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, 10, 11, 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Peng et al. [“CBCT-Based Synthetic CT Image Generation Using Conditional Denoising Diffusion Probabilistic Model”], hereinafter Peng.
As to claim 1, Peng discloses a computer implemented method for reconstructing a volumetric medical image of a patient from Cone Beam Computed Tomography (CBCT) projections of the patient (Abstract, Fig. 1(b)), the computer implemented method comprising:
using a shared neural field to generate a volumetric field of attenuation coefficients from the CBCT projections, wherein the shared neural field is modulated by a patient specific neural field; (Fig. 1(b), Section 2.3-The proposed conditional DDPM for synthetic CT from CBCT images; e.g. “shared neural field” corresponds to “conditional DDPM for synthetic CT from CBCT images”) and
mapping the volumetric field of attenuation coefficients to a volumetric image of the patient (Fig. 3, Sections 2.3, 2.4, 2.5).
As to claim 10, Peng discloses the computer implemented method of claim 1, further comprising: initiating values of the patient specific neural field to randomly generated initial values (Fig. 3, Sections 2.3, 2.4, 2.5, e.g., denoising diffusion probabilistic model (DDPM)).
As to claim 11, Peng discloses a computer implemented method for training a shared neural field for use in reconstructing a volumetric medical image of a patient from Cone Beam Computed Tomography (CBCT) projections of the patient, wherein the shared neural field is operable to generate a volumetric field of attenuation coefficients from the CBCT projections, and wherein the shared neural field is modulated by a patient specific neural field, the computer implemented method comprising:
training the shared neural field by using one or more ground truth volumetric medial images reconstructed from diagnostic CT projections of one or more individuals other than the patient for which the shared neural field will be used (Sections 2.3, 2.4, 2.5, 4).
As to claim 20, the claim recites features similar to those discussed above. Therefore, claim 20 is rejected for reasons similar to those discussed above.
Allowable Subject Matter
Claims 2-9, 12-19 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 dependent claims 2, 3, 5, 12, 13, 15.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
HINDLEY discloses a system for image registration and volumetric imaging of a patient.
ZHOU et al. relate to using deep learning (DL) networks or deep neural networks (DNNs) to improve the image quality of reconstructed medical images, and, more particularly, to providing a medical image processing apparatus for realizing DL networks to reduce noise and artifacts in images of reconstructed computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI).
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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