DETAILED ACTION
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 5/19/2026 has been entered.
Response to Arguments
Applicant’s arguments with respect to claim(s) 5/19/2026 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 102
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) 1-2, 10, 11-15, 19-20, 25 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Xu (PGPub 2021/0056688).
Xu discloses 1. A method for correcting artifacts in a computed tomography image data set of a recording region in which an at least substantially needle-shaped metal object is located, the computed tomography image data set being reconstructed from projection images recorded at least partially such that the at least substantially needle-shaped metal object is irradiated at least substantially in a longitudinal direction (Xu,Fig. 3), and the method comprising:
ascertaining an artifact data set describing at least one artifact in an image space, the atleast one artifact being caused by the at least substantially needle-shaped metal object, the ascertaining being based on prior knowledge about the at least one artifact; and (Xu, Fig. 1,#34, “Metal artifact image”; see paragraph 33-34, where the material is assumed to be titanium;) and the ascertaining including at least one of
ascertaining a first function of attenuation values of the at least one artifact with respect to a transverse direction perpendicular to the longitudinal direction of the at least substantially needle-shaped metal object in a longitudinal extension plane of the at least substantially needle-shaped metal object;(Xu,Fig. 1, #34 where the artifact image 35 assigns an attenuation value to every image pixels. Examiner Note : The BRI of “ascertaining” includes determining, finding, learning, discovering etc. By determining a row, column or the entire image, you have ascertained a first function of attenuation values; For example an image can be represented as F(x,y)={0,0,0;1,1,1;2,2,2}. That is a function. )
or
adapting the first function; and
subtracting the artifact data set from the computed tomography image data set. (Xu, Fig. 1 #36 & #40, subtracting the metal artifact image from the uncorrected image to get the corrected image)
Xu discloses 2. The method as claimed in claim 1, wherein during intervention monitoring with the at least substantially needle-shaped metal object as an intervention instrument at least one of
The method is performed for each recorded computed tomography image data set of a monitoring series,
an image plane of the computed tomography image data set is a longitudinal extension plane of the at least substantially needle-shaped metal object, or
the at least substantially needle-shaped metal object is an intervention needle. (Xu, paragraph 40, “As noted above, in experiments the CNN correction speed was about 80 images per second, which is practical for use in correcting “live” images generated by a C-arm 10 (e.g. FIG. 1) during an iGT procedure. Furthermore, as seen in FIGS. 3 and 4, the metal artifact image (second column from left in FIGS. 3 and 4) can provide effectively segmented representation of the metal artifact. Although this image exhibits blooming or other distortion compared with the actual boundaries of the metal object causing the artifact, it is seen that the metal artifact image provides an isolation image of the metal object that can, for example, be fitted to a known metal object geometry to provide for accurate live tracking of a biopsy needle, metal prosthesis, or other known metal object that is to be manipulated during the iGT procedure. In one approach, the corrected X-ray image 40 is displayed on the display 46 with the metal artifact image 34 (or an image derived from the metal artifact image 34, such as an image of the underlying metal object positioned to be spatially registered with the metal artifact image 34) is also displayed on the display 46, e.g. superimposed onto or otherwise fused with the display of the corrected X-ray image 40. As another application, the density of the image of the metal object captured in the metal artifact image 34 (or other information such as the extent of blooming) may be used to classify the metal object as to metal type, or the metal object depicted by the metal artifact image 34 may be identified based on shape, and/or so forth. In some embodiments, an identification approach such as one disclosed in Walker et al., U.S. Pub. No. 2012/0046971 A1 (published Feb. 23, 2012) may be used. In some embodiments, to maximize processing speed for live imaging during iGT or other time-critical imaging tasks, the image reconstruction method 26 does not include any metal artifact correction other than by applying the neural network 32 to the uncorrected X-ray image 30 to generate the metal artifact image 34 and generating the corrected X-ray image 40 by subtracting the metal artifact image from the uncorrected x ray image.”),
Xu discloses 10. The method as claimed in claim 1, wherein the ascertaining of the artifact data set comprises:
at least one of choosing or determining the artifact data set, at least partially from reference data sets present in a database, for at least one of various reference metal objects or various parameters of at least one of the at least substantially needle-shaped metal object or the various reference metal objects. (Xu, paragraph 40, “Furthermore, as seen in FIGS. 3 and 4, the metal artifact image (second column from left in FIGS. 3 and 4) can provide effectively segmented representation of the metal artifact. Although this image exhibits blooming or other distortion compared with the actual boundaries of the metal object causing the artifact, it is seen that the metal artifact image provides an isolation image of the metal object that can, for example, be fitted to a known metal object geometry to provide for accurate live tracking of a biopsy needle, metal prosthesis, or other known metal object that is to be manipulated during the iGT procedure. In one approach, the corrected X-ray image 40 is displayed on the display 46 with the metal artifact image 34 (or an image derived from the metal artifact image 34, such as an image of the underlying metal object positioned to be spatially registered with the metal artifact image 34) is also displayed on the display 46, e.g. superimposed onto or otherwise fused with the display of the corrected X-ray image 40. As another application, the density of the image of the metal object captured in the metal artifact image 34 (or other information such as the extent of blooming) may be used to classify the metal object as to metal type, or the metal object depicted by the metal artifact image 34 may be identified based on shape, and/or so forth. In some embodiments, an identification approach such as one disclosed in Walker et al., U.S. Pub. No. 2012/0046971 A1 (published Feb. 23, 2012) may be used. I”)
Xu discloses 11. The method as claimed in claim 10, wherein the reference data sets are based on learning measurements of at least one of the at least substantially needle-shaped metal object or at least one reference metal object of a same type in a phantom. (Xu, paragraph 40, “Furthermore, as seen in FIGS. 3 and 4, the metal artifact image (second column from left in FIGS. 3 and 4) can provide effectively segmented representation of the metal artifact. Although this image exhibits blooming or other distortion compared with the actual boundaries of the metal object causing the artifact, it is seen that the metal artifact image provides an isolation image of the metal object that can, for example, be fitted to a known metal object geometry to provide for accurate live tracking of a biopsy needle, metal prosthesis, or other known metal object that is to be manipulated during the iGT procedure. In one approach, the corrected X-ray image 40 is displayed on the display 46 with the metal artifact image 34 (or an image derived from the metal artifact image 34, such as an image of the underlying metal object positioned to be spatially registered with the metal artifact image 34) is also displayed on the display 46, e.g. superimposed onto or otherwise fused with the display of the corrected X-ray image 40. As another application, the density of the image of the metal object captured in the metal artifact image 34 (or other information such as the extent of blooming) may be used to classify the metal object as to metal type, or the metal object depicted by the metal artifact image 34 may be identified based on shape, and/or so forth. In some embodiments, an identification approach such as one disclosed in Walker et al., U.S. Pub. No. 2012/0046971 A1 (published Feb. 23, 2012) may be used. I”)
Xu discloses 19. The method as claimed in claim 11, wherein the phantom is a structureless phantom.(addressed the alternative)
Xu discloses 20. The method as claimed in claim 19, wherein the structureless phantom is a water phantom. (addressed the alternative)
Xu discloses 25. The method as claimed in claim 11, wherein at least part of the ascertaining of the artifact data set is performed via the database based on recording parameters that deviate with regard to the reference data sets. (Xu, paragraph 40, “Furthermore, as seen in FIGS. 3 and 4, the metal artifact image (second column from left in FIGS. 3 and 4) can provide effectively segmented representation of the metal artifact. Although this image exhibits blooming or other distortion compared with the actual boundaries of the metal object causing the artifact, it is seen that the metal artifact image provides an isolation image of the metal object that can, for example, be fitted to a known metal object geometry to provide for accurate live tracking of a biopsy needle, metal prosthesis, or other known metal object that is to be manipulated during the iGT procedure. In one approach, the corrected X-ray image 40 is displayed on the display 46 with the metal artifact image 34 (or an image derived from the metal artifact image 34, such as an image of the underlying metal object positioned to be spatially registered with the metal artifact image 34) is also displayed on the display 46, e.g. superimposed onto or otherwise fused with the display of the corrected X-ray image 40. As another application, the density of the image of the metal object captured in the metal artifact image 34 (or other information such as the extent of blooming) may be used to classify the metal object as to metal type, or the metal object depicted by the metal artifact image 34 may be identified based on shape, and/or so forth. In some embodiments, an identification approach such as one disclosed in Walker et al., U.S. Pub. No. 2012/0046971 A1 (published Feb. 23, 2012) may be used. I”)
Xu discloses 12. The method as claimed in claim 10, wherein at least part of the ascertaining of the artifact data set is performed via the database based on recording parameters that deviate with regard to the reference data sets. (Xu, paragraph 40, “Furthermore, as seen in FIGS. 3 and 4, the metal artifact image (second column from left in FIGS. 3 and 4) can provide effectively segmented representation of the metal artifact. Although this image exhibits blooming or other distortion compared with the actual boundaries of the metal object causing the artifact, it is seen that the metal artifact image provides an isolation image of the metal object that can, for example, be fitted to a known metal object geometry to provide for accurate live tracking of a biopsy needle, metal prosthesis, or other known metal object that is to be manipulated during the iGT procedure. In one approach, the corrected X-ray image 40 is displayed on the display 46 with the metal artifact image 34 (or an image derived from the metal artifact image 34, such as an image of the underlying metal object positioned to be spatially registered with the metal artifact image 34) is also displayed on the display 46, e.g. superimposed onto or otherwise fused with the display of the corrected X-ray image 40. As another application, the density of the image of the metal object captured in the metal artifact image 34 (or other information such as the extent of blooming) may be used to classify the metal object as to metal type, or the metal object depicted by the metal artifact image 34 may be identified based on shape, and/or so forth. In some embodiments, an identification approach such as one disclosed in Walker et al., U.S. Pub. No. 2012/0046971 A1 (published Feb. 23, 2012) may be used. I”)
Xu discloses 13. A computed tomography device including a control device configured to perform the method as claimed in claim 1. (see claim 1 above)
Xu discloses 14. A non-transitory computer-readable medium storing computer-executable instructions that, when executed at a control device of a computed tomography device, cause the computed tomography device to perform the method of claim 1. (see claim 1 above)
Xu discloses 15. A computed tomography device comprising:
a memory storing computer-executable instructions; and at least one processor configured to executed the computer-executable instructions to cause the computed tomography device (Xu, Abstract)
to ascertain an artifact data set describing, in an image space, at least one artifact caused by an at least substantially needle-shaped metal object, wherein the artifact data set is ascertained based on prior knowledge about the at least one artifact, and (Xu, Fig. 1,#34, “Metal artifact image”; paragraph 40, “As noted above, in experiments the CNN correction speed was about 80 images per second, which is practical for use in correcting “live” images generated by a C-arm 10 (e.g. FIG. 1) during an iGT procedure. Furthermore, as seen in FIGS. 3 and 4, the metal artifact image (second column from left in FIGS. 3 and 4) can provide effectively segmented representation of the metal artifact. Although this image exhibits blooming or other distortion compared with the actual boundaries of the metal object causing the artifact, it is seen that the metal artifact image provides an isolation image of the metal object that can, for example, be fitted to a known metal object geometry to provide for accurate live tracking of a biopsy needle, metal prosthesis, or other known metal object that is to be manipulated during the iGT procedure. In one approach, the corrected X-ray image 40 is displayed on the display 46 with the metal artifact image 34 (or an image derived from the metal artifact image 34, such as an image of the underlying metal object positioned to be spatially registered with the metal artifact image 34) is also displayed on the display 46, e.g. superimposed onto or otherwise fused with the display of the corrected X-ray image 40. As another application, the density of the image of the metal object captured in the metal artifact image 34 (or other information such as the extent of blooming) may be used to classify the metal object as to metal type, or the metal object depicted by the metal artifact image 34 may be identified based on shape, and/or so forth. In some embodiments, an identification approach such as one disclosed in Walker et al., U.S. Pub. No. 2012/0046971 A1 (published Feb. 23, 2012) may be used. In some embodiments, to maximize processing speed for live imaging during iGT or other time-critical imaging tasks, the image reconstruction method 26 does not include any metal artifact correction other than by applying the neural network 32 to the uncorrected X-ray image 30 to generate the metal artifact image 34 and generating the corrected X-ray image 40 by subtracting the metal artifact image from the uncorrected x ray image.” )
subtract the artifact data set from a computed tomography image data set of a recording region in which the at least substantially needle-shaped metal object is located, to correct for artifacts in the computed tomography image data set, (Xu, Fig. 1 #36 & #40, subtracting the metal artifact image from the uncorrected image to get the corrected image)
wherein the computed tomography image data set is reconstructed from projection images recorded at least partially such that the at least substantially needle-shaped metal object is irradiated at least substantially in a longitudinal direction. (Xu, Fig. 1 #16)
Claims 17-20 are rejected based on Xu (paragraph 33, Fig. 2)
Allowable Subject Matter
No Prior Art reads on claims 3-9, 12, 21-25.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GANDHI THIRUGNANAM whose telephone number is (571)270-3261. The examiner can normally be reached M-F 8:30-5PM.
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/GANDHI THIRUGNANAM/Primary Examiner, Art Unit 2672
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