DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/28/25 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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.
Claims 6 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim 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.
Claims 6 and 18 recite the limitation "the respective inversion recovery series". There is insufficient antecedent basis for this limitation in the claim. This is being interpreted as referring to first and second inversion recovery series.
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 1-23 are rejected under 35 U.S.C. 101.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of using mathematical formulas for MRI image processing, without significantly more.
The claim recites: “A method of processing magnetic resonance (MR) images, the method comprising:
obtaining a first series of MR image frames of a patient's anatomy over a first time period;
generating a first quantitative parameter map based on the first series of MR image frames;
obtaining a second series of MR image frames of the patient's anatomy over a second time period, wherein the first time period does not overlap with the second time period;
generating a second quantitative parameter map based on the second series of MR image frames;
selecting at least one MR image frame from the first series of MR image frames as a first representative image;
selecting at least one MR image frame from the second series of MR image frames as a second representative image;
calculating a misalignment valuation based on a comparison of the first representative image to the second representative image; and
comparing the first quantitative parameter map and the second quantitative map based on the misalignment valuation to generate a comparison map.”
The limitations, as drafted, are processes that, under their broadest reasonable interpretation, cover performance of the limitation in the mind and by mathematical formulas. The MR image obtaining amounts to insignificant, extra-solution activity (data collection). The generation of quantitative parameter maps, calculating misalignment valuation, and generating a comparison map amount to the use of mathematical formulas. The selecting of representative images amounts to mental processes.
This judicial exception is not integrated into a practical application. In particular, the claim does not recite additional elements. Accordingly, the abstract idea is not integrated into a practical application because there are no meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. It is therefore a judicial exception that is not integrated into a practical application, and does not include additional elements that are sufficient to amount to significantly more than the judicial exception. This claim is not patent eligible.
Claims 2-4 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of selecting (mental process) particular or multiple images, without significantly more. The claims are not patent eligible.
Claim 5 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of describing the collected data as an inversion recover series, which amounts to extra-solution activity (data collection), and describing the quantitative parameter maps as relaxation maps generated using an inversion recovery parameter fit, which is performable using mathematical formulas. The claim is not patent eligible.
Claim 6 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of describing the representative image acquisition, which amounts to extra-solution activity (data collection). The claim is not patent eligible.
Claim 7 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of describing the imaged anatomy as heart, which amounts to extra-solution activity (data collection), and the comparison map as an ECV map, which can be calculated using mathematical formulas. The claim is not patent eligible.
Claims 8, 12, and 13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of describing MR image acquisition, which amounts to extra-solution activity (data collection). The claims are not patent eligible.
Claim 9 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of describing the quantitative parameter maps as T1 or T2 maps, which can be calculated using mathematical formulas. The claim is not patent eligible.
Claims 10 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of motion compensation of the MR images, which can be calculated using mathematical formulas. The claims are not patent eligible.
Claims 14-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a system with generically recited additional elements (processor and memory) for performing the abstract idea of the method claims above. The claims are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claim(s) 1-3, 5, 7-15, 17, and 19-23 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kellman (Extracellular volume fraction mapping in the myocardium, part 1: evaluation of an automated method).
Regarding claim 1, Kellman teaches “A method of processing magnetic resonance (MR) images, the method comprising: obtaining a first series of MR image frames of a patient's anatomy over a first time period; generating a first quantitative parameter map based on the first series of MR image frames; obtaining a second series of MR image frames of the patient's anatomy over a second time period, wherein the first time period does not overlap with the second time period; generating a second quantitative parameter map based on the second series of MR image frames;” (Kellman, Figure 2 shows MOCO image series (first and second MR image frames over first and second time period) generated from pre- and post-contrast images of anatomy, and generating T1 maps (via T1-fitting) for the pre- and post-contrast MOCO image series, thereby generating first and second quantitative parameter maps.)
“selecting at least one MR image frame from the first series of MR image frames as a first representative image; selecting at least one MR image frame from the second series of MR image frames as a second representative image; calculating a misalignment valuation based on a comparison of the first representative image to the second representative image;” (Kellman, Figure 2 shows that for each series, representative images are selected (MOCO images at longest inversion times) and calculating a misalignment valuation based on their comparison (co-registration).)
“and comparing the first quantitative parameter map and the second quantitative map based on the misalignment valuation to generate a comparison map.” (Kellman, Figure 2 shows the ECV map generation (comparison map generation) which is, in part, based on the co-registration (misalignment valuation).)
Regarding claim 2, Kellman teaches “The method of claim 1,”
“wherein the first representative image is a last frame in the first series of MR image frames and the second representative image is a last frame in the second series of MR image frames.” (Kellman, Figure 2 and “Image Acquisition” Section, “T1-mapping is based on inverting the magnetization and acquiring images at different times during the magnetization recovery. The acquisition is ECG triggered and all images are acquired at the same cardiac phase in late diastole using a Modified Look Locker Inversion Recovery (MOLLI) approach [17]. This method has been validated [17, 18]. Methods for T1-measurement such as cine inversion recovery (IR) [8, 19] which acquire the measurements of magnetization at multiple cardiac phases may be used for measuring the T1 in the myocardium but are not suitable for mapping with pixel resolution. Following respiratory motion correction (described below), T1-fitting of the data at each pixel is performed by a non-linear least square fit to the exponential recovery curve abs(A – B·exp(−TI/T1*)), where TI is the measured inversion time for each acquired image, the absolute value (abs) is used since the images are magnitude detected. T1* is the effective time constant which includes the effect of the image readout [17] related to the desired T1 as T1 = T1* (B/A – 1). This “Look-Locker” correction used in MOLLI [17] was derived analytically [20] for the case of FLASH readout with continuous RF, whereas in the case of MOLLI is being applied to gated SSFP readout. This leads to bias errors in T1 [21] which are reduced by using a relatively low excitation flip angle. Imaging was performed on 1.5 T Siemens Avanto and Espree scanners (Siemens Medical Solutions, Erlangen, Germany), equipped with 45 mT/m and 200 mT/m/s, and 33 mT/m and 170 mT/m/s gradient systems, respectively. The study protocol typically included acquiring T1-maps for 2 slices (mid-ventricular short axis and four chamber long axis views) both pre-contrast and approx. 15–20 min following intravenous administration of 0.15 mmol/kg Gd-DTPA. Both pre- and post-contrast T1-maps were acquired with the same imaging parameters. The original published MOLLI protocol [17] acquired 11 images over 17 heartbeats. The MOLLI imaging protocol used in this study acquired data at 8 inversion times over an 11 heart beat breath-hold at end-expiration with 2 inversions. The initial protocol used acquired 3 images acquired after the first inversion, 3 heart beat pause, and 5 images acquired after the second inversion. The protocol was later modified to reduce heart rate variability by acquiring 5 images after the first inversion, followed by a 3 heartbeat pause and then acquire 3 images after the second inversion. Both protocols (3–5 and 5–3) acquired 8 images in 11 heartbeats. Typical imaging parameters were: non-selective inversion pulse, steady state free precession single shot read out with 35° excitation flip angle, field of view 360 × 270 mm2, slice thickness 6 mm, minimum inversion time 110 ms, inversion time increment 80 ms, matrix 192×130, voxel size 2.1 × 1.9 × 6.0 mm3, TR/TE 2.4/1.0 ms, 7/8 partial Fourier plus parallel imaging factor 2 with 140 ms readout imaging duration. The maximum inversion time was approximately 5 sec at 60 bpm. The protocol has subsequently been revised for increased spatial resolution using a matrix of 256×144, TR/TE 2.7/1.1 ms (200 ms readout imaging duration), with voxel size 1.9 × 1.4 × 6.0 mm3 for heart rates < 90 bpm, and 192×130 for hearts above 90 bpm.”; Note that Kellman uses the MOCO images at longest inversion time as the representative images (see Figure 2), which amounts to the last frame in the series, as MOLLI includes an ordering of images from low-to-high inversion times.)
Regarding claim 3, Kellman teaches “The method of claim 1,”
“wherein the first representative image is one of a last two frames in the first series of MR image frames and the second representative image is one of a last two frames in the second series of MR image frames.” (Kellman, Figure 2 and “Image Acquisition” Section, “T1-mapping is based on inverting the magnetization and acquiring images at different times during the magnetization recovery. The acquisition is ECG triggered and all images are acquired at the same cardiac phase in late diastole using a Modified Look Locker Inversion Recovery (MOLLI) approach [17]. This method has been validated [17, 18]. Methods for T1-measurement such as cine inversion recovery (IR) [8, 19] which acquire the measurements of magnetization at multiple cardiac phases may be used for measuring the T1 in the myocardium but are not suitable for mapping with pixel resolution. Following respiratory motion correction (described below), T1-fitting of the data at each pixel is performed by a non-linear least square fit to the exponential recovery curve abs(A – B·exp(−TI/T1*)), where TI is the measured inversion time for each acquired image, the absolute value (abs) is used since the images are magnitude detected. T1* is the effective time constant which includes the effect of the image readout [17] related to the desired T1 as T1 = T1* (B/A – 1). This “Look-Locker” correction used in MOLLI [17] was derived analytically [20] for the case of FLASH readout with continuous RF, whereas in the case of MOLLI is being applied to gated SSFP readout. This leads to bias errors in T1 [21] which are reduced by using a relatively low excitation flip angle. Imaging was performed on 1.5 T Siemens Avanto and Espree scanners (Siemens Medical Solutions, Erlangen, Germany), equipped with 45 mT/m and 200 mT/m/s, and 33 mT/m and 170 mT/m/s gradient systems, respectively. The study protocol typically included acquiring T1-maps for 2 slices (mid-ventricular short axis and four chamber long axis views) both pre-contrast and approx. 15–20 min following intravenous administration of 0.15 mmol/kg Gd-DTPA. Both pre- and post-contrast T1-maps were acquired with the same imaging parameters. The original published MOLLI protocol [17] acquired 11 images over 17 heartbeats. The MOLLI imaging protocol used in this study acquired data at 8 inversion times over an 11 heart beat breath-hold at end-expiration with 2 inversions. The initial protocol used acquired 3 images acquired after the first inversion, 3 heart beat pause, and 5 images acquired after the second inversion. The protocol was later modified to reduce heart rate variability by acquiring 5 images after the first inversion, followed by a 3 heartbeat pause and then acquire 3 images after the second inversion. Both protocols (3–5 and 5–3) acquired 8 images in 11 heartbeats. Typical imaging parameters were: non-selective inversion pulse, steady state free precession single shot read out with 35° excitation flip angle, field of view 360 × 270 mm2, slice thickness 6 mm, minimum inversion time 110 ms, inversion time increment 80 ms, matrix 192×130, voxel size 2.1 × 1.9 × 6.0 mm3, TR/TE 2.4/1.0 ms, 7/8 partial Fourier plus parallel imaging factor 2 with 140 ms readout imaging duration. The maximum inversion time was approximately 5 sec at 60 bpm. The protocol has subsequently been revised for increased spatial resolution using a matrix of 256×144, TR/TE 2.7/1.1 ms (200 ms readout imaging duration), with voxel size 1.9 × 1.4 × 6.0 mm3 for heart rates < 90 bpm, and 192×130 for hearts above 90 bpm.”; Note that Kellman uses the MOCO images at longest inversion time as the representative images (see Figure 2), which amounts to the last frame in the series, as MOLLI includes an ordering of images from low-to-high inversion times.)
Regarding claim 5, Kellman teaches “The method of claim 1,”
“wherein each of the first series of MR image frames and the second series of MR images frames is an inversion recovery series,” (See Kelley Figure 2 and rejection of claims 2-3, and note that MOLLI stands for “Modified Look-Locker inversion recovery”.)
“and wherein each of the first quantitative parameter map and the second quantitative parameter map is a relaxation map generated using an inversion recovery parameter fit.” (See Kelley, Figure 2, and note that a T1 map is a relaxation map. The last Paragraph of Section “T1-Mapping and motion correction (MOCO)” additionally describes, “Following motion correction, the T1 map is generated by a pixel-wise curve fitting using a three parameter signal model for MOLLI inversion recovery. The downhill simplex minimization algorithm was used [15]. At each pixel the sum-of-squares of the residual errors of the measured data to the exponential model fit was calculated as a goodness of fit map.”)
Regarding claim 7, Kellman teaches “The method of claim 5,”
“wherein the patient's anatomy is the patient's heart and the comparison map is an extracellular volume (ECV) map.” (Kellman, Figure 2 shows heart images (left) and the ECV comparison map (right).)
Regarding claim 8, Kellman teaches “The method of claim 5,”
“wherein the first series of MR image frames is obtained before injection of a contrast agent and the second series of MR image frames is obtained after injection of the contrast agent.” (Kellman, Figure 2 shows the first and second image series of MR image frames obtained before and after injection of a contrast agent.)
Regarding claim 9, Kellman teaches “The method of claim 5,”
“wherein the first quantitative parameter map and the second quantitative parameter map are either T1 maps or T2 maps.” (Kellman, Figure 2 shows T1-fitting and T1 maps for pre- and post-contrast quantitative parameter maps; Additionally, see last paragraph of section “T1-Mapping and motion correction (MOCO)”: “Following motion correction, the T1 map is generated by a pixel-wise curve fitting using a three parameter signal model for MOLLI inversion recovery. The downhill simplex minimization algorithm was used [15]. At each pixel the sum-of-squares of the residual errors of the measured data to the exponential model fit was calculated as a goodness of fit map.”)
Regarding claim 10, Kellman teaches “The method of claim 1,”
“wherein each of the first series of MR image frames are motion compensated.” (See Kellman Figure 2, in particular, “MOCO”, which is performed to generate the first series.)
Regarding claim 11, Kellman teaches “The method of claim 10,”
“wherein each of the second series of MR image frames are motion compensated.” (See Kellman Figure 2, in particular, “MOCO”, which is performed to generate the second series.)
Regarding claim 12, Kellman teaches “The method of claim 1,”
“wherein the first series of MR image frames is obtained before injection of a contrast agent and the second series of MR image frames is obtained after injection of the contrast agent.” (Kellman, Figure 2 shows the first and second image series of MR image frames obtained before and after injection of a contrast agent.)
Regarding claim 13, Kellman teaches “The method of claim 1,”
“wherein the first time period and the second time period are at least 10 minutes apart.” (Kellman, “Co-registration” Section, Paragraph 1, “Pre- and post-contrast image series are acquired in separate breath-holds which are typically 15–30 minutes apart. Even small differences in respiratory position or changes in patient position due to movement will cause significant misregistration of the images. Co-registration of the pre-and post-contrast images is performed (Figure 2) using a non-rigid image registration applied to the longest inversion time image of the already motion corrected images. In this way, the magnetization of the long inversion time images is almost completely recovered and the image contrast between pre- and post-contrast series will be very similar thereby facilitating intensity based image registration methods. Co-registration is achieved by applying the estimated motion deformation field to the post-contrast series. Rather than simply applying the deformation (warping) to the T1-map or to the already motion corrected image series, a composite deformation using the original respiratory motion fields of the post-contrast series combined with the deformation from the co-registration step is applied a single time to avoid an unnecessary loss of spatial resolution. The final T1-map for the post-contrast series is calculated by pixel-wise fitting.”)
Regarding claims 14, 15, 17, and 19-23, these claims recite a system with processor and memory with elements corresponding to the steps recited in Claims 1, 2, 5, 7-9, and 12-13. Therefore, the recited elements of these claims are mapped to the analogous steps in the corresponding method claims. Kellman discloses a system with processor and memory (Kellman, last Paragraph of Section “Co-registration”, “The complete processing workflow was fully automated. Once the user specifies the study, the software retrieves all images for appropriate paired series of pre and post contrast MOLLI acquisitions at the same slice location, performs all processing, and produces T1-maps and ECV maps in dicom format which may be pushed to the PACS. The processing time in the current implementation is approximately 45 sec per ECV series including pre- and post-contrast T1-mapping with MOCO and co-registration, and blood pool segmentation.” Note that the claimed processor with memory is inherent to the processing performed with an approximate processing time according to software instructions.)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kellman in view of Voskrebenzev (US20160367200A1).
Regarding claim 4, Kellman teaches “The method of claim 1,”
While Kellman discloses the use of one first representative image from the first series and one second representative image from the second series (See Kellman Figure 2), Kellman does not disclose that these representative images include multiple MR image frames.
Voskrebenzev teaches the use of multiple MR image frames for registration (Voskrebenzev, Paragraph 38, “The inventive step-wise registration of dynamic time series of MR images within groups can be applied not only in combination with the inventive method of processing MR lung images but rather generally for registering time series of MR images. Thus, it is emphasized that the proposed registration process provides an independent subject of the invention wherein the registering the MR images comprises providing groups of a time series of subsequent MR images, the MR images within each group having a quantitative dimension parameter within a common predetermined parameter interval, groupwise registering the collected MR images within the groups of collected MR images, and registering the group-wise registered MR images or an averaged MR image thereof, starting from parameter intervals corresponding to extrema image positions towards a parameter interval corresponding to an intermediate image position.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use multiple MR image frames, as taught above by Voskrebenzev, from the first and second series as the first and second representative images, respectively, of Kellman.
The motivation for doing so would have been to improve signal-to-noise ratio (Voskrebenzev, Paragraph 65, “Advantageously, this procedure minimizes misregistrations. Combining images into groups allows the averaging of the images, so that the SNR of the images to be registered is increased and the reliability of the registration is improved.”) Further, selecting only one registration image per series introduces potential inaccuracies, particularly if the single selected images contain artifacts or random noise. Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kellman with the above teaching of Voskrebenzev to fully disclose “wherein the first representative image includes multiple MR image frames in the first series of MR image frames and the second representative image includes multiple MR image frames in the second series of MR image frames.”
Regarding claim 16, this claim recites a system with processor and memory with elements corresponding to the steps recited in Claim 4. Therefore, the recited elements of this claim are mapped to the analogous steps in the corresponding method claim. Additionally, the rationale and motivation to combine the references are applied here. Kellman teaches the system with processor as described in the 35 USC 102 rejections.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kellman in view of Nordio (3D myocardial T1 mapping using saturation recovery).
Regarding claim 6, Kellman teaches “The method of claim 5,”
While Kellman teaches “wherein the first representative image and the second representative image are each obtained in the respective inversion recovery series after a magnetization of a tissue of the patient's anatomy” has almost completely returned to an equilibrium state (Kellman, “Co-Registration” Section Paragraph 1, “Pre- and post-contrast image series are acquired in separate breath-holds which are typically 15–30 minutes apart. Even small differences in respiratory position or changes in patient position due to movement will cause significant misregistration of the images. Co-registration of the pre-and post-contrast images is performed (Figure 2) using a non-rigid image registration applied to the longest inversion time image of the already motion corrected images. In this way, the magnetization of the long inversion time images is almost completely recovered and the image contrast between pre- and post-contrast series will be very similar thereby facilitating intensity based image registration methods. Co-registration is achieved by applying the estimated motion deformation field to the post-contrast series. Rather than simply applying the deformation (warping) to the T1-map or to the already motion corrected image series, a composite deformation using the original respiratory motion fields of the post-contrast series combined with the deformation from the co-registration step is applied a single time to avoid an unnecessary loss of spatial resolution. The final T1-map for the post-contrast series is calculated by pixel-wise fitting.”), Kellman does not disclose that these representative images are obtained after a magnetization of a tissue of the patient's anatomy has fully returned to an equilibrium state.
Nordio discloses the collection of MR images obtained when the magnetization has fully returned to an equilibrium state (Nordio, “Pulse Sequence Scheme” Section, Paragraph 1, “All data were acquired on a 1.5T Ingenia MR system (Philips, Best, The Netherlands). The proposed 3D SASHA pulse sequence enables the acquisition of whole-heart T1 mapping in free breathing. To this end, the 2D SASHA sequence7 was modified to make the sequence compatible with a 3D segmented k-space acquisition. The sampling scheme used for 3D SASHA is shown in Fig. 1a. First, all image k-space segments with no magnetization preparation were acquired, which we term “infinity image” (as we assume it is acquired after an infinite saturation delay time during data fitting). This was followed by the interleaved segmented acquisition with preceding saturation pulse and increasing saturation delays. To ensure all k-space segments of the infinity image were acquired at equilibrium magnetization, “pause” cardiac cycles were added between the acquisitions of these segments. During these pauses, all RF pulses and data acquisition were switched off to allow for full T1 recovery.”)
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to use images with fully equilibrated magnetization, as taught by Nordio, as the first and second registration images of Kellman.
The motivation for doing so is already disclosed in Kellman above (“the magnetization of the long inversion time images is almost completely recovered and the image contrast between pre- and post-contrast series will be very similar thereby facilitating intensity based image registration methods”). Thus, with this motivation of recovering magnetization for registration images, it would have been obvious to use fully recovered (magnetization-equilibrated) images to further improve registration. Further, one skilled in the art could have substituted one known element for another, and the substitution would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kellman with the above teaching of Nordio to fully disclose “wherein the first representative image and the second representative image are each obtained in the respective inversion recovery series after a magnetization of a tissue of the patient's anatomy returns to an equilibrium state.”
Regarding claim 18, this claim recites a system with processor and memory with elements corresponding to the steps recited in Claim 6. Therefore, the recited elements of this claim are mapped to the analogous steps in the corresponding method claim. Additionally, the rationale and motivation to combine the references are applied here. Kellman teaches the system with processor as described in the 35 USC 102 rejections.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Huizinga (PCA-based groupwise image registration for quantitative MRI) teaches a groupwise image registration method in comparison with a pairwise registration calculation.
Lee (Free-breathing 3D cardiac extracellular volume (ECV) mapping using a linear tangent space alignment (LTSA) model) teaches cardiac ECV mapping during free breathing imaging using a linear tangent space alignment model-based method.
Bhatia (Similarity Metrics for Groupwise Non-rigid Registration) teaches groupwise image registration and similarity metrics, evaluated on 3D MR data.
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/AARON JOSEPH SORRIN/
Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672
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