DETAILED ACTION
This Office action is responsive to communications filed on 09/22/2025. Claims 1-12, 14, 16-18, 22-25 have been amended. Presently, Claims 1-12, 14, 16-18, 22-25 remain pending and are hereinafter examined on the merits.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Previous rejections under 35 USC § 112(b) are withdrawn in view of the amendments filed on 09/22/2025.
The Applicant’s arguments with respect to rejections under 35 USC § 101 have been fully, considered, but are not persuasive. The Examiner directs the Applicant’s attention provided in the Office Action regarding the grounds for rejection of the claims under 35 U.S.C. 101 in view of the amendments filed on 09/22/2025. Specifically, the Examiner response is set forth in the rejection under 35 U.S.C. 101 below.
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection does not rely on Braun et al (US 2018/0232878 A1) as being anticipated under 35 U.S.C. 102(a)(1) applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The new grounds of rejection now relies on Dosenbach (US 2024/0418814 A1) as being anticipated under 35 U.S.C. 102(a)(1).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12, 14, 16-18, 22-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claims 1-12, 14, 16-18 are directed to a “method” which describes one of the four statutory categories of patentable subject matter, i.e., a process.
Claim 22 drawn to a “system” which describes one of the four statutory categories, i.e., a machine.
Claims 23-25 are drawn to a “system” which describes one of the four statutory categories, i.e., a machine.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claim 1 recites (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), & the abstract idea of “mathematical concepts” (MPEP 2106.04(a)(2).I.), substantially as follows:
“ quantifying changes in magnetic resonance (MR) signals captured during the MRI scan, wherein the quantifying the changes is performed prior to image reconstruction that uses the MR signals;
determining, prior to image reconstruction that uses the MR signals and based at least in part on a result of the quantifying of the changes in the MR signals, a diagnostic quality of the at least one image that would result from the MRI scan; and ”
Claim 22 recites (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), & the abstract idea of “mathematical concepts” (MPEP 2106.04(a)(2).I.), substantially as follows:
“ quantifying changes in magnetic resonance (MR) signals captured during the MRI scan, wherein the quantifying the changes is performed prior to image reconstruction that uses the MR signals;
determining, prior to image reconstruction that uses the MR signals and based at least in part on a result of the quantifying of the changes in the MR signals, a diagnostic quality of the at least one image that would result from the MRI scan; and “
Claim 23 recites (“sets forth” or “describes”) the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), & the abstract idea of “mathematical concepts” (MPEP 2106.04(a)(2).I.), substantially as follows:
“ estimating motion of a subject during an MRI scan based on measured magnetic resonance (MR) signals captured during the MRI scan, wherein the estimating of the motion of the subject is performed prior to image reconstruction that uses the MR signals;
determining, prior to image reconstruction that uses the MR signals and based at least in part on a result of the estimating of the motion of the subject, a diagnostic quality of the MRI scan; and “
In claims (1, 22, 23), the identified steps above set forth the abstract ideas because each recite operations that fundamentally amount to mathematical analysis and mental evaluation of information, rather than a concrete technological process. In particular, the claims describe the manipulation, evaluation, and interpretation of measured data to reach a conclusion about a diagnostic quality, which fails squarely within the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), & the abstract idea of “mathematical concepts” (MPEP 2106.04(a)(2).I.).
The recited step of “quantifying changes in [...] MR signals” or “estimating motion of a subject” set forth a mathematical concept because it requires applying numerical or statistical techniques to measured signal data in order to derive values or estimates. Quantifying changes in signal involves comparing signal values, calculating differences, or magnitudes, or performing mathematical operations of the data. Likewise, estimating motion from MR signals merely amounts to applying mathematical relationships to measure data to infer movement. These steps do not recite any specifics for performing the calculations, but instead broadly claim the result of the mathematical analysis performed on data, which is a characteristics of abstract mathematical concepts.
Additionally, the step of “determining .. a diagnostic quality” based at least in part of the quantified changes or estimated motion set forth a mental process. This limitation describes an evaluative judgement made by analyzing information and drawing a conclusion. A classic example of a mental process. Such a determination mirrors the type of assessment that could be performed by a human using their mind or with aid of pen and paper, by reviewing signal characteristics or motion estimates and deciding whether the resulting imaging would be diagnostically acceptable. The claims do no improve any meaningful constraints on how this determination is made beyond relying on previously derived data, and therefore describe a mental evaluation of information.
Therefore, the above noted limitations amount to the abstract idea of “a mental process” (MPEP 2106.04(a)(2).III.), & the abstract idea of “mathematical concepts” (MPEP 2106.04(a)(2).I.).
Prong Two: Claims (1, 22, 23) do not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites (1) additional steps “an MRI scanner; at least one processor: and at least one non-transitory computer-readable storage medium storing executable instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for evaluating at least one image that would result from a magnetic resonance image (MRI) scan, the method comprising:”-(claim 22), “at least one processor; and at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for evaluating a magnetic resonance image (MRI) scan, the method comprising:”-(claim 23); and (2) further “outputting an indication of the diagnostic quality of the at least one image.”-(claim1), “outputting an indication of the diagnostic quality of the at least one image.”-(claim 22), “outputting an indication of the diagnostic quality of the MRI scan.”-(claim 23)
The steps in (1) represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools (see below Step IIB for further details). Data gathering and mere instructions to implement an abstract idea on a computer do not integrate a judicial exception into a practical application (MPEP 2106.05 (f and g)).Regarding the processor language written at such a high level of generality of structural limitations, the processor language amounts to a generic computer component with mere instructions to implement the abstract idea on a computer.
The step in (2) represents merely outputting the abstract idea as a post-solution activity and is recited at a high level of generality.
As a whole, the additional elements merely serve to gather and feed information to the abstract idea and to output a notification based on the abstract idea, while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the estimated diagnostic information is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Further, there is no evidence of record that would support the assertion that this step is an improvement to a computer or technological solution to a technological problem. Ultimately, the Applicant’s describe improvement in the process of using diagnostic techniques, but this is not an improvement in the function of a computer or other technology (See MPEP 2106.05(a)(ii); “the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology”; See MPEP 2106.04(d)(1); 2106.05(a); and 2106.05(f)). The claims are directed to the abstract idea. Also, there does not appear to be any particular structure or machine, treatment or prophylaxis, transformation, or any other meaningful application that would render the claim eligible at step 2A, prong 2.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims (1, 22, 23) do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims recite additional steps of an MRI scanner; at least one processor: and at least one non-transitory computer-readable storage medium storing executable instructions that, when executed by the at least one processor, cause the at least one processor to perform a method for evaluating at least one image that would result from a magnetic resonance image (MRI) scan. These steps represents mere data gathering, data outputting or pre/post/extra-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality. Furthermore, as discussed above, limitations with respect to the processor languages/terms, respectively, amount to mere instructions to implement the abstract idea on a computer. As discussed with respect to Step 2A Prong Two, the additional elements in the claims amount to no more than insignificant extra solution activity and mere instructions to apply the exception using a generic computer component. The same analysis applies here in 2B and does not provide an inventive concept. The data gathering steps that were considered insignificant extra-solution activity in Step 2A Prong Two, have been re-evaluated in Step 2B and determined to be well-understood, routine, conventional activity in the field.
As evidenced by, OH et al (2014/0088984 A1), discloses,
¶0109, ‘image quality, where image quality is measured by subjective or objective determiners, such as doctors evaluating MRI images, or a mathematical value determined by an image quality evaluating method known in the art.’
As evidenced by, Yager et al (US 2014/0200437 A1), discloses,
¶0026, ‘Processor 160, which can be any type of computer processor known to those of skill in the art, can be used to generate an overlay image based on received image data, to control MRI machine 155, to execute instructions stored in memory 175, etc. Memory 175, which can be any type of computer memory or memories known to those of skill in the art, can be used to store image data obtained from MRI machine 155, to store instructions to be executed by processor 170, to store patient information, etc.’
For these reasons, there is no inventive concept. The claim is not patent eligible. Even when viewed as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent Claims
The following dependent claims merely further define the abstract idea and are, therefore, recite an abstract idea for similar reasons:
Defining wherein the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises: comparing the result of the evaluating of the MRI signals to a threshold; and determining that the diagnostic quality of the at least one image that would result from the MRI scan is non-diagnostic when the result of evaluating the MRI signals exceeds the threshold.-(claim 2)
Defining wherein the quantifying the changes in the MR signals captured during the MRI scan comprises determining a change in the MR signals over time.-(claim 3)
Defining wherein the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises: comparing the change in the MR signals to a threshold; and determining that the diagnostic quality of the at least one image that would result from the MRI scan is non-diagnostic when the change in the MR signals exceeds the threshold.-(claim 4)
Defining wherein the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises: comparing the at least one second metric to a threshold; and determining that the at least one image that would result from the MRI scan is non- diagnostic when the at least one second metric exceeds the threshold. – (claim 9).
Defining wherein the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises determining the diagnostic quality during the MRI scan, prior to image reconstruction. – (claim 10).
Defining wherein the evaluating of the MR signals captured during the MRI scan comprises evaluating MR signals corresponding to a center of k- space captured during an MRI scan in which the center of k-space was repeatedly sampled. – (claim 17).
Defining wherein the evaluating of the MR signals corresponding to the center of k-space captured during an MRI scan in which the center of k- space was repeatedly sampled comprises: evaluating MR signals that correspond to the center of k-space and that were captured without gradient encoding.- (claim 18)
Defining wherein the estimating of the motion based on the measured MR signals captured during the MRI scan comprises estimating the motion based on measured free-induction decay (FID) navigator signals captured during the MRI scan. – (claim 24)
Defining wherein the estimating of the motion based on the measured MR signals captured during the MRI scan comprises estimating the motion based on measured MR signals corresponding to a center of k-space captured during an MRI scan in which the center of k-space was repeatedly sampled – (claim 25).
The following dependent claims merely further describe the extra-solution activities and therefore, do not amount to significantly more than the judicial exception or integrate the abstract idea into a practical application for similar reasons:
Describing wherein: the MR signals comprise free-induction decay (FID) navigator signals captured by two or more receiver coils during the MRI scan; and the quantifying of the changes in the MR signals captured during the MRI scan comprises combining the MR signals captured by the two or more receiver coils to determine at least one first metric.-(claim 5). The data gathering steps and pre-solution activity are conventional and recited at high level of generality. As such, the abstract idea is not applied, relied on, or used in a meaningful way. No improved to the technology is evident, and the determined visualization of context is not outputted in any way such that the practical benefit is realized.
Describing wherein the combining of the MR signals comprises combining MR signals captured for a channel, for a time, and/or for the MRI scan. – (claim 6). The data gathering steps and pre-solution activity are conventional and recited at high level of generality. As such, the abstract idea is not applied, relied on, or used in a meaningful way. No improved to the technology is evident, and the determined visualization of context is not outputted in any way such that the practical benefit is realized.
Describing wherein the combining of the MR signals captured by the two or more receiver coils comprises determining a normalized mean absolute change in the MR signals and/or a cross correlation coefficient between projection vectors of the MR signals. – (claim 7). The data gathering steps and pre-solution activity are conventional and recited at high level of generality. As such, the abstract idea is not applied, relied on, or used in a meaningful way. No improved to the technology is evident, and the determined visualization of context is not outputted in any way such that the practical benefit is realized.
Describing wherein: the quantifying of the MR signals captured during the MRI scan comprises evaluating FID navigator signals captured during the MRI scan; and the evaluating of the FID navigator signals captured during the MRI scan further comprises integrating the at least one first metric with respect to a number of k-space lines to determine at least one second metric.- (claim 8). The data gathering steps and pre-solution activity are conventional and recited at high level of generality. As such, the abstract idea is not applied, relied on, or used in a meaningful way. No improved to the technology is evident, and the determined visualization of context is not outputted in any way such that the practical benefit is realized.
Describing wherein the outputting of the indication of the diagnostic quality of the at least one image that would result from the MRI scan comprises outputting the indication of the diagnostic quality during the MRI scan, prior to image reconstruction. – (claim 11). The data gathering steps and post-solution activity are conventional and recited at high level of generality. As such, the abstract idea is not applied, relied on, or used in a meaningful way. No improved to the technology is evident, and the determined visualization of context is not outputted in any way such that the practical benefit is realized.
Describing wherein the outputting of the indication of the diagnostic quality comprises recommending intervention when the diagnostic quality is determined to be non-diagnostic. – (claim 12). The data gathering steps and post-solution activity are conventional and recited at high level of generality. As such, the abstract idea is not applied, relied on, or used in a meaningful way. No improved to the technology is evident, and the determined visualization of context is not outputted in any way such that the practical benefit is realized.
Describing wherein the outputting of the indication of the diagnostic quality comprises displaying data through a user interface of an MRI system. – (claim 14). The data gathering steps and post-solution activity are conventional and recited at high level of generality. As such, the abstract idea is not applied, relied on, or used in a meaningful way. No improved to the technology is evident, and the determined visualization of context is not outputted in any way such that the practical benefit is realized.
Describing wherein the MR signals comprise FID navigator signals captured during an acquisition of one or more MRI sequences, and the one or more MRI sequences comprise embedded FID navigator modules following radio-frequency (RF) excitation pulses. – (claim 16). The data gathering steps and pre-solution activity are conventional and recited at high level of generality. As such, the abstract idea is not applied, relied on, or used in a meaningful way. No improved to the technology is evident, and the determined visualization of context is not outputted in any way such that the practical benefit is realized.
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Objections
The following claims are objected to because of the following informalities and should recite:
Claim 15: lines 3-4: there is a line break (i.e., an indent/space). Consistent formatting is needed.
Claim 6: line 2, “combining the MR signals”. Appropriate correction is required.
Claim 16, line 1, “free-induction decay (FID)”. Appropriate correction is required.
Claim 17:
line 1, “the qualifying
line 2, the MR signals”.
Claim 18:
line 1, “the qualifying
line 3-4, “evaluating the MR signals [[that]] corresponding to the center of k-space ; wherein the MR signals are captured without gradient encoding”.
Consistent claim language is required when referring to the same term. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-4, 10-12, 14, and 22-23 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dosenbach (US 2024/0418814 A1).
Claim 1: Dosenbach discloses, A method for evaluating at least one image that would result from a magnetic resonance image (MRI) scan, the method comprising:
-Dosenbach discloses determining data quality using k-space magnetic resonance (MRI) data. The K-space is explicitly defined as any from of raw or non-reconstructed MR data acquired with an MRI system, ¶Abstract, ¶0008, ¶0021, ¶0030.
quantifying changes in magnetic resonance (MR) signals captured during the MRI scan, wherein the quantifying the changes is performed prior to image reconstruction that uses the MR signals;
-Dosenbach discloses a method that involves receiving k-space data acquiring during the MRI scan, and analyzing the received k-space data to identify signs of decreased data quality, ¶0009-0010, Claim 1, Claim 2. K-space data is raw data, meaning this analysis occurs prior to image reconstruction, ¶0021. The system of Dosenbach uses the raw k-space data to calculate a data quality metric, which includes quantifying motion or displacement components, ¶0037, ¶0045, Claim 8-9. This quantifying may involve calculating relative motion between successive k-space data, ¶0034-0036.
determining, prior to image reconstruction that uses the MR signals and based at least in part on a result of the quantifying of the changes in the MR signals, a diagnostic quality of the at least one image that would result from the MRI scan; and
-Dosenbach discloses that the analysis determines the data quality in real-time during the MRI procedure, ¶0008, ¶0024. This data quality assessment is indicative of poor motion that substantially damages the clinical value of the resulting images, and can negate any diagnostic capability of the scan, ¶0023. By assessing k-space data quality (i.e., by calculating the total displacement and comparing it to a threshold, ¶0037, Claim 7), the method of Dosenbach effectively determines if the resulting data frames are low- or no movement datasets, or usable data, ¶0027, ¶0046, ¶0048.
outputting an indication of the diagnostic quality of the at least one image.
-Dosenbach discloses that the method includes displaying a real-time indication or report to an operator of the MRI system during the study, ¶0009, ¶0011, Claim 1, Claim 20. Dosenbach method indicates the decreased data quality and also shows the cumulative number of usable data obtained in the current scan and predicts the amount of scans that need to repeated, ¶0009, ¶0011, ¶0046, Claim 1, Claim 20. This indication is provided to the subject via sensor feedback based on the calculated data quality metrics, ¶0025, ¶0045, ¶0049-0050.
Claim 2: Dosenbach discloses al the elements above in claim 1, Dosenbach discloses, wherein the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises: comparing the result of the evaluating of the MRI signals to a threshold; and (¶0037, ¶0040-0041, ¶0056, Claim 5-7)
determining that the diagnostic quality of the at least one image that would result from the MRI scan is non-diagnostic when the result of evaluating the MRI signals exceeds the threshold. (¶0003-0004, ¶0023, ¶0040-0041)
Claim 3: Dosenbach discloses al the elements above in claim 1, Dosenbach discloses, wherein the quantifying the changes in the MR signals captured during the MRI scan comprises determining a change in the MR signals over time. (¶0004, ¶0035-0039, ¶0043)
Claim 4: Dosenbach discloses al the elements above in claim 3, Dosenbach discloses, the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises: comparing the change in the MR signals to a threshold (¶0035-0039, ¶0045, Claim 5-7, Claim 15-17); and determining that the diagnostic quality of the at least one image that would result from the MRI scan is non-diagnostic when the change in the MR signals exceeds the threshold. (¶0006, ¶0023, ¶0040-0041, Claim 5-7, Claim 15-17)
Claim 10: Dosenbach discloses all the elements above in claim 1, Dosenbach discloses, wherein the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises determining the diagnostic quality during the MRI scan, prior to image reconstruction. (¶Abstract, ¶0007-0009, ¶0011, ¶0021, ¶0023, Claim 1, Claim 20)
Claim 11: Dosenbach discloses all the elements above in claim 1, Dosenbach discloses, wherein the outputting of the indication of the diagnostic quality of the at least one image that would result from the MRI scan comprises outputting the indication of the diagnostic quality during the MRI scan, prior to image reconstruction. (¶Abstract, ¶0007-0011, ¶0021, ¶0024-0027, ¶0043, Claim 1, Claim 11-12, Claim 20)
Claim 12: Dosenbach discloses all the elements above in claim 1, Dosenbach discloses, wherein the outputting of the indication of the diagnostic quality comprises recommending intervention when the diagnostic quality is determined to be non-diagnostic. (¶0008-00011, ¶0025, ¶0028-0029, ¶0048, ¶0054-0055, Claim 1, Claim 20)
Claim 14: Dosenbach discloses all the elements above in claim 1, Dosenbach discloses, wherein the outputting of the indication of the diagnostic quality comprises displaying data through a user interface of an MRI system. (¶0009-0011, ¶0024, ¶0046, ¶0069, ¶0078, Claim 1, Claim 20)
Claim 22: Dosenbach discloses, A magnetic resonance imaging (MRI) system configured to evaluate a magnetic resonance image (MRI) scan, comprising:
-Dosenbach discloses determining data quality using k-space magnetic resonance (MRI) data. The K-space is explicitly defined as any from of raw or non-reconstructed MR data acquired with an MRI system, ¶Abstract, ¶0008, ¶0021, ¶0030.
an MRI scanner; (FIG. 5)
at least one processor (¶0068-0069): and
at least one non-transitory computer-readable storage medium storing executable instructions that, (¶0086, ¶0088-0089, ¶0091, ¶0096) when executed by the at least one processor, cause the at least one processor to perform a method for evaluating at least one image that would result from a magnetic resonance image (MRI) scan, the method comprising: (¶0009, ¶0067, ¶0088, ¶0023, Claim 1, Claim 11)
quantifying changes in magnetic resonance (MR) signals captured during the MRI scan, wherein the quantifying the changes is performed prior to image reconstruction that uses the MR signals;
-Dosenbach discloses a method that involves receiving k-space data acquiring during the MRI scan, and analyzing the received k-space data to identify signs of decreased data quality, ¶0009-0010, Claim 1, Claim 2. K-space data is raw data, meaning this analysis occurs prior to image reconstruction, ¶0021. The system of Dosenbach uses the raw k-space data to calculate a data quality metric, which includes quantifying motion or displacement components, ¶0037, ¶0045, Claim 8-9. This quantifying may involve calculating relative motion between successive k-space data, ¶0034-0036.
determining, prior to image reconstruction that uses the MR signals and based at least in part on a result of the quantifying of the changes in the MR signals, a diagnostic quality of the at least one image that would result from the MRI scan; and
-Dosenbach discloses that the analysis determines the data quality in real-time during the MRI procedure, ¶0008, ¶0024. This data quality assessment is indicative of poor motion that substantially damages the clinical value of the resulting images, and can negate any diagnostic capability of the scan, ¶0023. By assessing k-space data quality (i.e., by calculating the total displacement and comparing it to a threshold, ¶0037, Claim 7), the method of Dosenbach effectively determines if the resulting data frames are low- or no movement datasets, or usable data, ¶0027, ¶0046, ¶0048.
outputting an indication of the diagnostic quality of the at least one image.
-Dosenbach discloses that the method includes displaying a real-time indication or report to an operator of the MRI system during the study, ¶0009, ¶0011, Claim 1, Claim 20. Dosenbach method indicates the decreased data quality and also shows the cumulative number of usable data obtained in the current scan and predicts the amount of scans that need to repeated, ¶0009, ¶0011, ¶0046, Claim 1, Claim 20. This indication is provided to the subject via sensor feedback based on the calculated data quality metrics, ¶0025, ¶0045, ¶0049-0050.
Claim 23: Dosenbach discloses, A system configured to evaluate a magnetic resonance image (MRI) scan, comprising:
-Dosenbach discloses determining data quality using k-space magnetic resonance (MRI) data. The K-space is explicitly defined as any from of raw or non-reconstructed MR data acquired with an MRI system, ¶Abstract, ¶0008, ¶0021, ¶0030.
at least one processor; and (¶0068-0069)
at least one non-transitory computer-readable storage medium storing processor executable instructions that, (¶0086, ¶0088-0089, ¶0091, ¶0096) when executed by the at least one processor, cause the at least one processor to perform a method for evaluating a magnetic resonance image (MRI) scan, the method comprising: (¶0009, ¶0067, ¶0088, ¶0023, Claim 1, Claim 11)
estimating motion of a subject during an MRI scan based on measured magnetic resonance (MR) signals captured during the MRI scan, wherein the estimating of the motion of the subject is performed prior to image reconstruction that uses the MR signals;
-Dosenbach discloses a method that involves receiving k-space data acquiring during the MRI scan, and analyzing the received k-space data to identify signs of decreased data quality, ¶0009-0010, Claim 1, Claim 2. K-space data is raw data, meaning this analysis occurs prior to image reconstruction, ¶0021. The system of Dosenbach uses the raw k-space data to calculate a data quality metric, which includes quantifying motion or displacement components, ¶0037, ¶0045, Claim 8-9. This quantifying may involve calculating relative motion between successive k-space data, ¶0034-0036.
determining, prior to image reconstruction that uses the MR signals and based at least in part on a result of the estimating of the motion of the subject, a diagnostic quality of the MRI scan; and
-Dosenbach discloses that the analysis determines the data quality in real-time during the MRI procedure, ¶0008, ¶0024. This data quality assessment is indicative of poor motion that substantially damages the clinical value of the resulting images, and can negate any diagnostic capability of the scan, ¶0023. By assessing k-space data quality (i.e., by calculating the total displacement and comparing it to a threshold, ¶0037, Claim 7), the method of Dosenbach effectively determines if the resulting data frames are low- or no movement datasets, or usable data, ¶0027, ¶0046, ¶0048.
outputting an indication of the diagnostic quality of the MRI scan.
-Dosenbach discloses that the method includes displaying a real-time indication or report to an operator of the MRI system during the study, ¶0009, ¶0011, Claim 1, Claim 20. Dosenbach method indicates the decreased data quality and also shows the cumulative number of usable data obtained in the current scan and predicts the amount of scans that need to repeated, ¶0009, ¶0011, ¶0046, Claim 1, Claim 20. This indication is provided to the subject via sensor feedback based on the calculated data quality metrics, ¶0025, ¶0045, ¶0049-0050.
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 text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
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 5-6, 8, 16, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Dosenbach (US 2024/0418814 A1) in view of Nielsen et al (US 2021/0356547 A1).
Claim 5: Dosenbach discloses all the elements above in claim 1, Dosenbach fails to disclose: wherein: the MR signals comprise free-induction decay (FID) navigator signals captured by two or more receiver coils during the MRI scan; and evaluating the MR signals captured during the MRI scan comprises combining the MR signals captured by the two or more receiver coils to determine at least one first metric.
However, Nielsen in the context of motion compensation image reconstruction in magnetic resonance imaging discloses, wherein: the MR signals comprise free-induction decay (FID) navigator signals captured by two or more receiver coils during the MRI scan; and evaluating the MR signals captured during the MRI scan comprises combining the MR signals captured by the two or more receiver coils to determine at least one first metric. (¶0026, ‘a set of RF coils having different spatial sensitivity profiles is used for MR signal acquisition.’; ¶0032, ‘The MR signals may also be sampled according to a so-called “koosh ball”-sampling scheme. This techniques provides for virtually silent MR imaging, in which RF excitation as well as acquisition of MR signals are performed in the presence of a magnetic field gradient. The magnetic field gradient is applied for purely frequency-encoded, radial centre-out k-space encoding. The spatially non-selective excitation must uniformly cover the full frequency bandwidth spanned by the readout magnetic field gradient, which is typically accomplished by radiating short, hard RF pulses. The acquisition of a free induction decay (FID) signal starts immediately after radiation of the RF pulse. After the FID readout, only minimal time is required for setting of the next readout magnetic field gradient before the next RF pulse can be applied, thus enabling very short repetition times (TR).’)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the MR signals of Dosenbach such that the MR signals includes free-induction decay (FID) navigator signals captured by two or more receiver coils during the MRI scan; and evaluating the MR signals captured during the MRI scan comprises combining the MR signals captured by the two or more receiver coils to determine at least one first metric as taught by Nielsen. The motivation to do this yields predictable results such as improving the robustness to motion artifacts, Abstract & ¶0032 of Nielsen.
Claim 6: Modified Dosenbach discloses all the elements above in claim 5, Dosenbach discloses, wherein combining the MR signals comprises combining MR signals captured for a channel, for a time, and/or for the MRI scan. (¶0009, ¶0021, ¶0038-0039, ¶0046, ¶0053, ¶0072)
Claim 8: Modified Dosenbach discloses all the elements above in claim 5, Dosenbach fails to disclose: wherein: evaluating MR signals captured during the MRI scan comprises evaluating FID navigator signals captured during the MRI scan; and evaluating the FID navigator signals captured during the MRI scan further comprises integrating the at least one first metric with respect to a number of k-space lines to determine at least one second metric.
However, Nielsen is relied upon above discloses, wherein: evaluating MR signals captured during the MRI scan comprises evaluating FID navigator signals captured during the MRI scan; and evaluating the FID navigator signals captured during the MRI scan further comprises integrating the at least one first metric with respect to a number of k-space lines to determine at least one second metric. (¶0026, ‘a set of RF coils having different spatial sensitivity profiles is used for MR signal acquisition.’; ¶0032, ‘The MR signals may also be sampled according to a so-called “koosh ball”-sampling scheme. This techniques provides for virtually silent MR imaging, in which RF excitation as well as acquisition of MR signals are performed in the presence of a magnetic field gradient. The magnetic field gradient is applied for purely frequency-encoded, radial centre-out k-space encoding. The spatially non-selective excitation must uniformly cover the full frequency bandwidth spanned by the readout magnetic field gradient, which is typically accomplished by radiating short, hard RF pulses. The acquisition of a free induction decay (FID) signal starts immediately after radiation of the RF pulse. After the FID readout, only minimal time is required for setting of the next readout magnetic field gradient before the next RF pulse can be applied, thus enabling very short repetition times (TR)… Such radial centre-out k-space scanning techniques are referred to as “koosh ball”-scanning, with the radial k-space “spokes” and their arrangement in k-space resembling the filaments (strings) of the known toy ball design.’)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the MR signals of modified Dosenbach such that the MR signals evaluating FID navigator signals captured during the MRI scan; and evaluating the FID navigator signals captured during the MRI scan further comprises integrating the at least one first metric with respect to a number of k-space lines to determine at least one second metric as taught by Nielsen. The motivation to do this yields predictable results such as improving the robustness to motion artifacts, Abstract & ¶0032 of Nielsen.
Claim 16: Dosenbach discloses all the elements above in claim 1, Dosenbach fails to disclose: wherein the MR signals comprise FID navigator signals captured during an acquisition of one or more MRI sequences, and the one or more MRI sequences comprise embedded FID navigator modules following RF excitation pulses.
However, Nielsen in the context of motion compensation image reconstruction in magnetic resonance imaging discloses, wherein the MR signals comprise FID navigator signals captured during an acquisition of one or more MRI sequences, and the one or more MRI sequences comprise embedded FID navigator modules following RF excitation pulses. (¶0026, ‘a set of RF coils having different spatial sensitivity profiles is used for MR signal acquisition.’; ¶0032, ‘The MR signals may also be sampled according to a so-called “koosh ball”-sampling scheme. This techniques provides for virtually silent MR imaging, in which RF excitation as well as acquisition of MR signals are performed in the presence of a magnetic field gradient. The magnetic field gradient is applied for purely frequency-encoded, radial centre-out k-space encoding. The spatially non-selective excitation must uniformly cover the full frequency bandwidth spanned by the readout magnetic field gradient, which is typically accomplished by radiating short, hard RF pulses. The acquisition of a free induction decay (FID) signal starts immediately after radiation of the RF pulse. After the FID readout, only minimal time is required for setting of the next readout magnetic field gradient before the next RF pulse can be applied, thus enabling very short repetition times (TR).’)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the MR signals of Dosenbach such that the MR signals includes FID navigator signals captured during an acquisition of one or more MRI sequences, and the one or more MRI sequences comprise embedded FID navigator modules following RF excitation pulses as taught by Nielsen. The motivation to do this yields predictable results such as improving the robustness to motion artifacts, Abstract & ¶0032 of Nielsen.
Claim 24: Dosenbach discloses all the elements above in claim 23, Dosenbach fails to disclose: wherein estimating the motion based on the measured MR signals captured during the MRI scan comprises estimating the motion based on measured free-induction decay (FID) navigator signals captured during the MRI scan.
However, Nielsen in the context of motion compensation image reconstruction in magnetic resonance imaging discloses, wherein estimating the motion based on the measured MR signals captured during the MRI scan comprises estimating the motion based on measured free-induction decay (FID) navigator signals captured during the MRI scan. (¶0026, ‘a set of RF coils having different spatial sensitivity profiles is used for MR signal acquisition.’; ¶0032, ‘The MR signals may also be sampled according to a so-called “koosh ball”-sampling scheme. This techniques provides for virtually silent MR imaging, in which RF excitation as well as acquisition of MR signals are performed in the presence of a magnetic field gradient. The magnetic field gradient is applied for purely frequency-encoded, radial centre-out k-space encoding. The spatially non-selective excitation must uniformly cover the full frequency bandwidth spanned by the readout magnetic field gradient, which is typically accomplished by radiating short, hard RF pulses. The acquisition of a free induction decay (FID) signal starts immediately after radiation of the RF pulse. After the FID readout, only minimal time is required for setting of the next readout magnetic field gradient before the next RF pulse can be applied, thus enabling very short repetition times (TR)… Such radial centre-out k-space scanning techniques are referred to as “koosh ball”-scanning, with the radial k-space “spokes” and their arrangement in k-space resembling the filaments (strings) of the known toy ball design.’)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the estimated motion of Dosenbach such that the MR signals are based on the measured MR signals captured during the MRI scan comprises estimating the motion based on measured free-induction decay (FID) navigator signals captured during the MRI scan as taught by Nielsen. The motivation to do this yields predictable results such as improving the robustness to motion artifacts, Abstract & ¶0032 of Nielsen.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Dosenbach (US 2024/0418814 A1) in view of Nielsen et al (US 2021/0356547 A1), as applied to claim 5 above, in further view of Stemkens (Adaptive bulk motion exclusion for improved robustness of abdominal magnetic resonance imaging, 2017).
Claim 7: Modified Dosenbach discloses all the elements above in claim 5, Dosenbach fails to disclose: wherein combining the MR signals captured by the two or more receiver coils comprises determining a normalized mean absolute change in the MR signals and/or a cross correlation coefficient between projection vectors of the MR signals.
However, Stemkens in the field of motion correction for improved magnetic resonance imaging discloses, wherein combining the MR signals captured by the two or more receiver coils comprises cross correlation coefficient between projection vectors of the MR signals. (pg 3/2.2 Implementation, ‘Motion is detected by calculating the correlation coefficient between a reference projection vector and all other projection vectors.20 The correlation coefficient CC is defined as:
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where xi and yi denote the ith entry of the current projection vector x and reference projection y. ̅x and ̅𝑦 are the mean values of both vectors. A low correlation coefficient implies that the load distribution of the coil elements has changed. This indicates that the patient position has been altered between the projections. As it is not known beforehand if and when bulk motion occurs, the reference projection vector, which is defined as the projection with highest overall correlation to all previously acquired projections, is dynamically updated after each projection. The calculation of all correlation coefficients is computationally demanding and cannot always be performed in real time. In the current implementation, the reference projection is therefore only calculated within a sliding window containing the previous 100 projections. Outlier projections, which indicate bulk motion, are identified using a user‐defined acceptance threshold for the correlation coefficient.’)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify combining of the MR signals captured of modified Dosenbach such that it includes cross-correlation coefficient between projection vectors of the MR signals as taught by Stemkens. The motivation to do this yields predictable results such as improving the motion estimation between projections as suggested by Stemkens, pg. 3.
Claims 5-6, 8-9, 16, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over Dosenbach (US 2024/0418814 A1) in view of Nielsen et al (US 2021/0356547 A1), as applied to claim 8, alternatively in further view Braun et al (US 2018/0232878 A1).
Claim 9: Modified Dosenbach discloses all the elements above in claim 8, Dosenbach discloses, wherein determining the diagnostic quality of the at least one image that would result from the MRI scan comprises: comparing the at least one second metric to a threshold; and determining that the at least one image that would result from the MRI scan is non- diagnostic when the at least one second metric exceeds the threshold. (¶0004-0008, ¶0023, ¶0040-0041, Claim 5-7)
Alternatively, Braun in the context of image quality assessment in MRI discloses, wherein determining the diagnostic quality of the at least one image that would result from the MRI scan comprises: comparing the at least one second metric to a threshold; and determining that the at least one image that would result from the MRI scan is non- diagnostic when the at least one second metric exceeds the threshold. (¶0024, ‘network 120 may output more than one type of motion score. Quality evaluation component 140 may evaluate these scores (e.g., by comparing the scores against score thresholds corresponding to each type of motion score) and output an Acceptable or Unacceptable indicator based on the comparison.’) (¶0018-¶0024, -Quality evaluation component 140 outputs an “Acceptable” indicator if the motion score is less than the score threshold and an “Unacceptable” indicator if the motion score is greater than or equal to the score threshold. An “Unacceptable” indicator corresponds unacceptable quality to image quality for the clinical diagnosis (i.e. the MRI scan is non-diagnostic))
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the determining of the diagnostic quality of modifed Dosenbach in view of the teachings of Braun. The motivation to do this yield predictable results such as improving the system efficiency by ensuring sufficient quality of images, as suggested by Braun, ¶0003.
Claims 17 & 25 are rejected under 35 U.S.C. 103 as being unpatentable over Dosenbach (US 2024/0418814 A1), as applied to claim 1 & 23 respectively, alternatively in further view Braun et al (US 2018/0232878 A1).
Claim 17: Dosenbach discloses all the elements above in claim 1, Dosenbach discloses, wherein the evaluating of the MR signals captured during the MRI scan comprises evaluating MR signals corresponding to a center of k-space captured during an MRI scan in which the center of k-space was repeatedly sampled. (¶0006, ¶0042)
Alternatively, Braun in the context of image quality assessment in MRI discloses, wherein the evaluating of the MR signals captured during the MRI scan comprises evaluating MR signals corresponding to a center of k-space captured during an MRI scan in which the center of k-space was repeatedly sampled. (provide a network and training architecture to assess image quality, ¶0018-0024, raw data are also known as k-space data, where k-space represents the spatial frequency information in two or three dimensions of the imaged object. The k-space is defined as the space covered by the phase and frequency encoding data. Motion of an object during imaging causes a Fourier shift in the k-space data. The motion results in different phase shifts for different measurements taken at different times. Accordingly, motion corruption component 200 may receive known acceptable image Image1, convert image Image1 to raw k-space data (e.g., via an inverse Fourier transform), introduce the desired motion in the k-space data through phase-shifting the k-space data, and reconstruct image Image1C1, ¶0028-¶0032)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify evaluating of the MR signals of Dosenbach in view of the teachings of Braun. The motivation to do this yield predictable results such as improving the system efficiency by ensuring sufficient quality of images, as suggested by Braun, ¶0003.
Claim 25: Dosenbach discloses all the elements above in claim 23, Dosenbach discloses,
wherein the estimating of the motion based on the measured MR signals captured during the MRI scan comprises estimating the motion based on measured MR signals corresponding to a center of k-space captured during an MRI scan in which the center of k-space was repeatedly sampled. (¶0006, ¶0042)
Alternatively, Braun in the context of image quality assessment in MRI discloses,
wherein the estimating of the motion based on the measured MR signals captured during the MRI scan comprises estimating the motion based on measured MR signals corresponding to a center of k-space captured during an MRI scan in which the center of k-space was repeatedly sampled. (provide a network and training architecture to assess image quality, ¶0018-0024, raw data are also known as k-space data, where k-space represents the spatial frequency information in two or three dimensions of the imaged object. The k-space is defined as the space covered by the phase and frequency encoding data. Motion of an object during imaging causes a Fourier shift in the k-space data. The motion results in different phase shifts for different measurements taken at different times. Accordingly, motion corruption component 200 may receive known acceptable image Image1, convert image Image1 to raw k-space data (e.g., via an inverse Fourier transform), introduce the desired motion in the k-space data through phase-shifting the k-space data, and reconstruct image Image1C1, ¶0028-¶0032)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify the estimating of the motion of Dosenbach in view of the teachings of Braun. The motivation to do this yield predictable results such as improving the system efficiency by ensuring sufficient quality of images, as suggested by Braun, ¶0003.
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Dosenbach (US 2024/0418814 A1), as applied to claim 17, in further view Braun et al (US 2018/0232878 A1).
Claim 18: Dosenbach or alternatively modified Dosenbach discloses all the elements above in claim 17, Dosenbach discloses, wherein the evaluating of the MR signals corresponding to the center of k-space captured during an MRI scan in which the center of k-space was repeatedly sampled (¶0006, ¶0042)
Dosenbach fails to disclose: comprises evaluating MR signals that correspond to the center of k-space and that were captured without gradient encoding.
Alternatively, Braun is relied upon above discloses, wherein the evaluating of the MR signals corresponding to the center of k-space captured during an MRI scan in which the center of k-space was repeatedly sampled (provide a network and training architecture to assess image quality, ¶0018-0024, raw data are also known as k-space data, where k-space represents the spatial frequency information in two or three dimensions of the imaged object. The k-space is defined as the space covered by the phase and frequency encoding data. Motion of an object during imaging causes a Fourier shift in the k-space data. The motion results in different phase shifts for different measurements taken at different times. Accordingly, motion corruption component 200 may receive known acceptable image Image1, convert image Image1 to raw k-space data (e.g., via an inverse Fourier transform), introduce the desired motion in the k-space data through phase-shifting the k-space data, and reconstruct image Image1C1, ¶0028-¶0032)
comprises evaluating MR signals that correspond to the center of k-space and that were captured without gradient encoding. (provide a network and training architecture to assess image quality, ¶0018-0024, raw data are also known as k-space data, where k-space represents the spatial frequency information in two or three dimensions of the imaged object. The k-space is defined as the space covered by the phase and frequency encoding data. Motion of an object during imaging causes a Fourier shift in the k-space data. The motion results in different phase shifts for different measurements taken at different times… In some embodiments, motion corruption component 200 may receive raw (i.e., pre-reconstruction) k-space data, eliminating the need to convert a reconstructed image to k-space data. ¶0028-¶0032)
It would have been obvious to one of ordinary skilled in the art before the effective filing date of the claimed invention to modify evaluating of the MR signals of modified Dosenbach in view of the teachings of Braun. The motivation to do this yield predictable results such as improving the system efficiency by ensuring sufficient quality of images, as suggested by Braun, ¶0003.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Braun et al (US 2018/0232878 A1) discloses the following:
Braun discloses, re: Claim 2, wherein the determining of the diagnostic quality (¶0021, ‘Automated image quality assessment may improve operational efficiency and enforce image quality for clinical diagnosis.’) of the at least one image that would result from the MRI scan comprises: (¶0002-¶0003, Quality assessment of medical images access whether image quality is acceptable or unacceptable for diagnostic purposes, ¶0018-¶0024 - network 120 output more than one type of motion score. Quality evaluation component 140 evaluates the scores (e.g., by comparing scores against score threshold corresponding to each type of motion score) and outputs an Acceptable or Unacceptable indicator based on the comparison)
comparing the result of the evaluating of the MR signals to a threshold; and determining that the diagnostic quality of the at least one image that would result from the MRI scan is non-diagnostic when the result of evaluating the MR signals exceeds the threshold. (¶0018-¶0024, -Quality evaluation component 140 outputs an “Acceptable” indicator if the motion score is less than the score threshold and an “Unacceptable” indicator if the motion score is greater than or equal to the score threshold. An “Unacceptable” indicator corresponds unacceptable quality to image quality for the clinical diagnosis (i.e. the MRI scan is non-diagnostic))
Braun discloses, re: Claim 3, wherein the quantifying the changes in the MR signals captured during the MRI scan comprises determining a change in the MR signals over time. (¶0028-¶0032, raw MR data consists of transversal components of magnetization in an imaged object after excitation, sampled from a receive coil signal and stored as a function of time during the data acquisition portion of an MR imaging sequence)
Braun discloses, re: Claim 4, wherein the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises: comparing the change in the MR signals to a threshold; and determining that the diagnostic quality of the at least one image that would result from the MRI scan is non-diagnostic when the change in the MR signals exceeds the threshold. (¶0018-¶0026, -Network 120 outputs more than one type of motion score. Quality evaluation component 140 evaluates the scores (e.g., by comparing the scores against score thresholds corresponding to each type of motion score) and outputs an Acceptable or Unacceptable indicator based the comparison.)
Braun discloses, re: Claim 10, wherein the determining of the diagnostic quality of the at least one image that would result from the MRI scan comprises determining the diagnostic quality during the MRI scan, prior to image reconstruction. (Automated image quality assessment may improve operational efficiency and enforce image quality for clinical diagnosis. If an image is determined to exhibit unacceptable quality, an operator may adjust the protocol and reacquire the image before the patient leaves the scanner table or the exam room. Automated image quality assessment may also provide a confidence metric for subsequent post-processing, or a gating mechanism to control the workflow (e.g., whether or not to proceed with post-processing), (¶0018-¶0024))
Braun discloses, re: Claim 11, wherein the outputting of the indication of the diagnostic quality of the at least one image that would result from the MRI scan comprises outputting the indication of the diagnostic quality during the MRI scan, prior to image reconstruction. (Automated image quality assessment may improve operational efficiency and enforce image quality for clinical diagnosis. If an image is determined to exhibit unacceptable quality, an operator may adjust the protocol and reacquire the image before the patient leaves the scanner table or the exam room. - network 120 may output more than one type of motion score. Quality evaluation component 140 may evaluate these scores (e.g., by comparing the scores against score thresholds corresponding to each type of motion score) and output an Acceptable or Unacceptable indicator based on the comparison, (¶0018-¶0024))
Braun discloses, re: Claim 12, wherein the outputting of the indication of the diagnostic quality comprises recommending intervention when the diagnostic quality is determined to be non-diagnostic. (Automated image quality assessment may improve operational efficiency and enforce image quality for clinical diagnosis. If an image is determined to exhibit unacceptable quality, an operator may adjust the protocol and reacquire the image before the patient leaves the scanner table or the exam room. - network 120 may output more than one type of motion score. -Quality evaluation component 140 outputs an “Acceptable” indicator if the motion score is less than the score threshold and an “Unacceptable” indicator if the motion score is greater than or equal to the score threshold. An “Unacceptable” indicator corresponds unacceptable quality to image quality for the clinical diagnosis (i.e. the MRI scan is non-diagnostic)). -Quality evaluation component 140 may evaluate these scores (e.g., by comparing the scores against score thresholds corresponding to each type of motion score) and output an Acceptable or Unacceptable indicator based on the comparison, (¶0018-¶0024))
Braun discloses, re: Claim 14, wherein the outputting of the indication of the diagnostic quality comprises displaying data through a user interface of an MRI system. (Automated image quality assessment may improve operational efficiency and enforce image quality for clinical diagnosis. If an image is determined to exhibit unacceptable quality, an operator may adjust the protocol and reacquire the image before the patient leaves the scanner table or the exam room. - network 120 may output more than one type of motion score. -Quality evaluation component 140 outputs an “Acceptable” indicator if the motion score is less than the score threshold and an “Unacceptable” indicator if the motion score is greater than or equal to the score threshold. An “Unacceptable” indicator corresponds unacceptable quality to image quality for the clinical diagnosis (i.e. the MRI scan is non-diagnostic)). -Quality evaluation component 140 may evaluate these scores (e.g., by comparing the scores against score thresholds corresponding to each type of motion score) and output an Acceptable or Unacceptable indicator based on the comparison, (¶0018-¶0024)), -Terminal 1290 may simply comprise a display device and an input device coupled to system 1275. In some embodiments, terminal 1290 is a separate computing device such as, but not limited to, a desktop computer, a laptop computer, a tablet computer, and a smartphone., (¶0068-¶0072))
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/N.A.R./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798