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 .
Priority
Receipt is acknowledged that application claims priority to foreign application with application number DE10 2023 202 273.7 dated 14 March 2023. Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS dated 14 March 2024 has been considered and placed in the application file.
Specification - Title
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Using Scaled Noise Images to improve x-ray images.
Claim Interpretation
Under MPEP 2143.03, "All words in a claim must be considered in judging the patentability of that claim against the prior art." In re Wilson, 424 F.2d 1382, 1385, 165 USPQ 494, 496 (CCPA 1970). As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
Claims 1, 7, 13,15 and 19 recite “or.” Since “or” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. Claims 5-6, 14 and 18 recite “at least one.” Since “at least one” is disjunctive, any one of the elements found in the prior art is sufficient to reject the claim. While citations have been provided for completeness and rapid prosecution, only one element is required. Because, on balance, it appears the disjunctive interpretation enjoys the most specification support and for that reason the disjunctive interpretation (one of A, B OR C) is being adopted for the purposes of this Office Action. Applicant’s comments and/or amendments relating to this issue are invited to clarify the claim language and the prosecution history.
Claim Objections
Claim 13 is objected to because of the following informalities:
Claim 13, line 2, there is a missing ending parenthesis.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f), is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f), because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
“at least one computing unit being configured for X-ray imaging” in claim 13; and
“at least one computing unit configured to” in claim 13.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), they are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
1st 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.
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 and 4-15 (all claims not rejected below) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2017 0345132 A1, (Schluter et al.).
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Claim 1
Regarding Claim 1, Schluter et al. teach a method for X-ray imaging ("a virtual X-ray dose increase. This allows "simulating" a noise reduction that could have been achieved had the image been acquired with a higher dose," paragraph [0020]), the method comprising:
obtaining detector data by a flat panel X-ray detector and generating an input image based on the detector data, the input image showing an object to be mapped ("The imaging apparatus IMA includes an x-ray source (an x-ray tube) XR and an x-ray sensitive detector DT. Preferably, but not necessarily, the detector is of the digital flat panel type," paragraph [0047]);
generating a signal image, the generating of the signal image comprising applying a noise suppression algorithm to the input image ("the imaging arrangement 100 includes a noise reduction module NR that operates on the detected image ( or if applicable, on the converted image) to produce a noise reduced version rR of the image," paragraph [0051]);
generating a scaled signal image, the generating of the scaled signal image comprising scaling the signal image or an image dependent on the signal image using an interpolation method so that in a first image direction, a number of pixels of the scaled signal image is greater than a corresponding number of pixels of the input image ("Reference is now made to FIG. 8 where the upper row shows an example of a multi-scale decomposition of an image I into its components, low pass image L, high pass image H and two band pass images B0 and B 1 . It should be understood that the images in general have different sizes but are shown here scaled to equal size for illustrative purposes. The lower row shows the respective normalized high and band pass images with the applied noise reduction," paragraph [0109]);
generating a noise image, the generating of the noise image comprising forming a difference between the input image and the signal image ("low pass components L, are used to compute the signal dependent noise information (e.g. standard deviation) to quantify local noise at the respective image signal and to use this information in the normalization operation of the band pass and/or high pass images," paragraph [0053]);
modifying the noise image, the modifying of the noise image comprising extending a spatial frequency spectrum of the noise image so that a maximum spatial noise frequency is increased in accordance with the first image direction ("the image I to be processed or noise reduced is received at input port IN. The multi-scale spatial frequency decomposition module DEC operates to decompose the image into a hierarchy ("Laplacian" pyramid) of a different band-pass images B, and corresponding low pass images L," paragraph [0053]); and
generating a result image, the generating of the result image comprising adding the scaled signal image and the modified noise image ("The noise reduction module includes an image de-composer DEC, a normalizer NOR, a signal modifier MOD and a reconstruction unit RECON to reconstruct the previously decomposed image signals," paragraph [0052]).
It is recognized that the citations and evidence provided above are derived from potentially different embodiments of a single reference. Nevertheless, it 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 to employ combinations and sub-combinations of these complementary embodiments, because XXXX et al. explicitly motivates doing so at least in paragraphs [0004], [0050] and [0123] including “Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing a claimed invention, from a study of the drawings, the disclosure, and the dependent claims” and otherwise motivating experimentation and optimization.
The rejection of method claim 1 above applies mutatis mutandis to the corresponding limitations of apparatus claim 13, system claim 14 and computer readable storage medium claim 15 while noting that the rejection above cites to both device and method disclosures. Claims 13, 14 and 15 are mapped below for clarity of the record and to specify any new limitations not included in claim 1.
Claim 2
Regarding claim 2, Schluter et al. teach the method of claim 1, further comprising generating a raw image based on the detector data, wherein generating the input image comprises applying a variance-stabilizing transformation to the raw image ("the imaging arrangement 100 includes a noise reduction module NR that operates on the detected image ( or if applicable, on the converted image) to produce a noise reduced version rR of the image," paragraph [0051]).
Claim 4
Regarding claim 4, Schluter et al. teach the method of claim 2, wherein the generating of the scaled signal image comprises scaling the image dependent on the signal image using the interpolation method ("where d denotes a scaling constant. The log dose noise can be expressed as a function of the log dose signal s1og, by inverting the first equation and inserting the linear signal into the second equation. It turns out that the log dose noise decreases exponentially with increasing log dose signal," paragraph [0077] which explains equation 9, which is an interpolation method),
wherein the method further comprises generating the image dependent on the signal image, the generating of the image dependent on the signal image comprising performing at least one image processing step ("the decomposition is performed by separation of a high pass H and iterative calculation of multi scale low and band passes L, and B, as per the following recursive decomposition routine," paragraph [0062] where decomposition is within the interpretation of an image processing step), and
wherein the at least one image processing step includes an inverse variance-stabilizing transformation ("More particularly, the reciprocal of the squared slope is proportional to the reciprocal of the variance (that is the square for standard deviational of the noise at the respective pixel value) which in turn is proportional to the square of the local contrast noise ratio," paragraph [0100] where a reciprocal is an inverse).
Claim 5
Regarding claim 5, Schluter et al. teach the method of claim 4, wherein the at least one image processing step includes at least one grayscale transformation, an application of at least one frequency filter, or the at least one grayscale transformation and the application of at least one frequency filter("However, the detector signals may be first transformed into a different domain (for instance a logarithmic domain as explained earlier) and the noise model can then be transformed accordingly," paragraph [0068] and "the digital values of the x-ray image are passed on to a graphical renderer which maps the digital values according to scale such as a grey value palette or color palette," paragraph [0049]).
Claim 6
Regarding claim 6, Schluter et al. teach the method of claim 5, wherein the at least one image processing step includes the at least one grayscale transformation, and wherein the at least one grayscale transformation includes a logarithmic grayscale transformation ("However, the detector signals may be first transformed into a different domain (for instance a logarithmic domain as explained earlier) and the noise model can then be transformed accordingly," paragraph [0068] and "the digital values of the x-ray image are passed on to a graphical renderer which maps the digital values according to scale such as a grey value palette or color palette," paragraph [0049]).
Claim 7
Regarding claim 7, Schluter et al. teach the method of claim 1, further comprising displaying the result image or an image dependent on the result image on a screen ("The mapped values are then used to drive the monitor MT via suitable graphics software to effect the rendering on the screen MT," paragraph [0049]).
Claim 8
Regarding claim 8, Schluter et al. teach the method of claim 7, wherein the number of pixels of the scaled signal image in the first image direction corresponds to a number of pixels of the screen in the first image direction ("Reference is now made to FIG. 8 where the upper row shows an example of a multi-scale decomposition of an image I into its components, low pass image L, high pass image H and two band pass images B0 and B
It should be understood that the images in general have different sizes but are shown here scaled to equal size for illustrative purposes," paragraph [0109] where scaled for illustrative purposes teaches scaling to correspond to a number of pixels of the screen).
Claim 9
Regarding claim 9, Schluter et al. teach the method of claim 7, wherein the increased maximum spatial noise frequency corresponds to an inverted pixel size of the screen in the first image direction ("Since in 2-dimensional images noise amplitudes increase with spatial frequency, a rather simple way of noise reduction is the application of a low pass filter which leads to a smoothing of the image," paragraph [0002] where amplitudes increasing with frequency teaches the maximum frequency being the inverted pixel size, or half the Nyquist limit).
Claim 10
Regarding claim 10, Schluter et al. teach the method of claim 1, wherein the detector data includes respective pixel values for detector pixels of a predetermined section of a detector array of the flat panel X-ray detector ("The DAS includes in particular an analogue to digital (A/D)-conversion circuitry which converts analog signal into a digital value, that is, into a number, measured in least significant bits (lsb ). These digital values are also referred to herein as "pixel values" or "image signals". The collection or array of all digital values so obtained forms an x-ray image I=(k,l), with (k,l) denoting the array index of the respective pixel position," paragraph [0048]).
Claim 11
Regarding claim 11, Schluter et al. teach the method of claim 1, wherein the signal image or the image dependent on the signal image is scaled using the interpolation method so that in a second image direction, a number of pixels of the scaled signal image is greater than a corresponding number of pixels of the input image ("where d denotes a scaling constant. The log dose noise can be expressed as a function of the log dose signal s1og, by inverting the first equation and inserting the linear signal into the second equation. It turns out that the log dose noise decreases exponentially with increasing log dose signal," paragraph [0077] which explains equation 9, which is an interpolation method), and
wherein modifying the noise image comprises extending the spatial frequency spectrum of the noise image, such that a further maximum spatial noise frequency corresponding to the second image direction is increased ("To be more specific (down) removes every second pixel and (up) inserts zeros between any two neighboring pixels to form a "checkerboard" of zeros and the original image signal values," paragraph [0062] where a checkerboard teaches second image direction).
Claim 12
Regarding claim 12, Schluter et al. teach the method of claim 1, wherein obtaining the detector data comprises:
emitting X-ray radiation in a direction of the object by an X-ray source ("The imaging apparatus IMA includes an x-ray source (an x-ray tube) XR ," paragraph [0047]); and
generating the detector data using the flat panel X-ray detector based on portions of the X-ray radiation passing through the object ("The imaging apparatus IMA includes an x-ray source (an x-ray tube) XR and an x-ray sensitive detector DT. Preferably, but not necessarily, the detector is of the digital flat panel type," paragraph [0047]).
Claim 13
Regarding claim 13, Schluter et al. teach a data processing apparatus("a virtual X-ray dose increase. This allows "simulating" a noise reduction that could have been achieved had the image been acquired with a higher dose," paragraph [0020]) comprising:
at least one computing unit (configured for X-ray imaging, the at least one computing unit being configured for X-ray imaging comprising the at least one computing unit ("computer program may be loaded into a working memory of a data processor. The data processor may thus be equipped to carry out the method of the invention," paragraph [0116]) being configured to:
obtain detector data by a flat panel X-ray detector and generate an input image based on the detector data, the input image showing an object to be mapped ("The imaging apparatus IMA includes an x-ray source (an x-ray tube) XR and an x-ray sensitive detector DT. Preferably, but not necessarily, the detector is of the digital flat panel type," paragraph [0047]);
generate a signal image, the generation of the signal image comprising application of a noise suppression algorithm to the input image ("the imaging arrangement 100 includes a noise reduction module NR that operates on the detected image ( or if applicable, on the converted image) to produce a noise reduced version rR of the image," paragraph [0051]);
generate a scaled signal image, the generating of the scaled signal image comprising scaling the signal image or an image dependent on the signal image using an interpolation method so that in a first image direction, a number of pixels of the scaled signal image is greater than a corresponding number of pixels of the input image ("where d denotes a scaling constant. The log dose noise can be expressed as a function of the log dose signal s1og, by inverting the first equation and inserting the linear signal into the second equation. It turns out that the log dose noise decreases exponentially with increasing log dose signal," paragraph [0077] which explains equation 9, which is an interpolation method);
generating a noise image, the generating of the noise image comprising forming a difference between the input image and the signal image ("low pass components L, are used to compute the signal dependent noise information (e.g. standard deviation) to quantify local noise at the respective image signal and to use this information in the normalization operation of the band pass and/or high pass images," paragraph [0053]);
modifying the noise image, the modifying of the noise image comprising extending a spatial frequency spectrum of the noise image so that a maximum spatial noise frequency is increased in accordance with the first image direction ("the image I to be processed or noise reduced is received at input port IN. The multi-scale spatial frequency decomposition module DEC operates to decompose the image into a hierarchy ("Laplacian" pyramid) of a different band-pass images B, and corresponding low pass images L," paragraph [0053]); and
generating a result image, the generating of the result image comprising adding the scaled signal image and the modified noise image ("The noise reduction module includes an image de-composer DEC, a normalizer NOR, a signal modifier MOD and a reconstruction unit RECON to reconstruct the previously decomposed image signals," paragraph [0052]).
Claim 14
Regarding claim 14, Schluter et al. teach an X-ray imaging system ("a virtual X-ray dose increase. This allows "simulating" a noise reduction that could have been achieved had the image been acquired with a higher dose," paragraph [0020]) comprising:
an X-ray source that is configured to emit X-ray radiation in a direction of an object to be mapped("The imaging apparatus IMA includes an x-ray source (an x-ray tube) XR ," paragraph [0047]);
a flat panel X-ray detector that is configured to generate detector data based on portions of the X-ray radiation passing through the object ("The imaging apparatus IMA includes an x-ray source (an x-ray tube) XR and an x-ray sensitive detector DT. Preferably, but not necessarily, the detector is of the digital flat panel type," paragraph [0047]); and
at least one computing unit ("computer program may be loaded into a working memory of a data processor. The data processor may thus be equipped to carry out the method of the invention," paragraph [0116]) configured to:
generate an input image representing the object based on the detector data ("The imaging apparatus IMA includes an x-ray source (an x-ray tube) XR and an x-ray sensitive detector DT. Preferably, but not necessarily, the detector is of the digital flat panel type," paragraph [0047]);
apply a noise suppression algorithm to the input image, such that a signal image is generated ("the imaging arrangement 100 includes a noise reduction module NR that operates on the detected image ( or if applicable, on the converted image) to produce a noise reduced version rR of the image," paragraph [0051]);
scale the signal image or an image dependent on the signal image using an interpolation method, so that in a first image direction, a number of pixels of the scaled signal image is greater than a corresponding number of pixels of the input image ("where d denotes a scaling constant. The log dose noise can be expressed as a function of the log dose signal s1og, by inverting the first equation and inserting the linear signal into the second equation. It turns out that the log dose noise decreases exponentially with increasing log dose signal," paragraph [0077] which explains equation 9, which is an interpolation method);
generate a noise image, the at least one computing unit being configured to generate the noise image comprising the at least one computing unit being configured to form a difference between the input image and the signal image ("low pass components L, are used to compute the signal dependent noise information (e.g. standard deviation) to quantify local noise at the respective image signal and to use this information in the normalization operation of the band pass and/or high pass images," paragraph [0053]);
extend a spatial frequency spectrum of the noise image so that a maximum spatial noise frequency corresponding to the first image direction is increased, such that a modified noise image is generated ("the image I to be processed or noise reduced is received at input port IN. The multi-scale spatial frequency decomposition module DEC operates to decompose the image into a hierarchy ("Laplacian" pyramid) of a different band-pass images B, and corresponding low pass images L," paragraph [0053]); and
add the scaled signal image and the modified noise image, such that a result image is produced ("The noise reduction module includes an image de-composer DEC, a normalizer NOR, a signal modifier MOD and a reconstruction unit RECON to reconstruct the previously decomposed image signals," paragraph [0052]).
Claim 15
Regarding claim 15, Schluter et al. teach in a non-transitory computer-readable storage medium that stores instructions executable by one or more processors for X-ray imaging ("a virtual X-ray dose increase. This allows "simulating" a noise reduction that could have been achieved had the image been acquired with a higher dose," paragraph [0020]), the instructions comprising:
obtaining detector data by a flat panel X-ray detector and generating an input image based on the detector data, the input image showing an object to be mapped ("The imaging apparatus IMA includes an x-ray source (an x-ray tube) XR and an x-ray sensitive detector DT. Preferably, but not necessarily, the detector is of the digital flat panel type," paragraph [0047]);
generating a signal image, the generating of the signal image comprising applying a noise suppression algorithm to the input image ("the imaging arrangement 100 includes a noise reduction module NR that operates on the detected image ( or if applicable, on the converted image) to produce a noise reduced version rR of the image," paragraph [0051]);
generating a scaled signal image, the generating of the scaled signal image comprising scaling the signal image or an image dependent on the signal image using an interpolation method so that in a first image direction, a number of pixels of the scaled signal image is greater than a corresponding number of pixels of the input image ("where d denotes a scaling constant. The log dose noise can be expressed as a function of the log dose signal s1og, by inverting the first equation and inserting the linear signal into the second equation. It turns out that the log dose noise decreases exponentially with increasing log dose signal," paragraph [0077] which explains equation 9, which is an interpolation method);
generating a noise image, the generating of the noise image comprising forming a difference between the input image and the signal image ("low pass components L, are used to compute the signal dependent noise information (e.g. standard deviation) to quantify local noise at the respective image signal and to use this information in the normalization operation of the band pass and/or high pass images," paragraph [0053]);
modifying the noise image, the modifying of the noise image comprising extending a spatial frequency spectrum of the noise image so that a maximum spatial noise frequency is increased in accordance with the first image direction ("the image I to be processed or noise reduced is received at input port IN. The multi-scale spatial frequency decomposition module DEC operates to decompose the image into a hierarchy ("Laplacian" pyramid) of a different band-pass images B, and corresponding low pass images L," paragraph [0053]); and
generating a result image, the generating of the result image comprising adding the scaled signal image and the modified noise image ("The noise reduction module includes an image de-composer DEC, a normalizer NOR, a signal modifier MOD and a reconstruction unit RECON to reconstruct the previously decomposed image signals," paragraph [0052]).
2nd Claim Rejections - 35 USC § 103
Claims 3 and 16-19 (all claims not rejected above) are rejected under 35 U.S.C. 103 as obvious over US Patent Publication 2017 0345132 A1, (Schluter et al.) in view of US Patent Publication 2022 0277424 A1, (Kaethner et al.).
Claim 3
Regarding Claim 3, Schluter et al. teach 3. The method of claim 2, as noted above.
Schluter et al. do not explicitly teach all of Anscombe transforms.
However, Kaethner et al. teach wherein the variance-stabilizing transformation includes an Anscombe transform ("the noise variance stabilization may be based on the generalized Anscombe transform (GAT), as is generally known," paragraph [0024]).
Therefore, taking the teachings of Schluter et al. and Kaethner et al. as a whole, it would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify “Simulating Dose increase by noise model based multi-scale noise reduction” as taught by Schluter et al. to use Method of Noise reduction in an x-Ray image as taught by Kaethner et al. The suggestion/motivation for doing so would have been that, “Numerous different approaches to noise reduction in low-dose X-ray images have already been suggested in the prior art.” as noted by the Kaethner et al. disclosure in paragraph [0005], which also motivates combination because the combination would predictably have a better noise reduction as there is a reasonable expectation that scaling techniques can be used to reduce noise; and/or because doing so merely combines prior art elements according to known methods to yield predictable results.
Claim 16
Regarding claim 16, Schluter et al. teach the non-transitory computer-readable storage medium of claim 15, wherein the instructions further comprise generating a raw image based on the detector data ("the imaging arrangement 100 includes a noise reduction module NR that operates on the detected image ( or if applicable, on the converted image) to produce a noise reduced version rR of the image," paragraph [0051]),
Schluter et al. is not relied on to explicitly teach all of variance stabilizing transforms.
However, Kaethner et al. teach wherein generating the input image comprises applying a variance-stabilizing transformation to the raw image ("the noise variance stabilization may be based on the generalized Anscombe transform (GAT), as is generally known," paragraph [0024]).
Schluter et al. and Kaethner et al. are combined as per claim 3.
Claim 17
Regarding claim 17, Schluter et al. teach the non-transitory computer-readable storage medium of claim 16, as noted above.
Schluter et al. is not relied on to explicitly teach all of variance stabilizing transforms.
However, Kaethner et al. teach wherein the variance-stabilizing transformation includes an Anscombe transform ("the noise variance stabilization may be based on the generalized Anscombe transform (GAT), as is generally known," paragraph [0024]).
Schluter et al. and Kaethner et al. are combined as per claim 3.
Claim 18
Regarding claim 18, Schluter et al. teach the non-transitory computer-readable storage medium of claim 16, wherein the generating of the scaled signal image comprises scaling the image dependent on the signal image using the interpolation method ("where d denotes a scaling constant. The log dose noise can be expressed as a function of the log dose signal s1og, by inverting the first equation and inserting the linear signal into the second equation. It turns out that the log dose noise decreases exponentially with increasing log dose signal," paragraph [0077] which explains equation 9, which is an interpolation method),
wherein the instructions further comprise generating the image dependent on the signal image, the generating of the image dependent on the signal image comprising performing at least one image processing step ("the decomposition is performed by separation of a high pass H and iterative calculation of multi scale low and band passes L, and B, as per the following recursive decomposition routine," paragraph [0062] where decomposition is within the interpretation of an image processing step), and
wherein the at least one image processing step includes an inverse variance-stabilizing transformation ("More particularly, the reciprocal of the squared slope is proportional to the reciprocal of the variance (that is the square for standard deviational of the noise at the respective pixel value) which in turn is proportional to the square of the local contrast noise ratio," paragraph [0100] where a reciprocal is an inverse).
Claim 19
Regarding claim 19, Schluter et al. teach the non-transitory computer-readable storage medium of claim 18, wherein the at least one image processing step includes at least one grayscale transformation, an application of at least one frequency filter, or the at least one grayscale transformation and the application of at least one frequency filter ("However, the detector signals may be first transformed into a different domain (for instance a logarithmic domain as explained earlier) and the noise model can then be transformed accordingly," paragraph [0068] and "the digital values of the x-ray image are passed on to a graphical renderer which maps the digital values according to scale such as a grey value palette or color palette," paragraph [0049]).
Reference Cited
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
US Patent Publication 2021 0097736 A1 to Takahashi et al. discloses medical image processing apparatus and a medical image processing method capable of improving structural component detectability with respect to a noise component in a medical image. The medical image processing apparatus includes: a separation unit that separates the medical image into a first component including a structural component as a main component and a second component including a noise component as a main component; a modulation unit that modulates the second component with a spatial frequency; and a correction unit that generates a corrected image based on the first component and the modulated second component.
US Patent Publication 2019 0035058 A1 to Strobel et al. discloses processing at least one X-ray image is provided. A variance of noise is signal dependent. The method includes applying a variance-stabilizing transformation to image data of the at least X-ray image to generate variance stabilized data.
Conclusion
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/Heath E. Wells/Examiner, Art Unit 2664
Date: 25 February 2026