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 of certified copies of papers required by 37 CFR 1.55.
Drawings
Figures 1 and 2 are objected to as depicting a block diagram without “readily identifiable” descriptors of each block, as required by 37 CFR 1.84(n). Rule 84(n) requires “labeled representations” of graphical symbols, such as blocks; and any that are “not universally recognized may be used, subject to approval by the Office, if they are not likely to be confused with existing conventional symbols, and if they are readily identifiable.”
In the case of Figures 1 and 2, the blocks designated 2, 3, 4, 5, 10, F, and P are not readily identifiable per se and therefore require the insertion of text that identifies the function of that block. That is, each vacant block should be provided with a corresponding label identifying its function or purpose.
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(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.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited 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) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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) or pre-AIA 35 U.S.C. 112, sixth paragraph, 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 limitations are: “a selection unit”, “an adaptation unit”, and “a denoising unit” in claims 15 and 16.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/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) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (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) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 5 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites, “wherein, for denoising the action projection image, a bilateral filter is used, and wherein a filter kernel of which is based on the reference projection images or which uses the reference projection images as guidance images and/or a guided filter is used, a mask image of which is based on the reference projection images”. The intended interpretation of the recited claim features is uncertain as the claim can be interpreted as equivalent to the following scenarios:
(a) “for denoising the action projection image,” (i) “a bilateral filter is used, and wherein a filter kernel of which is based on the reference projection images or which uses the reference projection images as guidance images”, and/or (ii)“a guided filter is used, a mask image of which is based on the reference projection images”; or
(b) “for denoising the action projection image, a bilateral filter is used, and wherein a filter kernel of which is based on (i) the reference projection images or (ii) which uses the reference projection images as guidance images and/or (iii) a guided filter is used, a mask image of which is based on the reference projection images”.
For the purposes of further treating the Application on the merits, the Examiner assumes that the above scenario (a) is the intended interpretation of the claim.
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.
Claims 1, 3, 4, and 6-14 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (“Learning CT projection denoising from adjacent views”), herein Hong, in view of Hsieh et al. (“Conjugate cone-beam reconstruction algorithm”), herein Hsieh.
Regarding claim 1, Hong discloses a method for denoising a tomography recording with a plurality of projection images, the method comprising:
selecting an action projection image to be denoised (see Hong Fig. 1 and sect. 2.1 Method overview, where given sequential projection set, a middle projection is extracted as target and use the rest of the projection as input of the DNN model);
selecting reference projection images having recording angles that lie in a range of a recording angle of the action projection image and/or an opposite recording angle (see Hong Fig. 1 and sect. 2.1 Method overview, where given sequential projection set, a middle projection is extracted as target and the rest of the adjacent projections are used input of the DNN model); and
denoising the action projection image based on a noise of the reference projection images (see Hong sect. 2.1. Method Overview, where the network learns to synthesize the ideal projection from its adjacent views and is able to restore the content whereas unable to generate noise of the target projection).
Hong does not explicitly disclose adapting a binning of the reference projection images to the action projection image so that the reference projection images correspond to a projection geometry of the action projection image.
Hsieh teaches in a related and pertinent conjugate cone-beam reconstruction algorithm (see Hsieh Abstract) where a conjugate sampling pair which have opposite fan angles in a fan beam geometry are rebinned so that all projection angles for all samples in a rebinned view become identical, where the fan-to parallel rebinning improves performance in noise uniformity and artifact suppression in the reconstructed images (see Hsieh sect. 2 Conjugate Backprojection Algorithm).
At the time of filing, one of ordinary skill in the art would have found it obvious from the teachings of Hong and Hsieh that the ideal projection image can also be synthesized by rebinning the adjacent projection images and used to denoise the target projection image.
This modification is rationalized as some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
In this instance, Hong teaches that given sequential projection set, a middle projection is extracted as target and the rest of the adjacent projections are used input of a DNN model, where the network learns to synthesize the ideal projection from its adjacent views and is able to restore the content whereas unable to generate noise of the target projection; and Hsieh teaches that a conjugate sampling pair which have opposite fan angles in a fan beam geometry are rebinned so that all projection angles for all samples in a rebinned view become identical, where the fan-to parallel rebinning improves performance in noise uniformity and artifact suppression in the reconstructed images .
One of ordinary skill in the art would understand that the ideal projection image can also be synthesized by rebinning the adjacent projection images and used to denoise the target projection image.
Regarding claim 3, please see the above rejection of claim 1. Hong and Hsieh disclose the method of claim 1, wherein, for at least one reference projection image of the reference projection images with a relative recording angle to the recording angle of the action projection image, a further reference projection image with the relative recording angle to the recording angle of the action projection image is selected to provide at least one pair of reference projection images, and wherein information from the at least one pair of reference projection images is used to adapt the binning (see Hong Fig. 1 and sect. 2.1 Method overview, where given sequential projection set, a middle projection is extracted as target and the rest of the adjacent projection are used as input of the DNN model).
Regarding claim 4, please see the above rejection of claim 1. Hong and Hsieh disclose the method of claim 1, wherein the reference projection images are combined to form one virtual guidance projection image (see Hong Fig. 1 and sect. 2.1 Method overview, where the network learns to synthesize the ideal projection from the adjacent views), and wherein the action projection image and the virtual guidance projection image are averaged during denoising, and/or wherein, for denoising, a machine-learning model is used that has been trained on noise properties of action projection images relative to the virtual guidance projection image (see Hong Fig. 1 and sect. 2.1 Method overview, where a deep neural network model is train to perform the denoising).
Regarding claim 6, please see the above rejection of claim 1. Hong and Hsieh disclose the method of claim 1, wherein, when recording the projection images, further projection images are recorded at further recording angles before a first projection image and/or after a last projection image and used for denoising, and wherein the further projection images are not used for image reconstruction (see Hong sect. 2.1 Method overview, where the sequence of projections are limited to a number of adjacent projections; see Hong 2 Conjugate Backprojection Algorithm, where projection images can be recorded for the full rotation).
Regarding claim 7, please see the above rejection of claim 6. Hong and Hsieh disclose the method of claim 6, wherein the projection images are recorded at a larger angular range than an angular range defined for a recording (see Hong sect. 2.1 Method overview, where the sequence of projections are limited to a number of adjacent projections; see Hong 2 Conjugate Backprojection Algorithm, where projection images can be recorded for the full rotation).
Regarding claim 8, please see the above rejection of claim 1. Hong and Hsieh disclose the method of claim 1, wherein a previously denoised action projection image is used as a reference projection image for denoising another action projection image (see Hong sect. 2.1 Method overview, where a middle projection is extracted, and that when a new middle projection is to be denoised, the previously middle denoised projection would be an adjacent projection).
Regarding claim 9, please see the above rejection of claim 1. Hong and Hsieh disclose the method of claim 1, wherein the adapting of the binning comprises: using a beam-for-beam rebinning approach, and/or using a tomographic rebinning approach (see Hsieh sect. 2 Conjugate Backprojection Algorithm, where an interpolation function is also introduced for the rebinning the conjugate sampling pairs).
Regarding claim 10, please see the above rejection of claim 9. Hong and Hsieh disclose the method of claim 9, wherein the beam-for-beam rebinning approach uses trainable weights for redundant beams (see Hsieh sect. 2 Conjugate Backprojection Algorithm, where an interpolation function that is distance weighted is introduced for the rebinning the conjugate sampling pairs).
Regarding claim 11, please see the above rejection of claim 9. Hong and Hsieh disclose the method of claim 9, wherein the tomographic rebinning approach uses trainable redundancy weights, a trainable reconstruction kernel, trainable distance weighting, or a combination thereof (see Hsieh sect. 2 Conjugate Backprojection Algorithm, where an interpolation function that is distance weighted is introduced for the rebinning the conjugate sampling pairs).
Regarding claim 12, please see the above rejection of claim 1. Hong and Hsieh disclose the method of claim 1, further comprising: reconstructing the tomography recording from projection images recorded with a conical beam; and compiling a reference projection image from image elements of a plurality of projection images (see Hsieh sect. 2.6 Data, where the projection images are used in reconstructing CT images).
Regarding claim 13, please see the above rejection of claim 12. Hong and Hsieh disclose the method of claim 12, wherein the compiling of the reference projection image is from projection images having recording angles that originate from an area lying opposite the recording angle of the action projection image (see Hsieh sect. 2 Conjugate Backprojection Algorithm, where a conjugate sampling pair which have opposite fan angles in a fan beam geometry are rebinned).
Regarding claim 14, please see the above rejection of claim 13. Hong and Hsieh disclose the method of claim 13, wherein the compiling of the reference projection image comprises:
selecting a target projection image from a recording angle range of the action projection image having pixels to be simulated (see Hong Fig. 1 and sect. 2.1 Method overview, where given sequential projection set, a middle projection is extracted as target);
ascertaining recording geometry of the target projection image by ascertaining a respective beam path for each pixel at the recording angle of the target projection image (see Hong Fig. 1 and sect. 2.1 Method overview, where a the projection angle of middle projection extracted as target can be determined);
ascertaining, for each pixel of the target projection image, which pixel of the plurality of projection images corresponds to an inverted beam path through an object (see Hsieh sect. 2 Conjugate Backprojection Algorithm, where a conjugate sampling pair which have opposite fan angles in a fan beam geometry are rebinned);
selecting the pixel corresponding to the inverted beam path as a corresponding pixel for the target projection image (sect. 2 Conjugate Backprojection Algorithm, where a conjugate projection angle can be chosen); and
using the target projection image as the reference projection image (see Hong, 2.1 Method overview, where a shifted middle projection angle can use the previous middle projection as an adjacent projection).
Claim 2 is rejected under 35 U.S.C. 103 as being unpatentable over Hong and Hsieh as applied to claim 1 above, and further in view of Choi et al. (“Self-supervised denoising of projection data for low-dose cone-beam CT”), herein Choi.
Regarding claim 2, please see the above rejection of claim 1. Hong and Hsieh do not explicitly disclose the method of claim 1, wherein the recording angles of the selected reference projection images differ from the recording angle of the action projection image by at most 10°, and wherein the reference projection images have recording angles that are a next larger and/or a next smaller to the recording angle of the action projection image or the opposite recording angle.
Choi teaches in a related and pertinent self-supervised learning method that reduces noise in projection s acquired by ordinary CBCT scans (see Choi Abstract), where the number of projections acquired was 680 from a full 360 degrees or 340 for a 180degree rotation (see Choi sect. 3.1.1 Data acquisition).
At the time of filing, one of ordinary skill in the art would have found it obvious from the teachings of Hong, Hsieh, and Choi that the sequence of adjacent projection images used to denoise the target projection image would be similarly captured as in Choi and would yield a recording angle less than 10°.
This modification is rationalized as some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
In this instance, Hong and Hsieh teaches the use of a sequential projection set; and Choi teaches that the number of projections acquired was 680 from a full 360 degrees or 340 for a 180degree rotation scan .
One of ordinary skill in the art would understand that the sequence of adjacent projection images used to denoise the target projection image would be similarly captured as in Choi and would yield a recording angle less than 10°.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hong and Hsieh as applied to claim 1 above, and further in view of Manduca et al. (“Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT”), herein Manduca.
Regarding claim 5, please see the above rejection of claim 1. Hong and Hsieh do not explicitly disclose the method of claim 1, wherein, for denoising the action projection image, a bilateral filter is used, and wherein a filter kernel of which is based on the reference projection images or which uses the reference projection images as guidance images and/or a guided filter is used, a mask image of which is based on the reference projection images.
Manduca teaches in a related and pertinent denoising algorithm based on bilateral filtering (see Manduca Abstract), where noise model is incorporated in bilateral filter and applied to a sinogram to obtain a denoised sinogram (see Manduca sect. 11.E. Sinogram smoothing with bilateral filtering).
At the time of filing, one of ordinary skill in the art would have found it obvious to apply the teachings of Manduca to the teachings of Hong and Hsieh such that a bilateral filter can be applied to filter and denoise the projection images. This modification is rationalized as a simple substitution of one known element for another to obtain predictable results. In this instance, Hong and Hsieh disclose a base method for denoising projection images with a trained DNN model. Manduca teaches a known technique of incorporating a noise model into bilateral filter to be applied to denoise a sinogram. One of ordinary skill in the art could have substituted the use of the trained DNN model with the bilateral filter of Manduca to denoise the projection image, predictable resulting in a denoised projection image.
Claim 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Hong et al. (“Learning CT projection denoising from adjacent views”), herein Hong, in view of Hsieh et al. (“Conjugate cone-beam reconstruction algorithm”), herein Hsieh, and Matsuura et al. (US 2022/0139006), herein Matsuura.
Regarding claim 16, Hong and Hsieh discloses a system to
select an action projection image to be denoised and to select reference projection images having recording angles that lie in a range of a recording angle of the action projection image and/or an opposite recording angle (see Hong Fig. 1 and sect. 2.1 Method overview, where given sequential projection set, a middle projection is extracted as target and use the rest of the projection as input of the DNN mode);
adapt a binning of the reference projection images to the action projection image so that the reference projection images correspond to a projection geometry of the action projection image (see Hsieh sect. 2 Conjugate Backprojection Algorithm, where a conjugate sampling pair which have opposite fan angles in a fan beam geometry are rebinned so that all projection angles for all samples in a rebinned view become identical, where the fan-to parallel rebinning improves performance in noise uniformity and artifact suppression in the reconstructed images ); and
denoise the action projection image based on a noise of the reference projection images (see Hong sect. 2.1. Method Overview, where the network learns to synthesize the ideal projection from its adjacent views and is able to restore the content whereas unable to generate noise of the target projection).
Hong and Hsieh do not explicitly disclose a system comprising:
a control facility having an apparatus for denoising a tomography recording with a plurality of projection images, wherein the apparatus comprises: a selection unit, an adaptation unit, and a denoising unit.
Matsuura teaches a known medical image diagnostic apparatus and processing system (see Matsuura Abstract), where an X-ray CT apparatus is disclosed with processing circuitry to control the overall operation of the CT apparatus (see Matsuura Fig. 1A and [0024]-[0043]), and includes processing circuity which can be processor executing computer programs stored on memory to perform the functions of the X-ray CT apparatus (see Matsuura [0043]-[0050).
At the time of filing, one of ordinary skill in the art would have found it obvious from the teachings of Hong, Hsieh, and Matsuura that the disclosed teachings of Hong and Hsieh can be implemented in the X-ray CT apparatus of Matsuura.
This modification is rationalized as some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention.
In this instance, Hong and Hsieh teaches the denoising method for CT projection images; and Matsuura teaches a known X-ray CT apparatus that includes processing circuity which can be processor executing computer programs stored on memory to perform the functions of the X-ray CT apparatus.
One of ordinary skill in the art would understand that that the disclosed teachings of Hong and Hsieh can be implemented in the X-ray CT apparatus of Matsuura.
Regarding claim 17, please see the above rejection of claim 16. Hong, Hsieh, and Matsuura disclose the system of claim 16, wherein the system is a tomography system (see Matsuura Fig. 1A and [0024]-[0043], where an X-ray CT apparatus is disclosed ).
Regarding claim 18, please see the above rejection of claim 16. Hong, Hsieh, and Matsuura disclose the system of claim 17, wherein the tomography system is an angiography system or a computed tomography system (see Matsuura Fig. 1A and [0024]-[0043], where an X-ray CT apparatus is disclosed).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TIMOTHY WING HO CHOI whose telephone number is (571)270-3814. The examiner can normally be reached 9:00 AM to 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, VINCENT RUDOLPH can be reached at (571) 272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TIMOTHY CHOI/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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