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 .
DETAILED ACTION
Claims status
Claims 1, 3-8, 10-17 and 32-35 are pending as the applicant filed Preliminary Amendment on 06/03/2026.
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, 3-8, 10-17 and 32-35 are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Ikhlef (US Patent 7825370 B2, DATE PUBLISHED: 2010-11-02).
Regarding claim 1:
Ikhlef described a calibration method for imaging field (page 1), comprising: obtaining a calibration model of a target imaging device (page 2, calibrate CT image), wherein the calibration model includes at least one convolutional layer, the at least one convolutional layer includes at least one candidate convolution kernel (page 7, This matrix equation is de-convolved to obtain the collection vector intensities), and the target imaging device includes a computed tomography (CT) device with a detector and a radiation source (page 2, 3 radiation energy, tomography);determining a target convolution kernel based on the at least one candidate convolution kernel of the calibration model (page 2, detector); determining calibration information of the target imaging device based on the target convolution kernel (page 4, calibrate, page 7, This matrix equation is de-convolved to obtain the collection vector intensities), wherein the calibration information is used to calibrate at least one of a device parameter of the target imaging device (page 2, calibrate image device to remove artifacts from CT images) or imaging data acquired by the target imaging device, and the calibration information includes at least one of mechanical deviation information of the radiation source, mechanical deviation information of the detector, crosstalk information of the detector (page 2, cross talk both direction), or scattering information of the target imaging device; and calibrating, based on the calibration information, the at least one of the device parameter of the target imaging device or the imaging data acquired by the target imaging device (page 2, use on crosstalk and a module-to-module crosstalk).
Regarding claim 17:
Ikhlef described a calibration system for imaging field, comprising:
at least one storage medium storing a set of instructions; at least one processor in communication with the at least one storage medium, when executing the stored set of instructions, the at least one processor causes the
system to (page 4, computer program processor): obtain a calibration model of a target imaging device, wherein the calibration model includes at least one convolutional layer (page 4, calibrate, page 7, This matrix equation is de-convolved to obtain the collection vector intensities), the at least one convolutional layer includes at least one candidate convolution kernel, and the target imaging device includes a computed tomography (CT) device with a detector and a radiation source (page 2, 3 radiation energy, tomography); determine a target convolution kernel based on the at least one candidate convolution kernel of the calibration model (page 4, calibrate, page 7, This matrix equation is de-convolved to obtain the collection vector intensities); determine calibration information of the target imaging device based on the target
convolution kernel (page 4, calibrate, page 7, This matrix equation is de-convolved to obtain the collection vector intensities, page 2, calibrate image device to remove artifacts from CT images), the calibration information includes at least one of mechanical deviation information of the target imaging device, crosstalk information of the target imaging device (page 2, cross talk both direction, or scattering information of the target imaging device; calibrate, based on the calibration information, projection data acquired by the target imaging device in a medical scan of an object to obtain calibrated projection data; and generate a medical image of the object by performing image reconstruction based on the calibrated projection data (page 2, use on crosstalk and a module-to-module crosstalk, page 9, medical device imaging).
Regarding claim 32:
Ikhlef described a non-transitory computer readable medium including executable instructions, the instructions, when executed by at least one processor, causing the at least one processor to effectuate a method comprising (page 9, computer): obtaining a calibration model of a target imaging device, wherein the calibration model includes at least one convolutional layer (page 7, This matrix equation is de-convolved to obtain the collection vector intensities), the at least one convolutional layer includes at least one candidate convolution kernel (page 7, This matrix equation is de-convolved to obtain the collection vector intensities), and the target imaging device includes a computed tomography (CT) device with a detector and a radiation source
determining a target convolution kernel based on the at least one candidate convolution kernel of the calibration model (page 3, computed tomography, page 5, X-ray radiation directed, page 7, de-convolved to obtain the collection vector intensities); determining calibration information of the target imaging device based on the target convolution kernel (page 3, computed tomography, page 5, X-ray radiation directed, page 7, de-convolved to obtain the collection vector intensities), wherein calibration information includes positional deviation information of a component of the target imaging device (page 7, CT image); determining a direction and a distance that the component of the target imaging device needs to move based on the positional deviation information (page 4, angular position of x-ray source, page 5, correction of the z-crosstalk and for the calibration of CT); and controlling the component of the target imaging device to move based on the direction and the distance (page 5, correction of the z-crosstalk and for the calibration of CT).
Regarding claim 3, Ikhlef further described wherein the determining the target convolution kernel based on the at least one candidate convolution kernel of the calibration model includes: determining the target convolution kernel by convolving the at least one candidate convolution kernel (page 3, computed tomography, page 5, X-ray radiation directed, page 7, de-convolved to obtain the collection vector intensities).
Regarding claim 4, Ikhlef further described wherein the determining a target convolution kernel based on the at least one candidate convolution kernel of the calibration model includes: determining an input matrix based on the size of the at least one candidate convolution kernel; and determining the target convolution kernel by inputting the input matrix into the calibration model (page 7, written in form of the z-crosstalk matrix equation).
Regarding claim 5, Ikhlef further described obtaining first projection data of a reference object, wherein the first projection data is acquired by the target imaging device, and the first projection data includes deviation projection data; obtaining second projection data of the reference object, wherein the second projection data excludes the deviation projection data, and the second projection data is acquired by a standard imaging device that has been subjected to an error calibration, or the second projection data is acquired by calibrating the first projection data; determining training data based on the first projection data and the second projection data, and generating the calibration model by training a preliminary model using the training data (page 4, different projecting an X-ray attenuated beam).
Regarding claim 6, Ikhlef further described wherein the generating the calibration model by training a preliminary model using the training data includes one or more iterations, at least one of the one or more iterations comprising: determining an intermediate convolution kernel of an updated preliminary model generated in a previous iteration; determining a value of a loss function based on the first projection data, the second projection data, and the intermediate convolution kernel; and further updating the updated preliminary model to be used in a next iteration based on the value of the loss function (page 7, process of z-crosstalk matrix equation, this matrix equation is de-convolved in order to obtain the collection vector intensities values).
Regarding claim 7, Ikhlef further described wherein the determining a value of a loss function based on the first projection data, the second projection data, and the intermediate convolution kernel includes: determining the value of the loss function based on at least one of a value of a first loss function and a value of a second loss function, wherein the value of the first loss function is determined based on the intermediate convolution kernel, and the value of the second loss function is determined based on the first projection data and the second projection data (page 7-8, represents the ratio of difference between collection vector intensities).
Regarding claim 8, Ikhlef further described the detector including a plurality of detection units (page 2, detectors), and the calibration information including a positional deviation of a target detection unit among the plurality of detection units, wherein the determining calibration information of the target imaging device based on the target convolution kernel includes: (page 4, difference angular position of x-ray) determining at least one first difference between a central element of the target convolution kernel and at least one other element of the target convolution kernel; determining at least one second difference between a projection position of the target detection unit and at least one projection position of at least one other detection unit of the detector; and etermining the positional deviation of the target detection unit based on the at least one first difference and the at least one second difference (page 9, ratio of a difference and a corrected vector intensity).
Regarding claim 10, Ikhlef further described he detector including a plurality of detection units, and the calibration information including a crosstalk coefficient of a target detection unit among the plurality of detection units, wherein the determining calibration information of the target imaging device based on the target convolution kernel includes: determining, based on at least one difference between a central element of the target convolution kernel and at least one other element of the target convolution kernel, at least one crosstalk coefficient of the at least one other element with respect to the target detection unit (page 8, crosstalk correction equation).
Regarding claim 11, Ikhlef further described determining a first crosstalk coefficient of the target detection unit in the target direction based on a sum of the crosstalk coefficients of the at least two other elements with respect to the target detection unit (page 8, no crosstalk, crosstalk algorithm).
Regarding claim 12, Ikhlef further described determining a second crosstalk coefficient of the target detection unit in the target direction based on a difference between the crosstalk coefficients of the at least two other elements with respect to the target detection unit (page 8, no crosstalk, approximate correction of the crosstalk algorithm).
Regarding claim 13, Ikhlef further described the determining calibration information of the target imaging device based on the target convolution kernel includes: determining the scattering information of the target imaging device corresponding to at least one angle of view based on the target
convolution kernel; the imaging data includes projection data acquired by the target imaging device in a medical scan of an object, and the calibrating the imaging data acquired by the target imaging device includes: calibrating deviation projection data caused by scattering in the projection data based
on the scattering information (page 2, object being scanned, page 4, the rotational speed and angular position of X-ray, page 9, medical imaging).
Regarding claim 14, Ikhlef further described the first activation function is used to transform input data of the calibration model from projection data to data of a target type, the data of the target type being input to the at least one convolutional layer for processing; and the second activation function is used to transform output data of the at least one convolutional layer from the data of the target type to projection data (page 7, This matrix equation is de-convolved to obtain the collection vector intensities D.sub.1-D.sub.64.).
Regarding claim 15, Ikhlef further described wherein the calibration model also
includes a fusion unit, and the fusion unit is configured to fuse the input data and the output data of the at least one convolutional layer (page 7, This matrix equation is de-convolved to obtain the collection vector intensities D.sub.1-D.sub.64.).
Regarding claim 16, Ikhlef further described the calibration model also includes a data transformation unit, wherein the data transformation unit is configured to transform the data of the-first target type to determine transformed data, and the transformed data is input to the at least one convolutional layer for processing (page 7, The average z-crosstalk error values e.sub.N and e.sub.S are used for determining collection vector intensitiesD, this matrix equation is de-convolved to obtain the collection vector intensities D.sub.1-D.sub.64).
Regarding claim 33, Ikhlef further described wherein the calibration information
includes the crosstalk information of the target imaging device, and the imaging data includes projection data acquired by the target imaging device in a
medical scan of an object, and the calibrating the imaging data acquired by the target imaging device includes: calibrating, based on the crosstalk information, deviation projection data in the projection data, the deviation projection data being caused by crosstalk between detection units of the target imaging device (page 2, object being scanned, page 4, the rotational speed and angular position of X-ray, page 9, medical imaging).
Regarding claim 34, Ikhlef further described wherein the calibration model includes at least one of a mechanical deviation calibration model, a crosstalk calibration model (page 2, cross talk both direction), and a scattering calibration model, the target convolution kernel includes at least one of a first target convolution kernel determined based on the mechanical deviation calibration model, a second target convolution kernel determined based on the crosstalk calibration model (page 2, cross talk both direction), and a third target convolution kernel determined based on the scattering calibration model, and the mechanical deviation information is determined based on the first target convolution kernel, the crosstalk information is determined based on the second target convolution kernel, and the scattering information is determined based on the third target convolution kernel
.
Regarding claim 35, Ikhlef further described the target convolution kernel includes at least one of a first target convolution kernel determined based on the mechanical deviation calibration model, a second target convolution kernel determined based on the crosstalk calibration model, and a third target convolution kernel determined based on the scattering calibration model, and
the mechanical deviation information is determined based on the first target
convolution kernel, the crosstalk information is determined based on the second target convolution kernel, and the scattering information is determined based on the third target convolution kernel (page 2, photosensors, as the thickness of the back-illuminated photosensors increases the diffusion length before the photosensor collection junction.).
Response to Arguments
Applicant's arguments with respect to the amended claims have been considered but are moot in view of the new ground(s) of rejection. However, applicant's arguments filed 06/03/2026 have been fully considered but they are not persuasive.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact information
5. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
June 16, 2026
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