1. … wherein the scan mode includes a first mode or a second mode, the first mode is at least a static mode or a transmission mode for scanning the scan region, the second mode is a gating mode for scanning the scan region…
10. The method of claim 9, further comprising: adjusting a statistical characteristic of the at least one scan region according to at least one table position corresponding to the at least one scan region, wherein the statistical characteristic of one of the at least one scan region includes at least one of a scan time period of the scan region, a scan velocity of the scan region, or a number of data frames of the scan region.
7. The method of claim 1, wherein generating a PET image of the scan volume based on one or more PET sub-images includes adjusting statistical characteristics of the one or more scan regions, wherein the statistical characteristics include at least one of a scan time period of the scan region, a scan velocity of the scan region, or a number of data frames of the scan region.
11. The method of claim 9, further comprising: generating a PET image of the scan volume by stitching the at least one PET sub-image of the at least one scan region.
5. The method of claim 1, wherein the PET image of the scan volume is generated by stitching the PET sub-images of the one or more scan regions.
14. A system, comprising: at least one storage device storing a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to direct the system to perform operations including:
obtaining a motion curve of a portion of a subject in at least one scan region of the subject; determining a spectrum corresponding to the motion curve;
determining at least one spectrum segment in the spectrum based on a target frequency,
wherein the target frequency corresponds to a maximum signal intensity in the spectrum;
determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment;
determining a scan mode for scanning the at least one scan region based on whether there is the physiological motion in the at least one scan region; and
generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode.
8. A system, comprising: a computer-readable storage medium storing executable instructions, and at least one processor in communication with the computer-readable storage medium, when executing the executable instructions, causing the system to implement a method
for each scan region of the one or more scan regions, obtaining a motion curve of a center of mass of a portion of the subject in the scan region;
determining a spectrogram corresponding to the motion curve;
wherein the first spectrum segment includes a union of a spectrum range where a target frequency is located and a spectrum range where 2 times the target frequency is located, the second spectrum segment includes a spectrum range where noise is located, and the target frequency corresponds to a maximum signal intensity in the spectrogram;
determining whether there is a physiological motion in the scan region based on the ratio;
determining a scan mode for scanning the scan region based on whether there is the physiological motion in the scan region; and
generating a PET sub-image of the scan region based on the PET data of the scan region; and generating a PET image of the scan volume based on one or more PET sub-images.
15. The system of claim 14, wherein the at least one spectrum segment includes a first spectrum segment, the first spectrum segment is a spectrum range where a target frequency is located or a union of the spectrum range where the target frequency is located and a spectrum range where 2 times the target frequency is located.
8. ……dividing the spectrogram into a first spectrum segment and a second spectrum segment, wherein the first spectrum segment includes a union of a spectrum range where a target frequency is located and a spectrum range where 2 times the target frequency is located, the second spectrum segment includes a spectrum range where noise is located…
16. The system of claim 15, wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum; and determining whether there is a physiological motion in the at least one scan region based on the ratio.
8. …obtaining a first intensity value of the first spectrum segment and a second intensity value of the second spectrum segment; obtaining a ratio of the first intensity value of the first spectrum… segment to the second intensity value of the second spectrum segment; determining whether there is a physiological motion in the scan region based on the ratio…
17. The system of claim 15, wherein the at least one spectrum segment further includes a second spectrum segment, the second spectrum segment includes a spectrum range where a noise is located, and the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a third intensity value of the second spectrum segment; and determining whether there is a physiological motion in the at least one scan region based on the ratio.
8. … obtaining a first intensity value of the first spectrum segment and a second intensity value of the second spectrum segment; obtaining a ratio of the first intensity value of the first spectrum… segment to the second intensity value of the second spectrum segment; determining whether there is a physiological motion in the scan region based on the ratio…
18. The system of claim 14, wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region.
8. …wherein the scan mode includes a first mode or a second mode, the first mode is at least a static mode or a transmission mode for scanning the scan region, the second mode is a gating mode for scanning the scan region, the scan mode includes the first mode if it is determined that there is no physiological motion in the scan region, and the scan mode includes the second mode if it is determined that there is the physiological motion in the scan region…
19. The system of claim 14, wherein the scan mode includes a second mode if it is determined that there is the physiological motion in the at least one scan region, and the second mode includes a gating mode for scanning the at least one scan region.
8. …the second mode is a gating mode for scanning the scan region, the scan mode includes the first mode if it is determined that there is no physiological motion in the scan region, and the scan mode includes the second mode if it is determined that there is the physiological motion in the scan region…
20. A non-transitory computer readable medium, comprising a set of instructions, wherein when executed by at least one processor, the set of instructions direct the at least one processor to effectuate a method, the method comprising:
obtaining a motion curve of a portion of a subject in at least one scan region of the subject; determining a spectrum corresponding to the motion curve;
determining at least one spectrum segment in the spectrum based on a target frequency,
wherein the target frequency corresponds to a maximum signal intensity in the spectrum;
determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment;
determining a scan mode for scanning the at least one scan region based on whether there is the physiological motion in the at least one scan region; and
generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode.
12. A non-transitory computer readable medium, comprising: instructions being executed by at least one processor, causing the at least one processor to implement a method
for each scan region of the one or more scan regions, obtaining a motion curve of a center of mass of a portion of the subject in the scan region;
determining a spectrogram corresponding to the motion curve;
wherein the first spectrum segment includes a union of a spectrum range where a target frequency is located and a spectrum range where 2 times the target frequency is located, the second spectrum segment includes a spectrum range where noise is located, and the target frequency corresponds to a maximum signal intensity in the spectrogram;
determining whether there is a physiological motion in the scan region based on the ratio;
determining a scan mode for scanning the scan region based on whether there is the physiological motion in the scan region; and
generating a PET sub-image of the scan region based on the PET data of the scan region; and generating a PET image of the scan volume based on one or more PET sub-images.
Claims 1-3, 6-7, and 10-11 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3, and 5-7 of U.S. Patent No. 12115009 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the limitations recited in the claims mentioned above of the instant application are also recited in the claims mentioned above of the copending application.
Instant Application
US Pat. No. 12115009 B2
1. A method implemented on at least one machine each of which has at least one processor and a storage device, the method comprising:
obtaining a motion curve of a portion of a subject in at least one scan region of the subject; determining a spectrum corresponding to the motion curve;
determining at least one spectrum segment in the spectrum based on a target frequency, wherein the target frequency corresponds to a maximum signal intensity in the spectrum;
determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment;
determining a scan mode for scanning the at least one scan region based on whether there is the physiological motion in the at least one scan region; and generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode.
1. A method implemented on at least one machine each of which has at least one processor and a storage device, the method comprising:
obtaining a motion curve of a portion of a subject in at least one scan region of the subject; determining a spectrum corresponding to the motion curve;
dividing the spectrum into a plurality of spectrum segments, wherein the plurality of spectrum segments include a first spectrum segment, and the first spectrum segment includes a union of a spectrum range where a target frequency is located and a spectrum range where 2 times the target frequency is located, wherein the plurality of spectrum segments include a second spectrum segment, the second spectrum segment includes a spectrum range where a noise is located;
determining whether there is a physiological motion in the at least one scan region based on a first intensity value of the first spectrum segment by obtaining a ratio of the first intensity value of the first spectrum segment to a third intensity value of the second spectrum segment;
determining a scan mode for scanning the at least one scan region based on whether there is the physiological motion in the at least one scan region; and generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode.
2. The method of claim 1, wherein the at least one spectrum segment includes a first spectrum segment, the first spectrum segment is a spectrum range where a target frequency is located or a union of the spectrum range where the target frequency is located and a spectrum range where 2 times the target frequency is located.
1. …the first spectrum segment includes a union of a spectrum range where a target frequency is located and a spectrum range where 2 times the target frequency is located..
3. The method of claim 2, wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum; and determining whether there is a physiological motion in the at least one scan region based on the ratio.
1. …determining whether there is a physiological motion in the at least one scan region based on a first intensity value of the first spectrum segment by obtaining a ratio of the first intensity value of the first spectrum segment to a third intensity value of the second spectrum segment
6. The method of claim 1, wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region.
2. The method of claim 1, wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region.
7. The method of claim 1, wherein the scan mode includes a second mode if it is determined that there is the physiological motion in the at least one scan region, and the second mode includes a gating mode for scanning the at least one scan region.
3. The method of claim 1, wherein the scan mode includes a second mode if it is determined that there is the physiological motion in the at least one scan region, and the second mode includes a gating mode for scanning the at least one scan region.
9. The method of claim 1, wherein the method further comprises: determining a scan volume of the subject supported by a scan table; and dividing the scan volume into the at least one scan region, wherein each of the at least one scan region corresponds to a table position.
5. The method of claim 1, wherein the method further comprises: determining a scan volume of the subject supported by a scan table; and dividing the scan volume into the at least one scan region, wherein each of the at least one scan region corresponds to a table position.
10. The method of claim 9, further comprising: adjusting a statistical characteristic of the at least one scan region according to at least one table position corresponding to the at least one scan region, wherein the statistical characteristic of one of the at least one scan region includes at least one of a scan time period of the scan region, a scan velocity of the scan region, or a number of data frames of the scan region.
6. The method of claim 5, further comprising: adjusting a statistical characteristic of the at least one scan region according to at least one table position corresponding to the at least one scan region, wherein the statistical characteristic of one of the at least one scan region includes at least one of a scan time period of the scan region, a scan velocity of the scan region, or a number of data frames of the scan region.
11. The method of claim 9, further comprising: generating a PET image of the scan volume by stitching the at least one PET sub-image of the at least one scan region.
7. The method of claim 5, further comprising: generating a PET image of the scan volume by stitching the at least one PET sub-image of the at least one scan region.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-7, and 9-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “a method implemented on at least one machine each of which has at least one processor and a storage device”. However, because the claim fails to establish a plurality of machines, the recitation of “each of which” renders the claim indefinite. The preamble may be amended to recite at least one machine of a plurality of machines.
Claim 2 recites “wherein the at least one spectrum segment includes a first spectrum segment, the first spectrum segment is a spectrum range where a target frequency is located or a union of the spectrum range where the target frequency is located and a spectrum range where 2 times the target frequency is located”. It is unclear what the first spectrum segment entails as the limitation appear to present three criteria for defining the first spectrum segment but fails to stipulate if all three criteria must be met or only a single criteria is required. For purposes of the examination, the first spectrum segment is being defined as requiring ONLY one of the three criteria.
Claims 2-7 and 9-13 are rejected based on their respective dependencies on claim 1.
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 9-20 are rejected under 35 U.S.C. 103 as being unpatentable over Schleyer, et al., US 20050123183 A1 in view of Wollenweber, S.D., US 20160163042 (disclosed in the IDS filed 01/06/2025).
Regarding claim 1, Schleyer teaches a method implemented on at least one machine each of which has at least one processor and a storage device (the abstract discloses a system and method of correcting respiratory induced motion in nuclear medicine imaging. Images are acquired dynamically, and gated post-acquisition, generating a series of near motion-free bins. These bins are then aligned to produce a motion corrected image without extending the acquisition time), the method comprising:
obtaining a motion curve of a portion of a subject in at least one scan region of the subject ([0042] states that “the binning module 130 places each of the original, unfiltered frames in the appropriate bin by referencing the filtered counts-time series” and [0044] states that the binning module 130 divides the phase weighted counts into bins of equal count-range as opposed to equal time-ranges, as shown in FIG. 4. For example, a typical maximum amplitude of 2 cm for the phase weighted counts and R=16 (i.e., the number of near motion-free bins) translates to a maximum of 1.25 mm of respiratory induced motion in each near motion-free bin);
determining a spectrum corresponding to the motion curve (figs. 3A-3C depict frequency magnitude of pixels from liver spleen scans, showing background, edge of liver (respiratory frequency spike circled), and center of liver, respectively, due to motions of the liver spleen during the scans [0028] further discloses distinctions between the edge pixels and the central pixels. The frequency graphs in figs. 3A-3C correspond to the counts-time series graph of fig. 4);
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determining at least one spectrum segment in the spectrum based on a target frequency, wherein the target frequency corresponds to a maximum signal intensity in the spectrum ([0028] states that “By specifying upper and lower frequencies, F.sub.up and F.sub.lo respectively, around the estimated respiration frequency F.sub.r, the pixel classification module 110 calculates the average amplitude of the frequencies in the search window”. The search window F.sub.win is the at least one spectrum segment containing the upper frequency F.sub.up);
determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment ([0025] discloses that “The pixel classification module or routine 110 of the processor 100 generates a filtered set of data by temporally and spatially Gaussian smoothing (one pixel full width half maximum) the frames to eliminate pixels not demonstrating respiratory motion characteristics in step 210. Since the respiration cycle is quasi-sinusoidal, the respiratory induced motion contains a dominant frequency component with approximately the same period as the respiration cycle itself”);
determining a scan mode for scanning the at least one scan region based on whether there is the physiological motion in the at least one scan region ([0010] states that “the respiratory motion correction technique and system utilize a temporal spectral analysis to determine the spatial regions in a dynamic scan which are subject to respiration motion…determines where, in the displacement phase of the respiration cycle, each frame lies from the change in counts within these spatial regions which are subject to respiration motion throughout the dynamic scan…places these frames into bins which contain other frames from equal displacement phases of the respiratory cycle, thereby effectively data gating the acquisition with a displacement based trigger, rather than temporally based…the inventive system and method processes list mode acquired data and images acquired as a dynamic scan, of short frame duration relative to respiratory period, so that minimal motion occurs during each frame”, meaning that a dynamic scanning mode using displacement triggering is used in the stead of a temporal parameters to perform the data acquisition).
Schleyer does not teach generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode.
However, within the same field of endeavor, Wollenweber teaches a method (method 400 for imaging an object in [0037]) utilizing a sliding window to define segment sizes ([0037]) for PET listmode data acquisition ([0038]), the method comprising generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode ([0038] discloses defining a sliding window length, to correspond to a segment of PET data to be acquired, at step 404 and then based on the defined sliding window length, acquiring PET data for the defined data segment ([0039]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Schleyer for generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 2, Schleyer in view of Wollenweber teaches all the limitations of claim 1 above.
Schleyer further teaches wherein the at least one spectrum segment includes a first spectrum segment, the first spectrum segment is a spectrum range where a target frequency is located or a union of the spectrum range where the target frequency is located and a spectrum range where 2 times the target frequency is located ([0028] states that “By specifying upper and lower frequencies, F.sub.up and F.sub.lo respectively, around the estimated respiration frequency F.sub.r, the pixel classification module 110 calculates the average amplitude of the frequencies in the search window”. The search window F.sub.win is the at least one spectrum segment containing the upper frequency F.sub.up).
Regarding claim 3, Schleyer in view of Wollenweber teaches all the limitations of claim 2 above.
Schleyer in view of the embodiment in fig. 4 of Wollenweber relied upon above does not teach wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment ([0025] discloses that “The pixel classification module or routine 110 of the processor 100 generates a filtered set of data by temporally and spatially Gaussian smoothing (one pixel full width half maximum) the frames to eliminate pixels not demonstrating respiratory motion characteristics in step 210. Since the respiration cycle is quasi-sinusoidal, the respiratory induced motion contains a dominant frequency component with approximately the same period as the respiration cycle itself”) comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum; and determining whether there is a physiological motion in the at least one scan region based on the ratio.
However, in a separate embodiment, Wollenweber in fig. 1 discloses a method 100 of imaging an object ([0022]), the method comprising acquisition of PET data at step 102 ([0023]), determining a segment size at step 104 ([0026]). The method then analyzes an amount of motion for a current segment at step 106 ([0032]). Wollenweber teaches wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum (during the specifying of the amount of motion in a current segment at step 106, Wollenweber states in [0032] that “One or more metrics that may be determined using the PCA (or other computational technique) may be determined for each segment. The metric may describe or correspond to an amount of respiratory motion. For example, in some embodiments, R may be determined, where R is the ratio of a peak value in a respiratory frequency window to the mean value above the window for a Fourier transform of a waveform generated using the PCA”); and determining whether there is a physiological motion in the at least one scan region based on the ratio ([0032] discloses that an amount of motion for a current segment being analyzed is determined at step 106).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure modified Schleyer wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum; and determining whether there is a physiological motion in the at least one scan region based on the ratio, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 4, Schleyer in view of Wollenweber teaches all the limitations of claim 2 above.
Schleyer further teaches wherein the at least one spectrum segment further includes a second spectrum segment, the second spectrum segment includes a spectrum range where a noise is located ([0033] states that “the pixel classification module 110 applied a 3.times.3 median filter to the binary mask as defined by Equation (6) to reduce "salt and pepper" noise, i.e., data drop-out noise. The pixel classification module 110 applies this mask, which represents pixels of significant power, to the original frames to eliminate pixels not demonstrating respiratory motion characteristics”. With reference to the frequency graphs in figs. 3A-3C, the noise is contained within a window outside the search window F.sub.win).
Regarding claim 5, Schleyer in view of Wollenweber teaches all the limitations of claim 2 above.
Schleyer further teaches wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment ([0025] discloses that “The pixel classification module or routine 110 of the processor 100 generates a filtered set of data by temporally and spatially Gaussian smoothing (one pixel full width half maximum) the frames to eliminate pixels not demonstrating respiratory motion characteristics in step 210. Since the respiration cycle is quasi-sinusoidal, the respiratory induced motion contains a dominant frequency component with approximately the same period as the respiration cycle itself”) comprises:
obtaining a ratio of a first intensity value of the first spectrum segment to a third intensity value of the second spectrum segment ([0032] states that “The reference windows allow the pixel classification module 110 to determine a ratio of respiratory signal power to non-respiratory signal power in”, where the respiratory signal power is the first intensity value and the non-respiratory signal power is the second intensity value); and
determining whether there is a physiological motion in the at least one scan region based on the ratio ([0032] further discloses generating a mask as a selection criteria, and [0032] states that “The binary mask determines which regions in the X-Y plane contain the edge of a moving structure”).
Regarding claim 6, Schleyer in view of Wollenweber teaches all the limitations of claim 1 above.
Schleyer in view of the embodiment in fig. 4 of Wollenweber relied upon above does not teach wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region.
However, Wollenweber’s fig. 1 further teaches wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region ([0030] states that “In some embodiments, for each slice, a static image as well as a motion mitigation image may be generated, with the static and motion mitigation image combined using a weighting, with the sum of the weights for a segment being equal to 1”. [0030] then further states that “Slices immediately adjacent to slice 315 may be given a static image weight of 1/3, and slices spaced a distance of one slice from slice 315 may be given a static image weighting of 2/3, with all other slices given a static image weight of one. With a static weight of one, only the static image is used. Other weighting schemes may be employed in various embodiments. In various embodiments, a weight (e.g., a weight corresponding to a proportion between static and mitigated image information used) may be assigned to each of the sliding windows based on a detected sliding window amount of motion (e.g., an amount of motion for the slices of each sliding window, with the motion-mitigated data and non-motion-mitigated data for each slice combined based on the corresponding weight to provide information from the sliding window used for image reconstruction”, meaning the static image is acquired for regions devoid of motion).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure modified Schleyer wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 7, Schleyer in view of Wollenweber teaches all the limitations of claim 1 above.
Schleyer further teaches wherein the scan mode includes a second mode if it is determined that there is the physiological motion in the at least one scan region, and the second mode includes a gating mode for scanning the at least one scan region ([0023] states “The processor 100 receives a dynamic image of the target organ having a plurality of frames, preferably a moving organ or an organ subject to respiratory motion, from any known nuclear medicine image system…The binning module 130 bins or places frames into bins containing other frames from equal displacement phases of the respiratory cycle, effectively data gating the acquisition with a displacement based trigger”).
Regarding claim 9, Schleyer in view of Wollenweber teaches all the limitations of claim 1 above.
Schleyer in view of the embodiment in fig. 4 of Wollenweber relied upon above does not teach wherein the method further comprises: determining a scan volume of the subject supported by a scan table; and dividing the scan volume into the at least one scan region, wherein each of the at least one scan region corresponds to a table position.
However, However, in a separate embodiment, Wollenweber in fig. 6 discloses a method for determining scanning and/or processing parameters and/or imaging [0045], with step 606 of the method in fig. 6 calling for a scan range normalization ([0048]), such scan range normalization depicted in reproduced figure 7 below, and [0049] stating that “In view 710, a series of overlapping FOVs or bed positions 711, 712, 713, 714, 715, 716, 717, 718, 719, and 720 are shown. The illustrated FOVs correspond to bed positions having a 20% overlap, and to a range extending from a portion of the skull to about the knee. The various locations along the length of the patient are normalized in view 710. For example, the dashed line 730 corresponds to a superior-most location of the lung, and the dashed line 740 corresponds to an inferior-most location of the lung. The superior location 730 is assigned a normalized value of 1.0, and the inferior location 740 is assigned a normalized value of 0.0.”, thereby determining a scan volume of the subject supported by a scan table; and dividing the scan volume into the at least one scan region, wherein each of the at least one scan region corresponds to a table position, as required by the limitation. [0050] further describes the normalization steps from the view 750.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure modified Schleyer wherein the method further comprises: determining a scan volume of the subject supported by a scan table; and dividing the scan volume into the at least one scan region, wherein each of the at least one scan region corresponds to a table position, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 10, Schleyer in view of Wollenweber’s embodiment in fig. 4 and embodiment in fig. 6 teaches all the limitations of claim 9 above.
Schleyer in view of Wollenweber’s embodiment in fig. 4 does not teach adjusting a statistical characteristic of the at least one scan region according to at least one table position corresponding to the at least one scan region, wherein the statistical characteristic of one of the at least one scan region includes at least one of a scan time period of the scan region, a scan velocity of the scan region, or a number of data frames of the scan region.
However, Wollenweber’s embodiment in fig. 6 further teaches adjusting a statistical characteristic of the at least one scan region according to at least one table position corresponding to the at least one scan region, wherein the statistical characteristic of one of the at least one scan region includes at least one of a scan time period of the scan region, a scan velocity of the scan region, or a number of data frames of the scan region ([0050] states that “One or more scanning and/or acquisition settings for the positions identified as more likely affected to be affected by motion (e.g., positions 712, 713, 714 in the example discussed above) may be adjusted relative to the other portions to account for the anticipated motion”, and [0052] states that “the method proceeds to 612, and motion mitigation scanning and/or analysis parameters are applied. For example, a scan time or duration for the position being analyzed may be increased at 612 based on the value of R, with higher values of R resulting in longer scan times or durations”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure modified Schleyer for adjusting a statistical characteristic of the at least one scan region according to at least one table position corresponding to the at least one scan region, wherein the statistical characteristic of one of the at least one scan region includes at least one of a scan time period of the scan region, a scan velocity of the scan region, or a number of data frames of the scan region, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 11, Schleyer in view of Wollenweber’s embodiment in fig. 4 and embodiment in fig. 6 teaches all the limitations of claim 9 above.
Schleyer does not teach generating a PET image of the scan volume by stitching the at least one PET sub-image of the at least one scan region.
However, Wollenweber’s embodiment in fig. 4 further teaches generating a PET image of the scan volume by stitching the at least one PET sub-image of the at least one scan region, stating in [0040] that “If it is determined that all data segments have been analyzed, the method proceeds to 420. At 420, PET image data (e.g., slices of data and/or images from the various bed or detector positions) is stitched together, for example using conventional techniques”.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure modified Schleyer for generating a PET image of the scan volume by stitching the at least one PET sub-image of the at least one scan region, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 12, Schleyer in view of Wollenweber teaches all the limitations of claim 1 above.
Schleyer further teaches wherein the scan mode is a gating mode in response to determining that there is a physiological motion in the at least one scan region ([0023] states “The processor 100 receives a dynamic image of the target organ having a plurality of frames, preferably a moving organ or an organ subject to respiratory motion, from any known nuclear medicine image system…The binning module 130 bins or places frames into bins containing other frames from equal displacement phases of the respiratory cycle, effectively data gating the acquisition with a displacement based trigger”), the PET data is divided into multiple data frames and the count of the multiple data frames is determined based on an amplitude of the physiological motion of the at least one scan region ([0041] states that “The binning module 130 convolves frames with the phase weighted mask defined by phase weighting module 120, and obtains total counts per frame, i.e., counts-time series or phase weighted counts in step 230” and [0042] states that “the binning module 130 places each of the original, unfiltered frames in the appropriate bin by referencing the filtered counts-time series”).
Schleyer in view of the embodiment in fig. 4 of Wollenweber relied upon above does not teach that the at least one PET sub-image is generated by performing gating reconstruction on the PET data of the at least one scan region acquired in the gating mode, during the gating reconstruction.
However, the embodiment in fig. 1 of Wollenweber further discloses that the at least one PET sub-image is generated by performing gating reconstruction on the PET data of the at least one scan region acquired in the gating mode, during the gating reconstruction ([0034] states that “At 110, if the amount of motion for a given segment satisfied the threshold, motion mitigation is performed on the data for the particular segment. Various motion mitigation techniques may be employed in various embodiments. For example, in some embodiments, PET coincident data for a segment having motion above the threshold may be gated to produce 4D PET image volumes used to generate a motion mitigated image volume”).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure modified Schleyer such that the at least one PET sub-image is generated by performing gating reconstruction on the PET data of the at least one scan region acquired in the gating mode, during the gating reconstruction, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 13, Schleyer in view of Wollenweber teaches all the limitations of claim 1 above.
wherein the scan mode is a gating mode in response to determining that there is a physiological motion in the at least one scan region ([0023] states “The processor 100 receives a dynamic image of the target organ having a plurality of frames, preferably a moving organ or an organ subject to respiratory motion, from any known nuclear medicine image system…The binning module 130 bins or places frames into bins containing other frames from equal displacement phases of the respiratory cycle, effectively data gating the acquisition with a displacement based trigger”), and the method further comprises:
determining a scan duration based on an amplitude of the physiological motion of the at least one scan region; and collecting the PET data by performing a gating scan on the at least one scan region based on the scan duration ([0010] states that “the respiratory motion correction technique and system utilize a temporal spectral analysis to determine the spatial regions in a dynamic scan which are subject to respiration motion” and then further state that “the inventive system and method processes list mode acquired data and images acquired as a dynamic scan, of short frame duration relative to respiratory period, so that minimal motion occurs during each frame”. As [0028] discloses, the method specifies “upper and lower frequencies, F.sub.up and F.sub.lo respectively, around the estimated respiration frequency F.sub.r, the pixel classification module 110 calculates the average amplitude of the frequencies in the search window F.sub.win”).
Regarding claim 14, Schleyer teaches a system (the abstract discloses a system and method of correcting respiratory induced motion in nuclear medicine imaging. Images are acquired dynamically, and gated post-acquisition, generating a series of near motion-free bins. These bins are then aligned to produce a motion corrected image without extending the acquisition time), comprising:
at least one storage device storing a set of instructions; and at least one processor configured to communicate with the at least one storage device, wherein when executing the set of instructions, the at least one processor is configured to direct the system to perform operations ([0021] states “the data driven respiratory motion correction method for nuclear medicine imaging is a software program running on a processor or computer 100 of FIG. 1”, the processor 100 comprising one or more modules or routines performing various steps according to [0022] and configured for receiving dynamic image for processing [0023]. The computer inherently includes a computer readable medium for storing the software as evidenced by recitation in claims 24-27) including:
obtaining a motion curve of a portion of a subject in at least one scan region of the subject ([0042] states that “the binning module 130 places each of the original, unfiltered frames in the appropriate bin by referencing the filtered counts-time series” and [0044] states that the binning module 130 divides the phase weighted counts into bins of equal count-range as opposed to equal time-ranges, as shown in FIG. 4. For example, a typical maximum amplitude of 2 cm for the phase weighted counts and R=16 (i.e., the number of near motion-free bins) translates to a maximum of 1.25 mm of respiratory induced motion in each near motion-free bin);
determining a spectrum corresponding to the motion curve (figs. 3A-3C depict frequency magnitude of pixels from liver spleen scans, showing background, edge of liver (respiratory frequency spike circled), and center of liver, respectively, due to motions of the liver spleen during the scans [0028] further discloses distinctions between the edge pixels and the central pixels. The frequency graphs in figs. 3A-3C correspond to the counts-time series graph of fig. 4);
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determining at least one spectrum segment in the spectrum based on a target frequency, wherein the target frequency corresponds to a maximum signal intensity in the spectrum ([0028] states that “By specifying upper and lower frequencies, F.sub.up and F.sub.lo respectively, around the estimated respiration frequency F.sub.r, the pixel classification module 110 calculates the average amplitude of the frequencies in the search window”. The search window F.sub.win is the at least one spectrum segment containing the upper frequency F.sub.up);
determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment ([0025] discloses that “The pixel classification module or routine 110 of the processor 100 generates a filtered set of data by temporally and spatially Gaussian smoothing (one pixel full width half maximum) the frames to eliminate pixels not demonstrating respiratory motion characteristics in step 210. Since the respiration cycle is quasi-sinusoidal, the respiratory induced motion contains a dominant frequency component with approximately the same period as the respiration cycle itself”);
determining a scan mode for scanning the at least one scan region based on whether there is the physiological motion in the at least one scan region ([0010] states that “the respiratory motion correction technique and system utilize a temporal spectral analysis to determine the spatial regions in a dynamic scan which are subject to respiration motion…determines where, in the displacement phase of the respiration cycle, each frame lies from the change in counts within these spatial regions which are subject to respiration motion throughout the dynamic scan…places these frames into bins which contain other frames from equal displacement phases of the respiratory cycle, thereby effectively data gating the acquisition with a displacement based trigger, rather than temporally based…the inventive system and method processes list mode acquired data and images acquired as a dynamic scan, of short frame duration relative to respiratory period, so that minimal motion occurs during each frame”, meaning that a dynamic scanning mode using displacement triggering is used in the stead of a temporal parameters to perform the data acquisition).
Schleyer does not teach generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode.
However, within the same field of endeavor, Wollenweber teaches a method utilizing a sliding window to define segment sizes ([0037]) for PET listmode data acquisition ([0038]), the method comprising generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode ([0038] discloses defining a sliding window length, to correspond to a segment of PET data to be acquired, at step 404 and then based on the defined sliding window length, acquiring PET data for the defined data segment ([0039]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Schleyer for generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 15, Schleyer in view of Wollenweber teaches all the limitations of claim 14 above.
Schleyer further teaches wherein the at least one spectrum segment includes a first spectrum segment, the first spectrum segment is a spectrum range where a target frequency is located or a union of the spectrum range where the target frequency is located and a spectrum range where 2 times the target frequency is located ([0028] states that “By specifying upper and lower frequencies, F.sub.up and F.sub.lo respectively, around the estimated respiration frequency F.sub.r, the pixel classification module 110 calculates the average amplitude of the frequencies in the search window”. The search window F.sub.win is the at least one spectrum segment containing the upper frequency F.sub.up).
Regarding claim 16, Schleyer in view of Wollenweber teaches all the limitations of claim 15 above.
Schleyer in view of the embodiment in fig. 4 relied upon above does not teach wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment ([0025] discloses that “The pixel classification module or routine 110 of the processor 100 generates a filtered set of data by temporally and spatially Gaussian smoothing (one pixel full width half maximum) the frames to eliminate pixels not demonstrating respiratory motion characteristics in step 210. Since the respiration cycle is quasi-sinusoidal, the respiratory induced motion contains a dominant frequency component with approximately the same period as the respiration cycle itself”) comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum; and determining whether there is a physiological motion in the at least one scan region based on the ratio.
However, in a separate embodiment, Wollenweber in fig. 1 discloses a method 100 of imaging an object ([0022]), the method comprising acquisition of PET data at step 102 ([0023]), determining a segment size at step 104 ([0026]). The method then analyzes an amount of motion for a current segment at step 106 ([0032]). Wollenweber teaches wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum (during the specifying of the amount of motion in a current segment at step 106, Wollenweber states in [0032] that “One or more metrics that may be determined using the PCA (or other computational technique) may be determined for each segment. The metric may describe or correspond to an amount of respiratory motion. For example, in some embodiments, R may be determined, where R is the ratio of a peak value in a respiratory frequency window to the mean value above the window for a Fourier transform of a waveform generated using the PCA”); and determining whether there is a physiological motion in the at least one scan region based on the ratio ([0032] discloses that an amount of motion for a current segment being analyzed is determined at step 106).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure modified Schleyer wherein the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment comprises: obtaining a ratio of a first intensity value of the first spectrum segment to a second intensity value of the spectrum; and determining whether there is a physiological motion in the at least one scan region based on the ratio, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 17, Schleyer in view of Wollenweber teaches all the limitations of claim 15 above.
Schleyer further teaches wherein the at least one spectrum segment further includes a second spectrum segment, the second spectrum segment includes a spectrum range where a noise is located ([0033] states that “the pixel classification module 110 applied a 3.times.3 median filter to the binary mask as defined by Equation (6) to reduce "salt and pepper" noise, i.e., data drop-out noise. The pixel classification module 110 applies this mask, which represents pixels of significant power, to the original frames to eliminate pixels not demonstrating respiratory motion characteristics”. With reference to the frequency graphs in figs. 3A-3C, the noise is contained within a window outside the search window F.sub.win), and the determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment ([0025] discloses that “The pixel classification module or routine 110 of the processor 100 generates a filtered set of data by temporally and spatially Gaussian smoothing (one pixel full width half maximum) the frames to eliminate pixels not demonstrating respiratory motion characteristics in step 210. Since the respiration cycle is quasi-sinusoidal, the respiratory induced motion contains a dominant frequency component with approximately the same period as the respiration cycle itself”) comprises:
obtaining a ratio of a first intensity value of the first spectrum segment to a third intensity value of the second spectrum segment ([0032] states that “The reference windows allow the pixel classification module 110 to determine a ratio of respiratory signal power to non-respiratory signal power in”, where the respiratory signal power is the first intensity value and the non-respiratory signal power is the second intensity value); and
determining whether there is a physiological motion in the at least one scan region based on the ratio ([0032] further discloses generating a mask as a selection criteria, and [0032] states that “The binary mask determines which regions in the X-Y plane contain the edge of a moving structure”).
Regarding claim 18, Schleyer in view of Wollenweber teaches all the limitations of claim 14 above.
Schleyer in view of the embodiment in fig. 4 of Wollenweber relied upon above does not teach wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region.
However, Wollenweber’s fig. 1 further teaches wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region ([0030] states that “In some embodiments, for each slice, a static image as well as a motion mitigation image may be generated, with the static and motion mitigation image combined using a weighting, with the sum of the weights for a segment being equal to 1”. [0030] then further states that “Slices immediately adjacent to slice 315 may be given a static image weight of 1/3, and slices spaced a distance of one slice from slice 315 may be given a static image weighting of 2/3, with all other slices given a static image weight of one. With a static weight of one, only the static image is used. Other weighting schemes may be employed in various embodiments. In various embodiments, a weight (e.g., a weight corresponding to a proportion between static and mitigated image information used) may be assigned to each of the sliding windows based on a detected sliding window amount of motion (e.g., an amount of motion for the slices of each sliding window, with the motion-mitigated data and non-motion-mitigated data for each slice combined based on the corresponding weight to provide information from the sliding window used for image reconstruction”, meaning the static image is acquired for regions devoid of motion).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure modified Schleyer wherein the scan mode includes a first mode if it is determined that there is no physiological motion in the at least one scan region, and the first mode includes at least a static mode or a transmission mode for scanning the at least one scan region, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Regarding claim 19, Schleyer in view of Wollenweber teaches all the limitations of claim 14 above.
Schleyer further teaches wherein the scan mode includes a second mode if it is determined that there is the physiological motion in the at least one scan region, and the second mode includes a gating mode for scanning the at least one scan region ([0023] states “The processor 100 receives a dynamic image of the target organ having a plurality of frames, preferably a moving organ or an organ subject to respiratory motion, from any known nuclear medicine image system…The binning module 130 bins or places frames into bins containing other frames from equal displacement phases of the respiratory cycle, effectively data gating the acquisition with a displacement based trigger”).
Regarding claim 20, Schleyer teaches a non-transitory computer readable medium, comprising a set of instructions, wherein when executed by at least one processor, the set of instructions direct the at least one processor to effectuate a method ([0021] discloses a software program running on a processor or computer 100 of FIG. 1 for a data driven respiratory motion correction method), the method comprising:
obtaining a motion curve of a portion of a subject in at least one scan region of the subject ([0042] states that “the binning module 130 places each of the original, unfiltered frames in the appropriate bin by referencing the filtered counts-time series” and [0044] states that the binning module 130 divides the phase weighted counts into bins of equal count-range as opposed to equal time-ranges, as shown in FIG. 4. For example, a typical maximum amplitude of 2 cm for the phase weighted counts and R=16 (i.e., the number of near motion-free bins) translates to a maximum of 1.25 mm of respiratory induced motion in each near motion-free bin);
determining a spectrum corresponding to the motion curve (figs. 3A-3C depict frequency magnitude of pixels from liver spleen scans, showing background, edge of liver (respiratory frequency spike circled), and center of liver, respectively, due to motions of the liver spleen during the scans [0028] further discloses distinctions between the edge pixels and the central pixels. The frequency graphs in figs. 3A-3C correspond to the counts-time series graph of fig. 4);
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determining at least one spectrum segment in the spectrum based on a target frequency, wherein the target frequency corresponds to a maximum signal intensity in the spectrum ([0028] states that “By specifying upper and lower frequencies, F.sub.up and F.sub.lo respectively, around the estimated respiration frequency F.sub.r, the pixel classification module 110 calculates the average amplitude of the frequencies in the search window”. The search window F.sub.win is the at least one spectrum segment containing the upper frequency F.sub.up);
determining whether there is a physiological motion in the at least one scan region based on an intensity value of each of the at least one spectrum segment ([0025] discloses that “The pixel classification module or routine 110 of the processor 100 generates a filtered set of data by temporally and spatially Gaussian smoothing (one pixel full width half maximum) the frames to eliminate pixels not demonstrating respiratory motion characteristics in step 210. Since the respiration cycle is quasi-sinusoidal, the respiratory induced motion contains a dominant frequency component with approximately the same period as the respiration cycle itself”);
determining a scan mode for scanning the at least one scan region based on whether there is the physiological motion in the at least one scan region ([0010] states that “the respiratory motion correction technique and system utilize a temporal spectral analysis to determine the spatial regions in a dynamic scan which are subject to respiration motion…determines where, in the displacement phase of the respiration cycle, each frame lies from the change in counts within these spatial regions which are subject to respiration motion throughout the dynamic scan…places these frames into bins which contain other frames from equal displacement phases of the respiratory cycle, thereby effectively data gating the acquisition with a displacement based trigger, rather than temporally based…the inventive system and method processes list mode acquired data and images acquired as a dynamic scan, of short frame duration relative to respiratory period, so that minimal motion occurs during each frame”, meaning that a dynamic scanning mode using displacement triggering is used in the stead of a temporal parameters to perform the data acquisition).
Schleyer does not teach generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode.
However, within the same field of endeavor, Wollenweber teaches a method utilizing a sliding window to define segment sizes ([0037]) for PET listmode data acquisition ([0038]), the method comprising generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode ([0038] discloses defining a sliding window length, to correspond to a segment of PET data to be acquired, at step 404 and then based on the defined sliding window length, acquiring PET data for the defined data segment ([0039]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to configure Schleyer for generating at least one PET sub-image of the at least one scan region based on PET data of the at least one scan region acquired in the scan mode, as taught by Wollenweber, to reduce effects of motion artifacts in PET data while maintaining improved image quality ([0002]-[0003]), with a reasonable expectation of success, as Schleyer is also concerned with addressing limitations of motion correction protocols in PET image acquisitions ([0005]-[0004]).
Conclusion
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/FAROUK A BRUCE/ Examiner, Art Unit 3797