4DETAILED 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, 10, 11, 12, 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang1 (US-20150071514-A1) in view of Fu et al. (US-20180293762-A1) in view of Walczak (WO-2015179258-A2).
Regarding claim 1
Wang discloses
A method for optimizing an examination protocol for executing a magnetic resonance (MR) image acquisition ([0003]), the method comprising:
providing an examination protocol containing specifications of two or more
imaging sequences ([0060]);
Wang does not disclose
“in a computer, executing at least one algorithm processing said examination protocol as an input to perform an optimization with regard to a trade-off between
image quality and the-speed of execution of the examination protocol, taking into account diagnostic relevance weightings assigned to the imaging sequences
contained in the examination protocol, these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose; and
making an output available representing said optimized examination protocol to a user and/or executing the MR image acquisition on an MR scanner based on said optimized examination protocol”.
Fu, however, teaches
in a computer, executing at least one algorithm processing said examination protocol as an input to perform an optimization with regard to a trade-off between image quality and the-speed of execution of the examination protocol ([0003]),
Wang in view of Fu do not teach
“taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol, these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose; and making an output available representing said optimized examination protocol to a user and/or executing the MR image acquisition on an MR scanner based on said optimized examination protocol.”
Walczak, however, teaches
taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol (¶ or line 54 above “Claims”), these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose (¶ or line 54 above “Claims”); and
making an output available representing said optimized examination protocol to a user and/or executing the MR image acquisition on an MR scanner based on said optimized examination protocol (¶ or line 24 above “Claims”).
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 “optimization of MRI function” as taught by Fu as well as the “relevance weightings” as taught by Walczak in the method of Wang.
The justification for this modification would be to 1) optimize the MRI performance by trading off between two important imaging parameters—image quality and acquisition time, and 2) to introduce “relevance weightings” of certain imaging areas to know which areas could and could not trade off image quality.
Regarding claim 10
Wang in view of Fu in view of Walczak teach the method of claim 1,
Fu, applied to claim 10, further teaches
wherein the at least one algorithm further takes a quality trade-off weight into account which is a user-specified trade-off between quality of the MR images
resulting from the optimized examination protocol and the increase of execution speed achieved by the optimization ([0003]).
Regarding claim 11
Wang in view of Fu in view of Walczak teach the method of claim 1,
Fu, applied to claim 11, further teaches
wherein the algorithm determines a number of optimization options and selects one of the optimization options in accordance with an objective function that assigns a higher weighting to an optimization option which results in a higher image quality of those MR images associated with imaging sequences having higher diagnostic relevance weightings ([0033]) and
simultaneously in a lower image quality of those MR images associated with imaging sequences having lower diagnostic relevance weightings ([0052]).
Regarding claim 12
Wang discloses
A computer [([0003]) & ([0015])], configured to perform a method comprising:
read-reading a digital representation of an examination protocol containing
specifications of two or more imaging sequences ([0060]);
Wang does not disclose
“execute executing at least one algorithm processing said examination
protocol as an input to perform an optimization with regard to a trade-off between image quality and the speed of execution of the examination protocol,
taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol, these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose; and
make making an output available representing said optimized examination
protocol”.
Fu, however, discloses
execute executing at least one algorithm processing said examination
protocol as an input to perform an optimization with regard to a trade-off between image quality and the speed of execution of the examination protocol ([0003]),
Wang in view of Fu do not teach
“taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol, these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose; and
make making an output available representing said optimized examination
protocol”.
Walczak, however, discloses
taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol (¶ or line 54 above “Claims”), these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose (¶ or line 54 above “Claims”); and
make making an output available representing said optimized examination
protocol (¶ or line 24 above “Claims”).
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 “optimization of MRI function” as taught by Fu as well as the “relevance weightings” as taught by Walczak in the method of Wang.
The justification for this modification would be to 1) optimize the MRI performance by trading off between two important imaging parameters—image
quality and acquisition time, and 2) to introduce “relevance weightings” of certain imaging areas to know which areas could and could not trade off image quality.
Regarding claim 14
Wang discloses
A magnetic resonance (MR) scanner ([0010]) comprising at least one main magnet coil for generating a main magnetic field within an examination volume ([0002]), a number of gradient coils for generating switched magnetic field
gradients in different spatial directions within the examination volume ([0006]), at least one RF coil for generating RF pulses within the examination volume and/or for receiving MR signals from a body of a patient positioned in the examination volume ([0002]), a control computer for controlling a temporal succession of RF pulses and switched magnetic field gradients based on an examination protocol ([0030]), and a reconstruction unit for reconstructing MR images from the received MR signals ([0002]),
wherein the MR scanner ([0010] ) is arranged configured to perform the following steps:
reading a digital representation of the examination protocol containing specifications of two or more imaging sequences into the control computer ([0060]);
Wang does not disclose
“in the control computer executing execute at least one algorithm processing said examination protocol as an input to perform an optimization with regard
to a trade-off between image quality and the speed of execution of the examination
protocol, taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol, these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose; and
executing execute an MR image acquisition on the MR scanner based on said optimized examination protocol”.
Fu, however, teaches
in the control computer executing execute at least one algorithm processing said examination protocol as an input to perform an optimization with regard
to a trade-off between image quality and the speed of execution of the examination
protocol ([0003])
Wang in view of Fu do not teach
“taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol, these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose; and
executing execute an MR image acquisition on the MR scanner based on said optimized examination protocol.”
Walczak, however, discloses
taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol (¶ or line 54 above “Claims”), these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose (¶ or line 54 above “Claims”); and
executing execute an MR image acquisition on the MR scanner based on said optimized examination protocol (¶ or line 24 above “Claims”).
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 “optimization of MRI function” as taught by Fu as well as the “relevance weightings” as taught by Walczak in the method of Wang.
The justification for this modification would be to 1) optimize the MRI performance by trading off between two important imaging parameters—image
quality and acquisition time, and 2) to introduce “relevance weightings” of certain imaging areas to know which areas could and could not trade off image quality.
Claim(s) 2, 7, 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang1 (US-20150071514-A1) in view of Fu et al. (US-20180293762-A1) in view of Walczak (WO-2015179258-A2) in view of De Oliveira (EP-3502727-A1).
Regarding claim 2
Wang in view of Fu in view of Walczak teach the method of claim 1,
Wang in view of Fu in view of Walczak do not explicitly teach
“wherein the optimization involves a modification of acquisition parameters associated with at least one of the imaging sequences”.
De Oliveira, however, discloses
wherein the optimization involves a modification of acquisition parameters associated with at least one of the imaging sequences (¶ 30 & 31 under Description).
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 “modification of acquisition parameters” as taught by De Oliveira in the method of Wang in view of Fu in view of Walczak.
The justification for this modification would be to optimize the high contrast to noise ratio for good image fidelity.
Regarding claim 7
Wang in view of Fu in view of Walczak teach the method of claim 1,
Wang in view of Fu in view of Walczak do not teach
“wherein the optimization involves assigning an image reconstruction model to at least one of the imaging sequences which image reconstruction model uses
MR signal data or image data acquired by executing at least one of the other imaging sequences”.
De Oliveira, however, teaches
wherein the optimization involves assigning an image reconstruction model to at least one of the imaging sequences which image reconstruction model uses
MR signal data or image data acquired by executing at least one of the other imaging sequences (¶ 30 & 31 under Description).
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 “optimization model using a reconstruction sequence” as taught by De Oliveira in the method of Wang in view of Fu in view of Walczak.
The justification for this modification would be to maximize the contrast-to-noise ratio (¶ 30 & 31 under Description, De Oliveira).
Regarding claim 8
Wang in view of Fu in view of Walczak teach the method of claim 1,
Wang in view of Fu in view of Walczak do not teach
“wherein the execution of at least one of the imaging sequences is omitted in the optimized examination protocol, wherein an image reconstruction model is added to the optimized examination protocol to synthesize an MR image associated
with the omitted imaging sequence from MR signal data acquired by executing a least one of the other imaging sequences”.
De Oliveira, however, discloses
wherein the execution of at least one of the imaging sequences is omitted in the optimized examination protocol, wherein an image reconstruction model is added to the optimized examination protocol to synthesize an MR image associated
with the omitted imaging sequence from MR signal data acquired by executing a least one of the other imaging sequences (¶ 30 & 31 under Description).
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 “optimization model using a reconstruction sequence” as taught by De Oliveira in the method of Wang in view of Fu in view of Walczak.
The justification for this modification would be to maximize the contrast-to-noise ratio (¶ 30 & 31 under Description, De Oliveira).
Claim(s) 3, 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang1 (US-20150071514-A1) in view of Fu et al. (US-20180293762-A1) in view of Walczak (WO-2015179258-A2) in view of Wang2 et al. (US-20150108978-A1).
Regarding claim 3
Wang in view of Fu in view of Walczak teach the method of claim 1,
Wang in view of Fu in view of Walczak do not teach
“wherein the optimization involves a modification of the k-space sampling pattern and/or of the image reconstruction model associated with at least one of the imaging sequences”.
Wang2, however, teaches
wherein the optimization involves a modification of the k-space sampling pattern and/or of the image reconstruction model associated with at least one of the imaging sequences ([0038]).
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 “optimization of k-space” as taught by Wang2 in the method of Wang in view of Fu in view of Walczak.
The justification for this modification would be to maximize sampled k-space without increasing scan time ([0038], Wang2).
Regarding claim 4
Wang in view of Fu in view of Walczak in view of Wang2 teach the method of claim 3,
Wang2, applied to claim 4, further teaches
wherein the k-space sampling pattern is modified to result in an undersampling of k-space ([0003]).
Claim(s) 5, 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang1 (US-20150071514-A1) in view of Fu et al. (US-20180293762-A1) in view of Walczak (WO-2015179258-A2) in view of Miao (CN-209231768-U).
Regarding claim 5
Wang in view of Fu in view of Walczak teach the method of claim 1,
Wang in view of Fu in view of Walczak do not teach
“wherein the optimization involves assigning an artificial intelligence-based image reconstruction model to at least one of the imaging sequences”.
Miao, however, discloses
wherein the optimization involves assigning an artificial intelligence-based image reconstruction model to at least one of the imaging sequences (¶ 6 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 “artificial-based image reconstruction” as taught by Miao in the method of Wang in view of Fu in view of Walczak.
The justification for this modification would be to accelerate the imaging process.
Regarding claim 6
Wang in view of Fu in view of Walczak in view Miao teach the reconstruction model uses a machine learning method of claim 5,
Miao, applied to claim 6, further teaches
Wherein
the optimization involves training the artificial intelligence-based image reconstruction model and incorporating the trained artificial intelligence-based image reconstruction model into the output (¶ 5 under Summary Of The Invention).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang1 (US-20150071514-A1) in view of Fu et al. (US-20180293762-A1) in view of Walczak (WO-2015179258-A2) in view of Liu (CN-103246073-A).
Regarding claim 9
Wang in view of Fu in view of Walczak teach the method of claim 1,
Wang in view of Fu in view of Walczak do not teach
“wherein the optimization involves a modification of the order in which the imaging sequences contained in the examination protocol are executed”.
Liu, however, teaches
wherein the optimization involves a modification of the order in which the imaging sequences contained in the examination protocol are executed ([0030] & [0038]).
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 “order as optimization” as taught by Liu in the method of Wang in view of Fu in view of Walczak.
The justification for this modification would be to reduce artifacts and
increase efficiency.
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wang1 (US-20150071514-A1) in view of Fu et al. (US-20180293762-A1) in view of Walczak (WO-2015179258-A2) in view of Shah (CN-110167629-A).
Regarding claim 13
Wang1 discloses
Reading a digital representation of an examination protocol containing
specifications of two or more imaging sequences ([0060]);
Wang1 does not disclose
“executing at least one algorithm processing said examination protocol as
an input to perform an optimization with regard to a trade-off between image quality and the speed of execution of the examination protocol, taking into account
diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol, these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose; and
making an output available representing said optimized examination
protocol.
A computer program comprising instructions stored on a non-transitory computer readable medium to perform a method above”.
Fu, however, discloses
executing at least one algorithm processing said examination protocol as
an input to perform an optimization with regard to a trade-off between image quality and the speed of execution of the examination protocol ([0003]),
Wang1 in view of Fu do not teach
“taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol, these diagnostic relevance weights representing relevance of image quality for a specific diagnostic purpose; and
making an output available representing said optimized examination, protocol”.
Walczak, however, discloses
taking into account diagnostic relevance weightings assigned to the imaging sequences contained in the examination protocol (¶ or line 54 above “Claims”), these diagnostic relevance weights representing relevance of image quality for a
specific diagnostic purpose (¶ or line 54 above “Claims”); and
making an output available representing said optimized examination, protocol (¶ or line 24 above “Claims”),
Wang1 in view of Fu in view of Walczak do not teach
“A computer program comprising instructions stored on a non-transitory computer readable medium to perform a method above”.
Shah, however, teaches
A computer program comprising instructions stored on a non-transitory computer readable medium to perform a method above (Claim 9).
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 “modification of acquisition parameters” as taught by De Oliveira in the method of Wang in view of Fu in view of Walczak.
The justification for this modification would be to 1) optimize the high contrast to noise ratio for good image fidelity, and 2) to have a way to permanently store the MRI machine program in case of accidental power-down.
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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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