DETAILED ACTION
This Office action is responsive to communications filed on 02/17/2026. Claims 1, 9, 11, & 19 have been amended. Presently, Claims 1-20 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Previous objections to the Drawings are withdrawn in view of the amendments filed on 02/17/2026.
Previous rejections under 35 USC § 112(b) are withdrawn in view of the amendments filed on 02/17/2026.
Previous claim objections are withdrawn in view of the amendments filed on 02/17/2026.
The Applicant’s arguments with respect to rejections under 35 USC § 103 have been fully, considered, but are not persuasive. The rejection under 35 USC § 103 over Speier et al (US 2018/0353140 A1) in view of Schroeder et al. ("Two-Dimensional Respiratory-Motion Characterization for Continuous MR Measurements Using Pilot Tone Navigation." Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), Singapur 2016. 3103) is maintained.
The Applicant argues that Speier does not teach or disclose “selecting and storing at least two non-parallel weighting vectors [...] that allow the extract signal components that represent the cardiac movement”. The Applicant asserts that Speier represents cardiac motion using only a single vector or signal component.
The Examiner disagrees. This argument is not persuasive because Speier is relied upon for teaching that use of a blind source separation technique (ICA) on pilot tone channel signals to determine weighting vectors forming weighted combinations of the channel signals, storing the resulting demixing matrix, and applying those weights to the subsequent pilot tone data to extract motion signals. Speier explains that ICA extracts the independent components from multichannel signals and determines a demixing matrix comprising weighted vectors applied to the channel signals, ¶0010, ¶0025-0026, ¶0029, ¶0034, ¶0096.
Applicant argument that Speier ultimately selects a single cardiac component addresses a narrower embodiment and does not negate Speier’s disclosure that the ICA procedure generates a demixing matrix containing multiple vectors corresponding to the independent components of the multichannel. The rejection does not rely on Speier alone to teach selecting at least two non-parallel vectors representing cardiac movement. Rather, that aspect of the limitation is addressed by Schroeder. Accordingly, the Applicant’s argument directly to solely to Speier does not address the rejection as applied and therefore are not persuasive.
With respect to Schroeder, Applicant argues that the reference does not teach selecting and storing non-parallel weighting vectors that extract signal components representing cardiac movement because Schroeder characterizes respiratory motion. This argument is not persuasive because it mischaracterizes the role of Schroeder in the rejection. Schroeder was relied upon to teach determining and applying at least two distinct weighting vectors derived from pilot tone channel signals to produce a multi-dimensional motion representation. Schroeder discloses determining two optimal channel combination weight vectors corresponding to motion in different directions and applying these weights to Pilot tone signals to produce two pilot tone navigators forming a two dimensional motion characterization. These vectors are determine using a calibration phase and subsequently stored and applied to further pilot tone data. Specifically, Schroeder teaches selecting and storing non-parallel weighting vectors that extract signal components representing cardiac movement from multiple channels is demonstrated in the application of the Pilot Tone navigation. See Methods; The optimal channel coefficients (i.e., the weighted vectors) the process the PT signals are received by standard MR coils. To capture non-parallel respiratory movements, the extracted signal components for two orthogonal directions; the super-interior (SI) direction and the anterior-posterior (AP) direction. Thus Schroeder teaches selecting and storing at least two distinct weighting vectors and applying them to generate a multidimensional pilot tone motion signal.
The Applicant argument that Schroeder concerns respiratory motion rather than cardiac motion is not persuasive. The rejection relies of Speier the extraction of cardiac motion signals from pilot tone data and relies on Schroeder for the concept using multiple channel combination weighting vectors to obtain a multi-dimensional motion signal. Including Schroeder teachings of the multidimensional weighting vector technique into the pilot tone motion extraction framework of Speier would have been an obvious modification for a person of ordinary skill in the art, since both references address mitigation of physiological motion effects in MR imaging using pilot tone signals. Applying the Schroeder multidimensional weighting vector approach within Speier’s cardiac motion extraction framework would reasonably result in multiple weighting vectors extracting components representing cardiac motion from the pilot tone channel signals, thereby producing the multidimensional pilot tone signal as claimed.
The Applicant arguments referring these advantages allegedly associated with representing cardiac motion using multiple vectors is not reflected in the claims. The claim only requires selecting and storing at least two non-parallel weighting vectors capable of extracting cardiac movement components and using the resulting multidimensional pilot tone signal for gating or correction. These arguments are therefore not persuasive.
For these reasons, the 35 USC § 103 over Speier in view of Schroeder is therefore maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as failing to set forth the subject matter which the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the applicant regards as the invention.
Claim 11:
line 5: “separate channels”, it is unclear if the phrase refers to or is separate from the number of channel signals. For examination purposes, the Examiner assumes they refer to the same. Appropriate correction is required.
The dependent claims of the above rejected claims are rejected due to their dependency.
Claim Objections
The following claims are objected to because of the following informalities and should recite:
Claim 1:
line 4, “[[Tx]](RF) Pilot Tone signal”.
line 19, the acquired image data”. Consistent claim language is required when referring to the same term.
Claim 19:
line 5, “[[Tx]](RF) Pilot Tone signal”.
line 21, the acquired image data”.
Consistent claim language is required when referring to the same term. Appropriate correction is needed.
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 1-5, 9, 12-15, and 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over Speier et al (US 2018/0353140 A1) in view of Schroeder et al. ("Two-Dimensional Respiratory-Motion Characterization for Continuous MR Measurements Using Pilot Tone Navigation." Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), Singapur 2016. 3103).
Claim 1: Speier disclose: A method for acquiring image data in a radiological examination of a part of a human or animal body, wherein the part is subjected to a cardiac movement, the method comprising: (¶0010, ‘One or more of the present embodiments are directed to a method for generating a movement signal of a part of a human or an animal body, of which at least a portion is undergoing a cyclical movement (e.g., a cardiac and/or respiratory movement). The method includes providing a pilot tone signal acquired from the body part by a magnetic resonance receiver coil arrangement including a plurality of channels. The Pilot Tone signal includes a plurality of signal components associated with the plurality of channels. From a calibration portion of the Pilot Tone signal, a demixing matrix is calculated using an independent component analysis (ICA) algorithm, where the demixing matrix calculates the independent components from the plurality of signal components. The independent component(s) corresponding to at least one particular movement type (e.g., the cardiac movement) are selected. The demixing matrix is applied to the further portions of the pilot tone signal to obtain at least one movement signal representing one particular movement type (e.g., the cardiac movement). An adaptive, stochastic, or model-based filter is applied to the at least one movement signal representing one particular movement type (e.g., the cardiac movement) to obtain a filtered movement signal.’)
transmitting a radiofrequency Tx Pilot Tone signal via at least one RF transmit antenna; (¶0018, ‘The Pilot Tone (PT) signal is a frequency signal received by a magnetic resonance receiver coil arrangement (e.g., a standard MR local coil) that has a plurality of channels, outside the receive bandwidth of an MR scan of the body part. The PT signal may be generated by an independent continuous-wave radio frequency (RF) source’; ¶0105, ‘a pilot tone signal 16 is emitted by a pilot tone emitter 14 that may be a separate RF source. In one embodiment, the pilot tone emitter 14 is positioned close to the heart (e.g., strapped to the local coil 28 or included in the coil). The pilot tone signal is modulated by the movement of the heart 18 and the lung (not shown).’)
receiving a Pilot Tone signal from the body part via a radiofrequency receiver coil arrangement comprising a number of channels, wherein the Pilot Tone signal comprises a number of channel signals associated with the number of channels; (¶Abstract, ‘includes providing a pilot tone signal acquired from the body part by a magnetic resonance receiver coil arrangement.’; Claim 1, ‘providing a Pilot Tone signal acquired from the body part by a magnetic resonance receiver coil arrangement, the magnetic resonance receiver coil arrangement comprising a plurality of channels, wherein the Pilot Tone signal comprises a plurality of signal components associated with the plurality of channels;’; see also ¶0010)
carrying out a blind source separation algorithm on a training portion of the Pilot Tone signal and thereby determining weighting vectors to extract cardiac movement signals, wherein the weighting vectors allow to form weighted combinations of the number of channel signals; (¶Abstract, ¶0009-0012, Claim 1, Claims 3-4, 7, ¶0025-0026, ¶0029-0030, ¶0034, ¶0096: -Speier explicitly teaches a demixing matrix calculated from a calibration portion of the pilot tone signal using an independent component analysis. ICA is an algorithm that carries out a blind source separation algorithm where it used to extract a plurality of independent components from the calibration portion. ICA is applied for reliably extracting a cardiac movement signal and to separate the cardiac movement signal form other motion and signal components. This separation occurs because the demixing calculated by the ICA separates the independent components, corresponding to different movement types, from the plurality of signals. The result of this process is an optimal linear channel combination (i.e., demixing matric). This demixing matric weights the contributions of different channel elements accordingly (e.g., suppressing unwanted patient motion while maximizing sensitivity to cardiac motion.)
selecting and storing at least two non-parallel weighting vectors of the weighting vectors that allow to extract signal components that represent the cardiac movement from the number of channel signals;
-Speier discloses, the goal of ICA processing is to compute an optimal linear channel combination, which would weight contributions of different channel elements accordingly, ¶0029, ‘The goal is to compute an optimal linear channel combination, which would weigh the contributions of the different channel elements accordingly (e.g., suppressing contributions from unwanted patient motion while maximizing sensitivity to one particular movement type such as cardiac motion).’ Once the demixing matrix is calculated the ICA corresponding to the cardiac movement is selected, ¶Abstract, Claim 1. This demixing matrix provides the optimal linear channel contribution (i.e., the weighting vectors). In the case, where the method extracts a plurality of movement types, the demixing matrix is applied which consistes of multiple vectors, resulting in a plurality of movement signals. In this case, there are multiple vectors within the matrix used, ¶0025: ‘a demixing matrix is calculated from a calibration portion of the pilot tone signal by an independent component analysis (ICA). The ICA is performed on the several signal components corresponding to the plurality of channels. A demixing matrix generally separates the independent components (e.g., corresponding to different movement types) from a plurality of signal components. In this case, the demixing matrix, which is applied to the further portions of the pilot tone signal, is a demixing matrix when extracting a plurality of movement types, but becomes a demixing vector when only one movement type (e.g., independent component) is extracted. Thus, the term “demixing matrix” may also cover “demixing vector”, depending on whether one or several movement types are extracted. The demixing matrix, when applied to the pilot tone signal, will separate at least one particular movement type (e.g., the cardiac component) from the several signal components. Depending on the implementation of the ICA, this demixing matrix may be either complex or real-valued.’ see also ¶0026: “ICA is first used to extract a plurality of independent components from a calibration portion of the Pilot Tone signal, and when the independent component(s) corresponding to the desired movement types have been selected, a demixing matrix or vector that extracts these movement type(s) is applied to the further portions of the Pilot Tone signal (e.g., in real time). When several movement types are to be extracted, applying the demixing matrix may result in a plurality of movement signals, each representing one particular movement type.”
-Speier discloses, the weighting vectors are stored, at the processing act, ¶0097: “The demixing matrix W is then stored and applied to the incoming further Pilot Tone signal data 102.”
-Speier disclose several movement types are to be extracted resulting in a plurality of movement signals, ¶0025-0026. This demixing matrix would be composed of multiple weighting vectors, where each vector extracts a unique independent component, ¶0025-0026. As explicitly stated by Speier, ¶0030, ‘ICA is based on the assumption that individual components of a multivariate signal are non-Gaussian and that the individual components are statistically independent from each other.’, The existence of separate vectors to isolate statistically independent components, ¶0096 (like respiratory motion and cardiac motion) confirms the presence of at least two distinct, and thus non-parallel, weighting vectors within the demixing matrix for extracting signal components that represent cardiac movement from the number of channels.
applying the weighting vectors to further portions of the Pilot Tone signal to obtain a multi-dimensional Pilot Tone signal representing the cardiac movement,
-The pilot tone signal of Speier is multidimensional, as it is acquired from a magnetic resonance receiver coil arrangement comprising a plurality of channels, and thus comprising a plurality of signal components representing the cardiac movement, Claim 1, ¶Abstract, ¶0010. The demixing matrix is calculated using the ICA, Claim 1, ¶Abstract, ¶0010. These weighted vectors are applied to further portions of the pilot tone signal, ¶Abstract, ¶0010, ¶0025-0026.
using the multi-dimensional Pilot Tone signal for controlling acquisition of the image data or for retrospectively gating or correcting the acquired image data.
-The pilot tone signal of Speier is multidimensional, as it is acquired from a magnetic resonance receiver coil arrangement comprising a plurality of channels, and thus comprising a plurality of signal components representing the cardiac movement, Claim 1, ¶Abstract, ¶0010. The resulting filtered movement derived from the multi-dimensional pilot tone signal is used to extract time points for tiggering a scan of medical data and prost processing, ¶Abstract, ¶0053, Claim 14-15,
Speier fails to disclose:
wherein the multi-dimensional Pilot Tone signal has at least two dimensions;
However, Schroeder in the context of two-dimensional respiratory-motion characterization for continuous MR measurements using pilot tone navigation discloses:
selecting and storing at least two non-parallel weighting vectors of the weighting vectors that allow to extract signal components that represent respiratory movement from the number of channel signals;
-[Methods], [Conclusion]: Schroeder teaches selecting and storing at least two distinct weighting vectors (i.e., optimal channel combination coefficients, w) that allow for the extraction of signal components characterizing respiratory movement. The methodology provides two-dimensional characterization of respiratory motion, [Synopsis], [Conclusion], by determining two sets of optimal weights WSI (for superior-inferior motion) and WAP (for Anterior-Posterior motion), [Methods], FIGURE 2. These weights are found in a separate calibration phase by solving a least squire optimization problem using respiratory ground truth signals (gSI and gAP), [Methods]. Since the ground trush signals characterize motion in two orthogonal directions (SI and AP), the corresponding weights (WSI and WAP) must be non-parallel to distinguish between these two respiration modes. Once determined, these coefficient are stored and used in the subsequent application phase to generate PT navigators (right side of FIGURE 1).
applying the weighting vectors to further portions of the Pilot Tone signal to obtain a multi-dimensional Pilot Tone signal representing the respiratory movement, wherein the multi-dimensional Pilot Tone signal has at least two dimensions;
-Schroeder teaches applying the determined weighting vectors (w) to further portions of the PT signal a multi-dimensional PT signal representing respiratory movement, where the signal has at least two dimensions. The optimal channel combination coefficient (w) are determine during a separate calibration phase and are then used in the subsequent application phase to generate PT navigators for motion correction, [Methods]. The goal of Schroeder disclosure is to provide two-dimensional characterization of respiratory motion, [Synopsis]. This is achieved by determining and applying weighting vectors corresponding to the superior-inferior (WSI) and Anterior-Posterior (WAP), then applying these two vectors to the incoming PT amplitudes yielding two PT navigators, which together constitute a multi-dimensional (i.e., two-dimensional) PT signal representation of the respiratory movement, [Methods], [Results].
using the multi-dimensional Pilot Tone signal for controlling acquisition of the image data or for retrospectively gating or correcting the acquired image data.
-The optimal channel combination of Schroeder are used in the application phase to generate the PT navigators for motion correction. The quality of the resulting PT navigators is evaluated by sorting the calibration images according to the PT navigators into bins, [Methods]. The resulting process involves binning and averaging the images in two dimensions, which improves the image sharpness, [Methods], [Results]. Hence, the two-dimensional PT navigation technique of Schroeder provides an alternative motion correction methodology to establish navigation methods for handing motion to improve image sharpness, [Methods], [Results].
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 method of acquiring image data of Speier to have at least two dimensions (i.e., the two-dimensional PT navigation technique of Schroeder provides an alternative motion correction methodology to establish navigation methods for handing motion) to improve image sharpness, as suggested by Schroeder, [Methods], [Results].
Alternatively, 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 method of acquiring image data of Speier in view of known techniques applied to respiratory extraction (i.e., the two-dimensional PT navigation technique of Schroeder provides an alternative motion correction methodology to establish navigation methods for handing motion) to improve image sharpness, as suggested by Schroeder, [Methods], [Results]. The modified combination would disclose selecting and storing at least two non-parallel weighting vectors of the weighting vectors that allow to extract signal components that represent cardiac movement from the number of channel signals; applying the weighting vectors to further portions of the Pilot Tone signal to obtain a multi-dimensional Pilot Tone signal representing the cardiac movement, wherein the multi-dimensional Pilot Tone signal has at least two dimensions; using the multi-dimensional Pilot Tone signal for controlling acquisition of image data since both Speier & Schroeder mitigate motion artifacts from physiological motion.
Claim 2: Speier as modified discloses all the elements above in claim 1, Speier fails to disclose, wherein the multi-dimensional Pilot Tone signal representing the cardiac movement has between two and five dimensions.
However, Schroeder is relied upon above teaches: wherein the multi-dimensional Pilot Tone signal representing the respiratory movement has between two and five dimensions.
-Schroeder teaches applying the determined weighting vectors (w) to further portions of the PT signal a multi-dimensional PT signal representing respiratory movement, where the signal has at least two dimensions. The optimal channel combination coefficient (w) are determine during a separate calibration phase and are then used in the subsequent application phase to generate PT navigators for motion correction, [Methods]. The goal of Schroeder disclosure is to provide two-dimensional characterization of respiratory motion, [Synopsis]. This is achieved by determining and applying weighting vectors corresponding to the superior-inferior (WSI) and Anterior-Posterior (WAP), then applying these two vectors to the incoming PT amplitudes yielding two PT navigators, which together constitute a multi-dimensional (i.e., two-dimensional) PT signal representation of the respiratory movement, [Methods], [Results].
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 method of acquiring image data of modified Speier in view of known techniques applied to respiratory extraction (i.e., the two-dimensional PT navigation technique of Schroeder provides an alternative motion correction methodology to establish navigation methods for handing motion) to improve image sharpness, as suggested by Schroeder, [Methods], [Results]. The modified combination would disclose representing the cardiac movement as required by the claim since both Speier & Schroeder mitigate motion artifacts from physiological motion.
Claim 3: Speier as modified discloses all the elements above in claim 2, Speier fails to disclose, wherein the multi-dimensional Pilot Tone signal representing the respiratory movement has two or three dimensions.
However, Schroeder is relied upon above teaches: wherein the multi-dimensional Pilot Tone signal representing the respiratory movement has two or three dimensions.
-Schroeder teaches applying the determined weighting vectors (w) to further portions of the PT signal a multi-dimensional PT signal representing respiratory movement, where the signal has at least two dimensions. The optimal channel combination coefficient (w) are determine during a separate calibration phase and are then used in the subsequent application phase to generate PT navigators for motion correction, [Methods]. The goal of Schroeder disclosure is to provide two-dimensional characterization of respiratory motion, [Synopsis]. This is achieved by determining and applying weighting vectors corresponding to the superior-inferior (WSI) and Anterior-Posterior (WAP), then applying these two vectors to the incoming PT amplitudes yielding two PT navigators, which together constitute a multi-dimensional (i.e., two-dimensional) PT signal representation of the respiratory movement, [Methods], [Results].
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 method of acquiring image data of modified Speier in view of known techniques applied to respiratory extraction (i.e., the two-dimensional PT navigation technique of Schroeder provides an alternative motion correction methodology to establish navigation methods for handing motion) to improve image sharpness, as suggested by Schroeder, [Methods], [Results]. The modified combination would disclose representing the cardiac movement as required by the claim since both Speier & Schroeder mitigate motion artifacts from physiological motion.
Claim 4: Speier as modified discloses all the elements above in claim 1, Speier discloses, wherein the blind source separation algorithm utilizes one or more Principal Component Analysis operations. (¶0067-0069)
Claim 5: Speier as modified discloses all the elements above in claim 1, Speier discloses, wherein the blind source separation algorithm utilizes one or more Independent Component Analysis operations. (¶0029-0031)
Claim 6: Speier as modified discloses all the elements above in claim 1, Speier discloses, wherein the blind source separation algorithm is used to detect a strongest independent component corresponding to the cardiac movement, and wherein the method further comprises:
-Speier teaches where the ICA is used to selected independent component corresponding to the cardiac movement. The selection to cardiac movement is done by computing quality criteria, such as selecting the component that has the most signal energy in the cardiac frequency band, ¶0033, ¶0096.
using a strongest independent component to retrospectively analyze the training portion of the Pilot Tone signal; and detecting at least one further independent component corresponding to the cardiac movement from the retrospective analysis. (¶0010, ¶0025, ¶0096)
Claim 7: Speier as modified discloses all the elements above in claim 6, Speier discloses, wherein using the strongest independent component to retrospectively analyze the training portion of the Pilot Tone signal comprises using the strongest independent component to retrospectively analyze the training portion of the Pilot Tone signal to average the Pilot Tone signal over a plurality of cardiac intervals, the plurality of cardiac intervals having been determined from the strongest independent component. (¶0010, ¶0025, ¶0096-0100)
Claim 9: Speier as modified discloses all the elements above in claim 1, Speier discloses, wherein the number of channel signals of the received Pilot Tone signal are complex-valued, and wherein the number of channel signals are rotated in a complex plane before carrying out the blind source separation algorithm. (¶0020, ¶0066, ¶0096, Claim 1, Claim 20: -Speier teaches the method improves pre-processing where the channels are rotated in the complex plane (i.e., phase normalization) before carrying out the ICA, which is a blind source separation algorithm. All channels are normalized to a reference plane. This removes phase drift and wrapping problems. The phase normalization is achieved by multiplying with the complex conjugate of the phase of the sample from the reference channel. This multiplication achieves the rotation in the complex plane, and the resulting normalized complex pilot tone signals are then further processed (i.e. PCA then ICA) to separate the motion components)
Claim 12: Speier as modified discloses all the elements above in claim 1, Speier discloses, wherein a time derivative of the multi-dimensional Pilot Tone signal is used for controlling the acquisition of the image data.
-Speier teaches that the recited PT signal is multi-dimensional, Claim 1, ¶Abstract, ¶0010. The process involves using weighted vectors (i.e., the demixing matrix) on this multi-dimensional PT signal to obtain cardiac movement, Claim 1, ¶Abstract, ¶0010. It is the time derivative of this resulting and filtered movement that is used for controlling the acquisition of image data. The first and/or second derivative of the filtered movement signal to extract time points used for triggering, ¶0052-0053. The time derivative is indeed of the multi-dimensional PT signal, comprising a plurality of signal components from a plurality of channels, Claim 1, ¶Abstract, ¶0010. The demixing matrix is applied to obtain the cardiac movement signal, Claim 1, ¶Abstract, ¶0010. The filter is applied to this cardiac movement signal to obtain the filtered movement signal, Claim 1, ¶Abstract, ¶0010. The first and/or second derivative is then calculated from this filtered movement signal to determine control points like max velocity or max acceleration, ¶0052-0055.
Claim 13: Speier as modified discloses all the elements above in claim 1, Speier discloses, further comprising determining trigger time points for triggering the acquisition of the image data, the determining of the trigger time points comprising evaluating properties of the multi-dimensional Pilot Tone signal or of a time derivative of the multi-dimensional Pilot Tone signal.
-Speier teaches that the recited PT signal is multi-dimensional, Claim 1, ¶Abstract, ¶0010. The process involves using weighted vectors (i.e., the demixing matrix) on this multi-dimensional PT signal to obtain cardiac movement, Claim 1, ¶Abstract, ¶0010. It is the time derivative of this resulting and filtered movement that is used for controlling the acquisition of image data. The first and/or second derivative of the filtered movement signal to extract time points used for triggering, ¶0052-0053. The time derivative is indeed of the multi-dimensional PT signal, comprising a plurality of signal components from a plurality of channels, Claim 1, ¶Abstract, ¶0010. The demixing matrix is applied to obtain the cardiac movement signal, Claim 1, ¶Abstract, ¶0010. The filter is applied to this cardiac movement signal to obtain the filtered movement signal, Claim 1, ¶Abstract, ¶0010. The first and/or second derivative is then calculated from this filtered movement signal to determine control points like max velocity or max acceleration, ¶0052-0055.
Claim 14: Speier as modified discloses all the elements above in claim 13, Speier discloses, wherein evaluating properties of the multi-dimensional Pilot Tone signal or of the time derivative of the multi-dimensional Pilot Tone signal comprises evaluating position, direction, velocity, acceleration, change in direction in multi-dimensional signal space, or any combination thereof of the multi-dimensional Pilot Tone signal or the time derivative of the multi-dimensional Pilot Tone signal.
-Speier teaches that the recited PT signal is multi-dimensional, Claim 1, ¶Abstract, ¶0010. The process involves using weighted vectors (i.e., the demixing matrix) on this multi-dimensional PT signal to obtain cardiac movement, Claim 1, ¶Abstract, ¶0010. It is the time derivative of this resulting and filtered movement that is used for controlling the acquisition of image data. The first and/or second derivative of the filtered movement signal to extract time points used for triggering, ¶0052-0053. The time derivative is indeed of the multi-dimensional PT signal, comprising a plurality of signal components from a plurality of channels, Claim 1, ¶Abstract, ¶0010. The demixing matrix is applied to obtain the cardiac movement signal, Claim 1, ¶Abstract, ¶0010. The filter is applied to this cardiac movement signal to obtain the filtered movement signal, Claim 1, ¶Abstract, ¶0010. The first and/or second derivative is then calculated from this filtered movement signal to determine control points like max velocity or max acceleration, ¶0052-0055.
Claim 15: Speier as modified discloses all the elements above in claim 1, Speier discloses, wherein the acquisition of the image data is triggered at a defined time point within a cardiac cycle. (¶0003, ¶0013, ¶0101, Claim 14, Claim 16)
Claim 17: Speier as modified discloses all the elements above in claim 1, Speier discloses, further comprising applying an adaptive, stochastic, or model-based filter to the multi-dimensional Pilot Tone signal representing the cardiac movement, such that a filtered movement signal is obtained.
-Speier teaches that the recited PT signal is multi-dimensional, Claim 1, ¶Abstract, ¶0010. The process involves using weighted vectors (i.e., the demixing matrix) on this multi-dimensional PT signal to obtain cardiac movement, Claim 1, ¶Abstract, ¶0010. It is the time derivative of this resulting and filtered movement that is used for controlling the acquisition of image data. The first and/or second derivative of the filtered movement signal to extract time points used for triggering, ¶0052-0053. The time derivative is indeed of the multi-dimensional PT signal, comprising a plurality of signal components from a plurality of channels, Claim 1, ¶Abstract, ¶0010. The demixing matrix is applied to obtain the cardiac movement signal, Claim 1, ¶Abstract, ¶0010. The filter is applied to this cardiac movement signal to obtain the filtered movement signal, Claim 1, ¶Abstract, ¶0010. The first and/or second derivative is then calculated from this filtered movement signal to determine control points like max velocity or max acceleration, ¶0052-0055. Specifically, “An, adaptive stochastic, or model-based filter is applied to the signal representing the cardiac movement, to obtain a filtered movement signal.”-¶Abstract.
Claim 18: Speier as modified discloses all the elements above in claim 17, Speier discloses, wherein the adaptive, stochastic, or model-based filter is adapted to obtain properties of the multi-dimensional Pilot Tone signal, the properties of the multi-dimensional Pilot Tone signal being a velocity vector or an acceleration vector.
-Speier teaches that the recited PT signal is multi-dimensional, Claim 1, ¶Abstract, ¶0010. The process involves using weighted vectors (i.e., the demixing matrix) on this multi-dimensional PT signal to obtain cardiac movement, Claim 1, ¶Abstract, ¶0010. It is the time derivative of this resulting and filtered movement that is used for controlling the acquisition of image data. The first and/or second derivative of the filtered movement signal to extract time points used for triggering, ¶0052-0053. The time derivative is indeed of the multi-dimensional PT signal, comprising a plurality of signal components from a plurality of channels, Claim 1, ¶Abstract, ¶0010. The demixing matrix is applied to obtain the cardiac movement signal, Claim 1, ¶Abstract, ¶0010. The filter is applied to this cardiac movement signal to obtain the filtered movement signal, Claim 1, ¶Abstract, ¶0010. The first and/or second derivative is then calculated from this filtered movement signal to determine control points like max velocity or max acceleration, ¶0052-0055. Specifically, “An, adaptive stochastic, or model-based filter is applied to the signal representing the cardiac movement, to obtain a filtered movement signal.”-¶Abstract.
Claim 19: A control unit (control unit 24) comprising: a processor (claim 20) configured to acquire image data in a radiological examination of a part of a human or animal body, wherein the part is subjected to a cardiac movement, the processor being configured to acquire the image data comprising the processor being configured to: (¶0010, ‘One or more of the present embodiments are directed to a method for generating a movement signal of a part of a human or an animal body, of which at least a portion is undergoing a cyclical movement (e.g., a cardiac and/or respiratory movement). The method includes providing a pilot tone signal acquired from the body part by a magnetic resonance receiver coil arrangement including a plurality of channels. The Pilot Tone signal includes a plurality of signal components associated with the plurality of channels. From a calibration portion of the Pilot Tone signal, a demixing matrix is calculated using an independent component analysis (ICA) algorithm, where the demixing matrix calculates the independent components from the plurality of signal components. The independent component(s) corresponding to at least one particular movement type (e.g., the cardiac movement) are selected. The demixing matrix is applied to the further portions of the pilot tone signal to obtain at least one movement signal representing one particular movement type (e.g., the cardiac movement). An adaptive, stochastic, or model-based filter is applied to the at least one movement signal representing one particular movement type (e.g., the cardiac movement) to obtain a filtered movement signal.’; ¶0080, ‘One or more of the present embodiments are further directed to a control unit adapted for performing the method as described, where the control unit may be part of a computer and/or part of a magnetic resonance machine.’)
transmit a radiofrequency Tx Pilot Tone signal via at least one RF transmit antenna;
(¶0018, ‘The Pilot Tone (PT) signal is a frequency signal received by a magnetic resonance receiver coil arrangement (e.g., a standard MR local coil) that has a plurality of channels, outside the receive bandwidth of an MR scan of the body part. The PT signal may be generated by an independent continuous-wave radio frequency (RF) source’; ¶0105, ‘a pilot tone signal 16 is emitted by a pilot tone emitter 14 that may be a separate RF source. In one embodiment, the pilot tone emitter 14 is positioned close to the heart (e.g., strapped to the local coil 28 or included in the coil). The pilot tone signal is modulated by the movement of the heart 18 and the lung (not shown).’)
receive a Pilot Tone signal from the body part via a radiofrequency receiver coil arrangement comprising a number of channels, wherein the Pilot Tone signal comprises a number of channel signals associated with the number of channels; (¶Abstract, ‘includes providing a pilot tone signal acquired from the body part by a magnetic resonance receiver coil arrangement.’; Claim 1, ‘providing a Pilot Tone signal acquired from the body part by a magnetic resonance receiver coil arrangement, the magnetic resonance receiver coil arrangement comprising a plurality of channels, wherein the Pilot Tone signal comprises a plurality of signal components associated with the plurality of channels;’; see also ¶0010)
carry out a blind source separation algorithm on a training portion of the Pilot Tone signal and thereby determine weighting vectors to extract cardiac movement signals, wherein the weighting vectors allow to form weighted combinations of the number of channel signals; (¶Abstract, ¶0009-0012, Claim 1, Claims 3-4, 7, ¶0025-0026, ¶0029-0030, ¶0034, ¶0096: -Speier explicitly teaches a demixing matrix calculated from a calibration portion of the pilot tone signal using an independent component analysis. ICA is an algorithm that carries out a blind source separation algorithm where it used to extract a plurality of independent components from the calibration portion. ICA is applied for reliably extracting a cardiac movement signal and to separate the cardiac movement signal form other motion and signal components. This separation occurs because the demixing calculated by the ICA separates the independent components, corresponding to different movement types, from the plurality of signals. The result of this process is an optimal linear channel combination (i.e., demixing matric). This demixing matric weights the contributions of different channel elements accordingly (e.g., suppressing unwanted patient motion while maximizing sensitivity to cardiac motion.)
select and store at least two non-parallel weighting vectors of the weighting vectors that allow to extract signal components that represent the cardiac movement from the number of channel signals;
-Speier discloses, the goal of ICA processing is to compute an optimal linear channel combination, which would weight contributions of different channel elements accordingly, ¶0029, ‘The goal is to compute an optimal linear channel combination, which would weigh the contributions of the different channel elements accordingly (e.g., suppressing contributions from unwanted patient motion while maximizing sensitivity to one particular movement type such as cardiac motion).’ Once the demixing matrix is calculated the ICA corresponding to the cardiac movement is selected, ¶Abstract, Claim 1. This demixing matrix provides the optimal linear channel contribution (i.e., the weighting vectors). In the case, where the method extracts a plurality of movement types, the demixing matrix is applied which consistes of multiple vectors, resulting in a plurality of movement signals. In this case, there are multiple vectors within the matrix used, ¶0025: ‘a demixing matrix is calculated from a calibration portion of the pilot tone signal by an independent component analysis (ICA). The ICA is performed on the several signal components corresponding to the plurality of channels. A demixing matrix generally separates the independent components (e.g., corresponding to different movement types) from a plurality of signal components. In this case, the demixing matrix, which is applied to the further portions of the pilot tone signal, is a demixing matrix when extracting a plurality of movement types, but becomes a demixing vector when only one movement type (e.g., independent component) is extracted. Thus, the term “demixing matrix” may also cover “demixing vector”, depending on whether one or several movement types are extracted. The demixing matrix, when applied to the pilot tone signal, will separate at least one particular movement type (e.g., the cardiac component) from the several signal components. Depending on the implementation of the ICA, this demixing matrix may be either complex or real-valued.’ see also ¶0026: “ICA is first used to extract a plurality of independent components from a calibration portion of the Pilot Tone signal, and when the independent component(s) corresponding to the desired movement types have been selected, a demixing matrix or vector that extracts these movement type(s) is applied to the further portions of the Pilot Tone signal (e.g., in real time). When several movement types are to be extracted, applying the demixing matrix may result in a plurality of movement signals, each representing one particular movement type.”
-Speier discloses, the weighting vectors are stored, at the processing act, ¶0097: “The demixing matrix W is then stored and applied to the incoming further Pilot Tone signal data 102.”
-Speier disclose several movement types are to be extracted resulting in a plurality of movement signals, ¶0025-0026. This demixing matrix would be composed of multiple weighting vectors, where each vector extracts a unique independent component, ¶0025-0026. As explicitly stated by Speier, ¶0030, ‘ICA is based on the assumption that individual components of a multivariate signal are non-Gaussian and that the individual components are statistically independent from each other.’, The existence of separate vectors to isolate statistically independent components, ¶0096 (like respiratory motion and cardiac motion) confirms the presence of at least two distinct, and thus non-parallel, weighting vectors within the demixing matrix for extracting signal components that represent cardiac movement from the number of channels.
apply the weighting vectors to further portions of the Pilot Tone signal to obtain a multi-dimensional Pilot Tone signal representing the cardiac movement,
-The pilot tone signal of Speier is multidimensional, as it is acquired from a magnetic resonance receiver coil arrangement comprising a plurality of channels, and thus comprising a plurality of signal components representing the cardiac movement, Claim 1, ¶Abstract, ¶0010. The demixing matrix is calculated using the ICA, Claim 1, ¶Abstract, ¶0010. These weighted vectors are applied to further portions of the pilot tone signal, ¶Abstract, ¶0010, ¶0025-0026.
use the multi-dimensional Pilot Tone signal for control of acquisition of the image data or for retrospective gate or correction of the acquired image data,
-The pilot tone signal of Speier is multidimensional, as it is acquired from a magnetic resonance receiver coil arrangement comprising a plurality of channels, and thus comprising a plurality of signal components representing the cardiac movement, Claim 1, ¶Abstract, ¶0010. The resulting filtered movement derived from the multi-dimensional pilot tone signal is used to extract time points for tiggering a scan of medical data and prost processing, ¶Abstract, ¶0053, Claim 14-15,
wherein the control unit is part of a radiological imaging modality. (¶0080, ‘One or more of the present embodiments are further directed to a control unit adapted for performing the method as described, where the control unit may be part of a computer and/or part of a magnetic resonance machine.’; ¶0003, ‘Patient movement or motion during a diagnostic examination or scan of medical data (e.g., during radiological imaging) often causes artefacts in the acquired images. Magnetic resonance (MR) imaging is relatively slow, so that respiratory and cardiac movement will occur during the scan.’; see also ¶0017)
Speier fails to disclose: wherein the multi-dimensional Pilot Tone signal has at least two dimensions;
However, Schroeder in the context of two-dimensional respiratory-motion characterization for continuous MR measurements using pilot tone navigation discloses:
select and store at least two non-parallel weighting vectors of the weighting vectors that allow to extract signal components that represent respiratory movement from the number of channel signals;
-[Methods], [Conclusion]: Schroeder teaches selecting and storing at least two distinct weighting vectors (i.e., optimal channel combination coefficients, w) that allow for the extraction of signal components characterizing respiratory movement. The methodology provides two-dimensional characterization of respiratory motion, [Synopsis], [Conclusion], by determining two sets of optimal weights WSI (for superior-inferior motion) and WAP (for Anterior-Posterior motion), [Methods], FIGURE 2. These weights are found in a separate calibration phase by solving a least squire optimization problem using respiratory ground truth signals (gSI and gAP), [Methods]. Since the ground trush signals characterize motion in two orthogonal directions (SI and AP), the corresponding weights (WSI and WAP) must be non-parallel to distinguish between these two respiration modes. Once determined, these coefficient are stored and used in the subsequent application phase to generate PT navigators (right side of FIGURE 1).
apply the weighting vectors to further portions of the Pilot Tone signal to obtain a multi-dimensional Pilot Tone signal representing the respiratory movement, wherein the multi-dimensional Pilot Tone signal has at least two dimensions;
-Schroeder teaches applying the determined weighting vectors (w) to further portions of the PT signal a multi-dimensional PT signal representing respiratory movement, where the signal has at least two dimensions. The optimal channel combination coefficient (w) are determine during a separate calibration phase and are then used in the subsequent application phase to generate PT navigators for motion correction, [Methods]. The goal of Schroeder disclosure is to provide two-dimensional characterization of respiratory motion, [Synopsis]. This is achieved by determining and applying weighting vectors corresponding to the superior-inferior (WSI) and Anterior-Posterior (WAP), then applying these two vectors to the incoming PT amplitudes yielding two PT navigators, which together constitute a multi-dimensional (i.e., two-dimensional) PT signal representation of the respiratory movement, [Methods], [Results].
use the multi-dimensional Pilot Tone signal for control of acquisition of the image data or for retrospective gate or correction of the acquired image data,
-The optimal channel combination of Schroeder are used in the application phase to generate the PT navigators for motion correction. The quality of the resulting PT navigators is evaluated by sorting the calibration images according to the PT navigators into bins, [Methods]. The resulting process involves binning and averaging the images in two dimensions, which improves the image sharpness, [Methods], [Results]. Hence, the two-dimensional PT navigation technique of Schroeder provides an alternative motion correction methodology to establish navigation methods for handing motion to improve image sharpness, [Methods], [Results].
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 method of acquiring image data of Speier to have at least two dimensions (i.e., the two-dimensional PT navigation technique of Schroeder provides an alternative motion correction methodology to establish navigation methods for handing motion) to improve image sharpness, as suggested by Schroeder, [Methods], [Results].
Alternatively, 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 method of acquiring image data of Speier in view of known techniques applied to respiratory extraction (i.e., the two-dimensional PT navigation technique of Schroeder provides an alternative motion correction methodology to establish navigation methods for handing motion) to improve image sharpness, as suggested by Schroeder, [Methods], [Results]. The modified combination would disclose selecting and storing at least two non-parallel weighting vectors of the weighting vectors that allow to extract signal components that represent cardiac movement from the number of channel signals; applying the weighting vectors to further portions of the Pilot Tone signal to obtain a multi-dimensional Pilot Tone signal representing the cardiac movement, wherein the multi-dimensional Pilot Tone signal has at least two dimensions; using the multi-dimensional Pilot Tone signal for controlling acquisition of image data since both Speier & Schroeder mitigate motion artifacts from physiological motion.
Claim 20: Speier as modified discloses all the elements above in claim 19, Speier discloses, wherein the radiological imaging modality comprises a magnetic resonance system. (¶0080, ‘One or more of the present embodiments are further directed to a control unit adapted for performing the method as described, where the control unit may be part of a computer and/or part of a magnetic resonance machine.’; ¶0003, ‘Patient movement or motion during a diagnostic examination or scan of medical data (e.g., during radiological imaging) often causes artefacts in the acquired images. Magnetic resonance (MR) imaging is relatively slow, so that respiratory and cardiac movement will occur during the scan.’; see also ¶0017)
Claims 8 & 11 are rejected under 35 U.S.C. 103 as being unpatentable over Speier et al (US 2018/0353140 A1) in view of Schroeder et al. ("Two-Dimensional Respiratory-Motion Characterization for Continuous MR Measurements Using Pilot Tone Navigation." Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), Singapur 2016. 3103), as applied to claim 1, in further view of Razzell (US 2019/0379462 A1).
Claim 8: Speier as modified discloses all the elements above in claim 1, Speier discloses, wherein the number of channel signals of the received Pilot Tone signal are complex-valued, (¶0025, ‘The demixing matrix, when applied to the pilot tone signal, will separate at least one particular movement type (e.g., the cardiac component) from the several signal components. Depending on the implementation of the ICA, this demixing matrix may be either complex or real-valued.’)
Speier fails to explicitly disclose: and wherein the weighting vectors each extract a real or an imaginary part of a signal component.
However, Razzell in the context of systems and methods for polarization control using blind source separation for pilot tone signals, discloses, and wherein the weighting vectors each extract a real or an imaginary part of a signal component.
-Razzell describes four streems of signals are processed by real-valued blind source separation using weighted vectors (i.e., demixing matrix) to recover individual real components of the original signals, ¶0064, Claims 14-15. The in-phase signals are real parts of a signal component.
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 blind source separation algorithm of modified Speier in view of the known techniques taught by Razzell for the advantage of optimizing separation of independent components to estimate jones matrix using the BBS technique and to reduce computational complexity, as suggested by Razzell, ¶0029-0030.
Claim 11: Speier as modified discloses all the elements above in claim 1, Speier discloses, wherein the number of channel signals of the received Pilot Tone signal are complex, and the blind source separation algorithm is real-valued, and wherein the method further comprises:
-Speier discloses, the received pilot tone signal of the number is channel are complex, ¶0020, ¶0096. Note; its also inherent for pilot tone signals to be complex because that is how MR data is acquired and represented. Speier discloses, the blind source separation algorithm is real-valued, ¶0010, ¶0025, ¶0030-0032
Speier fails to disclose: generating a real-valued matrix in which real and imaginary parts of the complex channel signals form separate channels; and
performing the blind source separation algorithm on the real-valued matrix.
However, Razzell in the context of systems and methods for polarization control using blind source separation for pilot tone signals, explicitly discloses, generating a real-valued matrix in which real and imaginary parts of the complex channel signals form separate channels; and
-Razzell teaches for real data signals are used to form separate channels, The four row vectors receive change signals are concatenated to for a matrix. The approach uses a 4x4 real matrix, ¶0064, ¶0072-773.
performing the blind source separation algorithm on the real-valued matrix.
-Razzell teaches a real-valued BBS is performed to obtain a 4x4 demixing matrix. A real ICA algorithm may be used to estimate the mixing matrix in this manner, ¶0064-0065, ¶0074-0075.
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 method of modified Speier in view of the known techniques of Razzel; for the advantage of optimizing separation of independent components to estimate jones matrix using the BBS technique and to reduce computational complexity, as suggested by Razzel, ¶0029-0030.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Speier et al (US 2018/0353140 A1) in view of Schroeder et al. ("Two-Dimensional Respiratory-Motion Characterization for Continuous MR Measurements Using Pilot Tone Navigation." Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), Singapur 2016. 3103), as applied to claim 1, in further view of Wilkinson et al (Pilot-Tone Motion Estimation for Brain Imaging at Ultra-High Field Using FatNav Calibration, Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)).
Claim 10: Speier as modified discloses all the elements above in claim 9, Speier fails to disclose: wherein the number of channel signals are rotated in the complex plane so that a mean of a rotated signal lies on one diagonal of the complex plane.
However, Wilkinson in the context of pilot tone signals teaches, wherein the number of channel signals are rotated in the complex plane so that a mean of a rotated signal lies on one diagonal of the complex plane. ([Methods], ‘A hybrid k-space5 was generated by applying a Fourier transformation to each readout line in the acquired, oversampled k-space. The detected peak in the oversampled region was selected as the pilot-tone signal. This was amplitude normalized across coils and phase was referenced to the complex mean across coils.’)
-Wilkinson teaches, “This was amplitude normalized across coils and phase was referenced to the complex mean across coils” thereby rotating the dataset so that the mean lies along a fixed line in the complex plane. This step is the fundamental process of adjusting the phase of the individual coil signals relative to the calculated complex center point, which would involve the rotated signal that lies on at least one diagonal of the complex plane to normalize the data set. In other words, the teachings of Wilkinson are functionally equivalent.
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 training data of modified Spierer in view of the known techniques taught by Wilkinson. The motivation to do this yields predictable results such as improving the accuracy and speed of the training data, as suggested by Wilkinson, [Synopsis].
Claim 16 is rejected under 35 U.S.C. 103 as being unpatentable over Speier et al (US 2018/0353140 A1) in view of Schroeder et al. ("Two-Dimensional Respiratory-Motion Characterization for Continuous MR Measurements Using Pilot Tone Navigation." Proceedings of the 24th Annual Meeting of the ISMRM (ISMRM 2016), Singapur 2016. 3103), as applied to claim 15, in further view of Hu et al 9US 2015/0374237 A1).
Claim 16: Speier as modified discloses all the elements above in claim 15, Speier fails to disclose: wherein the defined time point is a time point between 250ms before and 50ms after an R-wave, between 200ms before the R-wave and the R-wave, or between 150ms and 20ms before an R-wave.
However, Hu in the context of cardiac motion self-gating in MRI discloses, wherein the defined time point is a time point between 250ms before and 50ms after an R-wave, between 200ms before the R-wave and the R-wave, -Hu teaches the mean trigger delay when compared with ECG R-wave was approximately 220-230 ms for short-axis views and approximately 170-180 ms for vertical long axis views, ¶264.
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 defined time point of modified Speier in view of the teachings of HU. The motivation to do this yields predictable results such as improving the cardiac self-getting signal, ¶0097-0101 as suggested by Hu.
Conclusion
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/N.A.R./Examiner, Art Unit 3798
/PASCAL M BUI PHO/Supervisory Patent Examiner, Art Unit 3798