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 .
Response to Amendment
This Office action is responsive to Applicant’s PRELIMINARY AMENDMENT, filed March 26, 2024. Claims 1-13 and 15 are pending.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 and 15 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “[a] computer-implemented method for prospective quality assessment for imaging examination prior to acquisition, the method comprising: … providing the generated quality metric for prospective quality assessment for imaging examination prior to acquisition … (Emphasis added) The providing step is unclear, for it does not set forth what is being acquired in the acquisition following the provision of the generated quality metric for prospective quality assessment. Therefore, claim 1 is deemed indefinite, and is thus rejected, along with corresponding dependent claims 2-11.
Claim 15, directed to a non-transitory computer-readable medium, similarly recites the limitations of claim 1, and is thus rejected for the reasons set forth above.
Claim Rejections - 35 USC § 102
6. 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.
7. 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
8. Claims 1-3, 8, 9, 11, 12 and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by U.S. Patent Application Publication US 2022/0398718 A1 (hereinafter “Rao”) (note: application filed June 11, 2021).
Regarding claim 1, Rao discloses a computer-implemented method for prospective quality assessment for imaging examination prior to acquisition (medical imaging system determines if one or more image quality metrics satisfy a corresponding image quality criteria (paragraph [0027]), after which the medical imaging system displays the medical image, the image quality metrics, and the image quality criteria via a display (paragraph [0028]), thereby enabling a viewer to acquire the results of the determination), the method comprising:
- receiving sensor data of a body part of a patient to be imaged by a medical imaging apparatus using a medical imaging modality (medical imaging system acquires a medical image of an anatomical region using a medical imaging device (paragraph [0024]));
- generating a quality metric from the received sensor data using a data-driven model, wherein the data-driven model has been trained based on a training dataset that comprises a plurality of training examples, each training example comprises sensor data of the body part acquired in an imaging session and an associated quality metric derived from image data acquired using the medical imaging modality in the imaging session (medical imaging system maps the medical image to one or more positional attributes of one or more anatomical features using a trained deep neural network (paragraph [0025]); medical imaging system determines one or more image quality metrics based on the positional attributes (paragraph [0026])); and
- providing the generated quality metric for prospective quality assessment for imaging examination prior to acquisition, wherein the quality metric is a vector of numerical values, each numerical value representing a deviation of a position and/or a rotation of an anatomical feature in the image data acquired using the medical imaging modality from a desired position and/or rotation of the anatomical feature (medical imaging system determines if the one or more image quality metrics satisfy the corresponding image quality criteria (paragraph [0027]); image quality metrics include an angle of rotation of an imaging subject with respect to a projection plane of the medical image and an angle of rotation of an imaging subject with respect to a plane perpendicular to the projection plane of a medical image (paragraph [0026]); an image quality metric is said to satisfy the corresponding image quality criterion if the image quality metric equals the value, or falls within the range of values, indicated by the image quality criterion (paragraph [0027])).
Regarding claim 2, Rao further comprises:
- generating the quality metric directly from the received the sensor data; or
- fitting an anatomy model of a target anatomy to the sensor data and generating the quality metric from the fitted anatomy model of the target anatomy (deep neural network may extract and/or encode features from the medical image, and map said features to one or more positional attributes of one or more anatomical features (paragraph [0025]); medical imaging system determines one or more image quality metrics based on the positional attributes (paragraph [0026])). (note: generating and fitting steps listed in the alternative, and thus only one of the steps is required)
Regarding claim 3, Rao further comprises:
- determining whether the generated quality metric meets a predetermined criterion (medical imaging system determines if the one or more image quality metrics satisfy the corresponding image quality criteria (paragraph [0027])); and
- generating a signal indicative of whether the generated quality metric meets the predetermined criterion (medical imaging system may display a status of the image quality criteria, wherein the status indicates if the currently displayed medical image satisfies the image quality criteria, based on the image quality metric (paragraph [0028])).
Regarding claim 8, Rao discloses wherein the sensor data is acquired by one or more of the following: an optical sensor, a depth sensor, a thermal sensor, a pressure sensor, an ultrasound sensor, and an array of radio frequency sensors (imaging device may comprise a 2D or 3D medical imaging device, including an ultrasound (paragraph [0040]))
Regarding claim 9, Rao discloses wherein the medical imaging modality comprises one or more of: magnetic resonance imaging; ultrasound imaging; X-ray imaging; computed tomography imaging; and positron-emission tomography imaging (imaging device may comprise a 2D or 3D medical imaging device, including but not limited to an x-ray imaging device, a CT imaging device, an MRI system, an ultrasound, and a PET imaging device (paragraph [0040])).
Regarding claim 11, Rao discloses an apparatus for prospective quality assessment for imaging examination prior to acquisition (medical imaging system determines if one or more image quality metrics satisfy a corresponding image quality criteria (paragraph [0027]), after which the medical imaging system displays the medical image, the image quality metrics, and the image quality criteria via a display (paragraph [0028]), thereby enabling a viewer to acquire the results of the determination), the apparatus comprising one or more processing unit(s) to generate a quality metric, wherein the processing unit(s) include instructions, which when executed on the one or more processing unit(s) perform the method of claim 1 (image processing device 202 includes a processor 204 configured to execute machine readable instructions stored in non-transitory memory 206 (paragraph [0033]); non-transitory memory may include image quality metric module 210, which comprises instructions for determining one or more image quality metrics based on at least a first positional attribute of an anatomical feature, and may include instructions that, when executed by processor, cause image processing device to conduct one or more of the steps of the method (paragraph [0035])).
Regarding claim 12, Rao discloses a system comprising:
- a medical imaging apparatus configured to acquire image data of a body part of patient (imaging system 200 includes imaging device 250 (paragraph [0040]); medical image of an anatomical region acquired using a medical imaging device (paragraph [0024]));
- a sensor configured to acquire sensor data of the body part of the patient (medical imaging device acquires sensor data (medical image) of the body part of the patient (anatomical region) (paragraph [0024])); and
- an apparatus configured to:
receive sensor data of a body part of a patient to be imaged by a medical imaging apparatus using a medical imaging modality (medical imaging system acquires a medical image of an anatomical region using a medical imaging device (paragraph [0024]));
- generate a quality metric from the received sensor data using a data-driven model, wherein the data-driven model has been trained based on a training dataset that comprises a plurality of training examples, each training example comprises sensor data of the body part acquired in an imaging session and an associated quality metric derived from image data acquired using the medical imaging modality in the imaging session (medical imaging system maps the medical image to one or more positional attributes of one or more anatomical features using a trained deep neural network (paragraph [0025]); medical imaging system determines one or more image quality metrics based on the positional attributes (paragraph [0026])); and
- provide the generated quality metric for prospective quality assessment for imaging examination prior to acquisition, wherein the quality metric is a vector of numerical values, each numerical value representing a deviation of a position and/or a rotation of an anatomical feature in the image data acquired using the medical imaging modality from a desired position and/or rotation of the anatomical feature (medical imaging system determines if the one or more image quality metrics satisfy the corresponding image quality criteria (paragraph [0027]); image quality metrics include an angle of rotation of an imaging subject with respect to a projection plane of the medical image and an angle of rotation of an imaging subject with respect to a plane perpendicular to the projection plane of a medical image (paragraph [0026]); an image quality metric is said to satisfy the corresponding image quality criterion if the image quality metric equals the value, or falls within the range of values, indicated by the image quality criterion (paragraph [0027])).
Regarding claim 15, Rao discloses a computer-readable medium for storing executable instructions which cause a method for prospective quality assessment for imaging examination prior to acquisition to be performed (medical imaging system determines if one or more image quality metrics satisfy a corresponding image quality criteria (paragraph [0027]), after which the medical imaging system displays the medical image, the image quality metrics, and the image quality criteria via a display (paragraph [0028]), thereby enabling a viewer to acquire the results of the determination; non-transitory memory 206 may include image quality metric module 210, which comprises instructions for determining one or more image quality metrics based on at least a first positional attribute of an anatomical feature, and may include instructions that, when executed by processor 204, cause image processing device 202 to conduct one or more of the steps of the method (paragraph [0035])), the method comprising:
- receiving sensor data of a body part of a patient to be imaged by a medical imaging apparatus using a medical imaging modality (medical imaging system acquires a medical image of an anatomical region using a medical imaging device (paragraph [0024]));
- generating a quality metric from the received sensor data using a data-driven model, wherein the data-driven model has been trained based on a training dataset that comprises a plurality of training examples, each training example comprises sensor data of the body part acquired in an imaging session and an associated quality metric derived from image data acquired using the medical imaging modality in the imaging session (medical imaging system maps the medical image to one or more positional attributes of one or more anatomical features using a trained deep neural network (paragraph [0025]); medical imaging system determines one or more image quality metrics based on the positional attributes (paragraph [0026])); and
- providing the generated quality metric for prospective quality assessment for imaging examination prior to acquisition, wherein the quality metric is a vector of numerical values, each numerical value representing a deviation of a position and/or a rotation of an anatomical feature in the image data acquired using the medical imaging modality from a desired position and/or rotation of the anatomical feature (medical imaging system determines if the one or more image quality metrics satisfy the corresponding image quality criteria (paragraph [0027]); image quality metrics include an angle of rotation of an imaging subject with respect to a projection plane of the medical image and an angle of rotation of an imaging subject with respect to a plane perpendicular to the projection plane of a medical image (paragraph [0026]); an image quality metric is said to satisfy the corresponding image quality criterion if the image quality metric equals the value, or falls within the range of values, indicated by the image quality criterion (paragraph [0027])).
Claim Rejections - 35 USC § 103
9. The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
10. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
11. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Rao as applied to claim 1 above, and further in view of U.S. Patent Application Publication US 2022/0001210 A1 (hereinafter “Letourneau”) (note: PCT filed October 11, 2019).
Regarding claim 10, Rao does not expressly disclose wherein the medical imaging modality comprises a hybrid modality including one or more of: MR-Linac; MR proton therapy; and cone beam computed tomography.
Letourneau discloses procedures to automate quality assurance testing for radiotherapy equipment that includes MR-Linac devices that are agnostic as to the manufacturer and vendor of the equipment (Abstract). One of ordinary skill in the art, in view of Letourneau, would have recognized the MR-Lince devices were subject to image quality assessment, and it would have been obvious, at the time of Applicant’s invention, to have applied the teaching of Rau to MR-Linac devices, so as to ensure precise imaging which is necessary in aiding in the diagnosing health issues in patients.
12. Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Rao.
Regarding claim 13, Rao does not expressly disclose wherein the medical imaging apparatus is configured to start image acquisition according to the provided quality metric; and/or wherein the system further comprises a device configured to inform whether the patient is ready for image acquisition based on the quality metric. However, Rao discloses automatic repositioning of the medical imaging system imaging device relative to an imaging subject based on the image quality criteria (paragraph [0030]), thereby suggesting starting the imaging device according to the provided quality metric, for there would be no need to reposition the imaging device relative to the imaging subject if another image acquisition were not required. It would have been obvious to one of ordinary skill in the art, at the time of Applicant’s invention, to further provide for the starting of the image acquisition by the medical imaging apparatus in Rao according to the provided quality metric, so as to produce to image of a higher quality which is essential for providing an accurate diagnosis to the patient.
Allowable Subject Matter
13. Claims 4-7 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
14. The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 4, the cited prior art fails to disclose or suggest Applicant’s computer-implemented method according to claim 3, wherein the signal comprises a signal for controlling a device to inform whether the patient is ready for image acquisition.
Regarding claim 5, the cited prior art fails to disclose or suggest Applicant’s computer-implemented method according to claim 3, wherein the signal comprises a signal for triggering the medical imaging apparatus to start image acquisition.
Regarding claim 6, the cited prior art fails to disclose or suggest Applicant’s computer-implemented method according to claim 1, further comprising:
- receiving image data of the body part of the patient after image acquisition;
- determining, based on the received imaged data, a further quality metric; and
- determining a difference between the quality metric generated from the sensor data before image acquisition and the further quality metric derived from the image data after image acquisition; and
- further training the data-driven model using the difference.
Claim 7 depends from claim 6.
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to THOMAS D LEE whose telephone number is (571)272-7436. The examiner can normally be reached Mon-Fri 7:30AM-5:00PM.
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/THOMAS D LEE/Primary Examiner, Art Unit 2683