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 .
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.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN-108957375-B) in view of Zhu (CN-108335339-B).
Regarding claim 1
Wang discloses
A method for magnetic resonance imaging (MRI) (¶ 2 under Specific Implementation Examples) comprising:
determining a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from a subject (¶ 3 under Specific Implementation Examples);
acquiring a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor (¶ 3 under Specific Implementation Examples.
The k-space data is collected using undersampling, accelerated parallel imaging SENSE OR GRAPPA);
Although strongly implied, Wang does not explicitly disclose
“reconstructing an image of the subject by processing the set of k-space data
using a deep learning (DL) network”.
Zhu, however, discloses
reconstructing an image of the subject by processing the set of k-space data
using a deep learning (DL) network (¶ 2 under Specific Implementation Examples).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “processing of k-space with a DL network” as taught by Zhu in the method of Wang.
The justification for this modification would be to interpolate missing frequency data, reduce artifacts and speed up acquisition times.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Yi (WO-2022040449-A1).
Wang in view of Zhu teach the method of claim 1,
Wang in view of Zhu do not teach
“wherein the PF factor is determined based on echo spacing”.
Yi, however, teaches
wherein the PF factor is determined based on echo spacing ([0119]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “PF factor based on echo spacing” as taught by Yi in the method of Wang in view of Zhu.
The justification for this modification would be to mostly minimize acquisition time.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Polak et al. (US-20210264645-A1)
Regarding claim 3
Wang in view of Zhu teach the method of claim 1,
Wang in view of Zhu do not teach
“wherein acquiring the set of k-space data from the subject using the PF factor along with the under-sampling technique comprises marking a portion of a k-space based on the PF factor and then a sub-portion in that portion is acquired using an under-sampling mask”.
Polak, however, teaches
wherein acquiring the set of k-space data from the subject using the PF factor ([0004]) along with the under-sampling technique ([0013]) comprises marking a portion of a k-space based on the PF factor and then a sub-portion in that portion is acquired using an under-sampling mask ([0071]—[0072] & [0133]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “under-sampling mask” as taught by Polak in the method of Wang in view of Zhu.
The justification for this modification would be to accelerate acquisition speeds.
Claim(s) 4, 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Karczmar et al. (US-20170061613-A1).
Regarding claim 4
Wang in view of Zhu teach the method of claim 1,
Wang in view of Zhu do not teach
“wherein the DL network is trained using under-sampled data corresponding to a range of partial Fourier factors and a range of acceleration levels”.
Karczmar, however, discloses
wherein the DL network is trained using under-sampled data corresponding to a range of partial Fourier factors and a range of acceleration levels ([0057]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “ranges of acceleration and PF factors” as taught by Karczmar in the systsem of Wang in view of Zhu.
The justification for this modification would be to achieve high temporal resolution ([0057]—[0058], Karczmar).
Regarding claim 5
Wang in view of Zhu in view of Karczmar teach the method of claim 4,
Karczmar, applied to claim 5, further teaches
wherein the DL network is trained using following steps:
randomly selecting a preliminary PF factor from the range of partial
Fourier factors and a desired acceleration factor from the range of acceleration
Levels ([0057]);
generating a preliminary PF mask based on the preliminary PF factor;
determining a preliminary acceleration factor based on the preliminary
PF mask ([0057]);
dropping additional k-space lines in an alternating pattern from a
periphery of the preliminary PF mask until the desired acceleration factor is
reached if the preliminary acceleration factor does not satisfy the desired
acceleration factor ([0057]).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Akcakaya (US-20210118200-A1).
Regarding claim 6
Wang in view of Zhu teach the method of claim 1,
Wang in view of Zhu do not teach
“wherein the DL network is based on an unrolled algorithm based deep learning reconstruction”.
Akcakaya, however, teaches
wherein the DL network is based on an unrolled algorithm based deep learning reconstruction ([0028]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “unrolled deep learning
algorithm” as taught by Akcakaya in the system of Wang in view of Zhu.
The justification for this modification would be to use a deep learning algorithm that is fast and flexible.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Zhong (CN-110009674-A).
Regarding claim 7
Wang in view of Zhu teach the method of claim 1,
Wang in view of Zhu do not teach
“wherein the DL network employs a weighted loss function that includes a weighted combination of a mean absolute error and a structural similarity index measure (SSIM)”.
Zhong, however, discloses
wherein the DL network employs a weighted loss function that includes a weighted combination of a mean absolute error and a structural similarity index measure (SSIM) (¶ or line 12 under Summary of the Invention).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “weighted loss function and SSIM” as taught by Zhong in the method of Wang in view of Zhu.
The justification for this modification would be to improve accuracy of he deep learning facility.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Brown (WO-2015197363-A1).
Regarding claim 9
Wang in view of Zhu teach the method of claim 1,
Although strongly implied, Wang in view of Zhu do not explicitly teach
“wherein acquiring the set of k-space data comprises acquiring the set of k-space data using only a single radio frequency coil”.
Brown, however, teaches
wherein acquiring the set of k-space data comprises acquiring the set of k-space data using only a single radio frequency coil (¶ 5 under DETAILED DESCRIPTION OF THE EMBODIMENTS).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “single radio frequency coil” as taught by Brown in the method of Wang in view of Zhu.
The justification for this modification would be to make a compact system with a single RF coil.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ye (US-20180017652-A1) in view of Wang (CN-108957375-B) in view of Zhu (CN-108335339-B).
Regarding claim 10
Ye discloses
A magnetic resonance imaging (MRI) system ([0002]), comprising:
a magnet configured to generate a polarizing magnetic field about at least a
portion of a subject arranged in the MRI system ([0053]);
a gradient coil assembly including a readout gradient coil, a phase gradient coil, a slice selection gradient coil configured to apply at least one gradient field to the polarizing magnetic field ([0053]);
a radio frequency (RF) system configured to apply an RF field to the subject
and to receive magnetic resonance signals from the subject ([0053]);
a processing system programmed ([0028]) to:
Ye does not disclose
“determine a Partial Fourier (PF) factor and an acceleration factor, for
acquiring k-space data from the subject;
acquire a set of k-space data from the subject using the PF factor along
with an under-sampling technique, wherein the under-sampling technique is
dependent on the acceleration factor;
reconstruct an image of the subject by processing the set of k-space data
using a deep learning (DL) network”.
Wang, however, discloses
determine a Partial Fourier (PF) factor and an acceleration factor, for
acquiring k-space data from the subject (¶ 3 under Specific Implementation Examples. The k-space data is collected using undersampling, accelerated parallel imaging SENSE OR GRAPPA);
acquire a set of k-space data from the subject using the PF factor along
with an under-sampling technique, wherein the under-sampling technique is
dependent on the acceleration factor (¶ 3 under Specific Implementation Examples.
The k-space data is collected using undersampling, accelerated parallel imaging SENSE OR GRAPPA).
Ye in view of Wang do not teach
“reconstruct an image of the subject by processing the set of k-space data
using a deep learning (DL) network”.
Zhu, however, discloses
reconstruct an image of the subject by processing the set of k-space data
using a deep learning (DL) network (¶ 2 under Specific Implementation Examples).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “processing of k-space with a DL network” as taught by Zhu in the method of Wang.
The justification for this modification would be to interpolate missing frequency data, reduce artifacts and speed up acquisition times and incorporate these facilities in a standard MRI machine.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ye (US-20180017652-A1) in view of Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Yi (WO-2022040449-A1).
Regarding claim 11
Ye in view of Wang in view of Zhu teach the MRI system of claim 10,
Ye in view of Wang in view of Zhu do not teach
“wherein the PF factor is determined based on echo spacing”.
Yi, however, teaches
wherein the PF factor is determined based on echo spacing ([0119]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “PF factor based on echo spacing” as taught by Yi in the method of Ye in view of Wang in view of Zhu.
The justification for this modification would be to mostly minimize acquisition time.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ye (US-20180017652-A1) in view of Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Karczmar et al. (US-20170061613-A1).
Regarding claim 12
Ye in view of Wang in view of Zhu teach the MRI system of claim 10,
Ye in view of Wang in view of Zhu do not teach
“wherein the DL network is trained using under-sampled data corresponding to a range of partial Fourier factors and a range of acceleration levels”.
Karczmar, however, teaches
wherein the DL network is trained using under-sampled data corresponding to a range of partial Fourier factors and a range of acceleration levels ([0057]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “ranges of acceleration and PF factors” as taught by Karczmar in the systsem of Ye in view of Wang in view of Zhu.
The justification for this modification would be to achieve high temporal resolution ([0057]—[0058], Karczmar).
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ye (US-20180017652-A1) in view of Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Akcakaya (US-20210118200-A1).
Regarding claim 14
Ye in view of Wang in view of Zhu teach the MRI system of claim 10,
Ye in view of Wang in view of Zhu do not teach
“wherein the DL network is based on an unrolled algorithm based deep learning reconstruction”.
Akcakaya, however, teaches
wherein the DL network is based on an unrolled algorithm based deep learning reconstruction ([0028]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “unrolled deep learning
algorithm” as taught by Akcakaya in the system of Ye in view of Wang in view of Zhu.
The justification for this modification would be to use a deep learning algorithm that is fast and flexible.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ye (US-20180017652-A1) in view of Wang (CN-108957375-B) in view of Zhu (CN-108335339-B) in view of Zhong (CN-110009674-A).
Regarding claim 15
Ye in view of Wang in view of Zhu teach the MRI system of claim 10,
Ye in view of Wang in view of Zhu do not teach
“wherein the DL network employs a weighted loss function that includes a weighted combination of a mean absolute error and a structural similarity index measure (SSIM)”.
Zhong, however, teaches
wherein the DL network employs a weighted loss function that includes a weighted combination of a mean absolute error and a structural similarity index measure (SSIM) (¶ or line 12 under Summary of the Invention).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “weighted loss function and SSIM” as taught by Zhong in the method of Wang in view of Zhu.
The justification for this modification would be to improve accuracy of he deep learning facility.
Claim(s) 18—20 is/are rejected under 35 U.S.C. 103 as being unpatentable over in view of Ye (US-20180017652-A1)in view of Wang (CN-108957375-B) in view of Zhu (CN-108335339-B).
Regarding claim 18
Ye discloses
A non-transitory computer-readable medium comprising instructions ([0050]), which when executed by a computer, cause the computer to carry out a method for Single Shot Fast Spin Echo (SSFSE) T2 magnetic resonance imaging ([0065]), the method comprising the steps of:
Ye does not disclose
“determining a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from a subject;
acquiring a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor;
reconstructing an image of the subject by processing the set of k-space data
using a deep learning (DL) network”.
Wang, however, teaches
determining a Partial Fourier (PF) factor and an acceleration factor for acquiring k-space data from a subject (¶ 3 under Specific Implementation Examples);
acquiring a set of k-space data from the subject using the PF factor along with an under-sampling technique, wherein the under-sampling technique is dependent on the acceleration factor (¶ 3 under Specific Implementation Examples.
The k-space data is collected using undersampling, accelerated parallel imaging SENSE OR GRAPPA)
Ye in view of Wang do not teach
“reconstructing an image of the subject by processing the set of k-space data
using a deep learning (DL) network”.
Zhu, however, teaches
reconstructing an image of the subject by processing the set of k-space data
using a deep learning (DL) network (¶ 2 under Specific Implementation Examples).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include the “processing of k-space with a DL network” as taught by Zhu in the method of Wang.
The justification for this modification would be to interpolate missing frequency data, reduce artifacts and speed up acquisition times and to include a
non-transitory way of storing the MRI machine program in case of accidental power-down.
Regarding claim 19
Ye in view of Wang in view of Zhu teach the non-transitory computer-readable medium of claim 18,
Wang, applied to claim 19, further teaches
wherein the instructions for acquiring the set of k-space data from the subject using the PF factor along with the under-sampling technique comprises instructions for marking a portion of a k-space based on the PF factor and then a sub-portion in that portion is acquired using an under-sampling mask (¶ 3 under Specific Implementation Examples).
Regarding claim 20
Ye in view of Wang in view of Zhu teach the non-transitory computer-readable medium of claim 18,
Wang, applied to claim 20, further teaches
wherein the DL network is trained using under-sampled data corresponding to a range of partial Fourier factors and a range of acceleration levels (¶ 3 under Specific Implementation Examples. The k-space data is collected using undersampling, accelerated parallel imaging SENSE OR GRAPPA).
Allowable Subject Matter
Claims 8, 13, 16, 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Regarding claim 8
Nothing in the prior art of record teaches or discloses
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where L(I, Î) is the weighted loss function showing error between actual input data I and the predicted output data Î, and α,ß represent weighting factors.
In conjunction with the rest of the claim language.
Regarding claim 13
Nothing in the prior art of record teaches or discloses
“dropping additional k-space lines in an alternating pattern from a
periphery of the preliminary PF mask until the desired acceleration factor is
reached if the preliminary acceleration factor does not satisfy the desired
acceleration factor”.
In conjunction with the rest of the claim language.
Regarding claim 16
Nothing in the prior art of record teaches or discloses
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65
731
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where L(I,Î) is the weighted loss function showing error between actual input data I and the predicted output data Î, and α, ß represent weighting factors.
In conjunction with the rest of the claim language.
Regarding claim 17
The claim is allowable due to its dependency on objected-to claim 16.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FREDERICK WENDEROTH whose telephone number is (571)270-1945. The examiner can normally be reached M-F 7 a.m. - 4 p.m.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Walter Lindsay can be reached at 571-272-1674. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WALTER L LINDSAY JR/Supervisory Patent Examiner, Art Unit 2852
/Frederick Wenderoth/
Examiner, Art Unit 2852