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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 1/27/2026 has been entered.
Response to Arguments
Applicant's arguments filed 12/18/2025 have been fully considered but they are not persuasive. In response the argument regarding the 112 rejection, in reference to the areas quoted on page 5 of the remarks, the spectral aspect is not present in the areas associated with the specification filed on 6/23/2022. Moreover, the quotes regarding pages 4 and 7 also did not include the spectral data information. As stated on page 14, the invention is disclosed at energy level 70 keV. The specification appears to state the dual energy is possible, but in the exemplary embodiment, it uses one. Also, on page 13, it discusses using some and not all of the low frequency data and all of the high frequency data that is specifically CT data. Based on the possibility being mentioned, but the exemplary embodiment explaining the invention using CT data and not specifically spectral CT data, the 112 1st rejection of the claims are maintained.
Regarding the 101 rejection of the claims, this is maintained. Without repeating the framework listed in the last arguments, the Examiner would like to point to Applicant’s own arguments for the reason the 101 rejection is maintained. The invention discloses an improved FS method. In Applicant’s invention, the improvement is realized when a Frequency Split method is selectively applied to different decomposed sinograms in order to improve the different types of data during the reconstruction of data when using FBP. This is not claimed in such a manner in the claims to capture the improved FS method. Since the claims do not capture the inventive concept, the 101 rejection of the claims is maintained.
Applicant’s arguments with respect to claim(s) 1-11 and 15 have been considered but are moot because the new ground of rejection does not rely on all references applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. The reference of Shechter et al is applied with the previously applied references and will be explained briefly below.
In particular, the Shechter reference is applied to specifically disclose using the frequency split method and during the reconstruction, using some of the low frequency data and all of the high frequency data. This is disclosed on page 3. It also discloses spreading Poisson noise on page 4 in the first paragraph. This reference, in combination with the previously applied references, performs the features of the amended claims.
Thus, based on the above, the features of the claims are disclosed below.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-11 and 15 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The invention is describes CT data in particular for the decomposing and spreading of noise and/or inconsistencies. The spectral data is explained as a possibility of working with this data but not in the specific terms of the operation of the invention, such as on page 13. Since spectral data is considered as operating within different energy levels than CT data and the invention is not explained working with multiple different energy level data, this is considered new matter in the independent claims. Claims 2-10 are rejected based on their dependency.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The claim(s) recite(s) an abstract idea in the non-uniformly spreading of noise and/or inconsistencies as a mathematical calculation. The application of the filter back projection (FBP) is also considered as a mathematical calculation or formula. This judicial exception is not integrated into a practical application because it is not clear how the reconstruction improves the image with the non-uniformly spread noise even with the addition of the last wherein clause of suppressed image cone beam artifacts. How is the image cone beam artifacts suppressed? The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the first two limitations of obtaining and decomposing are considered as data gathering steps. The use of some low frequency data and all of high frequency data for the reconstruction is also considered as data gathering steps. Moreover, this limitation is not specific as to how the different frequencies are selected to be included in the reconstruction. This claim limitation does not integrate the abstract ideas into a practical application nor is considered to amount to significantly more than the judicial exception.
Claims 11 is rejected based on the same rationale, but the use of the memory and processor are considered to detail the technological environment for the invention. Claim 15 is rejected based on the same rationale since the use of a non-transitory computer readable medium that stores instructions to perform a method lists the technological environment of the invention.
Claim 2 is a mathematical concept of a calculation using the unitary basis transformation by a matrix. This limitation is a more specific description of the third limitation in claim 1. This abstract idea does not incorporate the prior abstract idea into a practical application nor does amount to significantly more than the judicial exception. The wherein clause list the type of data that will be manipulated based on the use of the matrix, which is considered as insignificant extra solution activity. The wherein clause does not incorporate the abstract idea into a practical application or amount to significantly more than the judicial exception.
Claims 4-7 contain the filter split (FS) method, which is explained on page 13. This explanation describes the FS method uses FBP using different amounts of low frequencies than high frequencies of a base image for reconstruction. This is considered as a mathematical calculation or concept. These claims detail applying the abstract idea to a plurality of sinograms or varying aggressiveness of the FS method. This does not detail how this application would impact or improve the reconstruction of the image. The claims further state reducing aggressiveness based on an amount of low frequency noise or a scenario of low level of cone angle inconsistencies. These details describe scenarios how to apply the FS method. This also does not describe how this improves the reconstruction. None of these claim limitations, containing an aspect of the abstract idea, incorporates the abstract idea in claim 1 or 5 into a practical application or amount to significantly more than the judicial exception.
Claim 8 contains an abstract idea of a mathematical calculation of varying a cut-off or shape of a low-frequency filter. It is not clearly claimed how this variance impacts the reconstruction of the image into an improved image. With this improvement not specific, this is considered to not incorporate the abstract idea into a practical application nor amount to significantly more than the judicial exception.
Claim 9 contains an modifying a weighting scheme used in a mathematical relationship or calculation in the back projection, which is considered as an abstract idea. It is not clear how this claimed feature results in an improved reconstruction of an image. Since this claim feature does not claim the improvement, this is not considered to incorporate the abstract idea into a practical application. This claim limitation does not provide an amount significantly more than the judicial exception.
Claim 10 is a limitation that describes the technological environment of the invention. This claim does not incorporate the abstract idea into a practical application nor amount to significantly more than the judicial exception.
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 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.
Claim(s) 1, 3-8, 11 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang (US Pub 2018/0096476) in view of Baturin (US Pub 2014/0185896) and Shi et al (NPL Raw-Data-based Material Decomposition Using Modified U-Net for Low-Dose Spectral CT, (Pub Date: 10/2019)) and Shechter er al (The frequency split method for helical cone-beam reconstruction (Pub date: 5/27/2004)).
Amendments to the Claims A listing of the entire set of pending claims is submitted herewith per 37 CFR 1.121. This listing of claims will replace all prior versions, and listings, of claims in the application.
Re claim 1: (Currently Amended) Tang discloses a method of processing spectral data in cone beam computed tomography (CT), the method comprising:
obtaining the data generated during a CT scan (e.g. CT data can be obtained, which is taught in ¶ [48].);
[0048] In step 110 of method 100, computed tomography (CT) projection data is obtained, e.g., either from performing a CT scan or by recalling previously stored projection data from memory.
[[-]] non-uniformly spreading, between the decomposed sinograms, noise and/or inconsistencies that would lead to image cone beam artefacts (e.g. the statistical weighting matrix is considered as a matrix or function that contributes to the spreading of the noise within the projection data. The noise contributes to the streaks or cone beam artifacts within the image, which is taught in ¶ [49]-[51], [54] and [56].); and
[0049] In step 120 of method 100, the data-fidelity weight factors are determined. For example, the data-fidelity weights W can include a statistical weight matrix W.sub.s, which represents the uncertainty of the respective projection data (e.g., the standard deviation of the measured irradiance at each pixel). When the noise on each pixel is uncorrelated with the other pixels and projection angles, the statistical weighting matrix will be diagonal. For example, the SNR can be determined for each pixel by dividing the measured amplitude of the pixel by a combination of a Poisson noise contribution corresponding to the measured amplitude of the pixel and a predetermined dark noise value of the pixel. The dark noise value can be determined, for example, by a calibration process. Any known method of determining the statistical weight matrix W.sub.s can be used.
[0050] The redundancy weights can be determined using any known reconstruction method. For example, the redundancy weights can be the Parker weights for a half-scan reconstruction, or any other short-scan redundancy weights can be used. Other weights can include calibration weights accounting for pixel-dependent gain factors or other calibrations, for example.
[0051] In process 130 of method 100, the weight matrix is modified to trade-off the effects of the streak artifacts and the anisotropic and peripheral artifacts. For example, the statistical weight W.sub.s can be modified to generate modified weights Wc, which are used in place of the statistical weight W.sub.s. The use of modified weights Wc can decrease the representation of the peripheral rays in the reconstructed image, making the weights of each pixel less polarized, that is. The modified weights Wc can be determined as
Wc=F(W.sub.s),
wherein F can be any function including a polynomial, a log function, etc. For example, F can be a polynomial function, which is given by
Wc=W.sub.s.sup.p,0<p<1
A value of p=0 would reduce the modified weights to the case of IR with no statistical weight, whereas a value of p=1 would reduce the modified weights to the case of IR with statistical weights W.sub.5. For values 0<p<1, the modified weights mitigate streak artifacts (although perhaps not as well as when p=1) while simultaneously mitigating the anisotropic and peripheral artifacts (although perhaps not as well as when p=0).
[0054] FIGS. 5A and 5B respectively show reconstructed images for the modified cases of p=0.3 and p=1. The case p=1 is identical to IR using the unmodified statistical weights, as shown in FIG. 1B. FIG. 5A shows some streak artifacts, but less than those observed in FIG. 1A, corresponding to a case of p=0 (i.e., no statistical weighting). Also notable in FIGS. 5A and 5B is that less anisotropic and peripheral artifacts are observed in FIG. 5A than in FIG. 5B. FIGS. 5C and 5D show close up images of FIGS. 5A and 5B respectively.
[0056] It is observed that the low frequency component of statistical weights contribute to the anisotropic artifacts, whereas high-frequency components are related to the streak artifacts. Thus, to address both types of artifacts, the statistical weights can be separated into low- and high-frequency components in order to compress the low-frequency components of the statistical weights while preserving/amplifying the amplitudes (and sometimes inverting the amplitudes, as explained later) of the high-frequency components of statistical weights. Thus, through disparate treatment of the low- and high-frequency components, the various artifacts can each be more effectively mitigated.
However, Tang fails to specifically teach the features of obtaining the spectral data generated during a CT scan; non-uniformly spreading, between the decomposed sinograms, noise and/or inconsistencies; and wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization.
However, this is well known in the art as evidenced by Baturin. Similar to the primary reference, Baturin discloses adding noise to sinograms (same field of endeavor or reasonably pertinent to the problem).
Baturin discloses obtaining the spectral data generated during a CT scan (e.g. the invention discloses producing spectral data from a CT scanner, which is taught in ¶ [21].);
[0021] Initially referring to FIG. 1, a spectral imaging system 100 such as a spectral computed tomography (CT) scanner is illustrated. The illustrated spectral imaging system 100 utilizes kVp switching, as discussed in greater detail below, to produce spectral projection data. The spectral imaging system 100 includes a generally stationary gantry 102 and a rotating gantry 104, which is rotatably supported by the stationary gantry 102 and rotates around an examination region 106 about a z-axis.
non-uniformly spreading, between the decomposed sinograms, noise and/or inconsistencies (e.g. the invention discloses adding Poisson noise to different sinograms decomposed into an attenuation and phase shift sinograms, which is taught in ¶ [48]-[50]. This is an example of non-uniform noise spread between sinograms.); and
Digital Phantom
[0048] The breast is primarily composed of two types of tissue: glandular and adipose. Additionally, a thin layer of skin surrounds the breast. The digital phantom shown in FIG. 1 models the cross section of a compressed breast including three materials: 1) skin, 2) adipose tissue, and 3) glandular tissue. The background in the image was assumed to be air.
[0049] Each of the materials shown in FIG. 1 was assigned a particular code value. Using equation (6), the XZ cross sectional images of breast's .beta. and .delta. ("map" images) were generated. Then, the "map" images were substituted in equation (5) to get the line integrals along the direction of the x-ray beam propagation (i.e., z axis). The sinogram data was generated for 360 projection angles. Further, Poisson noise was added to both attenuation and phase shift sinograms to simulate the statistical fluctuations in the measurement for the 360 projection angles. Furthermore, a conventional filtered back projection algorithm was applied to obtain the XZ cross section of the breast.
[0050] The simulations were conducted for: a) single scan PCI CT, b) spectral PCI CT (with energy-resolving detector), and c) absorption-based spectral CT (with energy-resolving detector). In addition, the capability for identification of a cancerous tumor in the breast was demonstrated by simulation for a case of single scan PCI CT (although embodiments herein can be performed in spectral CT as well).
wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization (e.g. a filter is applied to the processed image to suppress artifacts within the image, which is taught in ¶ [65] and [70]-[72].).
[0065] In the current simulation for breast CT imaging, the glandular, skin, and tumor are the most difficult to differentiate materials, due to a strong overlap of their respective distributions. Thus, the neighboring distributions in particular have mutual contaminations, such as image of the skin shows glandular and tumor tissue, and image of tumor has contamination from skin. Cross contaminations between glandular and tumor tissues are almost not present since their distributions are significantly separated. Further, as shown in FIG. 12, the histogram of tumor image contains a reconstruction artifacts peak 1270 on the right hand side. According to embodiments of the application, such contamination can be removed by placing a hard cut on the histogram. Alternatively, the reconstruction artifacts can be modeled with a pdf and then identified as a separate material using likelihood routine to remove or reduce the reconstruction artifacts peak 1270.
[0070] Referring to the perspective view of FIG. 15, there is shown, in schematic form and using exaggerated distances for clarity of description, the activity of an exemplary conventional CT imaging apparatus for obtaining the individual 2-D images that are used to form a 3-D volume image. A radiation source 1522 directs radiation through a beam shaping apparatus (not shown) toward a subject 1520, such as a patient or other imaged subject. A sequence of images of subject 1520 is obtained in rapid succession at varying angles about the subject over a range of scan angles, such as one image at each 1-degree angle increment in a 360-degree orbit. A DR detector 1524 is moved to different imaging positions about subject 1520 in concert with corresponding movement of radiation source 1522. For example, such corresponding movement can have a prescribed 2D or 3D relationship. FIG. 15 shows a representative sampling of DR detector 1524 positions to illustrate how these images are obtained relative to the position of subject 1520. Once the needed 2-D projection images are captured in a prescribed sequence, a suitable imaging algorithm, such as FDK filtered back projection or other conventional technique, can be used for generating the 3-D volume image. Image acquisition and program execution are performed by a computer 1530 or by a networked group of computers 1530 that are in image data communication with DR detectors 1524. Image processing and storage is performed using a computer-accessible memory in image data communication with DR detectors 1524 such as computer-accessible memory 1532. The 3-D volume image or exemplary 2-D image data can be presented on a display 1534.
[0071] The logic flow diagram of FIG. 16 shows a conventional image processing sequence S1600 for CT reconstruction using partial scans. A scanning step S1610 directs cone beam exposure toward the subject, enabling collection of a sequence of 2-D raw data images for projection over a range of angles in an image data acquisition step S1620. An image correction step S1630 then performs standard processing of the projection images such as but not limited to geometric correction, scatter correction, gain and offset correction, and beam hardening correction. A logarithmic operation step S1640 obtains the line integral data that is used for conventional reconstruction methods, such as the FDK method well-known to those skilled in the volume image reconstruction arts.
[0072] An optional partial scan compensation step S1650 is then executed when it is necessary to correct for constrained scan data or image truncation and related problems that relate to positioning the detector about the imaged subject throughout the scan orbit. Optional step S1650 can be used for cone beam (CB) CT where typically a limited or partial angular scan (e.g., 220-degrees or 180-degrees plus fan angle) can be used. A ramp filtering step S1660 follows, providing row-wise linear filtering that is regularized with the noise suppression window in conventional processing. A back projection step S1670 is then executed and an image formation step S1680 reconstructs the 3-D volume image using one or more of the non-truncation corrected images. FDK processing generally encompasses the procedures of steps S1660 and S1670. The reconstructed 3-D image can then be stored in a computer-accessible memory and displayed.
Therefore, in view of Baturin, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of obtaining the spectral data generated during a CT scan; non-uniformly spreading, between the decomposed sinograms, noise and/or inconsistencies; and wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization, incorporated in the device of Tang, in order to add noise to decomposed sinograms, which can improve identification or decomposition techniques (as stated in Baturin ¶ [04]-[11]).
However, the combination above fails to specifically teach the feature of in a projection domain, using a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms; reconstructing one or more base images by applying a filter back projection to the decomposed sinograms.
However, this is well known in the art as evidenced by Shi et al. Similar to the primary reference, Shi et al discloses material decomposition of data from a CT scan (same field of endeavor or reasonably pertinent to the problem).
Shi et al discloses in a projection domain, using a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms (e.g. in the last paragraph of the Introduction section, the paper discusses performing material decomposition of raw data in a projection domain. The first paragraph in this section discusses getting basis material sinograms in the projection domain when performing projection domain-based methods of material decomposition.);
reconstructing one or more base images by applying a filter back projection to the decomposed sinograms (e.g. in the second column of the introduction, it is disclosed of acquiring the basis material sinograms in the projection domain and using a FBP algorithm on the material sinograms to reconstruct the material images. This is also discussed on page 5 in the discussion and conclusion).
Therefore, in view of Shi et al, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of in a projection domain, using a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms; reconstructing one or more base images by applying a filter back projection to the decomposed sinograms, incorporated in the device of Tang, as modified by Baturin, in order to use a modified U-Net to process sinograms, which can produce more accurate maps (as stated in Shi et al Abstract).
However, the combination above fails to specifically teach the features of wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data.
However, this is well known in the art as evidenced by Shechter et al. Similar to the primary reference, Shechter et al discloses the frequency split method (same field of endeavor or reasonably pertinent to the problem).
Shechter et al discloses wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data (e.g. the invention on page 3 in the frequency split method section discloses the high frequency is reconstructed using all of the redundant data while the low frequency image only uses little redundant data.).
Therefore, in view of Shechter et al, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data, incorporated in the device of Tang, as modified by Baturin and Shi et al, in order to utilize some redundant data for some frequencies and all for other frequencies, which is used to aid in reducing artifacts within the image (as stated in Shechter et al page 3).
Re claim 4: (Currently Amended) Tang discloses the method according to claim 1 wherein a Frequency Split method is selectively applied to the plurality of sinograms (e.g. ¶ [56] and [57] discloses a frequency split method for selectively applying weights to projection data.).
[0056] It is observed that the low frequency component of statistical weights contribute to the anisotropic artifacts, whereas high-frequency components are related to the streak artifacts. Thus, to address both types of artifacts, the statistical weights can be separated into low- and high-frequency components in order to compress the low-frequency components of the statistical weights while preserving/amplifying the amplitudes (and sometimes inverting the amplitudes, as explained later) of the high-frequency components of statistical weights. Thus, through disparate treatment of the low- and high-frequency components, the various artifacts can each be more effectively mitigated.
[0057] In step 210 of method 200, the data-fidelity weights of step 120 are split into high-frequency components and low-frequency components. Rather than a more conventional split, wherein the total weights are a superposition of the high-frequency components and the low-frequency components, the frequency components can be separated into conventional low-frequency components, and the high-frequency component can be represented by the ratio between the total weights and the low-frequency components. That is the frequency component split of statistical weight Ws can be expressed as
Ws=W.sub.sL.Math.W.sub.sH
wherein W.sub.sL is the low-frequency components of W.sub.s and W.sub.sH is the high-frequency components of W.sub.S.
However, the combination fails to specifically teach the features of decomposed sinograms.
However, this is well known in the art as evidenced by Baturin. Similar to the primary reference, Baturin discloses adding noise to sinograms (same field of endeavor or reasonably pertinent to the problem).
Baturin discloses decomposed sinograms (e.g. the invention discloses adding Poisson noise to different sinograms decomposed into an attenuation and phase shift sinograms, which is taught in ¶ [48]-[50]. This is an example of non-uniform noise spread between different sinograms.).
Therefore, in view of Baturin, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of decomposed sinograms, incorporated in the device of Tang, in order to add noise to decomposed sinograms, which can improve identification or decomposition techniques (as stated in Baturin ¶ [04]-[11]).
Re claim 5: (Currently Amended) Tang discloses the method according to
However, the combination fails to specifically teach the features of different sinograms of the plurality of decomposed sinograms.
However, this is well known in the art as evidenced by Baturin. Similar to the primary reference, Baturin discloses adding noise to sinograms (same field of endeavor or reasonably pertinent to the problem).
Baturin discloses different sinograms of the plurality of decomposed sinograms (e.g. the invention discloses adding Poisson noise to different sinograms decomposed into an attenuation and phase shift sinograms, which is taught in ¶ [48]-[50]. This is an example of non-uniform noise spread between different sinograms.).
Therefore, in view of Baturin, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of different sinograms of the plurality of decomposed sinograms, incorporated in the device of Tang, in order to add noise to decomposed sinograms, which can improve identification or decomposition techniques (as stated in Baturin ¶ [04]-[11]).
Re claim 6: (Currently Amended) Tang discloses the method according to claim 5, wherein the aggressiveness of the Frequency Split
[0058] FIG. 7A shows a plot of statistical weight Ws as a function of pixel number. FIGS. 7B and 7D respectively show low- and high-frequency components of the statistical weight Ws. The frequency split can be performed in either the spatial or the frequency domain, and the low-frequency components can be determined using a low-pass filter F, which is a Savitzky-Golay filter, a Gaussian filter, or any other known low-pass filter. Thus, the low-frequency components can be determined as by
W.sub.sL=F(W.sub.s),
and the high-frequency components can be determined
[00003] 𝑊sH=𝑊𝑠𝐹~(𝑊𝑠).
[0059] After determining the high- and low-frequency components, the two components can then be separately operated on to better address the various types of artifacts. For example, the high- and low-frequency components can be separately compressed/magnified using functions G and F respectively, which can be empirically optimized to minimize the artifacts in the reconstructed image. That is, the modified weights can be expressed by
Wc=F(W.sub.sL).Math.G(W.sub.sH)
wherein F and G are two functions. For example, F and G can be polynomial functions, which are given by
[00004] Wc=(𝐹~(𝑊𝑠))𝑝.Math.(𝑊𝑠𝐹~(𝑊𝑠))𝑞,
wherein p≧0 and q≧0.
Thus, if p<1 the low-frequency components are compressed, and if p>1 the low-frequency components are magnified. Similarly, if q<1 high-frequency components are compressed, and if q>1 the high-frequency components are magnified.
[0060] In step 215 of method 200, the low-frequency components are compressed. For example, the low-frequency components can be operated on by a function F that is a polynomial of order less than 1 (e.g., F(x)=x.sup.p). FIG. 7C shows a plot of low-frequency components after they have been compressed using function F, wherein F is a polynomial function of order p=0.5.
Re claim 7: (Currently Amended) Tang discloses the method according to claim 5
Re claim 8. (Currently Amended) Tang discloses the method according to
Re claim 11: (Currently Amended) Tang discloses a system for processing spectral computed tomography (CT) data in cone beam computed tomography (CT), the system comprising;
a memory that stores a plurality of instructions; and processor circuitry that coupled to the memory and is configured to execute the plurality of instruction (e.g. the invention discloses a memory that can store instructions and a system controller executes the program of the CT scanner process, which is taught in ¶ [76]-[78].) to:
[0076] The above-described data is sent to a preprocessing device 506, which is housed in a console outside the radiography gantry 500 through a non-contact data transmitter 505. The preprocessing device 506 performs certain corrections, such as sensitivity correction on the raw data. A memory 512 stores the resultant data, which is also called projection data at a stage immediately before reconstruction processing. The memory 512 is connected to a system controller 510 through a data/control bus 511, together with a reconstruction device 514, input device 515, and display 516. The system controller 510 controls a current regulator 513 that limits the current to a level sufficient for driving the CT system.
[0077] The detectors are rotated and/or fixed with respect to the patient among various generations of the CT scanner systems. In one implementation, the above-described CT system can be an example of a combined third-generation geometry and fourth-generation geometry system. In the third-generation system, the X-ray tube 501 and the X-ray detector 503 are diametrically mounted on the annular frame 502 and are rotated around the object OBJ as the annular frame 502 is rotated about the rotation axis RA. In the fourth-generation geometry system, the detectors are fixedly placed around the patient and an X-ray tube rotates around the patient. In an alternative embodiment, the radiography gantry 500 has multiple detectors arranged on the annular frame 502, which is supported by a C-arm and a stand.
[0078] The memory 512 can store the measurement value representative of the irradiance of the X-rays at the X-ray detector unit 503. Further, the memory 512 can store a dedicated program for executing the CT image reconstruction methods, including methods to perform method 100 and method 200 discussed herein.
obtaining the data generated during a CT scan (e.g. CT data can be obtained, which is taught in ¶ [48] above.);
non-uniformly spreading between the sinograms noise and/or inconsistencies that would lead to image cone beam artefacts (e.g. the statistical weighting matrix is considered as a unitary transformation matrix or function that contributes to the spreading of the noise non-uniformly within the projection data. The noise contributes to the streaks or cone beam artifacts within the image, which is taught in ¶ [49]-[51] above.).
However, Tang fails to specifically teach the features of obtain the spectral data generated during a CT scan; non-uniformly spread between the decomposed sinograms noise and/or inconsistencies; and wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization.
However, this is well known in the art as evidenced by Baturin. Similar to the primary reference, Baturin discloses adding noise to sinograms (same field of endeavor or reasonably pertinent to the problem).
Baturin discloses obtain the spectral data generated during a CT scan (e.g. the invention discloses producing spectral data from a CT scanner, which is taught in ¶ [21].);
non-uniformly spread between the decomposed sinograms noise and/or inconsistencies (e.g. the invention discloses adding Poisson noise to different sinograms decomposed into an attenuation and phase shift sinograms, which is taught in ¶ [48]-[50]. This is an example of non-uniform noise spread between sinograms.); and
wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization (e.g. a filter is applied to the processed image to suppress artifacts within the image, which is taught in ¶ [65] and [70]-[72].).
.
Therefore, in view of Baturin, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of obtain the spectral data generated during a CT scan; non-uniformly spread between the decomposed sinograms noise and/or inconsistencies; and wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization, incorporated in the device of Tang, in order to add noise to decomposed sinograms, which can improve identification or decomposition techniques (as stated in Baturin ¶ [04]-[11]).
However, the combination above fails to specifically teach the feature of in a projection domain, use a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms; reconstruct one or more base images by applying a filter back projection to the decomposed sinograms.
However, this is well known in the art as evidenced by Shi et al. Similar to the primary reference, Shi et al discloses material decomposition of data from a CT scan (same field of endeavor or reasonably pertinent to the problem).
Shi et al discloses in a projection domain, use a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms (e.g. in the last paragraph of the Introduction section, the paper discusses performing material decomposition of raw data in a projection domain. The first paragraph in this section discusses getting basis material sinograms in the projection domain when performing projection domain-based methods of material decomposition.);
reconstruct one or more base images by applying a filter back projection to the decomposed sinograms (e.g. in the second column of the introduction, it is disclosed of acquiring the basis material sinograms in the projection domain and using a FBP algorithm on the material sinograms to reconstruct the material images. This is also discussed on page 5 in the discussion and conclusion).
Therefore, in view of Shi et al, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of in a projection domain, use a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms; reconstruct one or more base images by applying a filter back projection to the decomposed sinograms, incorporated in the device of Tang, as modified by Baturin, in order to use a modified U-Net to process sinograms, which can produce more accurate maps (as stated in Shi et al Abstract).
However, the combination above fails to specifically teach the features of wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data.
However, this is well known in the art as evidenced by Shechter et al. Similar to the primary reference, Shechter et al discloses the frequency split method (same field of endeavor or reasonably pertinent to the problem).
Shechter et al discloses wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data (e.g. the invention on page 3 in the frequency split method section discloses the high frequency is reconstructed using all of the redundant data while the low frequency image only uses little redundant data.).
Therefore, in view of Shechter et al, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data, incorporated in the device of Tang, as modified by Baturin and Shi et al, in order to utilize some redundant data for some frequencies and all for other frequencies, which is used to aid in reducing artifacts within the image (as stated in Shechter et al page 3).
12 - 14. (Cancelled without prejudice).
Re claim 15: (New) Tang discloses a non-transitory computer-readable medium for storing executable instructions, which cause a method to be performed to process spectral data in cone beam computed tomography (CT) (e.g. the invention discloses a memory that can store instructions and a system controller executes the program of the CT scanner process, which is taught in ¶ [76]-[78] above.), the method comprising:
obtaining the data generated during a CT scan (e.g. CT data can be obtained, which is taught in ¶ [48] above.);
non-uniformly spreading, between the sinograms, noise and/or inconsistencies that would lead to image cone beam artefacts (e.g. the statistical weighting matrix is considered as a unitary transformation matrix or function that contributes to the spreading of the noise non-uniformly within the projection data. The noise contributes to the streaks or cone beam artifacts within the image, which is taught in ¶ [49]-[51] above.).
However, Tang fails to specifically teach the features of obtaining the spectral data generated during a CT scan; non-uniformly spread between the decomposed sinograms noise and/or inconsistencies; and wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization.
However, this is well known in the art as evidenced by Baturin. Similar to the primary reference, Baturin discloses adding noise to sinograms (same field of endeavor or reasonably pertinent to the problem).
Baturin discloses obtaining the spectral data generated during a CT scan (e.g. the invention discloses producing spectral data from a CT scanner, which is taught in ¶ [21].);
non-uniformly spread between the decomposed sinograms noise and/or inconsistencies (e.g. the invention discloses adding Poisson noise to different sinograms decomposed into an attenuation and phase shift sinograms, which is taught in ¶ [48]-[50]. This is an example of non-uniform noise spread between sinograms.); and
wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization (e.g. a filter is applied to the processed image to suppress artifacts within the image, which is taught in ¶ [65] and [70]-[72] above.).
Therefore, in view of Baturin, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of obtaining the spectral data generated during a CT scan; non-uniformly spread between the decomposed sinograms noise and/or inconsistencies; and wherein the image cone beam artifacts are suppressed in the base images thereby improving image visualization, incorporated in the device of Tang, in order to add noise to decomposed sinograms, which can improve identification or decomposition techniques (as stated in Baturin ¶ [04]-[11]).
However, the combination above fails to specifically teach the feature of in a projection domain, using a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms; reconstructing one or more base images by applying a filter back projection to the decomposed sinograms.
However, this is well known in the art as evidenced by Shi et al. Similar to the primary reference, Shi et al discloses material decomposition of data from a CT scan (same field of endeavor or reasonably pertinent to the problem).
Shi et al discloses in a projection domain, using a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms (e.g. in the last paragraph of the Introduction section, the paper discusses performing material decomposition of raw data in a projection domain. The first paragraph in this section discusses getting basis material sinograms in the projection domain when performing projection domain-based methods of material decomposition.);
reconstructing one or more base images by applying a filter back projection to the decomposed sinograms (e.g. in the second column of the introduction, it is disclosed of acquiring the basis material sinograms in the projection domain and using a FBP algorithm on the material sinograms to reconstruct the material images. This is also discussed on page 5 in the discussion and conclusion).
Therefore, in view of Shi et al, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of in a projection domain, using a material decomposition to decompose the obtained spectral data to obtain a plurality of decomposed sinograms; reconstructing one or more base images by applying a filter back projection to the decomposed sinograms, incorporated in the device of Tang, as modified by Baturin, in order to use a modified U-Net to process sinograms, which can produce more accurate maps (as stated in Shi et al Abstract).
However, the combination above fails to specifically teach the features of wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data.
However, this is well known in the art as evidenced by Shechter et al. Similar to the primary reference, Shechter et al discloses the frequency split method (same field of endeavor or reasonably pertinent to the problem).
Shechter et al discloses wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data (e.g. the invention on page 3 in the frequency split method section discloses the high frequency is reconstructed using all of the redundant data while the low frequency image only uses little redundant data.).
Therefore, in view of Shechter et al, it would have been obvious to one of ordinary skill before the effective filing date of the claimed invention was made to have the feature of wherein at least some low frequencies of the base image are reconstructed using not all data of the spectral CT data, while high frequencies of the base image are reconstructed using all data of the spectral CT data, incorporated in the device of Tang, as modified by Baturin and Shi et al, in order to utilize some redundant data for some frequencies and all for other frequencies, which is used to aid in reducing artifacts within the image (as stated in Shechter et al page 3).
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang, as modified by Baturin and Shi et al, as applied to claim 1 above and further in view of Yu (US Pub 2017/0206635).
Re claim 2: (Currently amended) Tang discloses the method according to claim 1, further comprising applying a basis transformation on different sinograms of the plurality of sinograms in the projection domain, wherein the noise and/or inconsistencies that would lead to the image cone beam artefacts are as non-uniformly spread between the decomposed sinograms (e.g. the weighting matrix combined with the transpose of the system matrix and the other system matrix are considered as a basis transformation or function that contributes to the spreading of the noise non-uniformly within the projection data, which is seen in ¶ [42] and [43]. The noise contributes to the streaks or cone beam artifacts within the image, which is taught in ¶ [49]-[51] above. The pixels that are impacted by the matrix are considered as a part of the projection data that makeup the sinogram that reflects information detected from different projection angles.).
[0042] However, rays transmitted through the periphery of a subject, as opposed to rays transmitted through the core, experience weaker attenuation. Thus, a reconstruction favoring high SNR data will disproportionately rely on peripheral rays that tangentially pass through the subject. This over representation of peripheral rays in the reconstructed image generates other artifacts, instead of the streak artifacts. The methods described herein mitigate these other artifacts by modifying the weights W applied in the data fidelity term of an objective function used for iterative reconstruction.
[0043] In certain implementations, statistical IR uses a penalized weighted least square (PWLS) approach, which generates a reconstructed image by solving an optimization problem with a cost/objective function C(.), which is given by
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wherein A is the system matrix (also referred to as the forward projection operator), x is the image to be reconstructed, y is the measured projection data, U(x) is the regularization function (also referred to as the penalty function), and β is the regularization parameter. The weighting is performed by W which is a diagonal weighting matrix. Generally, the weighting matrix can be expressed by using a factored model
W=W.sub.rW.sub.sW.sub.v
wherein W.sub.r is a redundant weight matrix, W.sub.s is a statistical weight matrix, and W.sub.v is another weight matrix. The term on the left-hand side of the objective function is the data fidelity term, and the term on the right-hand side of the objective function is the regularization term.
However, Tang fails to specifically teach the feature of applying a unitary basis transformation.
However, this is well known in the art as evidenced by Schwartzman. Similar to the primary reference, Schwartzman discloses utilizing a unitary matrix with other matrices or vectors (same field of endeavor or reasonably pertinent to the problem).
Schwartzman discloses applying a unitary basis transformation (e.g. the system discloses a unitary basis transformation matrix is applied to other matrices in order to ensure that the components that correspond to a signal received are uncorrelated, which is taught in ¶ [80]-[86].).
[0079] Extracting a Feature Vector from the Normalized Electrogram
[0080] As shown in FIG. 3A, the next step in the method of the invention is extracting a feature vector from the normalized electrogram 64. The method of the invention is based on the assumption that individual electrograms may be represented as being composed of basic elements (referred as u.sub.m, m=1, . . . , M, below). Having identified these elements (as described in "Training," below), we may calculate components, i.e., coefficients, which represent the extent to which each of the basic elements contributes to a given electrogram.
[0081] Let X.sub.L.times.N=[x.sub.1, . . . , x.sub.N] be a collection of N synchronized, scaled and centered electrograms recorded at a plurality of sites in a heart, each being an L-dimensional column vector:
x.sub.n=[x.sub.n.sup.scc(t)],t=-L1, . . . L2
[0082] A vector basis of size M U.sub.L.times.M=[u.sub.1, . . . , u.sub.M] is a unitary matrix, which is a collection of M fixed column vectors wherein u.sub.m=[u.sub.m(t)], t=-L1, . . . ,L2. This vector basis is used as a matrix transformation applied to the data matrix X.sub.L.times.N, as follows:
Y=U.sup.TX
[0083] In the matrix Y.sub.M.times.N=[y.sub.1,. . . , y.sub.N] obtained from the above operation, the column y.sub.n=[y.sub.1,n, . . . , y.sub.M,n].sup.T is a feature vector which is a component representation of the electrogram x.sub.n. Each of the M components, y.sub.m,n, of each of the electrograms is computed, within the matrix transformation, as: 5 y m , n = u m T x n = t = 1 L u m ( t ) x n ( t )
[0084] The components y.sub.m,n represent the contribution of each of the basis set vectors u.sub.m to each of the electrograms x.sub.n.
[0085] If the matrix U is unitary (all of the u.sub.m.sup.'s are mutually orthogonal), the components y.sub.m,n belonging to a particular electrogram x.sub.n are uncorrelated. This means that every component contains information that cannot be obtained from the other components.
[0086] FIG. 5D depicts the first three vector basis elements, u.sub.m, corresponding to the centered, scaled, synchronized electrogram of FIG. 5C. The matrix elements in FIG. 5D are scaled according to their components, and were computed as described herein. FIG. 5E is a graphical depiction of the first 15 components of the feature vector corresponding to the scaled, centered, synchronized electrogram of FIG. 5C. As can be seen from FIG. 5E, the majority of the variation in the electrogram may be accounted for by the first few components of the feature vector.
Therefore, in view of Schwartzman, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of applying a unitary basis transformation, incorporated in the device of Tang, in order to apply a unitary basis transformation onto vectors within an image signal to uncorrelated components, which aid in calculating the contribution of component elements to an electrogram (as stated in Schwartzman ¶ [80]).
However, the combination fails to specifically teach the features of different sinograms of the plurality of sinograms decomposed in the projection domain, non-uniformly spread between the decomposed sinograms.
However, this is well known in the art as evidenced by Baturin. Similar to the primary reference, Baturin discloses adding noise to sinograms (same field of endeavor or reasonably pertinent to the problem).
Baturin discloses different sinograms of the plurality of sinograms decomposed in the projection domain, non-uniformly spread between the decomposed sinograms (e.g. the invention discloses adding Poisson noise to different sinograms decomposed into an attenuation and phase shift sinograms, which is taught in ¶ [48]-[50]. This is an example of non-uniform noise spread between different sinograms.).
Therefore, in view of Baturin, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of different sinograms of the plurality of sinograms decomposed in the projection domain, non-uniformly spread between the decomposed sinograms, incorporated in the device of Tang, as modified by Schwartzman, in order to add noise to decomposed sinograms, which can improve identification or decomposition techniques (as stated in Baturin ¶ [04]-[11]).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang, as modified by Baturin and Shi et al, as applied to claim 5 above, and further in view of Liang (US Pub 2016/0055658).
Re claim 9: (Currently Amended) Tang discloses the method according to
controlling the variation of the aggressiveness of the Frequency Split method for the different sinograms by modifying a weighting scheme used to generate a low-frequency image (e.g. the invention discloses varying the frequency split for the projection data that allows for modifying the weights used to generate a compressed component image that can be comprised of low frequency. The section that describes this is ¶ [58]-[60] above.).
However, Tang fails to specifically teach the features of by modifying a back projection weighting scheme.
However, this is well known in the art as evidenced by Liang. Similar to the primary reference, Liang discloses iterative reconstruction that uses FBP (same field of endeavor or reasonably pertinent to the problem).
Liang discloses by modifying a back projection weighting scheme (e.g. the system discloses adjusting weights and filters for the back projection when reconstructing the image, which is taught in ¶ [40]-[42] and [45].).
[0040] In regards to implementation of the PWLS-PINL method, the current CT images and the initial high-dose CT images are first reconstructed by the FBP method, and then a B-spline based image registration technique, see R. Szeliski, et al., Spline-Based Image Registration, International Journal of Computer Vision, Vol. 22, no. 3, pp. 199-218, 1997, is adopted to generate the registered prior image from the initial high-dose CT image, with the effectiveness of the B-spline based image registration algorithm having been extensively validated by numerous registration experiments. Based on the transformation matrix, all voxels of the prior CT images are then transformed into the current CT images to obtain globally aligned prior images. Similar to previous studies in low-dose CT image restoration, see Ma, et al., through two roughly aligned images, the rich redundant information in the prior image can be effectively used to induce the PINL regularization for low-dose image reconstruction, as described below.
[0041] After obtaining the registered prior image μ.sub.prior.sup.reg, weight w(k, j) in Equation (6) is regarded as a function of current estimation μ and registered prior image μ.sub.prior.sup.reg. Because of nonlinearity in calculating weight w(k, j), minimizing the objective function of Equation (9) is difficult for a closed-solution. To address this problem, a binary optimal scheme is used to minimize the objective function of Equation (9). See, J. Ma, et al, Generalized Gibbs Priors Based Positron Emission Tomography Reconstruction, Comput. Biol. Med., Vol. 40, no. 6, pp. 565-571, June 2010, and J. Ma, et al., Iterative Image Reconstruction for Cerebral Perfusion CT Using a Pre-contrast Scan Induced Edge-preserving Prior, Phys. Med. Biol., Vol. 57, no. 22, pp. 7519-7542, November 2012. Specifically, weight w(k, j) is automatically adjusted according to the similarity between patch-windows in current estimation μ.sup.n, with n as an iterative index, and the registered prior-image μ.sub.prior.sup.reg during each iteration.
[0042] The iterative successive over-relaxation algorithm of J. Wang, et al., and J. A. Fessler, Penalized Weighted Least-squares Image Reconstruction for Positron Emission Tomography, IEEE Trans. Med. Imaging, Vol. 13, no. 2, pp. 290-300, 1994, is modified to calculate the solution of Equation (2). In the implementation, the variance σ.sub.i.sup.2, i.e., the element of weight matrix in Equation (2), is updated in each iteration according to Equation (3) to more accurately estimate the sinogram variance, with the implementation referred to herein as an PWLS-PINL algorithm, with presented PWLS-PINL algorithm summarized in Table 1.
[0045] FIG. 1(b) shows the CT image reconstructed by the Filtered Back-Projection (FBP) reconstruction method with an optimized Hamming filter from the sinogram data acquired at 100 mAs, 120 kVp. The deformed images were simulated by mechanically performing a cosine transform warped distortion on images reconstructed by the FBP method from the sinogram data acquired at 100 mAs, 120 kVp. To obtain the registered prior images, the deformed image volume was registered to the images reconstructed by the FBP method from the sinogram data acquired at 17 and 40 mAs, respectively.
Therefore, in view of Liang, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of by modifying a back projection weighting scheme, incorporated in the device of Tang, as modified by Baturin and Shi et al, in order to optimize the weights used to generate an image, which improve the current CT scan image quality (as stated in Liang ¶ [11] and [12]).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tang, as modified by Baturin and Shi et al, as applied to claim 1 above, and further in view of Common Knowledge (CT scanners) (Official Notice).
Re claim 10: (Currently Amended) Tang teaches the features of the method according to claim 1, wherein the data originate from a helical scan (e.g. the invention discloses a helical scan of a person using a CT scanner, which is seen in figure 12 and described in ¶ [72].).
[0072] FIG. 12 illustrates an implementation of the radiography gantry included in a CT apparatus or scanner. As shown in FIG. 12, a radiography gantry 500 is illustrated from a side view and further includes an X-ray tube 501, an annular frame 502, and a multi-row or two-dimensional-array-type X-ray detector 503. The X-ray tube 501 and X-ray detector 503 are diametrically mounted across an object OBJ on the annular frame 502, which is rotatably supported around a rotation axis RA. A rotating unit 507 rotates the annular frame 502 at a high speed, such as 0.4 sec/rotation, while the object OBJ is being moved along the axis RA into or out of the illustrated page.
However, Tang fails to specifically teach the feature of non-gated helical scan. However, this is well known in the art as evidenced by Common Knowledge (CT scanners). Similar to the primary reference, Common Knowledge (CT scanners) discloses scanning using a CT scanner (same field of endeavor or reasonably pertinent to the problem).
Common Knowledge (CT scanners) discloses non-gated helical scan (e.g. a non-gated CT scanner can be used to scan a person without the additional equipment or processing of taking into consideration the heart functioning, which can speed up processing images.).
Therefore, in view of Common Knowledge, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of non-gated helical scan, incorporated in the device of Tang, in order to provide a CT scanner used to capture images of a subject without monitoring heart functions, which can improve processing speed of the captured image.
However, the combination fails to specifically teach the features of the spectral data.
However, this is well known in the art as evidenced by Baturin. Similar to the primary reference, Baturin discloses adding noise to sinograms (same field of endeavor or reasonably pertinent to the problem).
Baturin discloses the spectral data (e.g. the invention discloses producing spectral data from a CT scanner, which is taught in ¶ [21] above.);
Therefore, in view of Baturin, it would have been obvious to one of ordinary skill at the time the invention was made to have the feature of the spectral data, incorporated in the device of Tang, as modified by Common Knowledge (CT scanners), in order to add noise to decomposed sinograms, which can improve identification or decomposition techniques (as stated in Baturin ¶ [04]-[11]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Petschke discloses balancing the noise in an image.
The Frequency Split Method for Helical Cone-Beam Reconstruction shows the frequency split method.
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/CHAD DICKERSON/ Primary Examiner, Art Unit 2681