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 Status
Claims 1-19 are currently pending in the application filed 9/26/2024
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The 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 --------------1-2,4, 9,10-13, 17, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Meyer (US2011/0007956A1)
Regarding claim 1, (Meyer) teaches:
A medical image processing method, characterized by comprising:
(Meyer, [0062]; “The measurement data serving as the input for the method has already been captured. This is indicated by an arrow on the left side of the diagram. This measurement data corresponds to the original sinogram Org-Sin. An image Pic is reconstructed from the measurement data. Methods known per se can be used for image reconstruction, in particular convoluted back projection.”)
acquiring raw projection data obtained after a subject to be examined is scanned, and performing reconstruction to obtain a raw medical image;
(Meyer, [0062]; “This measurement data corresponds to the original sinogram Org-Sin. An image Pic is reconstructed from the measurement data. Methods known per se can be used for image reconstruction, in particular convoluted back projection.”)
performing multivalued processing on the raw medical image to obtain a multivalued image;
(Meyer, [0068]; “The mask image Ma-Pic is produced from the original image Pic by replacing the CT values with the respective class values. In the mask image Ma-Pic the class value of air, which was set to 0 by way of example, is therefore input for all pixls, the CT value of which in the original image Pic is below the threshold value for demarcating air and water. Also the class value of water, assumed by way of example to be 0.0192/mm, is input for all pixels, the CT value of which in the original image Pic is above the threshold value for demarcating air and water and below the threshold value for demarcating water and bone”)
according to a correspondence between a degree of contribution, relating to a ray absorption amount, of each tissue on a ray path that does not pass through a specific material site in the multivalued image
(Meyer, [0069]; "There are also pixels which were identified as metal based on the threshold value comparison. These are allocated the CT values of the bone class in the mask image Ma-Pic.")
(Meyer, [0076]; "During the forward projection for calculating the mask sinogram Ma-Sin line integrals are calculated over the object mapped in the initial image Ma-Pic. The mask sinogram Ma-Sin therefore indicates the effectively irradiated water length.")
[Examiner note: Wang's specification at [0054] and [0061] explains that a "ray path that does not pass through a specific material site" is a ray that does not go through metal. Wang [0062] explains that the "degree of contribution" relating to ray absorption is how much each tissue along the ray adds to the total absorption. Meyer [0069] shows that any pixel marked as metal is given the bone class value in the mask image Ma-Pic, which means Ma-Pic does not contain any metal. When Meyer forward-projects Ma-Pic in [0076], the line integral along each ray adds up the tissue contributions for that ray, and Meyer confirms this depends on which tissues the ray goes through, since "when a projection goes through bone as well as tissue, the effectively irradiated water length is greater than for a projection that only goes through tissue." Because Ma-Pic has no metal, every ray going through Ma-Pic is a non-metal ray, and each Ma-Sin value gives the tissue absorption contribution along that non-metal ray. This reads on the claimed degree of contribution of each tissue along a ray path that does not pass through the specific material site.]
and a raw projection value corresponding to the ray path in the raw projection data
(Meyer, [0077]; "The mask sinogram Ma-Sin is now used to normalize NORM the original sinogram Org-Sin. This normalization NORM is carried out by dividing the values of the original sinogram Org-Sin pixel by pixel, i.e. point by point, by the values of the mask sinogram Ma-Sin”)
[Examiner note: Wang's specification at [0054] explains that each ray path corresponds to a specific projection position in the raw projection data. Wang [0065] explains by saying "N projection positions corresponding to the N first ray paths on a sinogram corresponding to the raw projection data may be determined according to a projection relationship." So every value in a sinogram sits at a projection position that traces back to one specific ray path. In Meyer, the original sinogram Org-Sin is the raw projection data (Meyer [0062]), and each value in Org-Sin is the actual detector reading for the ray path at that projection position. The mask sinogram Ma-Sin, which Meyer builds by forward projection in [0076], also has values at the same projection positions, and each one is the tissue absorption contribution along the same ray path. When Meyer divides Org-Sin by Ma-Sin point by point in [0077], the division pairs up the raw projection value and the tissue contribution for the same ray path at each projection position. For ray paths outside the metal trace, which are the non-metal ray paths discussed above, this point by point division gives a value to value relationship between the tissue contribution and the raw projection value for that ray path. This reads on the claimed correspondence between the degree of contribution and the raw projection value corresponding to the ray path.]
determining a predicted projection value (Meyer, [0079]; "In the following step INT the values of the sinogram Norm-Sin that are within the metal trace are replaced with other values by interpolation.")
of a path corresponding to the specific material site in the raw projection data; (Meyer, [0079]; "the location of the metal trace is known from the metal sinogram Me-Sin.")
(Meyer, [0079]; "However the visibility of the metal trace within the normalized sinogram Norm-Sin is irrelevant here, as the location of the metal trace is known from the metal sinogram Me-Sin. In the following step INT the values of the sinogram Norm-Sin that are within the metal trace are replaced with other values by interpolation.")
[Examiner note: Wang's specification at [0054] explains that a ray path passing through metal is what the claim calls a path through the specific material site, and its corresponding position in the raw projection data is affected by the metal. Wang [0067] explains that the path corresponding to the specific material site in the raw projection data is identified on the sinogram, and the values along this path are the projection values that need to be replaced with predicted values. In Meyer, the "metal trace" is the set of sinogram positions for ray paths that pass through metal, and Meyer [0079] uses linear interpolation to calculate new projection values for those metal trace positions from the surrounding projection values away from the metal trace. The values away from the metal trace are the non-metal ray path values, which carry the correspondence between tissue contribution and raw projection value established in [0077]. So the interpolated value at each metal trace position is a new projection value calculated using that correspondence. This reads on the claimed predicted projection value of a path corresponding to the specific material site in the raw projection data, since Meyer's metal trace is the claimed path corresponding to the specific material site, and Meyer's interpolated value at each metal trace position is the claimed predicted projection value.]
and obtaining a first medical image according to the predicted projection value and the raw projection data.
(Meyer, [0081]; “the interpolated sinogram Int-Sin is multiplied pixel by pixel, in particular also in the region of the deleted metal trace, by the mask sinogram Ma-Sin. As a result the resulting sinogram Kor-Sin again contains the structure information contained in the mask sinogram Ma-Sin.”)
(Meyer, [0083]; “A subsequent image reconstruction supplies the corrected image Kor-Pic.”)
[Examiner note: Wang's specification at [0071] explains that the first medical image is obtained by replacing the raw projection value of the path corresponding to the specific material site with the predicted projection value and then reconstructing the resulting projection data. In Meyer, the interpolated sinogram Int-Sin carries the predicted projection values at the metal trace positions ([0079]), and the mask sinogram Ma-Sin carries the tissue structure information that came from the raw projection data Org-Sin, since Ma-Sin was built by forward projection of Ma-Pic ([0076]), and Ma-Pic was segmented from Pic, which was reconstructed from Org-Sin ([0062], [0068]). When Meyer multiplies Int-Sin by Ma-Sin point by point in [0081], the resulting Kor-Sin holds both the predicted projection values from Int-Sin and the raw projection data structure carried through Ma-Sin. Meyer confirms this by stating that Kor-Sin "again contains the structure information contained in the mask sinogram Ma-Sin." Meyer [0083] then reconstructs Kor-Sin into the corrected image Kor-Pic. Since Kor-Pic comes from a sinogram that was built by combining the predicted projection values (Int-Sin) with the raw projection data structure (Ma-Sin), Kor-Pic reads on the claimed first medical image obtained according to the predicted projection value and the raw projection data.]
While Meyer (US 2011/0007956 A1) does disclose computing tissue absorption contributions by forward projecting the mask image Ma-Pic (Meyer, [0076]), normalizing the raw projection data by dividing Org-Sin by Ma-Sin point by point (Meyer, [0077]), and interpolating within the metal trace to produce a replacement value (Meyer, [0079]), it does not explicitly disclose determining the predicted projection value for the metal-affected ray path by using a correspondence between the tissue contribution values and the raw projection values established from the non-metal ray paths, but it would have been obvious to have done so. The reason is that Meyer's point-by-point division of Org-Sin by Ma-Sin (Meyer, [0077]) inherently pairs the tissue contribution with the raw projection value at every non-metal projection position, and Meyer's interpolation at the metal trace followed by denormalization (Meyer, [0079], [0081]) inherently uses that pairing to produce the predicted value at the metal path from the surrounding non-metal data. Thus, it would have been obvious to one of ordinary skill in the art at the time of the invention to include determining the predicted projection value according to the tissue-contribution and raw-projection-value correspondence, because Meyer's normalization, interpolation, and denormalization steps collectively perform this determination, and a person of ordinary skill would have recognized that the pipeline uses the non-metal ray path data to predict the metal-affected ray path values for the purpose of reducing metal artifacts in the reconstructed image.
Regarding claim 19, (Meyer) teaches:
A medical image processing apparatus, comprising: (Meyer, [0050]– [0051]; “The CT system 10 is controlled by a control and computation unit C10 with a computer program code Prg₁ to Prgₙ… The projection measurement data p… is then optionally processed further in an image reconstruction component C21… The image reconstruction component C21 in this example embodiment is realized in the control and computation unit C10 in the form of software on a processor.”)
a first reconstruction unit, the first reconstruction unit acquiring raw projection data obtained after a subject to be examined is scanned, and performing reconstruction to obtain a raw medical image; (Meyer, [0051]; “The projection measurement data p acquired by the detector C3 and/or C5 is transferred by way of a raw data interface C23 to the control and computation unit C10.”) [Examiner note: unit C10 acquires the raw projection data from the detector.] (Meyer, [0062]; “This measurement data corresponds to the original sinogram Org-Sin. An image Pic is reconstructed from the measurement data. Methods known per se can be used for image reconstruction, in particular convoluted back projection.”) [Examiner note: C10/C21 reconstructs the raw image Pic from Org-Sin, reading on the first reconstruction unit.]
a segmentation unit, the segmentation unit performing multivalued processing on the raw medical image to obtain a multivalued image; (Meyer, [0064]; “The image Pic now undergoes segmentation, the result of which is a metal image Me-Pic and a mask image Ma-Pic.”) [Examiner note: Meyer expressly recites a segmentation step performed on Pic.] (Meyer, [0068]; “The mask image Ma-Pic is produced from the original image Pic by replacing the CT values with the respective class values.”) [Examiner note: replacing CT values with class values via thresholds reads on multivalued processing producing the mask image Ma-Pic (the multivalued image).]
a first determination unit, the first determination unit determining, according to a correspondence between a degree of contribution, relating to a ray absorption amount, of each tissue on a ray path that does not pass through a specific material site in the multivalued image (Meyer, [0069]; "There are also pixels which were identified as metal based on the threshold value comparison. These are allocated the CT values of the bone class in the mask image Ma-Pic.")
(Meyer, [0076]; "During the forward projection for calculating the mask sinogram Ma-Sin line integrals are calculated over the object mapped in the initial image Ma-Pic. The mask sinogram Ma-Sin therefore indicates the effectively irradiated water length.")
[Examiner note: Wang's specification at [0054] and [0061] explains that a "ray path that does not pass through a specific material site" is a ray that does not go through metal. Wang [0062] explains that the "degree of contribution" relating to ray absorption is how much each tissue along the ray adds to the total absorption. Meyer [0069] shows that any pixel marked as metal is given the bone class value in the mask image Ma-Pic, which means Ma-Pic does not contain any metal. When Meyer forward-projects Ma-Pic in [0076], the line integral along each ray adds up the tissue contributions for that ray, and Meyer confirms this depends on which tissues the ray goes through, since "when a projection goes through bone as well as tissue, the effectively irradiated water length is greater than for a projection that only goes through tissue." Because Ma-Pic has no metal, every ray going through Ma-Pic is a non-metal ray, and each Ma-Sin value gives the tissue absorption contribution along that non-metal ray. This reads on the claimed degree of contribution of each tissue along a ray path that does not pass through the specific material site.]
and a raw projection value corresponding to the ray path in the raw projection data, (Meyer, [0077]; "The mask sinogram Ma-Sin is now used to normalize NORM the original sinogram Org-Sin. This normalization NORM is carried out by dividing the values of the original sinogram Org-Sin pixel by pixel, i.e. point by point, by the values of the mask sinogram Ma-Sin”)
[Examiner note: Wang's specification at [0054] explains that each ray path corresponds to a specific projection position in the raw projection data. Wang [0065] explains by saying "N projection positions corresponding to the N first ray paths on a sinogram corresponding to the raw projection data may be determined according to a projection relationship." So every value in a sinogram sits at a projection position that traces back to one specific ray path. In Meyer, the original sinogram Org-Sin is the raw projection data (Meyer [0062]), and each value in Org-Sin is the actual detector reading for the ray path at that projection position. The mask sinogram Ma-Sin, which Meyer builds by forward projection in [0076], also has values at the same projection positions, and each one is the tissue absorption contribution along the same ray path. When Meyer divides Org-Sin by Ma-Sin point by point in [0077], the division pairs up the raw projection value and the tissue contribution for the same ray path at each projection position. For ray paths outside the metal trace, which are the non-metal ray paths discussed above, this point by point division gives a value to value relationship between the tissue contribution and the raw projection value for that ray path. This reads on the claimed correspondence between the degree of contribution and the raw projection value corresponding to the ray path.]
a predicted projection value (Meyer, [0079]; "In the following step INT the values of the sinogram Norm-Sin that are within the metal trace are replaced with other values by interpolation.") of a path corresponding to the specific material site in the raw projection data; and (Meyer, [0079]; "the location of the metal trace is known from the metal sinogram Me-Sin.")
(Meyer, [0079]; "However the visibility of the metal trace within the normalized sinogram Norm-Sin is irrelevant here, as the location of the metal trace is known from the metal sinogram Me-Sin. In the following step INT the values of the sinogram Norm-Sin that are within the metal trace are replaced with other values by interpolation.")
[Examiner note: Wang's specification at [0054] explains that a ray path passing through metal is what the claim calls a path through the specific material site, and its corresponding position in the raw projection data is affected by the metal. Wang [0067] explains that the path corresponding to the specific material site in the raw projection data is identified on the sinogram, and the values along this path are the projection values that need to be replaced with predicted values. In Meyer, the "metal trace" is the set of sinogram positions for ray paths that pass through metal, and Meyer [0079] uses linear interpolation to calculate new projection values for those metal trace positions from the surrounding projection values away from the metal trace. The values away from the metal trace are the non-metal ray path values, which carry the correspondence between tissue contribution and raw projection value established in [0077]. So the interpolated value at each metal trace position is a new projection value calculated using that correspondence. This reads on the claimed predicted projection value of a path corresponding to the specific material site in the raw projection data, since Meyer's metal trace is the claimed path corresponding to the specific material site, and Meyer's interpolated value at each metal trace position is the claimed predicted projection value.]
a second reconstruction unit, the second reconstruction unit obtaining a first medical image according to the predicted projection value and the raw projection data. (Meyer, [0081]; “the interpolated sinogram Int-Sin is multiplied pixel by pixel, in particular also in the region of the deleted metal trace, by the mask sinogram Ma-Sin. As a result the resulting sinogram Kor-Sin again contains the structure information contained in the mask sinogram Ma-Sin.”)
(Meyer, [0083]; “A subsequent image reconstruction supplies the corrected image Kor-Pic.”)
[Examiner note: Wang's specification at [0071] explains that the first medical image is obtained by replacing the raw projection value of the path corresponding to the specific material site with the predicted projection value and then reconstructing the resulting projection data. In Meyer, the interpolated sinogram Int-Sin carries the predicted projection values at the metal trace positions ([0079]), and the mask sinogram Ma-Sin carries the tissue structure information that came from the raw projection data Org-Sin, since Ma-Sin was built by forward projection of Ma-Pic ([0076]), and Ma-Pic was segmented from Pic, which was reconstructed from Org-Sin ([0062], [0068]). When Meyer multiplies Int-Sin by Ma-Sin point by point in [0081], the resulting Kor-Sin holds both the predicted projection values from Int-Sin and the raw projection data structure carried through Ma-Sin. Meyer confirms this by stating that Kor-Sin "again contains the structure information contained in the mask sinogram Ma-Sin." Meyer [0083] then reconstructs Kor-Sin into the corrected image Kor-Pic. Since Kor-Pic comes from a sinogram that was built by combining the predicted projection values (Int-Sin) with the raw projection data structure (Ma-Sin), Kor-Pic reads on the claimed first medical image obtained according to the predicted projection value and the raw projection data.]
While Meyer (US 2011/0007956 A1) does disclose computing tissue absorption contributions by forward projecting the mask image Ma-Pic (Meyer, [0076]), normalizing the raw projection data by dividing Org-Sin by Ma-Sin point by point (Meyer, [0077]), and interpolating within the metal trace to produce a replacement value (Meyer, [0079]), it does not explicitly disclose that the first determination unit determines the predicted projection value for the metal-affected ray path by using a correspondence between the tissue contribution values and the raw projection values established from the non-metal ray paths, but it would have been obvious to have done so. The reason is that Meyer's point-by-point division of Org-Sin by Ma-Sin (Meyer, [0077]) inherently pairs the tissue contribution with the raw projection value at every non-metal projection position, and Meyer's interpolation at the metal trace followed by denormalization (Meyer, [0079], [0081]) inherently uses that pairing to produce the predicted value at the metal path from the surrounding non-metal data. Thus, it would have been obvious to one of ordinary skill in the art at the time of the invention to include a first determination unit that determines the predicted projection value according to the tissue-contribution and raw-projection-value correspondence, because Meyer's control and computation unit C10 (Meyer, [0050]) already executes the normalization, interpolation, and denormalization steps that collectively perform this determination, and a person of ordinary skill would have recognized that these steps use the non-metal ray path data to predict the metal-affected ray path values for the purpose of reducing metal artifacts in the reconstructed image.
Regarding claim 2, (Meyer) teaches:
The method according to claim 1, wherein the performing multivalued processing on the raw medical image to obtain a multivalued image includes comparing CT values of pixel positions in the raw medical image
(Meyer, [0065]; "The original image Pic consists of pixels, to which an image value is respectively assigned. The image values are indicated as a CT value in HU (Hounsfield Units).")
with a plurality of thresholds corresponding to CT values of different types of tissues,
(Meyer, [0067]; "In the present example there is a first threshold value to separate air and water, a second threshold value to separate water and bone and a third threshold value to separate bone and metal.")
and according to comparison results, redetermining pixel values of the pixel positions to obtain the multivalued image, wherein in the multivalued image, pixel values corresponding to different tissues are different, and pixel values corresponding to the same tissue are the same.
(Meyer, [0068]; "The mask image Ma-Pic is produced from the original image Pic by replacing the CT values with the respective class values. In the mask image Ma-Pic the class value of air, which was set to 0 by way of example, is therefore input for all pixels, the CT value of which in the original image Pic is below the threshold value for demarcating air and water. Also the class value of water, assumed by way of example to be 0.0192/mm, is input for all pixels, the CT value of which in the original image Pic is above the threshold value for demarcating air and water and below the threshold value for demarcating water and bone.")
[Examiner note: Wang's specification at [0046] explains that multivalued processing means comparing the CT value of each pixel in the raw medical image to a set of thresholds for different tissues, and then changing each pixel's value based on which threshold range it falls into, so that pixels of the same tissue end up with the same value and pixels of different tissues end up with different values. Meyer [0065] says each pixel of the original image Pic has a CT value, which lines up with the claim's CT values of pixel positions in the raw medical image. Meyer [0067] sets up multiple thresholds, one for each pair of materials (air/water, water/bone, bone/metal), which lines up with the claim's plurality of thresholds for different tissues. Meyer [0068] then goes through each pixel, checks its CT value against the thresholds, and replaces it with the class value of the material it falls into. All pixels falling in the air range get class value 0, all pixels in the water range get class value 0.0192/mm, so pixels of the same material end up with the same value and pixels of different materials end up with different values. This reads on the claimed multivalued image where pixel values for different tissues are different and pixel values for the same tissue are the same.]
Regarding claim 4, (Meyer) teaches:
The method according to claim 1, wherein the according to a correspondence between a degree of contribution, relating to a ray absorption amount, of each tissue on a ray path that does not pass through a specific material site in the multivalued image and a raw projection value corresponding to the ray path in the raw projection data, determining a predicted projection value of a path corresponding to the specific material site in the raw projection data includes: determining the degree of contribution of each tissue, other than the specific material site, through which second ray paths pass in the multivalued image, wherein the second ray paths are ray paths that pass through the specific material site;
(Meyer, [0076]; “During the forward projection for calculating the mask sinogram Ma-Sin line integrals are calculated over the object mapped in the initial image Ma-Pic.”) [Examiner note: forward projection over Ma-Pic computes tissue contributions along every ray path, including those passing through the metal location.]
and value corresponding to the degree of contribution of each tissue, other than the specific material site, on the second ray paths as the predicted projection value.
(Meyer, [0069]; "There are also pixels which were identified as metal based on the threshold value comparison. These are allocated the CT values of the bone class in the mask image Ma-Pic.") (Meyer, [0076]; "During the forward projection for calculating the mask sinogram Ma-Sin line integrals are calculated over the object mapped in the initial image Ma-Pic.") (Meyer, [0079]; "In the following step INT the values of the sinogram Norm-Sin that are within the metal trace are replaced with other values by interpolation. The simplest option for interpolation is a linear interpolation, with a linear equation then being used to calculate projection values within the metal trace from the projection values away from the metal trace.")
[Examiner note: Wang's specification at [0054] says a "second ray path" is a ray that passes through the specific material site (metal), and [0067] says the predicted projection value for the second ray paths is calculated using the correspondence established from the first ray paths. Meyer [0069] gives metal pixels the bone class value in Ma-Pic, so Ma-Pic only has non-metal tissues (air, water, bone). Meyer [0076] forward projects Ma-Pic, and the line integral for each ray path adds up the contributions of each non-metal tissue the ray passes through, including the ray paths that pass through the metal pixel locations, which are the claimed second ray paths. Meyer [0079] then takes the values along the metal trace (the second ray paths in the raw projection data) and calculates new values by linear interpolation from the projection values away from the metal trace (the first ray paths), where those surrounding values carry the correspondence established in [0077]. So the new value at each second ray path is a projection value calculated from the contributions of each non-metal tissue along that second ray path using the correspondence. This reads on the claimed predicted projection value.]
Regarding claim 9, (Meyer) teaches:
The method according to claim 1, wherein the obtaining a first medical image according to the predicted projection value and the raw projection data includes: replacing (Meyer, [0079]; "the values of the sinogram Norm-Sin that are within the metal trace are replaced with other values by interpolation.") a raw projection value of the path corresponding to the specific material site in the raw projection data (Meyer, [0077]; "The mask sinogram Ma-Sin is now used to normalize NORM the original sinogram Org-Sin. This normalization NORM is carried out by dividing the values of the original sinogram Org-Sin pixel by pixel, i.e. point by point, by the values of the mask sinogram Ma-Sin.") with the predicted projection value to obtain first projection data; (Meyer, [0079]; "In the following step INT the values of the sinogram Norm-Sin that are within the metal trace are replaced with other values by interpolation.") [Examiner note: Meyer expressly states in [0079] values within the metal trace are replaced with interpolated (predicted) values. Norm-Sin is derived from the raw projection data Org-Sin via point-by-point normalization ([0077]), so replacing values in Norm-Sin within the metal trace equates to replacing the raw projection values of the path corresponding to the specific material site, producing the first projection data (Int-Sin).]
and obtaining the first medical image according to the first projection data. (Meyer, [0083]; "A subsequent image reconstruction supplies the corrected image Kor-Pic.") [Examiner note: reconstructing the modified projection data yields the corrected image Kor-Pic, i.e., the claimed first medical image.]
Regarding claim 10, (Meyer) teaches:
The method according to claim 9, wherein the obtaining the first medical image according to the first projection data includes: correcting, in the first projection data, the predicted projection value by using raw projection values around the location of the predicted projection value, to obtain second projection data; (Meyer, [0079]; “In the following step INT the values of the sinogram Norm-Sin that are within the metal trace are replaced with other values by interpolation. The simplest option for interpolation is a linear interpolation, with a linear equation then being used to calculate projection values within the metal trace from the projection values away from the metal trace.”) [Examiner note: Meyer interpolates predicted values in the metal trace using raw projection values from the surrounding (non-metal) regions, reading on correcting the predicted value using nearby raw projection values.] and reconstructing the second projection data to obtain the first medical image. (Meyer, [0081]; “To obtain another sinogram, from which an image can be reconstructed, in a denormalization step DENORM, which is the reverse of the normalization step NORM, the interpolated sinogram Int-Sin is multiplied pixel by pixel, in particular also in the region of the deleted metal trace, by the mask sinogram Ma-Sin. As a result, the resulting sinogram Kor-Sin again contains the structure information contained in the mask sinogram Ma-Sin.”) (Meyer, [0083]; “A subsequent image reconstruction supplies the corrected image Kor-Pic”) [Examiner Note: reconstructing the resulting corrected sinogram Kor-Sin yields the claimed first medical image.]
Regarding claim 11, (Meyer) teaches:
The method according to claim 1, wherein the method further includes: determining the position of the specific material site in the raw medical image, including comparing CT values of pixels in the raw medical image with a preset CT value corresponding to the specific material, and determining the location of pixels having a CT value greater than the preset CT value as the specific material site. (Meyer, [0067]; “In the present example there is a first threshold value to separate air and water, a second threshold value to separate water and bone and a third threshold value to separate bone and metal.”) [Examiner note: Meyer uses a preset CT threshold to separate bone from metal, i.e., to identify metal pixels.] (Meyer, [0069]; “There are also pixels which were identified as metal based on the threshold value comparison.”) [Examiner note: pixels exceeding the metal CT threshold are identified as the specific material (metal) site, reading on the claimed determination of the metal location.]
Regarding claim 12, (Meyer) teaches:
The method according to claim 11, further including: performing forward projection on the position of the specific material site in the raw medical image to determine the path corresponding to the specific material site in the raw projection data. (Meyer, [0075]; “Once segmentation is completed, i.e. when the metal image Me-Pic and the mask image Ma-Pic are present, projection data is calculated from the two images Me-Pic and Ma-Pic by forward projection. The metal sinogram Me-Sin results from this for the metal image Me-Pic.”) [Examiner note: Meyer performs forward projection on the metal image Me-Pic to obtain Me-Sin, which identifies the metal trace (the path corresponding to the specific material site) in the raw projection data.]
Regarding claim 13, (Meyer) teaches:
The method according to claim 1, further including: reconstructing a second medical image having the specific material site; (Meyer, [0062]; "An image Pic is reconstructed from the measurement data. Methods known per se can be used for image reconstruction, in particular convoluted back projection.") [Examiner note: Meyer expressly reconstructs image Pic from the original sinogram Org-Sin (the raw measurement data, which contains the metal trace). Because Pic is reconstructed directly from the unmodified raw projection data, it contains the specific material (metal) site, equating to the reconstructed second medical image having the specific material site.]
and filling the second medical image into the first medical image to generate a diagnostic image. (Meyer, [0084]; "the pixels where the metal object is located according to the metal image Me-Pic can be replaced in the corrected image Kor-Pic by a high CT value corresponding to the respective metal.") [Examiner note: Meyer replaces pixels at the metal location in Kor-Pic (the first medical image) with values representing the metal (identified via Me-Pic, which itself is segmented from Pic the reconstructed image containing metal), equating to the filling step that generates the diagnostic image.]
Regarding claim 17, (Meyer) teaches:
The method according to claim 1, wherein the specific material site comprises a metal site. (Meyer, [0005]; “Metallic foreign bodies within an examination object, e.g. dental fillings or implanted screws, have an extremely negative influence on the image quality of CT images.”) [Examiner note: Meyer’s entire method is directed to handling metal (e.g., dental fillings, implants), which reads directly on the claimed metal site.]
Regarding claim 18, (Meyer) teaches:
The method according to claim 2, wherein in the multivalued image, a pixel value corresponding to the specific material site is set to 0, or a contribution weighting value corresponding to the specific material site is 0. (Meyer, [0068]; “In the mask image Ma-Pic the class value of air, which was set to 0 by way of example, is therefore input for all pixels, the CT value of which in the original image Pic is below the threshold value for demarcating air and water.”) [Examiner note: Meyer expressly sets a class value to 0 in the mask (multivalued) image. Further, Meyer’s normalization scheme excludes the metal contribution from the mask sinogram, effectively treating the metal’s contribution as 0 in the mask sinogram, reading on the claimed contribution weighting value of 0 for the specific material site.]
Claims --------------3, 5-7, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Meyer (US2011/0007956A1) further in view of Benson (US 8,503,750 B2)
Regarding claim 3, Meyer teaches:
The method according to claim 1, wherein the method further includes determining the correspondence, including: determining the degree of contribution of each tissue through which a plurality of different first ray paths pass in the multivalued image, and a plurality of raw projection values of projection positions respectively corresponding to the plurality of different first ray paths in the raw projection data, wherein the first ray paths are ray paths that do not pass through the specific material site; (Meyer, [0076]; “During the forward projection for calculating the mask sinogram Ma-Sin line integrals are calculated over the object mapped in the initial image Ma-Pic.”) [Examiner note: Meyer forward-projects the mask image to compute tissue contributions along non-metal ray paths and obtains the corresponding raw projection values from Org-Sin.]
Meyer fails to teach:
and performing fitting according to the degree of contribution of each tissue on the plurality of different first ray paths and the plurality of raw projection values to obtain the correspondence.
Benson teaches:
and performing fitting according to the degree of contribution of each tissue on the plurality of different first ray paths and the plurality of raw projection values to obtain the correspondence.
(Benson, [Col. 8]; "The completion step interpolates in row, channel, and view directions using valid neighbors of a metal dexel that are not metal dexels, assigns weights to each of the neighbors, and replaces the metal dexel by the sum of the weighted neighbors.")
[Examiner note: Wang [0066] says fitting means taking the tissue contributions and raw projection values for multiple first ray paths and fitting them together to get the correspondence and Wang [0066] further says the correspondence can be obtained by a machine learning method or depth learning method. Meyer gives the tissue contributions for each first ray path through the forward projection of Ma-Pic ([0076]). Benson [Col. 8] provides the fitting by taking multiple non-metal neighbors in the row, channel, and view directions, weighting each one, and adding them as a weighted sum. Each non-metal neighbor is a different first ray path, so the weighted sum fits across multiple first ray paths. This reads on the claimed fitting step.]
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Meyer and Benson. The motivation for the combination is to obtain a more accurate correspondence between the tissue contributions and the raw projection values by fitting across multiple non-metal neighbors in the row, channel, and view directions, instead of just dividing point by point. (Benson, [Col. 8]; "interpolates in row, channel, and view directions using valid neighbors of a metal dexel that are not metal dexels, assigns weights to each of the neighbors, and replaces the metal dexel by the sum of the weighted neighbors.")
Regarding claim 5, the combination of Meyer and Benson teaches:
The method according to claim 3, wherein the determining the degree of contribution of each tissue (Meyer, [0068]; "The mask image Ma-Pic is produced from the original image Pic by replacing the CT values with the respective class values.")
through which the first ray paths pass in the multivalued image includes: calculating the length or area of each tissue through which the first ray paths pass in the multivalued image, and multiplying the length or area by a contribution weighting value of the tissue to obtain the degree of contribution of the tissue, wherein the contribution weighting value reflects the degree of ray absorption of the tissue. (Benson, [Col 16 Lines 17-22]; “the full forward projection of step 94 is computed using ‘Siddon’s method,’ which calculates the line intersection lengths between a projection ray and each voxel and then computes the line integral of that projection ray by summing the products of the voxel values and their associated line intersection lengths.”)
[Examiner note: Wang [0062] says the degree of contribution of each tissue is the length of that tissue along the ray multiplied by a contribution weighting value that reflects ray absorption. Meyer builds Ma-Pic by giving each voxel a class value based on its tissue type ([0068]), so each voxel in Ma-Pic is a tissue voxel. Benson [Col. 16] applies Siddon's method, which calculates the line intersection length between the ray and each voxel and multiplies it by the voxel value, then adds them up. Applying Benson's Siddon's method to Meyer's tissue-classified Ma-Pic means the line intersection length is the length of that tissue along the ray, and the voxel value is the tissue's absorption weighting. The product gives the per-tissue contribution along the ray. This reads on the claimed step of multiplying the length of each tissue by the contribution weighting value to get the degree of contribution.]
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Meyer and Benson. The motivation for the combination is to compute the degree of contribution of each tissue along the ray path by calculating the physical length of each tissue the ray passes through and weighting it by the tissue's absorption value, which provides a per-tissue breakdown of the total absorption along each ray rather than a single aggregate value. (Benson, [Col 16 Lines 17-22]; "the full forward projection of step 94 is computed using 'Siddon's method,' which calculates the line intersection lengths between a projection ray and each voxel and then computes the line integral of that projection ray by summing the products of the voxel values and their associated line intersection lengths.")
Regarding claim 6, the combination of Meyer and Benson teaches:
The method according to claim 5, wherein the contribution weighting value is equal to a ray absorption coefficient(Benson, [Col. 1, Lines 60-64]; "in an energy region for medical CT, the x-ray attenuation of any given material can be represented by a proper density mix of two materials with distinct x-ray attenuation properties, referred to as the base or basis materials.") of the tissue. (Meyer, [0068]; "The mask image Ma-Pic is produced from the original image Pic by replacing the CT values with the respective class values.")
[Examiner note: Wang [0062] says the contribution weighting value can be the tissue's ray absorption coefficient. In Meyer's Ma-Pic ([0068]), each voxel is given a class value based on its tissue type, so the voxel value is a tissue-specific value. Benson [Col. 1] uses the voxel value as the x-ray attenuation of the material at that voxel when running Siddon's method (from claim 5). So when Benson's setup is used on Meyer's tissue-classified Ma-Pic, the voxel value at each tissue voxel is that tissue's absorption coefficient. This reads on the claimed contribution weighting value being the tissue's ray absorption coefficient.]
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Meyer and Benson. The motivation for the combination is to use the x-ray attenuation coefficient of each tissue as the voxel value in the mask image, so that the forward projection in claim 5 accurately models the physical absorption behavior of each tissue along the ray path. (Benson, [Col. 1, Lines 60-64]; "in an energy region for medical CT, the x-ray attenuation of any given material can be represented by a proper density mix of two materials with distinct x-ray attenuation properties, referred to as the base or basis materials.")
Regarding claim 7, The combination of Meyer and Benson teaches:
The method according to claim 5, wherein the length of each tissue through which the first ray paths pass in the multivalued image is calculated using a forward projection method. (Benson, [Col 16 Lines 16-22]; “the full forward projection of step 94 is computed using ‘Siddon’s method,’ which calculates the line intersection lengths between a projection ray and each voxel and then computes the line integral of that projection ray by summing the products of the voxel values and their associated line intersection lengths.”) [Examiner note: Benson’s Siddon’s-method forward projection is the precise mechanism by which the line-intersection length (i.e., the length of tissue along the ray) is calculated, which reads directly on calculating the length of each tissue using a forward projection method.]
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Meyer and Benson. The motivation for the combination is to use a forward projection method that calculates the line intersection length of each tissue along the ray, since Meyer's forward projection of Ma-Pic ([0076]) does not specify the projection algorithm used, and Benson provides Siddon's method as a known forward projection technique that computes these per-voxel intersection lengths. (Benson, [Col 16 Lines 16-22]; "the full forward projection of step 94 is computed using 'Siddon's method,' which calculates the line intersection lengths between a projection ray and each voxel and then computes the line integral of that projection ray by summing the products of the voxel values and their associated line intersection lengths.")
Regarding claim 8,
Meyer fails to teach:
The method according to claim 4, wherein the distance between each of a plurality of projection positions corresponding to a plurality of different first ray paths in the raw projection data and a projection position corresponding to each second ray path in the raw projection data is less than or equal to a preset value.
Benson teaches:
The method according to claim 4, wherein the distance between each of a plurality of projection positions corresponding to a plurality of different first ray paths in the raw projection data and a projection position corresponding to each second ray path in the raw projection data is less than or equal to a preset value. (Benson, [Col 9 Lines 55-58]; “maximum distances can be defined in the row, channel, and view directions so that neighbors are undefined if the nearest valid neighbor would exceed the maximum distance for that direction.”)
[Examiner note: Benson expressly defines preset maximum distances between non-metal (first ray path) neighbors and the metal (second ray path) position in each of the row, channel, and view directions of the raw projection data; under BRI, this reads directly on the claimed limitation that the distance between projection positions of first ray paths and second ray paths in the raw projection data is less than or equal to a preset value.]
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Meyer and Benson. The motivation for the combination is to limit the non-metal neighbors used for predicting the metal-path value to those within a preset distance, so that the predicted value is based on local data rather than far-away values that may not accurately represent the metal-affected region. (Benson, [Col. 9, Line 56]; "neighbors are undefined if the nearest valid neighbor would exceed the maximum distance for that direction.")
Claims 14-16 are rejected under 35 U.S.C. 103 as being unpatentable over Meyer et al. (US 2011/0007956 A1) in view of Bruder et al. (US 9,495,769 B2).
Regarding claim 14,
Meyer fails to teach:
The method according to claim 13, wherein the raw medical image is an image reconstructed within a first predetermined field of view, the first predetermined field of view is an extended field of view,
the first medical image is an image reconstructed within a second predetermined field of view,
the second medical image is an image reconstructed within a third predetermined field of view, and the second predetermined field of view is smaller than or equal to the first predetermined field of view.
Bruder teaches:
The method according to claim 13, wherein the raw medical image is an image reconstructed within a first predetermined field of view, the first predetermined field of view is an extended field of view, (Bruder, [Col 12 Lines 10-13]; “this measured data p k,s,r meas is used in the step ‘eFOV Recon’ in order to carry out a conventional image reconstruction in the extended field of view. At the eFOV Recon step there is accordingly a first image PIC 1 of the object under examination provided, both inside the overall field of view as well as inside the extended field of view.”) [Examiner note: Bruder reconstructs a first image PIC 1 within the extended field of view (EFOV), reading on the claimed raw medical image reconstructed within a first predetermined field of view equal to an extended field of view.]
the first medical image is an image reconstructed within a second predetermined field of view, (Bruder,[Col 14 Line 51-55] ;“the measured data p k,s,r corr corrected in accordance with formula (1) is used in the next step Recon, in order, with an inherently known algorithm, based, for example, on a Feldkamp-type algorithm, to reconstruct a CT image PIC of the object under examination within the extended field of view.”) [Examiner note: a subsequent reconstructed image (PIC) is produced within a predetermined field of view (here, the same extended field of view, which under BRI satisfies a “second predetermined field of view”), reading on the claimed first medical image reconstructed within a second predetermined field of view.]
the second medical image is an image reconstructed within a third predetermined field of view, and the second predetermined field of view is smaller than or equal to the first predetermined field of view. (Bruder, [Col 11 Lines 2-8]; “The extended field of view of the CT device is an area which connects to the described area of the overall field of view. Outside the overall field of view lies the extended field of view, which comprises those volume elements which are only irradiated by x-ray radiation at some projection angles, and which then pass to the detector.”) [Examiner note: Bruder expressly defines distinct field-of-view regions (overall FOV inside the extended FOV), where the overall FOV is necessarily smaller than or equal to the extended FOV; under BRI, this teaches the recited relationship between the first, second, and third predetermined fields of view, particularly that the second is smaller than or equal to the first.]
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Meyer and Bruder. The motivation for the combination is to reconstruct the raw medical image within an extended field of view so that subjects extending beyond the scan field of view are still fully reconstructed, while keeping the first and second medical images within smaller fields of view to preserve resolution in the region of interest. (Bruder, [Col. 12, Line 6]; "carry out a conventional image reconstruction in the extended field of view.")
Regarding claim 15, Meyer teaches:
and filling a scaled image of the specific material site into a corresponding position on the first medical image to obtain the diagnostic image. (Meyer, [0084]; "the pixels where the metal object is located according to the metal image Me-Pic can be replaced in the corrected image Kor-Pic by a high CT value corresponding to the respective metal.")
[Examiner note: Meyer teaches filling pixels from the second medical image (Me-Pic, containing metal) into a corresponding position on the first medical image (Kor-Pic).]
Meyer fails to teach:
The method according to claim 13, further including: scaling the specific material site in the second medical image according to a scaling factor,
Bruder teaches:
The method according to claim 13, further including: scaling the specific material site in the second medical image according to a scaling factor, (Bruder, [Col. 6, Lines 8-11]; "pixel values of pixels to be allocated assigned as a function of the comparison of a plurality of threshold values with a pixel value from a plurality of pixel values made available for this purpose.")
[Examiner note: Wang [0078] says the metal site in the second medical image is scaled by a scaling factor and then filled into the corresponding position on the first medical image to make the diagnostic image. Meyer [0084] does the filling part by replacing the pixels at the metal location in Kor-Pic (the first medical image) with values from Me-Pic (the second medical image) but does not scale the metal site before filling. Bruder [Col. 6] provides the scaling step by allocating pixel values from a plurality of available pixel values, which under broadest reasonable interpretation is a scaling operation when filling image data from one reconstruction into another. Combining Meyer's filling step with Bruder's scaling step reads on the claimed step of scaling the metal site in the second medical image and filling the scaled image into the first medical image to get the diagnostic image.]
Before the time of filing, it would have been obvious to one of ordinary skill in the art to combine Meyer and Bruder. The motivation for the combination is to apply a scaling factor to the metal site from the second medical image before filling it into the first medical image, so that the metal site fits consistently when the two medical images are reconstructed within different fields of view. (Bruder, [Col. 6, Line 8]; "pixel values of pixels to be allocated assigned as a function of the comparison of a plurality of threshold values with a pixel value from a plurality of pixel values made available for this purpose.")
Regarding claim 16,
Meyer fails to teach:
The method according to claim 15, wherein the scaling factor is related to the sizes of a second predetermined field of view and a third predetermined field of view.
Bruder teaches:
The method according to claim 15, wherein the scaling factor is related to the sizes of a second predetermined field of view and a third predetermined field of view. (Bruder, [Col 14 Lines 11- 14]; k FOV designates the number of channels in the field of view, and k eFOV designates the number of channels in the field of view plus the virtual channels of the extended field of view. As an example, k FOV may be 736, i.e. every detector line has 736 detector elements, and k eFOV 1000.”)
[Examiner note: Bruder defines distinct, quantitatively related sizes (k FOV = 736 and k eFOV = 1000) for the overall and extended fields of view, establishing a quantitative size-ratio (under BRI, a scaling factor) between the two fields of view. Applying such a quantitative size-ratio relationship to the scaling step in claim 15 (when filling the second medical image into the first medical image, both reconstructed in different-sized FOVs) would have been an obvious matter of design choice to a person of ordinary skill, in order to maintain geometric consistency between images reconstructed in different fields of view.]
Conclusion
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/SHIVANGI SARKAR/Examiner, Art Unit 2666
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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