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 .
Response to Arguments
103 Rejections
Applicant's arguments filed 6/11/26 have been fully considered but they are not persuasive.
Applicant argues that Fleming does not teach the features of claim 1, lines 14-17:
generating the projection as a 2D image using the mapped points, the generating the projection as a 2D image comprising grouping a plurality of points from the magnetic resonance image into a plurality of sagittal planes and arranging the plurality of sagittal planes adjacent to one another.
Applicant attempts to support this contention by stating that:
[a]mended claim 1 requires a specific projection-generation operation in which points from the magnetic resonance image are used in the generation of a 2D image projection after the claimed point-cloud generation and mapping steps. Remarks at 9.
First, Applicant appears to have a mistaken understanding of the scope of the required order of steps of the claimed method when compared to the prior art. The recitation of “generating the projection as a 2D image using the mapped points, the generating the projection as a 2D image comprising grouping a plurality of points… and arranging…” only differentiates from prior art wherein the prior art requires the grouping and arranging steps to be taken prior to the steps of generating a 3D representation, generating a point cloud from the 3D representation, and mapping points. As discussed below, Fleming does not require that the grouping and arranging steps to be taken prior to these steps. In fact, given its proper context, the 2D image generated from the interpolation/projection to the 2D plane is generated from grouping points of the magnetic resonance image to form a stack of sagittal planes.
Second, Applicant appears to isolate the teachings of Fleming, paragraph [0174] from the context of the teachings cited from Fleming in the Non-Final Rejection mailed 3/11/26 (“NF”) at P.4-7 thereby depriving the analysis of the disclosure presented to the PHOSITA by the Fleming reference. The proper context for the teachings of Fleming regarding the features of claim 1, lines 14-17 must include the disclosures cited from Fleming regarding the alleged point-cloud generation and mapping steps. For example, regarding the alleged point-cloud generation, the NF at 7 stated that Fleming discloses:
generating a point cloud from the 3D representation (the set of values for voxels representing the volume of the patient are arranged as points in a NxMxO spatial coordinate system, i.e., a point cloud, [0019]), wherein each point of the point cloud corresponds to a spatial position of a voxel of the 3D representation (each point in the NxMxO arrangement corresponds to a spatial position of the set of values for voxels representing the volume of the patient, [0019]) and comprises an intensity corresponding to the intensity of at least one point in the magnetic resonance image (each point in the NxMxO spatial coordinate system corresponds to a value of a voxel in the set of values for voxels representing the volume of the patient including intensity value, [0019]-[0023], [0059], [0062], [0070]).
In short, Fleming discloses an automated process by which the 3D representation is arranged as points in a spatial coordinate system, i.e., a point cloud. Further, regarding the alleged mapping steps, the NF at 7 stated that Fleming discloses:
mapping points from the point cloud into non-overlapping coordinates in a 2D plane (values of voxels from the set of values for voxels arranged as points in the NxMxO spatial coordinate system are interpolated to a plane, [0007], [0018]-[0023], [0049]-[0050]).
In short, Fleming discloses an automated process by which the points in the spatial coordinate system generated from the 3D representation are interpolated/mapped to a 2D plane. Further, regarding the alleged generating of the projection as a 2D image using the mapped points, the NF at 7 stated that Fleming discloses”
generating the projection as a 2D image using the mapped points (the interpolation to the plane is generated as a two-dimensional image using the values of voxels from the set of values for voxels arranged as points in the NxMxO spatial coordinate system, [0007], [0018]-[0023], [0049]-[0050]).
In short, Fleming discloses an automated process by which the interpolated/maps points on the 2D plane from the points in the spatial coordinate system generated from the 3D representation are used to form a 2D image. These teachings from Fleming provide context for the cited disclosure from Fleming in the NF at 9 that Fleming discloses:
generating the projection from the magnetic resonance image comprises:
grouping a plurality of points from the magnetic resonance image into a plurality of sagittal planes and arranging the plurality of sagittal planes adjacent to one another (acquire a plurality of 3D MR images of the knee and arrange the acquired images in a MR image stack in the sagittal plane, wherein an MR image stack in the sagittal plane is a grouping of the 3D MRI imaging points into sagittal planes arranged adjacent to one another, [0174]).
In short, Fleming paragraph [0174] discloses that the 2D image generated from the interpolation/projection to the 2D plane is generated from grouping points of the magnetic resonance image to form a stack of sagittal planes, i.e., a plurality of sagittal planes adjacent to one another, whereupon the 2D image can be segmented therefrom.
In addition, Applicant argues that paragraph [0174] contemplates only manual segmentation of the 2D image. Remarks at 9. However, the context discussed above, sets forth that Fleming’s disclosure is not limited to manual segmentation of the 2D image as Fleming alternatively discloses automated segmentation of the 2D image which involves the asserted steps of automated point-cloud generation and mapping that take place prior to the steps of automated segmentation/interpolation/mapping/projecting as discussed above.
Read together and in the proper context, Fleming discloses not only that the process of generating a 2D image projection comprises the alleged “grouping a plurality of points from the magnetic resonance image into a plurality of sagittal planes and arranging the plurality of sagittal planes adjacent to one another,” but also that the process of automatically generating a 2D image projection using the mapped points comes after the steps of point-cloud generation and mapping. Therefore, as Fleming discloses the alleged claim features, Applicant’s arguments are not persuasive.
Information Disclosure Statement
Applicant is reminded of their duty to disclose all information known to be material to patentability to the Office. The inventors and have a significant amount of undisclosed publication and patent application filing history that predate the effective filing date of the claimed invention.
Specification
The substitute specification filed 6/11/26 is entered.
Claim Rejections - 35 USC § 103
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 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 1-6, 8-9, and 11-16 are rejected under 35 U.S.C. 103 as being unpatentable over Fleming et al (U.S. Pub. No. 2020/0069257 sharing the same disclosure as WO2018204404 published 11/08/2018), hereinafter “Fleming,” in further view of Reisman et al. (U.S. Pub. No.2013/0137968), hereinafter “Reisman.”
Regarding claim 1, Fleming discloses a method of determining a condition of a tissue of a patient from analysis of a magnetic resonance image (method of determining a quality and likelihood of failure of the ACL or ACL graft in a patient, [0027], [0063]), the method comprising:
generating a projection from a magnetic resonance image depicting the tissue (ACL is segmented from the 3D MR image stack to form a 3D model of the ACL from which a 2D slice is generated, [0174], [0269], [0338], [0379], Fig. 23; see also medial mensiscus is segmented from the 3D MR image stack to form a 3D model of the MCL from which a 2D slice is generated, [0252], Fig. 18), wherein generating the projection comprises determining a value at a point in the projection based on at least one value at a position of the magnetic resonance image corresponding to the point in the projection (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; see also 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18), wherein generating the projection from the magnetic resonance image comprises:
generating a 3D representation of the tissue from the magnetic resonance image (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18);
wherein each point of the 3D representation corresponds to a spatial position of a voxel of the 3D representation and comprises an intensity corresponding to the intensity of at least one point in the magnetic resonance image (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; see also 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18);
mapping points from the 3D representation into non-overlapping coordinates in a 2D plane (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23); and
generating the projection as a 2D image using the mapped points (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times output as a 2D slice image, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31), the generating the projection as a 2D image comprising grouping a plurality of points from the magnetic resonance image into a plurality of sagittal planes and arranging the plurality of sagittal planes adjacent to one another (acquire a plurality of 3D MR images of the knee and arrange the acquired images in a MR image stack in the sagittal plane, wherein an MR image stack in the sagittal plane is a grouping of the 3D MRI imaging points into sagittal planes arranged adjacent to one another, [0174]);
determining, for each of a plurality of locations in the projection, the condition of the tissue at a location, wherein determining the condition of the tissue at the location comprises determining the condition based at least in part on at least one value of the projection at the location (determine the tissue quality using the signal intensity/grayscale values within the 2D slice, lower signal intensity/grayscale values within the 2D slice or shorter T2* relaxation times corresponds to higher quality tissue, [0160], [0171], [0173], [0099], [0240], [0262], [0263], [0333], [0334], [0339], [0376], [0378], [0388]); and
outputting the determined condition at the plurality of locations of the projection (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31).
However, Fleming does not appear to teach generating a point cloud from the 3D representation.
However, in the same field of endeavor MR imaging and in solving substantially the same problem of reconstructing 2D planar images of a patient’s tissue from 3D MR images, Resiman teaches generating a projection from a magnetic resonance image depicting the tissue (generate a planar, two-dimensional image from a 3D magnetic resonance image depicting patient tissue, [0007], [0018]-[0023], [0049]-[0050]), wherein generating the projection comprises determining a value at a point in the projection based on at least one value at a position of the magnetic resonance image corresponding to the point in the projection (generating the two-dimensional image comprises determining the value of a point in the plane based on at least one value of a voxel at a position of the 3D magnetic resonance image corresponding to the point in the plane, [0007], [0018]-[0023], [0049]-[0050]), wherein generating the projection from the magnetic resonance image comprises:
generating a 3D representation of the tissue from the magnetic resonance image (3D magnetic resonance imaging data is assigned to a set of values for voxels representing a volume of the patient, [0018]-[0023]);
generating a point cloud from the 3D representation (the set of values for voxels representing the volume of the patient are arranged as points in a NxMxO spatial coordinate system, i.e., a point cloud, [0019]), wherein each point of the point cloud corresponds to a spatial position of a voxel of the 3D representation (each point in the NxMxO arrangement corresponds to a spatial position of the set of values for voxels representing the volume of the patient, [0019]) and comprises an intensity corresponding to the intensity of at least one point in the magnetic resonance image (each point in the NxMxO spatial coordinate system corresponds to a value of a voxel in the set of values for voxels representing the volume of the patient including intensity value, [0019]-[0023], [0059], [0062], [0070]);
mapping points from the point cloud into non-overlapping coordinates in a 2D plane (values of voxels from the set of values for voxels arranged as points in the NxMxO spatial coordinate system are interpolated to a plane, [0007], [0018]-[0023], [0049]-[0050]); and
generating the projection as a 2D image using the mapped points (the interpolation to the plane is generated as a two-dimensional image using the values of voxels from the set of values for voxels arranged as points in the NxMxO spatial coordinate system, [0007], [0018]-[0023], [0049]-[0050]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Reisman’s known technique of arranging the spatial position and intensity values of voxels in a spatial coordinate system to Fleming’s known process of generating a 3D volume of the tissue corresponding to the spatial position and intensity values of voxels and generating a two-dimensional image therefrom to achieve the predictable result that arranging the set of values for voxels representing a volume of the patient in an NxMxO arrangement allows for the correction of distortion in magnetic resonance imaging that improves image quality with minimal additional acquisition time resource cost. See, e.g., Reisman, [0006]-[0007] and [0051].
Regarding claim 2, Fleming discloses the magnetic resonance image depicting the tissue comprises a magnetic resonance image depicting a knee of the patient (MR imaging and images of the knee of a patient, [0008], [0010], Figs. 5, 7, 20, 31).
Regarding claim 3, Fleming discloses the magnetic resonance image depicting the knee comprises a magnetic resonance image depicting a connective tissue of the knee of the patient (MR imaging and images of the knee of the patient comprises an image of the ACL, Abstract, [0008], [0013], [0020], [0026], [0027], [0044], [0063]; see also MR imaging and images of the knee comprises an image of the medial meniscus, [0252]).
Regarding claim 4, Fleming discloses the magnetic resonance image depicting the connective tissue comprises a magnetic resonance image depicting an Anterior Cruciate Ligament (ACL) of the patient (MR imaging and images of the knee of the patient comprises an image of the ACL, Abstract, [0008], [0013], [0020], [0026], [0027], [0044], [0063]).
Regarding claim 5, Fleming discloses the magnetic resonance image depicts only the ACL of the patient (ACL is segmented from the 3D MR image stack to form a 3D model of the ACL from which a 2D slice is generated, [0174], [0269], [0338], [0379], Fig. 23; 3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23).
Regarding claim 6, Fleming discloses segmenting a first magnetic resonance image of the knee of the patient to yield the magnetic resonance image depicting only the ACL of the patient (ACL is segmented from the 3D MR image stack to form a 3D model of the ACL from which a 2D slice is generated, [0174], [0269], [0338], [0379], Fig. 23; 3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23).
Regarding claim 8, Fleming discloses normalizing signal intensity of the magnetic resonance image, wherein normalizing the signal intensity comprises normalizing the signal intensity to an intensity in the image associated with a reference tissue (signal intensity/grayscale values are normalized by the value of the femoral cortical bone of a patient, [0174], [0240]).
Regarding claim 9, Fleming discloses the reference tissue comprises an intensity associated with a femoral cortical bone of the patient (signal intensity/grayscale values are normalized by the value of the femoral cortical bone of a patient, [0174], [0240]).
Regarding claim 11, Fleming discloses determining an average image intensity of intact tissue values (mean/average signal intensity from intact/contralateral/uninjured ligament, [0019], [0052], [0055], [0056], [0057], [0129], [0164], [0174], [0176], [0177], [0252], [0254], Fig.2, Fig. 35);
calculating at least one threshold indicative of the condition of the tissue using the average image intensity (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times, corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and corresponding to high signal intensity/grayscale values or longer T2* relaxation times are thresholded into a respective quartile/bin, respectively, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31); and
determining the condition of the tissue at a location of the plurality of locations in the projection comprises:
comparing one or more values of the projection at the location to the at least one threshold (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times, corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and corresponding to high signal intensity/grayscale values or longer T2* relaxation times are thresholded into a respective quartile/bin, respectively, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31); and
determining the condition of the tissue based on a result of the comparing (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times, corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and corresponding to high signal intensity/grayscale values or longer T2* relaxation times are identified corresponding to above-average, average, and below-average tissue quality, respectively, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31).
Regarding claim 12, Fleming discloses the at least one threshold comprises a first threshold intensity that is a standard deviation larger than the average intensity (voxels of the ACL are thresholded into a quartile/bin corresponding to higher than mean/average/median signal intensity/grayscale values or longer than mean/average/median T2* relaxation times, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31).
Regarding claim 13, Fleming discloses the at least one threshold comprises a second threshold intensity that is a standard deviation less than the average intensity (voxels of the ACL are thresholded into a quartile/bin corresponding to lower than mean/average/median signal intensity/grayscale values or shorter than mean/average/median T2* relaxation times, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31).
Regarding claim 14, Fleming discloses determining the condition of the tissue at a location comprises categorizing tissue quality at the location, based on one or more values of the projection at the location, as being of above-average, average, or below-average tissue quality (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times, corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and corresponding to high signal intensity/grayscale values or longer T2* relaxation times are identified corresponding to above-average, average, and below-average tissue quality, respectively, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31).
Regarding claim 15, Fleming discloses outputting the determined condition at the plurality of locations comprises:
visually annotating the projection at each location based on the categorized tissue quality at the location (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times output as a 2D slice image, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31); and
outputting the annotated projection (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times output as a 2D slice image, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31).
Regarding claim 16, Fleming discloses annotating the projection comprises assigning a color to each location indicative of the categorized tissue quality at the location (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times output as a 2D slice image, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Fleming in further view of Reisman, as applied to claim 1 above, and further in view of Lee et al. (“Segmentation of anterior cruciate ligament in knee MR images using graph cuts with patient-specific shape constraints and label refinement” 2014), hereinafter “Lee.”
Regarding claim 10, Fleming in further view of Reisman does not appear to teach mapping the points comprises calculating a transformation such that nearest neighboring points within slices of the magnetic resonance image are nearest neighboring points in the 2D image.
However, in the same field of endeavor MR imaging of the ACL and in solving substantially the same problem of reconstructing 2D slice images of a patient’s ACL from a 3D MR imaging stack, Lee teaches mapping the points comprises calculating a transformation such that nearest neighboring points within slices of the magnetic resonance image are nearest neighboring points in the 2D image (transforming the 3D MR imaging stack of the patient’s ACL to a 2D slice image in the sagittal plane involves a “neighbor” shape cost such that nearest neighboring points in the 3D MR imaging stack are nearest neighboring points in the 2D slice image, 1. Introduction, ¶3, 2. Proposed method, 2.2 Graph cuts with patient-specific shape constraints, ¶7-8, Fig. 5).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Lee’s known technique of using a nearest neighbor cost function to ensure that nearest neighboring points in the 3D MR imaging stack are nearest neighboring points in the 2D slice image in creating 2D ACL slice images from a 3D ACL MR imaging stack to Fleming in further view of Reisman’s known process of creating 2D ACL slice images from a 3D ACL MR imaging stack to achieve the predictable result that the nearest neighbor cost function enables the segmented 2D ACL slice images “to avoid leakage into regions of similar intensity.” See, e.g., Lee, 1. Introduction, ¶3.
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Fleming in further view of Reisman.
Regarding claim 17, Fleming discloses a computer system (computing system/computer, [0064], [0066]), comprising:
at least one processor (a processor, [0026], [0060], [0064], [0066]); and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the at least one processor, cause the processor to perform (a processor configured to execute computer-executable instructions to perform the method, [0026]; a non-transitory computer program product storing instructions for a computing system, [0064], [0066]) a method of determining a condition of a tissue of a patient from analysis of a magnetic resonance image (method of determining a quality and likelihood of failure of the ACL or ACL graft in a patient, [0027], [0063]), the method comprising:
generating a projection from a magnetic resonance image depicting the tissue (ACL is segmented from the 3D MR image stack to form a 3D model of the ACL from which a 2D slice is generated, [0174], [0269], [0338], [0379], Fig. 23; see also medial mensiscus is segmented from the 3D MR image stack to form a 3D model of the MCL from which a 2D slice is generated, [0252], Fig. 18), wherein generating the projection comprises determining a value at a point in the projection based on at least one value at a position of the magnetic resonance image corresponding to the point in the projection (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; see also 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18), wherein generating the projection from the magnetic resonance image comprises:
generating a 3D representation of the tissue from the magnetic resonance image (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18);
wherein each point of the 3D representation corresponds to a spatial position of a voxel of the 3D representation and comprises an intensity corresponding to the intensity of at least one point in the magnetic resonance image (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; see also 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18);
mapping points from the 3D representation into non-overlapping coordinates in a 2D plane (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23); and
generating the projection as a 2D image using the mapped points (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times output as a 2D slice image, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31), the generating the projection as a 2D image comprising grouping a plurality of points from the magnetic resonance image into a plurality of sagittal planes and arranging the plurality of sagittal planes adjacent to one another (acquire a plurality of 3D MR images of the knee and arrange the acquired images in a MR image stack in the sagittal plane, wherein an MR image stack in the sagittal plane is a grouping of the 3D MRI imaging points into sagittal planes arranged adjacent to one another, [0174]);
determining, for each of a plurality of locations in the projection, the condition of the tissue at a location, wherein determining the condition of the tissue at the location comprises determining the condition based at least in part on at least one value of the projection at the location (determine the tissue quality using the signal intensity/grayscale values within the 2D slice, lower signal intensity/grayscale values within the 2D slice or shorter T2* relaxation times corresponds to higher quality tissue, [0160], [0171], [0173], [0099], [0240], [0262], [0263], [0333], [0334], [0339], [0376], [0378], [0388]); and
outputting the determined condition at the plurality of locations of the projection (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31).
However, Fleming does not appear to teach generating a point cloud from the 3D representation.
However, in the same field of endeavor MR imaging and in solving substantially the same problem of reconstructing 2D planar images of a patient’s tissue from 3D MR images, Resiman teaches generating a projection from a magnetic resonance image depicting the tissue (generate a planar, two-dimensional image from a 3D magnetic resonance image depicting patient tissue, [0007], [0018]-[0023], [0049]-[0050]), wherein generating the projection comprises determining a value at a point in the projection based on at least one value at a position of the magnetic resonance image corresponding to the point in the projection (generating the two-dimensional image comprises determining the value of a point in the plane based on at least one value of a voxel at a position of the 3D magnetic resonance image corresponding to the point in the plane, [0007], [0018]-[0023], [0049]-[0050]), wherein generating the projection from the magnetic resonance image comprises:
generating a 3D representation of the tissue from the magnetic resonance image (3D magnetic resonance imaging data is assigned to a set of values for voxels representing a volume of the patient, [0018]-[0023]);
generating a point cloud from the 3D representation (the set of values for voxels representing the volume of the patient are arranged as points in a NxMxO spatial coordinate system, i.e., a point cloud, [0019]), wherein each point of the point cloud corresponds to a spatial position of a voxel of the 3D representation (each point in the NxMxO arrangement corresponds to a spatial position of the set of values for voxels representing the volume of the patient, [0019]) and comprises an intensity corresponding to the intensity of at least one point in the magnetic resonance image (each point in the NxMxO spatial coordinate system corresponds to a value of a voxel in the set of values for voxels representing the volume of the patient including intensity value, [0019]-[0023], [0059], [0062], [0070]);
mapping points from the point cloud into non-overlapping coordinates in a 2D plane (values of voxels from the set of values for voxels arranged as points in the NxMxO spatial coordinate system are interpolated to a plane, [0007], [0018]-[0023], [0049]-[0050]); and
generating the projection as a 2D image using the mapped points (the interpolation to the plane is generated as a two-dimensional image using the values of voxels from the set of values for voxels arranged as points in the NxMxO spatial coordinate system, [0007], [0018]-[0023], [0049]-[0050]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Reisman’s known technique of arranging the spatial position and intensity values of voxels in a spatial coordinate system to Fleming’s known apparatus configure for generating a 3D volume of the tissue corresponding to the spatial position and intensity values of voxels and generating a two-dimensional image therefrom to achieve the predictable result that arranging the set of values for voxels representing a volume of the patient in an NxMxO arrangement allows for the correction of distortion in magnetic resonance imaging that improves image quality with minimal additional acquisition time resource cost. See, e.g., Reisman, [0006]-[0007] and [0051].
Claims 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Fleming in further view of Reisman.
Regarding claim 18, Fleming discloses at least one non-transitory computer-readable storage medium storing executable instructions that, when executed by at least one processor, cause the at least one processor to perform (a processor configured to execute computer-executable instructions to perform the method, [0026]; a non-transitory computer program product storing instructions for a computing system, [0064], [0066]) a method of determining a condition of a tissue of a patient from analysis of a magnetic resonance image (method of determining a quality and likelihood of failure of the ACL or ACL graft in a patient, [0027], [0063]), the method comprising:
generating a projection from a magnetic resonance image depicting the tissue (ACL is segmented from the 3D MR image stack to form a 3D model of the ACL from which a 2D slice is generated, [0174], [0269], [0338], [0379], Fig. 23; see also medial mensiscus is segmented from the 3D MR image stack to form a 3D model of the MCL from which a 2D slice is generated, [0252], Fig. 18), wherein generating the projection comprises determining a value at a point in the projection based on at least one value at a position of the magnetic resonance image corresponding to the point in the projection (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; see also 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18), wherein generating the projection from the magnetic resonance image comprises:
generating a 3D representation of the tissue from the magnetic resonance image (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18);
wherein each point of the 3D representation corresponds to a spatial position of a voxel of the 3D representation and comprises an intensity corresponding to the intensity of at least one point in the magnetic resonance image (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23; see also 3D model of the medial meniscus is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0252], Fig. 18);
mapping points from the 3D representation into non-overlapping coordinates in a 2D plane (3D model of the ACL is generated by determining the value at a point in the 3D model based on at least one signal intensity/grayscale value at a position of the MR image corresponding to the voxel in the 3D model and a 2D slice of the 3D model is generated having corresponding values from the 3D model, [0174], [0269], [0338], [0379], Fig. 23); and
generating the projection as a 2D image using the mapped points (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times output as a 2D slice image, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31), the generating the projection as a 2D image comprising grouping a plurality of points from the magnetic resonance image into a plurality of sagittal planes and arranging the plurality of sagittal planes adjacent to one another (acquire a plurality of 3D MR images of the knee and arrange the acquired images in a MR image stack in the sagittal plane, wherein an MR image stack in the sagittal plane is a grouping of the 3D MRI imaging points into sagittal planes arranged adjacent to one another, [0174]);
determining, for each of a plurality of locations in the projection, the condition of the tissue at a location, wherein determining the condition of the tissue at the location comprises determining the condition based at least in part on at least one value of the projection at the location (determine the tissue quality using the signal intensity/grayscale values within the 2D slice, lower signal intensity/grayscale values within the 2D slice or shorter T2* relaxation times corresponds to higher quality tissue, [0160], [0171], [0173], [0099], [0240], [0262], [0263], [0333], [0334], [0339], [0376], [0378], [0388]); and
outputting the determined condition at the plurality of locations of the projection (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times are indicated in colors differing from voxels corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and high signal intensity/grayscale values or longer T2* relaxation times, [0024], [0031], [0072], [0073], [0158], [0160], [0095], [0279], [0106], [0388], Figs. 5, 7, 20, 31).
However, Fleming does not appear to teach generating a point cloud from the 3D representation.
However, in the same field of endeavor MR imaging and in solving substantially the same problem of reconstructing 2D planar images of a patient’s tissue from 3D MR images, Resiman teaches generating a projection from a magnetic resonance image depicting the tissue (generate a planar, two-dimensional image from a 3D magnetic resonance image depicting patient tissue, [0007], [0018]-[0023], [0049]-[0050]), wherein generating the projection comprises determining a value at a point in the projection based on at least one value at a position of the magnetic resonance image corresponding to the point in the projection (generating the two-dimensional image comprises determining the value of a point in the plane based on at least one value of a voxel at a position of the 3D magnetic resonance image corresponding to the point in the plane, [0007], [0018]-[0023], [0049]-[0050]), wherein generating the projection from the magnetic resonance image comprises:
generating a 3D representation of the tissue from the magnetic resonance image (3D magnetic resonance imaging data is assigned to a set of values for voxels representing a volume of the patient, [0018]-[0023]);
generating a point cloud from the 3D representation (the set of values for voxels representing the volume of the patient are arranged as points in a NxMxO spatial coordinate system, i.e., a point cloud, [0019]), wherein each point of the point cloud corresponds to a spatial position of a voxel of the 3D representation (each point in the NxMxO arrangement corresponds to a spatial position of the set of values for voxels representing the volume of the patient, [0019]) and comprises an intensity corresponding to the intensity of at least one point in the magnetic resonance image (each point in the NxMxO spatial coordinate system corresponds to a value of a voxel in the set of values for voxels representing the volume of the patient including intensity value, [0019]-[0023], [0059], [0062], [0070]);
mapping points from the point cloud into non-overlapping coordinates in a 2D plane (values of voxels from the set of values for voxels arranged as points in the NxMxO spatial coordinate system are interpolated to a plane, [0007], [0018]-[0023], [0049]-[0050]); and
generating the projection as a 2D image using the mapped points (the interpolation to the plane is generated as a two-dimensional image using the values of voxels from the set of values for voxels arranged as points in the NxMxO spatial coordinate system, [0007], [0018]-[0023], [0049]-[0050]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have applied Reisman’s known technique of arranging the spatial position and intensity values of voxels in a spatial coordinate system to Fleming’s known apparatus configure for generating a 3D volume of the tissue corresponding to the spatial position and intensity values of voxels and generating a two-dimensional image therefrom to achieve the predictable result that arranging the set of values for voxels representing a volume of the patient in an NxMxO arrangement allows for the correction of distortion in magnetic resonance imaging that improves image quality with minimal additional acquisition time resource cost. See, e.g., Reisman, [0006]-[0007] and [0051].
Regarding claim 20, determining an average image intensity of intact tissue values (mean/average signal intensity from intact/contralateral/uninjured ligament, [0019], [0052], [0055], [0056], [0057], [0129], [0164], [0174], [0176], [0177], [0252], [0254], Fig.2, Fig. 35);
calculating at least one threshold indicative of the condition of the tissue using the average image intensity (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times, corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and corresponding to high signal intensity/grayscale values or longer T2* relaxation times are thresholded into a respective quartile/bin, respectively, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31); and
determining the condition of the tissue at a location of the plurality of locations in the projection comprises:
comparing one or more values of the projection at the location to the least one threshold (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times, corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and corresponding to high signal intensity/grayscale values or longer T2* relaxation times are thresholded into a respective quartile/bin, respectively, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31); and
determining the condition of the tissue based on a result of the comparing (voxels of the ACL corresponding to low signal intensity/grayscale values or shorter T2* relaxation times, corresponding to mean/average/median low signal intensity/grayscale values or T2* relaxation times, and corresponding to high signal intensity/grayscale values or longer T2* relaxation times are identified corresponding to above-average, average, and below-average tissue quality, respectively, [0024], [0031], [0072], [0073], [0095], [0099], [0106], [0158], [0159], [0160], [0171], [0173], [0240], [0262], [0263], [0279], [0333], [0334], [0339], [0376], [0378], [0388], Figs. 5, 7, 20, 31).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Endo (U.S. Pub. No. 2018/0025548) discloses generating a 3D volume and voxels/points from MRI image data wherein each voxel/point is mapped to a 2D plane to generate a 2D image projection of the MRI image data including axial, sagittal, coronal, and arbitrary planes.
Kiapour (“Changes in cross-sectional area and signal intensity of healing anterior cruciate ligaments and grafts in the first 2 years after surgery” 2019) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing signal intensity information.
Beveridge (“Sensitivity of ACL volume and T2* relaxation time to magnetic resonance imaging scan conditions” 2017) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing T2* relaxation times.
Beveridge (“Magnetic resonance measurements of tissue quantity and quality using T2* relaxometry predict temporal changes in the biomechanical properties of the healing ACL” 2017) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing T2* relaxation times.
Fleming (“The use of magnetic resonance imaging to predict ACL graft structural properties” 2011) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing T2* relaxation times.
Biercevicz (“T2* MR relaxometry and ligament volume are associated with the structural properties of the healing ACL” 2013) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing T2* relaxation times.
Murray (“Predictors of healing ligament size and magnetic resonance signal intensity at 6 months after bridge-enhanced anterior cruciate ligament repair” 2019) determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing signal intensity information.
Biercevicz (“In situ, noninvasive, T2*-weighted MRI-derived parameters predict ex vivo structural properties of an anterior cruciate ligament reconstruction or bioenhanced primary repair in a porcine model” 2013) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing signal intensity information.
Biercevicz (“MRI volume and signal intensity of ACL graft predict clinical, functional, and patient-oriented outcome measures after ACL reconstruction” 2014) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing signal intensity information.
Biercevicz (“T2* Relaxometry and volume predict semi-quantitative histological scoring of an ACL bridge-enhanced primary repair in a porcine model” 2015) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing T2* relaxation times.
Monu (“Cluster analysis of quantitative MRI T2 and T1r relaxation times of cartilage identifies differences between healthy and ACL-injured individuals at 3T” 2016) discloses determining the quality of healing ACL tissue from analysis of 3D MR images including segmenting and labeling voxels and depicting slices of the 3D MR image stack as 2D images containing signal intensity information using projection to a stack of sagittal planes.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.M./Examiner, Art Unit 3798
/KEITH M RAYMOND/Supervisory Patent Examiner, Art Unit 3798
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