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 .
Claim Objections
Claim 4 is objected to because of the following informalities:
Claim 4 as recited is ambiguous as “the calculated compactness” lacks antecedent basis. For the record, the examiner recommends claim 4 to be rewritten as follow, and interpretation will be as such until clarification is made of record or applicant accepts this proposal and makes changes accordingly.
4. The apparatus of claim [[2]]3, wherein the vessel mask to be used during the estimation of the vessel motion field is of a circular shape, the size of the vessel mask is characterized by a radius thereof, and the processing circuitry is further configured to: determine the size of the vessel mask by using the calculated compactness as a key to obtain the radius of the vessel mask from a look-up table.
Claim 5 is objected to because of the following informalities:
Claim 5 as recited is ambiguous as “the calculated compactness” lacks antecedent basis. For the record, the examiner recommends claim 5 to be rewritten as follow, and interpretation will be as such until clarification is made of record or applicant accepts this proposal and makes changes accordingly.
5. The apparatus of claim [[2]]3, wherein the vessel mask to be used during the estimation of the vessel motion field is of a circular shape, the size of the vessel mask is characterized by a radius thereof, and the processing circuitry is further configured to: determine the size of the vessel mask by applying the calculated compactness to a trained neural network to obtain the radius of the vessel mask from outputs of the neural network.
Claim 7 is objected to because of the following informalities:
Claim 7 as recited is ambiguous as it is missing the word “on”. For the record, the examiner recommends claim 7 to be rewritten as follow, and interpretation will be as such until clarification is made of record or applicant accepts this proposal and makes changes accordingly.
7. The apparatus of claim 6, wherein the processing circuitry is further configured to: track the identified vessel to estimate a corresponding length of each vessel slice of the plurality of vessel slices included in the identified vessel, and obtain the estimated length of the identified vessel, based on the estimated corresponding lengths of the plurality of vessel slices.
Claim 9 is objected to because of the following informalities:
Claim 9 as recited is ambiguous as “the predefined range” lacks antecedent basis. For the record, the examiner recommends claim 9 to be rewritten as follow, and interpretation will be as such until clarification is made of record or applicant accepts this proposal and makes changes accordingly.
9. The apparatus of claim [[6]]8, wherein the predefined range is from 15 millimeters to 20 millimeters.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 16 and 20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Rohkohl (Improving best-phase image quality in cardiac CT by motion correction with MAM optimization, Med. Phys. 40 (3), March 2013).
Regarding claim 1, Rohkohl teaches an apparatus for performing parameter adaptation for motion compensation in a computed tomography (CT) imaging system, the apparatus comprising: processing circuitry configured to receive projection data acquired from imaging an object using the CT imaging system (
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); reconstruct, based on the received projection data, an image of the object, without performing motion compensation (Therefore, the first step of the algorithm is the definition of a region of interest Ω for motion estimation (M-ROI). It is determined by a segmentation of the coronary arteries from the initial reconstructed image. Page 3 right column 3rd paragraph); identify a vessel in the reconstructed image (Both the MAM-optimization and a 3-D/3-D registration-based motion estimation algorithm were investigated by means of a computer-simulated vessel with a cardiac motion profile. Page 1 Methods), the vessel including a plurality of vessel slices (The image matrix size was 512 × 512, covering a 90 mm field of view and a z-range of 4 mm with a slice increment of 0.4 mm. Page 6 right column last paragraph); determine, based on features of the identified vessel (It is based on a simple but realistic model of the right coronary artery (RCA) with regard to morphology and motion curve. Page 6 right column 3rd paragraph), parameters to be used during motion estimation of the identified vessel (A local parametric motion model M(t, x, s) is used to describe local deformable motion as a function of time. The parameters s are local motion vectors. Page 3 right column 3rd paragraph); estimate a vessel motion field using the determined parameters (A local parametric motion model M(t, x, s) is used to describe local deformable motion as a function of time. The parameters s are local motion vectors. Page 3 right column 3rd paragraph); and reconstruct, based on the received projection data and the estimated vessel motion field (An analytic reconstruction algorithm based on the FDK-algorithm but incorporating the motion model is used to compute a motion-compensated image. Page 3 right column 3rd paragraph), a motion-compensated image of the object (An analytic reconstruction algorithm based on the FDK-algorithm but incorporating the motion model is used to compute a motion-compensated image. Page 3 right column 3rd paragraph).
Method claim 16 is drawn to the method of using the corresponding apparatus claimed in claim 1. Therefore method claim 16 corresponds to apparatus claim 1 and is rejected for the same reasons of anticipation as used above.
Claim 20 is drawn to a non-transitory computer readable storage medium storing instructions executable by a processor for executing the method of using the corresponding apparatus as claimed in claim 1. Therefore, claim 20 corresponds to apparatus claims 1, and is rejected for the same reasons of anticipation as used above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 2 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Rohkohl (Improving best-phase image quality in cardiac CT by motion correction with MAM optimization, Med. Phys. 40 (3), March 2013), hereinafter Rohkohl, in view of Lauritsch (US Patent No.: US 11,379,994 B2), hereinafter Lauritsch.
Regarding claim 2, Rohkohl teaches the apparatus of claim 1, wherein the parameters include a size of a vessel mask to be used during the estimation of the vessel motion field (Therefore, the first step of the algorithm is the definition of a region of interest Ω (which reads on “a size of a vessel mask”) for motion estimation (M-ROI). Page 3 right column 3rd paragraph), and the processing circuitry is further configured to: determine the plurality of vessel slices included in the identified vessel (The vessel template (left column, Fig. 3) was matched to each slice of the reconstructed volume, and the overall maximum of NCC was recorded as quality index. Page 9 left column last paragraph), crop, based on a default vessel mask of a predefined default size, a vessel region around each of the plurality of vessel slices (In order to obtain the M-ROI, the segmented region was dilated by several millimeters to cover the whole range of motion related artifacts originating from the contrast filled vessels. Page 4 right column last paragraph), obtain a maximum motion artifact within each of the cropped vessel regions (In order to obtain the M-ROI, the segmented region was dilated by several millimeters to cover the whole range of motion related artifacts (In other words, the maximum motion artifact is obtained.) originating from the contrast filled vessels. Page 4 right column last paragraph), calculate a morphological metric with respect to each vessel slice of the plurality of vessel slices (In the following we propose two different metrics to assess the relative amount of motion artifacts: entropy and positivity. Page 5 left column 2nd paragraph. The intensity variations in a CT image caused by data inconsistencies due to motion artifacts increase entropy (In other words, entropy is a morphological metric.). Page 5 left column 3rd paragraph), based on the obtained maximum motion artifact within the vessel region around the vessel slice (In order to obtain the M-ROI, the segmented region was dilated by several millimeters to cover the whole range of motion related artifacts (which includes the maximum motion artifact) originating from the contrast filled vessels. Page 4 right column last paragraph).
Rohkohl does not teach the following limitations as further recited, but Lauritsch further teaches determine a mask size for each vessel slice of the plurality of vessel slices (In an advantageous development, the local or spatial restriction (i.e., a mask size) ensues according to a predetermined weighting function, by which, starting from a respective non-static region, regions that are more distant are considered to a lesser extent in the determination and/or application of the motion field. In other words, it is possible therefore for a so-called Fade-Out of the motion compensation or of the motion field to be provided, starting from a moving structure. As a parameter for the distance, a Euclidean Distance Transform may be used or applied. Column 10 line 36), based on the calculated morphological metric with respect to the vessel slice (In a further advantageous embodiment, the iterative determination of the motion field is implemented as an optimization problem with a given cost function, which defines, as a metric for the optimization problem, a correlation between the respective reference image and an image of the target object that has been generated in a motion-compensated manner using the respective current motion field. Column 7 line 52).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rohkohl to incorporate the teachings of Lauritsch to determine a mask size for each vessel slice of the plurality of vessel slices based on the calculated morphological metric with respect to the vessel slice in order to save computation time and improve efficiency.
Lauritsch in the combination further teaches obtain a maximum motion artifact within each of the cropped vessel regions (The reference image 13 shows at least one part of the vascular tree 12 but may be restricted to the maximum-intensity regions thereof. Column 13 line 37), through a thresholding process based on a set of predefined CT value thresholds (In a process act S3, the initial reconstructed image 11 is segmented, in particular automatically, for example by using an intensity-based thresholding method. Column 13 line 26).
Method claim 17 is drawn to the method of using the corresponding apparatus claimed in claim 2. Therefore method claim 17 corresponds to apparatus claim 2 and is rejected for the same reasons of obviousness as used above.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Rohkohl (Improving best-phase image quality in cardiac CT by motion correction with MAM optimization, Med. Phys. 40 (3), March 2013), hereinafter Rohkohl, in view of Lauritsch (US Patent No.: US 11,379,994 B2), hereinafter Lauritsch, further in view of Begelman (US Pub. No.: US 2008/0101674 A1), hereinafter Begelman.
Regarding claim 3, Rohkohl and Lauritsch teach all of the elements of the claimed invention as stated in claim 2 except for the following limitations as further recited. However, Begelman teaches wherein the morphological metric is represented by compactness of the obtained maximum motion artifact within the vessel region around the vessel slice (In an operation 508, a compactness of each identified CC is determined. [0059]. A person having ordinary skill in the art would recognize Begelman’s approach to define the compactness based on the lumen can also be applied to the obtained maximum motion artifact.), and the processing circuitry is further configured to calculate the compactness as
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where P represents a perimeter of the obtained maximum motion artifact, and A represents an area of the obtained maximum motion artifact (A compactness measure of a shape is a ratio of the area of the shape to the area of a circle (the most compact shape) having the same perimeter. The ratio may be expressed mathematically as M=4π(area)/2(perimeter). [0059]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rohkohl and Lauritsch to incorporate the teachings of Begelman to represent the morphological metric by compactness of the obtained maximum motion artifact as
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where P represents a perimeter of the obtained maximum motion artifact, and A represents an area of the obtained maximum motion artifact in order to automatically locate and quantify maximum motion artifact of a blood vessel.
Regarding claim 4, Begelman in the combination teaches the apparatus of claim [[2]]3, wherein the vessel mask (Imaging slices including the aorta bounding box and a mask of the aorta detected at a previous slice are processed to detect the aorta at the current slice. [0064]) to be used during the estimation of the vessel motion field is of a circular shape (A compactness measure of a shape is a ratio of the area of the shape to the area of a circle (the most compact shape) having the same perimeter. The ratio may be expressed mathematically as M=4π(area)/2(perimeter). [0059]), the size of the vessel mask is characterized by a radius thereof (A compactness measure of a shape is a ratio of the area of the shape to the area of a circle (the most compact shape) having the same perimeter. The ratio may be expressed mathematically as M=4π(area)/2(perimeter). [0059]), and the processing circuitry is further configured to: determine the size of the vessel mask by using the calculated compactness as a key to obtain the radius of the vessel mask from a look-up table (A compactness measure of a shape is a ratio of the area of the shape to the area of a circle (the most compact shape) having the same perimeter. The ratio may be expressed mathematically as M=4π(area)/2(perimeter). [0059]. A person having ordinary skill in the art would recognize the radius of the vessel mask can be obtained from the equation to calculate compactness in claim 3 or by using a look-up table.).
Regarding claim 5, Begelman in the combination teaches the apparatus of claim [[2]]3, wherein the vessel mask (Imaging slices including the aorta bounding box and a mask of the aorta detected at a previous slice are processed to detect the aorta at the current slice. [0064]) to be used during the estimation of the vessel motion field is of a circular shape (A compactness measure of a shape is a ratio of the area of the shape to the area of a circle (the most compact shape) having the same perimeter. The ratio may be expressed mathematically as M=4π(area)/2(perimeter). [0059]), the size of the vessel mask is characterized by a radius thereof (A compactness measure of a shape is a ratio of the area of the shape to the area of a circle (the most compact shape) having the same perimeter. The ratio may be expressed mathematically as M=4π(area)/2(perimeter). [0059]), and the processing circuitry is further configured to: determine the size of the vessel mask by applying the calculated compactness to a trained neural network to obtain the radius of the vessel mask from outputs of the neural network (A compactness measure of a shape is a ratio of the area of the shape to the area of a circle (the most compact shape) having the same perimeter. The ratio may be expressed mathematically as M=4π(area)/2(perimeter). [0059]. A person having ordinary skill in the art would recognize the radius of the vessel mask can be obtained from the equation to calculate compactness in claim 3 or by using a trained neural network.).
Claim 6-8, 10-15 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Rohkohl (Improving best-phase image quality in cardiac CT by motion correction with MAM optimization, Med. Phys. 40 (3), March 2013), hereinafter Rohkohl, in view of Lauritsch (US Patent No.: US 11,379,994 B2), hereinafter Lauritsch, further in view of Yasuhiro (Japan Patent Pub. No.: JP2004201873A), hereinafter Yasuhiro.
Regarding claim 6, Rohkohl teaches the apparatus of claim 2, wherein the parameters further include a number of control points to be used during the estimation of the vessel motion field (One important building block of any motion correction framework is a motion model that formally describes the motion. Most commonly, a set of control points is placed in space and time. A motion vector is assigned to each control point. The motion at any other point and time is then obtained by a weighted combination of the motion vectors of the neighboring control points. Page 3 right column 4th paragraph), and the processing circuitry is further configured to: obtain an interval between adjacent control points (For all motion models the spatial distance of the control points was set to 7.5 mm in each direction and the temporal distance was set to 2.5% of the cardiac cycle. Page 6 right column last paragraph), and determine the number of the control points (An implicit regularization of the motion field can be obtained by varying the number of control points. Page 6 left column last paragraph), based on the estimated length and the obtained interval (For all motion models the spatial distance of the control points was set to 7.5 mm in each direction and the temporal distance was set to 2.5% of the cardiac cycle. Page 6 right column last paragraph. It is common knowledge that the number of the control points can be determined from the length of the vessel and the interval between adjacent control points.).
The combination of Rohkohl and Lauritsch does not teach the following limitations as further recited, but Yasuhiro further teaches estimate a length of the identified vessel (obtaining the length of the vascular wall (which reads on “a length of the identified vessel”) by cumulatively adding the minute distances obtained between the vertices of the adjacent polygonal cross-sections. [0008]).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rohkohl and Lauritsch to incorporate the teachings of Yasuhiro to estimate a length of the identified vessel in order to calculate the number of control points needed for the motion model.
Regarding claim 7, Yasuhiro in the combination teaches the apparatus of claim 6, wherein the processing circuitry is further configured to: track the identified vessel to estimate a corresponding length of each vessel slice of the plurality of vessel slices included in the identified vessel, and obtain the estimated length of the identified vessel, based on the estimated corresponding lengths of the plurality of vessel slices (The above problem is solved by, for example, a configuration shown in FIG. 1. That is, the image processing method of the present invention (1) comprises the steps of obtaining the centraI point O of the CT cross-section of the vascular portion based on the CT tomographic image of the vascular portion, and forming the central line of the vascular portion by interpolating between the central points O1 to On of the adjacent CT cross-sections of the vascular portion with a smooth curve, preparing the vertical cross-section of the vascular portion perpendicular to the central line C for each minute section of the central line C and approximating the contour shape of the vertical cross-section of the vascular portion with a polygon, and obtaining the length of the vascular wall (which reads on the “length of the identified vessel”) by cumulatively adding the minute distances obtained between the vertices of the adjacent polygonal cross-sections (which reads on the “length of each vessel slice”). [0008].
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).
Regarding claim 8, Rohkohl in the combination teaches the apparatus of claim 6, wherein the processing circuitry is further configured to: receive an interval inputted by an operator of the CT imaging system, as the obtained interval, or derive an interval within a predefined range, as the obtained interval (For all motion models the spatial distance of the control points was set to 7.5 mm in each direction and the temporal distance was set to 2.5% of the cardiac cycle. Page 6 right column last paragraph).
Regarding claim 10, Rohkohl in the combination teaches the apparatus of claim 6, wherein the parameters further include respective positions of each of the control points to be used during the estimation of the vessel motion field (One important building block of any motion correction framework is a motion model that formally describes the motion. Most commonly, a set of control points is placed in space and time. A motion vector is assigned to each control point. The motion at any other point and time is then obtained by a weighted combination of the motion vectors of the neighboring control points. Page 3 right column 4th paragraph), and the processing circuitry is further configured to: calculate a motion artifact metric with respect to each vessel slice of the plurality of vessel slices included in the identified vessel (In the following we propose two different metrics to assess the relative amount of motion artifacts: entropy and positivity. Page 4 left column 2nd paragraph.
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).
Lauritsch in the combination further teaches determine, based on the calculated motion artifact metrics (In a further advantageous embodiment, the iterative determination of the motion field is implemented as an optimization problem with a given cost function, which defines, as a metric for the optimization problem, a correlation between the respective reference image and an image of the target object that has been generated in a motion-compensated manner using the respective current motion field. Column 7 line 52), the respective positions of each of the determined number of control points (The motion field, that is, the vectors thereof s, may initially be determined on a predetermined grid (In other words, the control point locations change as the optimization progresses.), for example with a width, that is, a point spacing, of 25 mm for instance. Column 14 line 12. A further predetermined parameter is an update interval K, which specifies after how many iterative acts k the reference image 13 is updated. To update the reference image 13, the most current iterative image 17 in each case is described as the provisional motion-compensated image, as for process act S3, and segmented as indicated here by a program path 18 in order to obtain a new or updated reference image 13, the respective existing reference image 13 being replaced. Column 14 line 57).
Regarding claim 11, Rohkohl in the combination teaches the apparatus of claim 10, wherein the processing circuitry is further configured to: extract a vessel region around each vessel slice of the plurality of vessel slices (Therefore, the first step of the algorithm is the definition of a region of interest Ω for motion estimation (M-ROI). Page 3 right column 3rd paragraph), based on a vessel mask of a particular one of the determined mask sizes that corresponds to the vessel slice (In order to obtain the M-ROI, the segmented region was dilated by several millimeters to cover the whole range of motion related artifacts originating from the contrast filled vessels. Page 4 right column last paragraph), and calculate a motion artifact level with respect to each vessel region of the extracted vessel regions, as the calculated motion artifact metrics (The metrics are described by a function L(s) which computes the MAM value for the motion compensated reconstruction corresponding to the motion model parameters s in a region of interest Ω. In the following we propose two different metrics to assess the relative amount of motion artifacts: entropy and positivity. Page 5 left column 2nd paragraph).
Regarding claim 12, Rohkohl in the combination teaches the apparatus of claim 11, wherein the processing circuitry is further configured to: identify a motion artifact within each of the extracted vessel regions (The intensity variations in a CT image caused by data inconsistencies due to motion artifacts increase entropy. Page 5 left column last paragraph), and calculating entropy (The authors use two different MAMs, entropy, and positivity. Page 1 Methods), compactness, or a circular score for each of the identified motion artifacts, as the calculated motion artifact levels (The metrics are described by a function L(s) which computes the MAM value for the motion compensated reconstruction corresponding to the motion model parameters s in a region of interest Ω. In the following we propose two different metrics to assess the relative amount of motion artifacts: entropy and positivity. Page 5 left column 2nd paragraph).
Regarding claim 13, Lauritsch in the combination teaches the apparatus of claim 10, wherein the processing circuitry is further configured to determine the respective positions of the determined number of control points (The motion field, that is, the vectors thereof s, may initially be determined on a predetermined grid (In other words, the control point positions change as the optimization progresses.), for example with a width, that is, a point spacing, of 25 mm for instance. Column 14 line 12. A further predetermined parameter is an update interval K, which specifies after how many iterative acts k the reference image 13 is updated. To update the reference image 13, the most current iterative image 17 in each case is described as the provisional motion-compensated image, as for process act S3, and segmented as indicated here by a program path 18 in order to obtain a new or updated reference image 13, the respective existing reference image 13 being replaced. Column 14 line 57) based on a magnitude distribution of the calculated motion artifact metrics along the identified vessel (As a termination condition, a predetermined number of iterative acts or a similarity criterion or a quality criterion may be specified for the iterative image 17, for example. For example, the following equation (1) may apply:
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where x is the spatial variable, the index i counts the projection images, the term f (x, s) specifies a motion-compensated back projection from the projection images, that is, specifies a motion-compensated reconstructed image, and the term fr(x) describes the reference image 13. Column 14 line 30).
Regarding claim 14, Lauritsch in the combination teaches the apparatus of claim 13, wherein the processing circuitry is further configured to determine the respective positions of the determined number of control points by assigning more control points to a portion of the identified vessel including more vessel slices with respect to which the calculated motion artifact metrics are beyond a predefined threshold, compared with another portion of the identified vessel including fewer vessel slices with respect to which the calculated motion artifact metrics are beyond the predefined threshold (In a process act S3, the initial reconstructed image 11 is segmented, in particular automatically, for example by using an intensity-based thresholding method. The result is a reference image 13, which may be thinly or sparsely populated-even compared with the initial reconstructed image 11. For example, just one out of 1000 voxels in the reference image 13 may include image element values that differ from 0, because in the segmentation or in the intensity-based or image element values-based thresholding method, image element values that are below the respective threshold value or that have not been acquired by the threshold may be replaced by 0 in process act S3. The reference image 13 shows at least one part of the vascular tree 12 but may be restricted to the maximum-intensity regions thereof. Column 13 line 26.
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Regarding claim 15, Lauritsch in the combination teaches the apparatus of claim 13, wherein the processing circuitry is further configured not to assign a control point to a vessel slice with respect to which the calculated motion artifact metric is below a predefined threshold (For example, just one out of 1000 voxels in the reference image 13 may include image element values that differ from 0, because in the segmentation or in the intensity-based or image element values-based thresholding method, image element values that are below the respective threshold value or that have not been acquired by the threshold may be replaced by 0 in process act S3. Column 13 line 31. A person having ordinary skill in the art would recognize Lauritsch’s approach can be applied to the calculated motion artifact metric.).
Method claims 18 and 19 are drawn to the method of using the corresponding apparatus claimed in claims 6 and 10. Therefore method claims 18 and 19 correspond to apparatus claims 6 and 10 and are rejected for the same reasons of obviousness as used above.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Rohkohl (Improving best-phase image quality in cardiac CT by motion correction with MAM optimization, Med. Phys. 40 (3), March 2013), hereinafter Rohkohl, in view of Lauritsch (US Patent No.: US 11,379,994 B2), hereinafter Lauritsch, further in view of Yasuhiro (Japan Patent Pub. No.: JP2004201873A), hereinafter Yasuhiro, further in view of Tu (Assessment of obstruction length and optimal viewing angle from biplane X-ray angiograms, Int J Cardiovasc Imaging (2010) 26:5–17), hereinafter Tu.
Regarding claim 9, Rohkohl, Lauritsch and Yasuhiro teaches all of the elements of the claimed invention as stated in claim [[6]]8 except for the following limitations as further recited. However, Tu teaches wherein the predefined range is from 15 millimeters to 20 millimeters (Twelve segments with length ranging from 16.5 to 39.0 mm were defined by the markers on the wire phantoms. Page 12 left column last paragraph).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Rohkohl, Lauritsch and Yasuhiro to incorporate the teachings of Tu to predefine range from 15 millimeters to 20 millimeters in order to obtain a better estimate of the number of control points for cardiac vessel motion compensation.
Conclusion
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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