Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3, 7-9, 13 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Sahbaee Bagherzadeh (US 2020/0281543).
As to claim 1, Sahbaee Bagherzadeh discloses a computer-implemented method for material decomposition in dual-energy X-ray imaging, the computer-implemented method comprising:
obtaining a first X-ray image dataset corresponding to a first X-ray energy spectrum, and a second X-ray image dataset corresponding to a second X-ray energy spectrum (para. 0017, 0018, e.g., “Dual energy or photon counting CT systems may be used. Using spectral CT, measurements reconstructed in Hounsfield or other density or attenuation values from different energies may be used to derive material composition”); and
generating at least one material-specific image dataset, the generating of the at least one material-specific image dataset comprising applying a decomposition module that contains a first sequence of processing steps or machine learning function to input data that depends on the first X-ray image dataset and the second X-ray image dataset (para. 0046),
wherein, before applying the decomposition module, a filter module, an artifact-reduction module, or the filter module and the artifact-reduction module are applied to the input data (para. 0013, 0031, 0039, 0043).
As to claim 2, Sahbaee Bagherzadeh discloses the computer-implemented method of claim 1, wherein the first X-ray image dataset corresponds to a first X-ray projection image, and the second X-ray image dataset corresponds to a second X-ray projection image, or wherein the first X-ray image dataset corresponds to a first reconstructed volume, and the second X-ray image dataset corresponds to a second reconstructed volume (para. 0017, 0046, 0054).
As to claim 3, Sahbaee Bagherzadeh discloses the computer-implemented method of claim 1, wherein the at least one material-specific image dataset includes a contrast-agent image dataset, a virtual non-contrast image dataset, or the contrast-agent image dataset and the virtual non-contrast image dataset (para. 0021, 0024, 0031, 0046).
As to claim 7, Sahbaee Bagherzadeh discloses a method for dual-energy X-ray imaging, the method comprising:
generating a first X-ray image dataset that represents an object to be imaged, the generating of the first X-ray image (para. 0017, 0018, e.g., “Dual energy or photon counting CT systems may be used. Using spectral CT, measurements reconstructed in Hounsfield or other density or attenuation values from different energies may be used to derive material composition”)comprising:
generating first X-ray radiation corresponding to a first X-ray energy spectrum (para. 0017, 0018); and
detecting portions of the first X-ray radiation that pass through the object (para. 0017, 0018);
generating a second X-ray image dataset that represents the object, the generating of the second X-ray image (para. 0017, 0018, e.g., “Dual energy or photon counting CT systems may be used. Using spectral CT, measurements reconstructed in Hounsfield or other density or attenuation values from different energies may be used to derive material composition”) comprising:
generating second X-ray radiation corresponding to a second X-ray energy spectrum (para. 0017, 0018); and
detecting portions of the first X-ray radiation that pass through the object (para. 0017, 0018); and
performing a computer-implemented method for material decomposition in dual-energy X-ray imaging, the computer-implemented method comprising:
generating at least one material-specific image dataset, the generating of the at least one material-specific image dataset comprising applying a decomposition module that contains a first sequence of processing steps or machine learning function to input data that depends on the first X-ray image dataset and the second X-ray image dataset (para. 0046),
wherein, before applying the decomposition module, a filter module, an artifact-reduction module, or the filter module and the artifact-reduction module are applied to the input data (para. 0013, 0031, 0039, 0043).
As to claims 8-9, 13, these claims recite features similar to those discussed above. Therefore, they are rejected for reasons similar to those discussed above.
Allowable Subject Matter
Claim 12 is allowed.
Claims 4-6, 10-11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art discloses the claim limitations discussed above, but fails to disclose the combined features required by each of claims 12, 4-5, 10-12.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Chen et al. disclose a system and method for reconstructing an image of a subject acquired using a tomographic imaging system includes at least one computer processor configured to form an image reconstruction pipeline.
Ramani et al. disclose a method for processing data acquired utilizing multi-energy computed tomography imaging. The method includes acquiring multiple multi-energy spectral scan datasets and computing basis material images representative of multiple basis materials from the multi-energy spectral scan datasets, wherein the multiple basis material images include correlated noise. The method also includes jointly denoising the multiple basis material images in at least a spectral domain utilizing a deep learning-based denoising network to generate multiple de-noised basis material images.
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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