DETAILED ACTIONS
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (“IDS”) filed on 04/15/2024, 05/15/2025, and 10/16/2025 were reviewed and the listed references were noted.
Drawings
The 26-page drawings have been considered and placed on record in the file.
Status of Claims
Claims 1-11 and 20 are pending. Claims 12-19 are withdrawn.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 1-4, 6-7, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Panin et al. (US 2022/0309718 A1), hereinafter referred to as Panin.
Claim 1
Panin discloses a method (Panin, Fig. 3A) implemented on a device (Panin, Fig. 12) including at least one processor (Panin, [0139], “The central controller 28 further includes one or more processors 920 coupled with the system bus 921 for processing the information.”) and at least one storage (Panin, [0120], “The memory 26 is a single device or group of two or more devices”), comprising:
obtaining a movement parameter of a movement of a phantom (Panin, [0048], “the list mode data file for the axially short phantom is available and a CT scan of the axially short phantom does not need to be taken because the parameters of the axially short phantom is known and can determine the position of the phantom from PET data.”), the movement of the phantom being performed during a scanning process for scanning the phantom using the PET system (Panin, [0112], “The axially short cylinder phantom 60 is mounted on the patient bed 206 that moves the phantom during the CBM acquisition of the phantom 60 as shown in FIG. 7A. The method comprises (a) generating TOF data of the axially short cylinder phantom 60 in a CBM acquisition mode, while accounting for the axial motion of the patient bed 206 during the CBM acquisition, as a first data set (see Box 610); (b) generating a complementary data set by integrating the TOF data of the axially short cylinder phantom 60 over the axial motion of the patient bed 206 during the CBM acquisition (see Box 620); and (c) simultaneously reconstructing the activity and CE normalization coefficient from the complementary data set (see Box 630)..);
determining a system effective scanning time based on the movement parameter (Panin, [0116], “the detection time efficiency at different times and corresponding positions of the bed or patient during the PET scan is determined based, in part, on the velocity.”);
obtaining scanning data of the phantom during the scanning process, the scanning data including an event count (Panin, [0065], “The bed motion data is stored in the list mode data file and is reported with a fraction of a second interval with a precision of a fraction of a millimeter. This provides the bed motion knowledge. The CBM data generation is a dynamic process of assignment list mode events into proper sonogram planes. The list mode events histogramming into CBM sonogram space utilize the nearest neighbor interpolation in the axial direction. In other words, when the CBM bed is shifted by a distance equal to that of the separation between sonogram planes, the event from the same detector pair will be assigned to the next image data plane. The normalization array n is also generated (box 412) utilizing the CBM bed motion knowledge (box 411))”; and
determining a performance parameter of the PET system based on the system effective scanning time and the scanning data (Panin, [0168], “A line source phantom was placed at the center of scanner to evaluate time resolution according to the National Electrical Manufacturers Association (NEMA) standard. List mode data was acquired without downloading TOs. TOs were applied during list mode data processing by changing TOF bin indexing. In this way, the same data set is used to estimate the performance of various TA procedures.”, [0005], “, the quality of TOF reconstruction strongly depends on the correctness of time information. In order to ensure accurate time information for TOF reconstruction in PET scanners, a TA procedure, also referred to as timing calibration, is performed on a regular basis in PET scanners”).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine each of the example embodiments of the prior art. All the claimed elements were known in the prior art and one skilled in the art could have combined the embodiments as claimed by known method with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art at the time of the invention. Panin disclosed that although the subject matter has been described in terms of exemplary embodiments, it is not limited thereto, rather it should be construed broadly, to include other variants and embodiments, which may be made by those skilled in the art (Panin, [0182]).
Claim 2
Panin discloses the method of claim 1 (Panin, Fig. 3A), wherein the movement parameter (Panin, [0048], “the list mode data file for the axially short phantom is available and a CT scan of the axially short phantom does not need to be taken because the parameters of the axially short phantom is known and can determine the position of the phantom from PET data.”) includes at least one of a movement time span of the phantom, a count of at least one movement segment included in the movement of the phantom, a movement speed of the phantom in the movement of the phantom, or a corresponding relationship between phantom positions of the phantom and time in the movement of the phantom, the movement segment referring to a movement throughout an axial length of a field of view (FOV) of the PET system (Panin[0065], “The bed motion data is stored in the list mode data file and is reported with a fraction of a second interval with a precision of a fraction of a millimeter. This provides the bed motion knowledge. The CBM data generation is a dynamic process of assignment list mode events into proper sonogram planes. The list mode events histogramming into CBM sonogram space utilize the nearest neighbor interpolation in the axial direction. In other words, when the CBM bed is shifted by a distance equal to that of the separation between sonogram planes, the event from the same detector pair will be assigned to the next image data plane. The normalization array n is also generated (box 412) utilizing the CBM bed motion knowledge (box 411). The normalization array n is computed by simulating the movement of an object through the scanner, assisted by the monitoring of basic scanner acquisition parameters such as the singles rate.”, [0178], “the axially short phantom in CBM mode of the PET scanner can be used to check normalization coefficients for the PET detectors. By performing a PET scan while the axially short phantom is moved through the patient tunnel of the PET gantry, the uniformity of the PET detectors along the PET scanner FOV can be measured and used to determine the normalization coefficients for the detectors.”).
Claim 3
Panin discloses the method of claim 2 (Panin, Fig. 3A), wherein determining the system effective scanning time based on the movement parameter (Panin, [0116], “the detection time efficiency at different times and corresponding positions of the bed or patient during the PET scan is determined based, in part, on the velocity.”) includes: determining the system effective scanning time based on the count of the at least one movement segment (Panin, [0065], “The normalization array n is computed by simulating the movement of an object through the scanner, assisted by the monitoring of basic scanner acquisition parameters such as the singles rate.”), an axial length of the phantom (Panin, [0176], “As PET scanner length becomes longer in an attempt to gain sensitivity, phantoms whose axial lengths are shorter than the scanner axial FOV is more desirable option than making longer phantoms for the system setup. The stationary scan can be used to cover only a portion of the available LORs. Since it is beneficial to cover all possible LORs, multiple or CBM scans can be exploited. The presented results verified that the CBM TA procedure using a short uniform cylindrical phantom produced the practically identical TO correction compared to standard TA.”), and the movement speed of the phantom (Panin, [0116], “The central controller 28 accounts for the velocity variation of the bed and patient. As the velocity of the bed changes, the detection time efficiency changes. The detection time efficiency at different times and corresponding positions of the bed or patient during the PET scan is determined based, in part, on the velocity.”).
Claim 4
Panin discloses the method of claim 2 (Panin, Fig. 3A), wherein determining the system effective scanning time based on the movement parameter (Panin, [0116], “the detection time efficiency at different times and corresponding positions of the bed or patient during the PET scan is determined based, in part, on the velocity.”) includes: determining the system effective scanning time based on an axial length of the FOV (Panin, [0160], “The “axially short phantom” for the CBM TA procedure and the CE estimation described herein has an axial length that is shorter than the FOV of the PET scanner. The axially short phantom of the present disclosure comprises a cylindrical outer shell that has a conventional length, e.g. about 28 cm long. However, only the central part that is shorter than the FOV of the PET scanner is filled with activity. “Axially short” refers to the fact that the activity filled part of the phantom is shorter than the FOV of the PET scanner. FIG. 7A shows an example of such axially short phantom 600 placed on a standard holder mounted on a CBM bed 260 according to the present disclosure.”), an axial length of the phantom (Panin, [0176], “As PET scanner length becomes longer in an attempt to gain sensitivity, phantoms whose axial lengths are shorter than the scanner axial FOV is more desirable option than making longer phantoms for the system setup. The stationary scan can be used to cover only a portion of the available LORs. Since it is beneficial to cover all possible LORs, multiple or CBM scans can be exploited. The presented results verified that the CBM TA procedure using a short uniform cylindrical phantom produced the practically identical TO correction compared to standard TA.”), and the movement time span (Panin, [0020], “a method for time alignment (TA) for a PET scanner system is disclosed. The method comprises receiving list mode data; generating non-time of flight (TOF) projection data, scanner efficiency normalization array n, and attenuation factor a; reconstructing image f from the non-TOF projection data, corrected for scanner efficiency by a normalization array n, and for attenuation by attenuation factor a; modeling non-TOF and TOF scatter S and non-TOF and TOF projections p; for each line of response, modeling TOF center-of-mass (modeled TOF COM) of true coincidence distribution from zero and first order moments M.sub.0 and M.sub.1 of the modeled TOF data; for each line of response, computing measured TOF center-of-mass (measured TOF COM); and determining TOF time offsets, to, by taking the difference between the measured TOF COM and the modeled TOF COM.”).
Claim 6
Panin discloses the method of claim 2 (Panin, Fig. 3A), wherein obtaining the scanning data of the phantom during the scanning process (Panin, [0130], “FIG. 5 is a flowchart 600 illustrating a method for generating CE normalization estimation for a PET scanner by CBM scanning an axially short cylinder phantom 60”, Fig. 6, step 610, generating TOF data of an axially short phantom in a CBM acquisition mode) includes: obtaining original PET data relating to the phantom during the scanning process (Fig. 6, step 610, generating TOF data of an axially short phantom in a CBM acquisition mode); obtaining corrected PET data by performing decay correction on the original PET data (Panin, [0111], “The central controller 28 operates pursuant to stored instructions to perform various acts described herein, such as determining decay correction efficiency, determining a singles rate for a given time, determining detection time efficiency, calculating the normalization coefficients, normalizing the line-of-response data, and/or reconstruction.”); and obtaining the scanning data based on the corrected PET data (Panin, Abstract, “In the CE estimation, TOF reconstruction of CBM data on the axially short cylinder phantom is performed. Alternating between TOF image reconstruction and CE updates eventually lead to the correct estimation of activity and CE component”, [0020], “generating non-time of flight (TOF) projection data, scanner efficiency normalization array n, and attenuation factor a; reconstructing image f from the non-TOF projection data, corrected for scanner efficiency by a normalization array n, and for attenuation by attenuation factor a; modeling non-TOF and TOF scatter S and non-TOF and TOF projections p; for each line of response, modeling TOF center-of-mass (modeled TOF COM) of true coincidence distribution from zero and first order moments M.sub.0 and M.sub.1 of the modeled TOF data; for each line of response, computing measured TOF center-of-mass (measured TOF COM); and determining TOF time offsets, to, by taking the difference between the measured TOF COM and the modeled TOF COM.”).
Claim 7
Panin discloses the method of claim 6 (Panin, Fig. 3A), wherein obtaining the original PET data relating to the phantom during the scanning process (Panin, [0130], “FIG. 5 is a flowchart 600 illustrating a method for generating CE normalization estimation for a PET scanner by CBM scanning an axially short cylinder phantom 60”, Fig. 6, step 610, generating TOF data of an axially short phantom in a CBM acquisition mode) includes: causing, by a motion controller, the phantom to perform the movement at a plurality of phantom positions, wherein the phantom is placed on a bed, and the motion controller is configured to move the bed to drive the phantom to the plurality of phantom positions (Panin, [0112], “The axially short cylinder phantom 60 is mounted on the patient bed 206 that moves the phantom during the CBM acquisition of the phantom 60 as shown in FIG. 7A. The method comprises (a) generating TOF data of the axially short cylinder phantom 60 in a CBM acquisition mode, while accounting for the axial motion of the patient bed 206 during the CBM acquisition, as a first data set (see Box 610); (b) generating a complementary data set by integrating the TOF data of the axially short cylinder phantom 60 over the axial motion of the patient bed 206 during the CBM acquisition (see Box 620); and (c) simultaneously reconstructing the activity and CE normalization coefficient from the complementary data set (see Box 630).”, The scanner is in continuous bed motion therefore the bed is being controlled to perform movement at plurality of positions); and acquiring, by a PET scanner of the PET system, the original PET data relating to the phantom at the plurality of phantom positions (Panin, [0130], “FIG. 5 is a flowchart 600 illustrating a method for generating CE normalization estimation for a PET scanner by CBM scanning an axially short cylinder phantom 60”, Fig. 6, step 610, generating TOF data of an axially short phantom in a CBM acquisition mode, the PET scanner is scanning in continuous bed motion which means plurality of positions).
Claim 11
Panin discloses the method of claim 1 (Panin, Fig. 3A), wherein the performance parameter of the PET system (Panin, [0168], “A line source phantom was placed at the center of scanner to evaluate time resolution according to the National Electrical Manufacturers Association (NEMA) standard. List mode data was acquired without downloading TOs. TOs were applied during list mode data processing by changing TOF bin indexing. In this way, the same data set is used to estimate the performance of various TA procedures.”, [0005], “, the quality of TOF reconstruction strongly depends on the correctness of time information. In order to ensure accurate time information for TOF reconstruction in PET scanners, a TA procedure, also referred to as timing calibration, is performed on a regular basis in PET scanners”) includes at least one of a system sensitivity, a event count rate, a scatter fraction, an equivalent noise count rate, or a time resolution (Panin, [0168], “A line source phantom was placed at the center of scanner to evaluate time resolution according to the National Electrical Manufacturers Association (NEMA) standard. List mode data was acquired without downloading TOs. TOs were applied during list mode data processing by changing TOF bin indexing. In this way, the same data set is used to estimate the performance of various TA procedures.”).
Allowable Subject Matter
Claim 5 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The claimed features such as “wherein determining the system effective scanning time based on the movement parameter includes: for each of a plurality of detector units of a detector of the PET system, obtaining the corresponding relationship; determining, based on a position of the detector unit, at least one phantom position where the phantom covers the detector unit ;determining time information corresponding to the at least one phantom position based on the corresponding relationship; and determining an effective scanning period corresponding to the detector unit based on the time information of the at least one phantom position; and determining the system effective scanning time based on the effective scanning periods corresponding to the plurality of detector units” claimed in dependent claim 8, in combination with the remainder of the limitations of the claims, are neither anticipated nor obvious in view of the prior art of record.
In the closest prior art of record, Panin, teaches determining a system effective scanning time based on a movement parameter. However, Panin fails to teach that for each plurality of detector units, a corresponding relationship is obtained which is based on the position of the detector unit. Therefore claim 5 would be allowable for claiming the limitation “wherein determining the system effective scanning time based on the movement parameter includes: for each of a plurality of detector units of a detector of the PET system, obtaining the corresponding relationship; determining, based on a position of the detector unit, at least one phantom position where the phantom covers the detector unit ;determining time information corresponding to the at least one phantom position based on the corresponding relationship; and determining an effective scanning period corresponding to the detector unit based on the time information of the at least one phantom position; and determining the system effective scanning time based on the effective scanning periods corresponding to the plurality of detector units”, in combination with the remainder of the limitations of the claims.
Claim 8 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The claimed features such as “wherein obtaining the corrected PET data by performing decay correction on the original PET data includes: dividing the original PET data into a plurality of data sets; for each of the plurality of data sets, determining, based on an initial activity of the phantom, a first activity of the phantom at an acquisition time corresponding to the data set; obtaining a corrected data set by performing, based on the initial activity and the first activity, the decay correction on the data set; and obtaining the corrected PET data based on the plurality of corrected data sets” claimed in dependent claim 5, in combination with the remainder of the limitations of the claims, are neither anticipated nor obvious in view of the prior art of record.
In the closest prior art of record, Panin, teaches performing a decay correction to reconstruct the PET data. However, Panin fails to teach dividing the original PET data into a plurality of data sets and determining an initial activity of the phantom to obtain a corrected data along with the delay correction. Therefore claim 8 would be allowable for claiming the limitation “wherein obtaining the corrected PET data by performing decay correction on the original PET data includes: dividing the original PET data into a plurality of data sets; for each of the plurality of data sets, determining, based on an initial activity of the phantom, a first activity of the phantom at an acquisition time corresponding to the data set; obtaining a corrected data set by performing, based on the initial activity and the first activity, the decay correction on the data set; and obtaining the corrected PET data based on the plurality of corrected data sets”, in combination with the remainder of the limitations of the claims.
Claim 9-10 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The claimed features such as “wherein obtaining the scanning data based on the corrected PET data include: generating a first sinogram based on the corrected PET data; determining a coincidence event count and a random event count based on the first sinogram; generating a second sinogram by subtracting the random event count from the first sinogram; generating a one-dimensional distribution map based on the second sinogram; determining a true event count and a scatter event count based on the one- dimensional distribution map; and obtaining the scanning data based on the coincidence event count, the random event count, the true event count, and the scatter event count” claimed in dependent claim 9, in combination with the remainder of the limitations of the claims, are neither anticipated nor obvious in view of the prior art of record.
In the closest prior art of record, Panin, teaches obtaining the scanning data based on the corrected PET data. However, Panin fails to teach determining a coincidence event count and a random event count based on a sinogram and using those to generate a one-dimensional distribution map. Therefore claim 8 would be allowable for claiming the limitation “wherein obtaining the scanning data based on the corrected PET data include: generating a first sinogram based on the corrected PET data; determining a coincidence event count and a random event count based on the first sinogram; generating a second sinogram by subtracting the random event count from the first sinogram; generating a one-dimensional distribution map based on the second sinogram; determining a true event count and a scatter event count based on the one- dimensional distribution map; and obtaining the scanning data based on the coincidence event count, the random event count, the true event count, and the scatter event count”, in combination with the remainder of the limitations of the claims.
Because the cited prior art of records does not teach or suggest each and every feature of dependent claim 9, this claim would be allowable. Claim 10 would be allowable by virtue of their dependency on dependent claim 9.
Conclusion
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/DENISE G ALFONSO/Examiner, Art Unit 2662
/AMANDEEP SAINI/Supervisory Patent Examiner, Art Unit 2662
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