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 .
Status of the Claims
Claims 1,5-13, and 17 are currently pending in the present application, with claims 1, 11, and 13 being independent.
Response to Amendments / Arguments
Applicant’s arguments, see Pg. 8-13, filed 08/03/2026, with respect to the rejection(s) of claim(s) 1,5-13, and 17 under 35 U.S.C. §103 have been fully considered and are persuasive. However, upon further consideration, a new ground(s) of rejection is made in view of different interpretation of the previously applied reference.
Regarding the remaining arguments: Applicant argues with respect to the amended claim language, which is fully addressed in the prior art rejections set forth below.
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 following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 5-9, 11-13, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ségonne, Florent et al. "Geometrically accurate topology-correction of cortical surfaces using nonseparating loops." IEEE transactions on medical imaging 26, no. 4 (2007): 518-529, hereinafter referred to as “Ségonne”, in view of Zwicker et al. "Meshing Point Clouds Using Spherical Parameterization." In PBG, pp. 173-180. 2004, hereinafter referred to as “Zwicker”, in further view of Yotter et al. "Topological correction of brain surface meshes using spherical harmonics." Human brain mapping 32, no. 7 (2011): 1109-1124, hereinafter referred to as “Yotter”, and in further view of Hormann et al. "Triangulating point clouds with spherical topology." Curve and Surface Design: Saint-Malo (2002): 215-224, hereinafter referred to as “Hormann”.
Regarding claim 1, Ségonne discloses a computer-implemented method for correcting topological defects on a surface mesh representing an organ that is homeomorphic to a sphere (Abstract; we propose a technique to accurately correct the spherical topology of cortical surfaces. Section I; each cortical hemisphere can be viewed as a simply connected 2-D sheet of neurons that carries the simple topology of a sphere), the surface mesh being obtained from a medical image (Abstract; The proposed method if a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on magnetic resonance imaging (MRI) intensity profile. Section I; Magnetic resonance images (MRIs)), the method comprising:
applying a transformation to the surface mesh, the transformation distributing positions of vertices of the surface mesh into a spherical point cloud (Section II.C; Generate a mapping from the original cortical surface onto the sphere that is as close as possible to a homeomorphism…Section III.A; The algorithm consists of iteratively updating the position of each vertex based on a smoothness force Fs, and a radial spherical force Fr…Eq. (1)-(3)…) and maintaining, in the distribution, vertices neighborhoods of the surface mesh (Section III.A; Nk set of vertices neighboring the kth vertex…The smoothness term Fs moves each vertex in the direction of the centroid of its neighbors…);
Ségonne does not disclose meshing the points of the spherical point cloud according to a triangular surface mesh, the triangular surface mesh presenting the following properties:
a union of triangles of the triangular surface mesh forms a convex hull of the points of the spherical point cloud and an intersection between any first triangle of the triangular surface mesh and any second triangle of the triangular surface mesh intersecting the first triangle is a vertex of the first triangle or an edge of the first triangle; and
mapping back the meshing onto the vertices of the surface mesh by reversing the transformation, thereby mapping positions of vertices corresponding to the meshed points of the spherical point cloud to respective positions of vertices of the triangular surface mesh, wherein the method further includes:
identifying outlier vertices that result from the reversed transformation, the identifying of the outlier vertices further including detecting vertices that are close on the meshing of the points of the spherical point cloud with respect to a Euclidean distance on the sphere and which are remote in the mesh resulting from the reserved transformation with respect to the Euclidean distance on the mesh, and
computing ratios between lengths of edges linking vertices on the meshing of the points of the spherical point cloud and lengths of edges linking vertices on the reversed transformation, the detecting including determining if the ratios are above a predetermined threshold.
In the same art of surface-mesh correction using spherical representation, Zwicker discloses meshing the points of the spherical point cloud according to a triangular surface mesh (Abstract; We first embed the neighborhood graph on a sphere using an iterative procedure…we triangulate the embedded points and apply the resulting mesh connectivity to the input points…Fig. 2; …2. Embed G on the sphere. Call the spherical point set V′. 3. Form G′ = ⟨V′,E′⟩ by triangulating V′ (e.g. by spherical Delaunay triangulation). 4. Use E′ to form M =⟨V,E′⟩) the triangular surface mesh presenting the following properties:
a union of triangles of the triangular surface mesh forms a convex hull of the points of the spherical point cloud (Section 3.1; To triangulate the points embedded on the sphere, we used the convex hull routine qconvex implemented in the qhull package [qhu]. The resulting spherical triangulation is a Delaunay triangulation with relatively short edge lengths), and
an intersection between any first triangle of the triangular surface mesh and any second triangle of the triangular surface mesh intersecting the first triangle is a vertex of the first triangle or an edge of the first triangle (Section 3; The result will, by definition, be a closed triangular genus-0 manifold mesh…Fig. 2); and
mapping back the meshing onto the vertices of the surface mesh by reversing the transformation (Abstract; …we triangulate the embedded points and apply the resulting mesh connectivity to the input points. Fig. 2; 4. Use E′ to form M =⟨V,E′⟩.), thereby mapping positions of vertices corresponding to the meshed points of the spherical point cloud to respective positions of vertices of the triangular surface mesh (Fig. 2 and Section 3; 2. Embed G on the sphere. Call the spherical point set V'. 3. Form G' = ⟨V', E'⟩ by triangulating V' (e.g. by spherical Delaunay triangulation. 4. Use E' to form M = ⟨V, E'⟩…Let us denote the positions of the input points in the set V by pi and the points embedded on the sphere by ui…),
wherein the method further includes:
the identifying of the outlier vertices further including detecting vertices that are close on the meshing of the points of the spherical point cloud (Section 2; short-range distances should be preserved as much as possible…where the edge lengths are to be preserved as much as possible. Section 3.1, Eq. (4))
computing ratios between lengths of edges linking vertices on the meshing of the points of the spherical point cloud and lengths of edges linking vertices on the reversed transformation (Section 3; denote the positions of the input points in the set V by pi and the points embedded on the sphere by ui…Section 3.1, Eq. (4); we compute adaptive weights as wij =∥ui−uj∥/∥pi−pj∥ (4)…this minimizes the stretch or distortion of the embedding),
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Zwicker’s spherical meshing technique into Segonne’s topology-correction framework. Doing so provides a simple and robust manner of producing closed manifold genus-0 representations while maintaining corresponding with the input points. This modification would predictably improve the reliability of the correction process by providing well-defined intermediate representation, and reducing geometric distortion before transferring the corrected connectivity back to the anatomical surface, overall improving the fidelity of the final reconstructed organ surface.
Ségonne in view of Zwicker does not disclose identifying outlier vertices that result from the reversed transformation, and the detecting including determining if the ratios are above a predetermined threshold.
In the same art of spherical-domain topology correction of anatomical surfaces, Yotter discloses identifying outlier vertices that result from the reversed transformation (Pg. 126, Section 2; an inverse Fourier transform that reconstructs the harmonic data into two distance surfaces…Pg. 128, Section 2.3; Topologically spiked regions are found by calculating a sharpness value for each point...If sharpness is above a threshold, then this point and a set of neighbors are replaced with corresponding points in the bandwidth-limited cortical surface…measure the distance between the points surrounding the patch and their corresponding points…any points that were more than 2 mm distance…),
detecting including determining if the ratios are above a predetermined threshold (Section 2.3; If sharpness is above a threshold, then this point and a set of neighbors are replaced with corresponding points…a threshold of ts = 110° fulfilled these criteria…measure the distance between the points surrounding the patch and their corresponding points…any points that were more than 2 mm distance…)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Yotter’s error-detection techniques into the combined system of Segonne and Zwicker. Doing so automatically identifies residual geometric abnormalities during reconstruction. This modification would predictably improve the accuracy and reliability of the corrected anatomical surface by allowing localized errors to be detected and corrected without unnecessarily modifying unaffected regions (Yotter Section 1; spherical harmonics processing can accurately and quickly repair topological defects. The spherical harmonics approach retrospectively corrects topological errors directly on the brain surface mesh, and results in an accurately reconstructed cortical surface free from topological defects and large artifacts). Further, it would have been obvious to a person of ordinary skill in the art to apply Yotter’s threshold-based error classification to Segonne and Zwicker’s combined quantitative distortion measure in order to provide an objective criterion for distinguishing ordinary deformation from geometrically significant reconstruction error. This modification predictably enables more consistent and automated identification of regions requiring correction, and reducing subjective inspection by providing a repeatable measure
Ségonne in view of Zwicker and in further view of Yotter does not disclose identifying of the outlier vertices further including detecting vertices that are close on the meshing points of the spherical point cloud with respect to a Euclidean distance on the sphere and which are remote in the mesh resulting from the reversed transformation with respect to the Euclidean distance on the mesh.
In the same art of spherical topology, Hormann discloses the identifying of the outlier vertices further including detecting vertices that are close on the meshing of the points of the spherical point cloud with respect to a Euclidean distance on the sphere and which are remote in the mesh resulting from the reserved transformation with respect to the Euclidean distance on the mesh (Section 2; Suppose the underlying surface is a two-manifold M, and let d(v, q) be the geodesic distance, i.e., the Euclidean length of the shortest path in M connecting v and w. Then we can define for each v ∈ M the (open) r-neighbourhood…Nr(v) = {w ∈ V : 0 < ||v-w|| < r});
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Hormann’s local distance-based neighborhood technique into the combined system of Segonne, Zwicker, and Yotter. Doing so provides an objective comparison to identify vertices whose local relationships were not preserved by the reverse transformation, yielding predictable results in improved local neighborhood reliability, and efficient outlier detection by measuring inconsistencies between proximity on the spherical domain and the reconstructed anatomical mesh.
Regarding claim 5, Ségonne in view of Zwicker, in view of Yotter, and in further view of Hormann discloses the method of claim 1, but Ségonne in view of Zwicker does not disclose processing the identified outlier vertices, the processing reducing a distance between the identified outlier vertices with respect to remaining vertices of the reversed transformation.
In the same art of spherical-domain topology correction of anatomical surfaces, Yotter discloses processing the identified outlier vertices, the processing reducing a distance between the identified outlier vertices with respect to remaining vertices of the reversed transformation (Abstract; a low-pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. Self-intersections are repaired using a local smoothing algorithm that limits the number of affected points to less than 0.1% of the total, and as a last step, all modified points are adjusted based on the T1 intensity… Correcting Self-Intersections section and Fig. 2; (e)-(g)).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate localized correction of defective regions, as taught by Yotter, into Segonne and Zwicker’s system. Doing so allows correction of identified outlier regions in a target manner so that the reconstructed surface closely conforms to neighboring vertices, yielding predictable results in fewer residual artifacts.
Regarding claim 6, Ségonne in view of Zwicker, in view of Yotter, and in further view of Hormann discloses the method of claim 5, and further discloses wherein the processing further comprises one or more of a smoothing of the identified outlier vertices or a cropping of the identified outlier vertices (Ségonne Section IV.B; once a nonseparating loop has been found, we discard the faces of the loop and seal the surface…).
Ségonne, Zwicker, Yotter, and Hormann are combined for the reasons set forth above with respect to claim 5.
Regarding claim 7, Ségonne in view of Zwicker, in view of Yotter, and in further view of Hormann discloses the method of claim 1, but Ségonne in view of Zwicker does not disclose selecting vertices from the reversed transformation remaining from the identified outlier vertices, and re-meshing the selected vertices according to the topology of the surface mesh.
In the same art of spherical-domain topology correction of anatomical surfaces, Yotter discloses selecting vertices from the reversed transformation remaining from the identified outlier vertices, and re-meshing the selected vertices according to the topology of the surface mesh (Fig. 2; (d)-(g)).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate selective correction and reconstruction of defective surface regions, as taught by Yotter, into Segonne and Zwicker’s system. Doing so allows efficient re-meshing of of selected vertices or regions while preserving topology and geometry of unaffected portions of the surface mesh.
Regarding claim 8, Ségonne in view of Zwicker, in view of Yotter, and in further view of Hormann discloses the method of claim 1, and further discloses wherein applying the transformation further comprises iteratively inflating the mesh into a spherical point cloud, using an inflation transformation constrained by a vertex neighborhood preservation constraint (Ségonne Section III.A; Initialization of the Mapping: Spherical Inflation…The algorithm consists of iteratively updating the position of each vertex based on a smoothness force Fs, and a radial spherical force Fr …Eq. (1)-(3)…spherical inflation).
Ségonne, Zwicker, Yotter, and Hormann are combined for the reasons set forth above with respect to claim 1.
Regarding claim 9, Ségonne in view of Zwicker, in view of Yotter, and in further view of Hormann discloses the method of claim 8, and further discloses wherein the inflation transformation is of a type, for each current vertex v (Ségonne Section III.A):
Ct+1v=Ctv+dv+dvnCvnSv+λrr0-rvr0rv, wherein dv=1#NAv∑v'∈NAvCtv'-Ctv, rv=Ctv-ot+1,
where A denotes the surface mesh (Ségonne Section III.A; folded cortical surface…tessellation),
v denotes the current vertex (Ségonne Section III.A; kth vertex),
NAv represents the vertex neighborhood of the current vertex vin the surface mesh A (Ségonne Section III.A; Nk set of vertices neighboring the kth vertex),
v' denotes a neighbor vertex of a set Nav (Ségonne Section III.A; Eq. (2) …Nk set of vertices neighboring the kth vertex…),
#N_A(v) denotes a number of vertices in a set Nav (Ségonne Section III.A; V number of vertices in the tessellation),
Ctv and Ctv' denote positions of the vertices v and v' positioned on a spherical point cloud S (Ségonne Section III.A; Eq. (1) …xk is the position of the kth vertex at iteration number t…Eq. (2)…xi…the surface is projected to lie precisely on the surface of a sphere of radius R…),
Ct+1v denotes an updated position of vertex v (Ségonne Section III.A; algorithm consists of iteratively updating the position of each vertex…Eq. (1) …xk is the position of the kth vertex at iteration number t),
ot+1=1#A∑v∈ACtv denotes an average position of vertices on the spherical point cloud S (Ségonne Section III.A; Eq. (2) …smoothness term Fs moves each vertex in the direction of the centroid of its neighbors),
nSv denotes an outward normal of the current vertex v (Ségonne Section III.A; nk and nk` surface normals at location k and its transpose),
dvnCv denotes an inner product between dv and nCv (Ségonne Section III.A; Eq. (2) …inward movement created over the entire surface…),
λr and r0 are predetermined parameters (Ségonne Section III.A; We use an R on the order of 100 mm to generate a sphere with roughly the same total surface area as an average cortex, and a λR of 0.25).
Ségonne, Zwicker, Yotter, and Hormann are combined for the reasons set forth above with respect to claim 1.
Regarding claim 11, Ségonne discloses a computer-implemented method for obtaining a surface model representing an organ that is homeomorphic to a sphere (Section II.B; an accurate and automated method for generating topologically correct models of the human cerebral cortex... Section II.C; Generate a mapping from the original cortical surface onto the sphere that is as close as possible to a homeomorphism), the method comprising:
obtaining a medical image of the organ (Abstract; The proposed method if a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on magnetic resonance imaging (MRI) intensity profile. Section I; Magnetic resonance images (MRIs));
obtaining a surface mesh from the medical image (Section III; cortical surface C); and
correcting topological defects on the surface mesh by correcting topological defects on a surface mesh representing an organ that is homeomorphic to a sphere, the surface mesh being obtained from a medical image, the correcting topological defects (Section II-IV and Algorithm 1) including:
applying a transformation to the surface mesh, the transformation distributing positions of vertices of the surface mesh into a spherical point cloud (Section II.C; Generate a mapping from the original cortical surface onto the sphere that is as close as possible to a homeomorphism…Section III.A; The algorithm consists of iteratively updating the position of each vertex based on a smoothness force Fs, and a radial spherical force Fr…Eq. (1)-(3)…) and maintaining, in the distribution, vertices neighborhoods of the surface mesh (Section III.A; Nk set of vertices neighboring the kth vertex…The smoothness term Fs moves each vertex in the direction of the centroid of its neighbors…).
Ségonne does not disclose meshing the points of the spherical point cloud according to a triangular surface mesh, the triangular surface mesh presenting the following properties:
a union of triangles of the triangular surface mesh forms a convex hull of the points of the spherical point cloud, and an intersection between any first triangle of the triangular surface mesh and any second triangle of the triangular surface mesh intersecting the first triangle is a vertex of the first triangle or an edge of the first triangle; and
mapping back the meshing onto the vertices of the surface mesh by reversing the transformation, thereby mapping positions of vertices corresponding to the meshed points of the spherical point cloud to respective positions of vertices of the triangular surface mesh, wherein the method further includes:
identifying outlier vertices that result from the reversed transformation, the identifying of the outlier vertices further including detecting vertices that are close on the meshing of the points of the spherical point cloud with respect to a Euclidean distance on the sphere and which are remote in the mesh resulting from the reserved transformation with respect to the Euclidean distance on the mesh, and
computing ratios between lengths of edges linking vertices on the meshing of the points of the spherical point cloud and lengths of edges linking vertices on the reversed transformation, the detecting including determining if the ratios are above a predetermined threshold.
In the same art of surface-mesh correction using spherical representation, Zwicker discloses meshing the points of the spherical point cloud according to a triangular surface mesh (Abstract; We first embed the neighborhood graph on a sphere using an iterative procedure…we triangulate the embedded points and apply the resulting mesh connectivity to the input points…Fig. 2; …2. Embed G on the sphere. Call the spherical point set V′. 3. Form G′ = ⟨V′,E′⟩ by triangulating V′ (e.g. by spherical Delaunay triangulation). 4. Use E′ to form M =⟨V,E′⟩) the triangular surface mesh presenting the following properties:
a union of triangles of the triangular surface mesh forms a convex hull of the points of the spherical point cloud (Section 3.1; To triangulate the points embedded on the sphere, we used the convex hull routine qconvex implemented in the qhull package [qhu]. The resulting spherical triangulation is a Delaunay triangulation with relatively short edge lengths), and
an intersection between any first triangle of the triangular surface mesh and any second triangle of the triangular surface mesh intersecting the first triangle is a vertex of the first triangle or an edge of the first triangle (Section 3; The result will, by definition, be a closed triangular genus-0 manifold mesh…Fig. 2); and
mapping back the meshing onto the vertices of the surface mesh by reversing the transformation (Abstract; …we triangulate the embedded points and apply the resulting mesh connectivity to the input points. Fig. 2; 4. Use E′ to form M =⟨V,E′⟩.), thereby mapping positions of vertices corresponding to the meshed points of the spherical point cloud to respective positions of vertices of the triangular surface mesh (Fig. 2 and Section 3; 2. Embed G on the sphere. Call the spherical point set V'. 3. Form G' = ⟨V', E'⟩ by triangulating V' (e.g. by spherical Delaunay triangulation. 4. Use E' to form M = ⟨V, E'⟩…Let us denote the positions of the input points in the set V by pi and the points embedded on the sphere by ui…),
wherein the method further includes:
the identifying of the outlier vertices further including detecting vertices that are close on the meshing of the points of the spherical point cloud (Section 2; short-range distances should be preserved as much as possible…where the edge lengths are to be preserved as much as possible. Section 3.1, Eq. (4))
computing ratios between lengths of edges linking vertices on the meshing of the points of the spherical point cloud and lengths of edges linking vertices on the reversed transformation (Section 3; denote the positions of the input points in the set V by pi and the points embedded on the sphere by ui…Section 3.1, Eq. (4); we compute adaptive weights as wij =∥ui−uj∥/∥pi−pj∥ (4)…this minimizes the stretch or distortion of the embedding),
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Zwicker’s spherical meshing technique into Segonne’s topology-correction framework. Doing so provides a simple and robust manner of producing closed manifold genus-0 representations while maintaining corresponding with the input points. This modification would predictably improve the reliability of the correction process by providing well-defined intermediate representation, and reducing geometric distortion before transferring the corrected connectivity back to the anatomical surface, overall improving the fidelity of the final reconstructed organ surface.
Ségonne in view of Zwicker does not disclose identifying outlier vertices that result from the reversed transformation, and the detecting including determining if the ratios are above a predetermined threshold.
In the same art of spherical-domain topology correction of anatomical surfaces, Yotter discloses identifying outlier vertices that result from the reversed transformation (Pg. 126, Section 2; an inverse Fourier transform that reconstructs the harmonic data into two distance surfaces…Pg. 128, Section 2.3; Topologically spiked regions are found by calculating a sharpness value for each point...If sharpness is above a threshold, then this point and a set of neighbors are replaced with corresponding points in the bandwidth-limited cortical surface…measure the distance between the points surrounding the patch and their corresponding points…any points that were more than 2 mm distance…),
detecting including determining if the ratios are above a predetermined threshold (Section 2.3; If sharpness is above a threshold, then this point and a set of neighbors are replaced with corresponding points…a threshold of ts = 110° fulfilled these criteria…measure the distance between the points surrounding the patch and their corresponding points…any points that were more than 2 mm distance…)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Yotter’s error-detection techniques into the combined system of Segonne and Zwicker. Doing so automatically identifies residual geometric abnormalities during reconstruction. This modification would predictably improve the accuracy and reliability of the corrected anatomical surface by allowing localized errors to be detected and corrected without unnecessarily modifying unaffected regions (Yotter Section 1; spherical harmonics processing can accurately and quickly repair topological defects. The spherical harmonics approach retrospectively corrects topological errors directly on the brain surface mesh, and results in an accurately reconstructed cortical surface free from topological defects and large artifacts). Further, it would have been obvious to a person of ordinary skill in the art to apply Yotter’s threshold-based error classification to Segonne and Zwicker’s combined quantitative distortion measure in order to provide an objective criterion for distinguishing ordinary deformation from geometrically significant reconstruction error. This modification predictably enables more consistent and automated identification of regions requiring correction, and reducing subjective inspection by providing a repeatable measure
Ségonne in view of Zwicker and in further view of Yotter does not disclose identifying of the outlier vertices further including detecting vertices that are close on the meshing points of the spherical point cloud with respect to a Euclidean distance on the sphere and which are remote in the mesh resulting from the reversed transformation with respect to the Euclidean distance on the mesh.
In the same art of spherical topology, Hormann discloses the identifying of the outlier vertices further including detecting vertices that are close on the meshing of the points of the spherical point cloud with respect to a Euclidean distance on the sphere and which are remote in the mesh resulting from the reserved transformation with respect to the Euclidean distance on the mesh (Section 2; Suppose the underlying surface is a two-manifold M, and let d(v, q) be the geodesic distance, i.e., the Euclidean length of the shortest path in M connecting v and w. Then we can define for each v ∈ M the (open) r-neighbourhood…Nr(v) = {w ∈ V : 0 < ||v-w|| < r});
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Hormann’s local distance-based neighborhood technique into the combined system of Segonne, Zwicker, and Yotter. Doing so provides an objective comparison to identify vertices whose local relationships were not preserved by the reverse transformation, yielding predictable results in improved local neighborhood reliability, and efficient outlier detection by measuring inconsistencies between proximity on the spherical domain and the reconstructed anatomical mesh.
Regarding claim 12, Ségonne in view of Zwicker, in view of Yotter, and in further view of Hormann discloses the method of claim 11, but Ségonne in view of Zwicker does not disclose wherein obtaining the surface mesh further comprises obtaining a tissue mask from the medical image and applying a mesh reconstruction method to the tissue mask.
In the same art of spherical-domain topology correction of anatomical surfaces, Yotter discloses wherein obtaining the surface mesh further comprises obtaining a tissue mask from the medical image and applying a mesh reconstruction method to the tissue mask (Introduction section and Fig. 1; intensity gradient in the T1 image is used to classify tissue and a machine cubes algorithm generates the surface at the boundary between the gray and white tissue segmentation maps)).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Yotter’s tissue-segmentation map as the input stage for Segonne and Zwicker’s system. Doing so provides a standard MRI-derived cortical mesh on which topology correction could then be performed in an automated pipeline, yielding predictable results in improving pipeline compatibility.
Regarding claim 13, claim 13 is the CRM claim (Ségonne Section V; 1-GHz Pentium IV machine) of method claim 1 and/or method claim 11, and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 1 and 11.
Regarding claim 17, claim 17 is the device claim (Ségonne Section V; 1-GHz Pentium IV machine) of CRM claim 13, and is accordingly rejected using substantially similar rationale as to that which is set for with respect to claim 13.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ségonne et al. "Geometrically accurate topology-correction of cortical surfaces using nonseparating loops." IEEE transactions on medical imaging 26, no. 4 (2007): 518-529, hereinafter referred to as “Ségonne”, in view of Zwicker et al. "Meshing Point Clouds Using Spherical Parameterization." In PBG, pp. 173-180. 2004, hereinafter referred to as “Zwicker”, in further view of Yotter et al. "Topological correction of brain surface meshes using spherical harmonics." Human brain mapping 32, no. 7 (2011): 1109-1124, hereinafter referred to as “Yotter”, and in further view of Hormann et al. "Triangulating point clouds with spherical topology." Curve and Surface Design: Saint-Malo (2002): 215-224, hereinafter referred to as “Hormann”, in further view of Praun et al. "Spherical parametrization and remeshing." ACM transactions on graphics (TOG) 22, no. 3 (2003): 340-349, hereinafter referred to as “Praun”.
Regarding claim 10, Ségonne in view of Zwicker, in view of Yotter, and in further view of Hormann discloses the method of claim 8, but does not disclose wherein the vertex neighborhood preservation constraint is of a type: v'∉NAv⇒dCv,Cv'≥min {dCv,Cv1,dCv,Cv2}, wherein A denotes the surface mesh, v denotes a current vertex, NAv represents the vertex neighborhood of the current vertex vin the surface mesh A,v1 and v2 denote vertices belonging to a vertex neighborhood NAv of the current vertex v, v'denotes a vertex not belonging to the intersection of vertices neighborhoods NAv1 and NAv2, and d∙,∙ is a distance function.
In the same art of spherical parameterization and remeshing, Praun discloses wherein the vertex neighborhood preservation constraint is of a type:
v'∉NAv⇒d(Cv,Cv')≥ min {dCv,Cv1,dCv,Cv2} (Section 3.5; To prevent flipping, we only consider pertrubing the vertex within the kernel of its 1-ring),
wherein A denotes the surface mesh (Section 3.5; Each vertex split in the progressive mesh specifies a ring of vertices that will be the neighbors of the new
vertex), v denotes a current vertex (Section 3.5; new vertex), NAv represents the vertex neighborhood of the current vertex vin the surface mesh A (Section 3.5; neighbors of the new vertex), v1 and v2 denote vertices belonging to a vertex neighborhood NAv of the current vertex v, v' denotes a vertex not belonging to the intersection of vertices neighborhoods NAv1 and NAv2 (Section 3.5; After inserting a new vertex, we optimize vertices in the neighborhood one at a time. To optimize one vertex, we minimize the stretch metric summed on its adjacent triangles…Each time the number of vertices has increased by a constant factor (e.g. 2), we sweep through all mesh vertices and optimize each one in turn. More precisely, we place all vertices in a priority queue ordered by the amount of change in their neighborhood), and d∙,∙ is a distance function (Section 3.4; distances around P).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention, to incorporate Praun’s neighborhood-preserving spherical-embedding constraint into Segonne, Zwicker, Yotter, and Hormann’s combined system. Doing so allows vertex updates remain locally valid during movement to the sphere, yielding predictable results in reduced distortion and improving stability of the spherical mapping used for topology correction.
Conclusion
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/JENNY N TRAN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615
