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 .
Claim Rejections - 35 USC § 103
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.
Claim(s) 11-13, 18-19, and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharma (PGPUB: 20180071452 A1) in view of Hoornaert (CN 110996798 A), and in view of Milioni (CN 108852388 A).
Regarding claims 11 and 21. Sharma a method comprising the implementation, by a data processor of a second server, of steps of:
(a) obtaining a pre-contrast image depicting a body part prior to an injection of contrast agent in the patient, wherein said pre-contrast image is acquired by a medical imaging device connected to the second server (see Fig. 1, paragraph 32, workstation 102 may assist the clinician in imaging subject 118 (e.g., a patient) for a medical procedure. Workstation 102 may receive medical imaging data generated by medical imaging system 112. Medical imaging system 112 may be of any modality, such as, e.g., x-ray, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound (US), single-photon emission computed tomography (SPECT), positron emission tomography (PET), or any other suitable modality or combination of modalities) (see paragraph 114, systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship. Typically, in such a system, the client computers are located remotely from the server computer and interact via a network. The client-server relationship may be defined and controlled by computer programs running on the respective client and server computers);
(b) determining candidate value(s) of at least one injection parameter for injection the contrast agent by application of a prediction model to said pre-contrast image (see Fig. 2, paragraph 40, target imaging parameters for imaging the region of interest are determined based on the desired attributes of the images to be generated for the region on interest. The imaging parameters for imaging the region of interest may include, for example, parameters of the contrast agent (e.g., the concentration of the contrast agent in the region of interest) and parameters of the medical imaging system (e.g., tube voltage, tube current, exposure time (i.e., duration of scan), table speed, the reconstruction algorithm (e.g., filter, convolution kernel properties, etc.), medical imaging system specifications (e.g., beam spectra, geometry, x-ray beam collimation, etc.), etc.)),;
(c) providing said determined candidate value(s) of said injection parameter(s) to the medical imaging device (see Fig. 1-2, paragraph 49, administration parameters for administering a contrast agent are determined based on the imaging parameters for imaging the region of interest (as determined at step 204) using the generated computational model. The administration parameters for administering the contrast agent may include, e.g., the volume of the contrast agent to be administered or injected into the patient, the concentration of the contrast agent to be injected into the patient, and the rate and profile of the injection of the contrast agent into the patient), and
obtaining in response a real contrast image depicting said body part during injection of contrast agent in accordance with said determined candidate value(s) of said injection parameter(s), wherein said real contrast image is acquired by said medical imaging device, (see Fig. 1-2, paragraph 54, the region of interest of the patient is caused to be imaged based on the administration characteristics for administering the contrast agent and the trigger time. For example, in one embodiment, imaging of the region of interest of the patient is automatically initiated (e.g., by optimization module 120 of FIG. 1) after the trigger time has elapsed upon administering the contrast agent to the patient in accordance with the administration parameters. In another embodiment, imaging of the region of interest of the patient is caused to be manually initiated by a user, e.g., via a notification or any other indication or instruction (e.g., from optimization module 120 of FIG. 1))
However, Sharma does not expressly teach:
(d) determining, by application of a classification model to the real contrast image, a real quality level of said real contrast image; and comparing said real quality level with a target quality level.
Hoornaert teaches:
the adjusted image contrast enhancement may also be referred to as a parameter for improved enhanced image quality. the adjusted image contrast enhancement may also be referred to as a dynamic contrast enhanced or predictive dynamic contrast enhancement.
In an example, the processing unit is further configured to determine the evaluation comparison parameter is less than, equal to or greater than the predetermined threshold.
In an example, the processing unit based on the evaluation comparison parameter performed for determining an algorithm for image contrast by adjusting structure for enhancing the common blood vessel (see page 5, lines 23-30).
Therefore, the contrast-adjusted of enhancement depends on based on information for current injection provided to improve the image quality. In other words, the image contrast of the adjusted enhancement is dynamic image, such as contrast enhancement process application of the current injection setting is determined, the current injection is set then depends on the patient details (such as nephropathy) (see page 6, lines 16-20).
In an example, for a typical patient at the typical geometrical structure to typical of vascular contrast (= high) iodine concentration in the obtained image can be assumed to be the expected or reference comparison, or the so-called threshold. it has reference value for the contrast strengthen parameter (see page 6, lines 27-30).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Sharma by Hoornaert for providing the adjusted image contrast enhancement may also be referred to as a parameter for improved enhanced image quality; for a typical patient at the typical geometrical structure to typical of vascular contrast (= high) iodine concentration in the obtained image can be assumed to be the expected or reference comparison, or the so-called threshold. it has reference value for the contrast strengthen parameter, as determining, by application of a classification model to the real contrast image, a real quality level of said real contrast image; and comparing said real quality level with a target quality level. Therefore, combining the elements from prior arts according to known methods and technique, such as adjusted image contrast enhancement may also be referred to as a parameter for improved enhanced image quality and for a typical patient at the typical geometrical structure to typical of vascular contrast (= high) iodine concentration in the obtained image can be assumed to be the expected or reference comparison, or the so-called threshold would yield predictable results.
However, the combination does not expressly teach quality level among a predefined plurality of possible quality levels.
Milioni teaches that the motion model 70 applied to data obtained from each image 30 the contrast to calculating one or more images 30 of each image of the contrast agent in the ROI 28 measures/estimates. The awareness to the motion model 70 can be based at least in part on fluid dynamics, the motion model 70 can simulate of the contrast agent in the patient 12 flux (i.e., volume per unit time) (see Fig. 3-4, page 11, lines 17-21); motion model 70 based at least in part on one or more images 30 of each image of the contrast in the measurement/estimate to generate a prediction curve of the amount C of the contrast agent in the ROI (represented by solid line 68). The awareness to, in an embodiment, the prediction curve 68 indicates a period of time t of the contrast agent in the patient 12 weight C. For example, shown in FIG. 3 is the ti0 at the time when the contrast medium is injected into patient 12 after the controller 22 at time t1, t2 and t3 to obtain an embodiment having three images I1, I2 and I3 C1, C2 and C3 for measuring/comparing the estimated amount. prediction curve motion model 70 then at least partially generated based on values of C1, C2 and C3 shown in 68. In other words, the motion model 70 is used to make the contrast data (e.g., C1, C2, and C3) and the prediction curve 68. The awareness to the motion model 70 can use/incorporating additional parameters and/or constraints to generate a prediction curve 68 (see Fig. 3-4, page 11, lines 22-33); the motion model 70 may be calculated/estimated contrast decay time tCd, which represents a time is a quantity of contrast agent in 12 is predicted at that time drops below/exceeds the contrast threshold 72. the contrast threshold 72 may correspond to Cd of contrast agent in the patient 12, it is not sufficient to maintain the desired image quality image In (i.e., I1, I2, I3 is collected before the current time tp, and In is collected after tp) has not been collecting. Similarly, the motion model 70 may be calculated/estimated contrast saturation time tCs, which represents time is the amount of contrast agent in the patient 12 is predicted at that time rises to high than/exceeds a contrast threshold 74 (see Fig. 3-4, page 12, lines 1-10).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the combination by Milioni to obtain three images I1, I2 and I3 C1, C2 and C3 for measuring/comparing the estimated amount. prediction curve motion model 70 then at least partially generated based on values of C1, C2 and C3 shown in 68 for providing, the motion model 70 may be calculated/estimated contrast decay time tCd, which represents a time is a quantity of contrast agent in 12 is predicted at that time drops below/exceeds the contrast threshold 72, and the motion model 70 may be calculated/estimated contrast saturation time tCs, which represents time is the amount of contrast agent in the patient 12 is predicted at that time rises to high than/exceeds a contrast threshold 74, in order to provide quality level among a predefined plurality of possible quality levels. Therefore, combining the elements from prior arts according to known methods and technique would yield predictable results.
Regarding claim 12. The combination teaches a method according to claim 11, wherein step (a) also comprises obtaining value(s) of at least one context parameter of said pre-contrast image and wherein said prediction model uses said at least one context parameter as input at step (b) (see Sharma, paragraph 50, the system of nonlinear equations may be represented as the following system of nonlinear equations f(x.sub.i) in Equation (2) to estimate administration parameters x.sub.i representing the characteristics for administering the contrast agent (e.g., injected volume, concentration, and injection rate/profile), where each equation represents the residual error between the computed and desired values of the administration parameters for imaging the region of interest (e.g., the concentration of the contrast agent in the region of interest, and the scan exposure time)).
Regarding claim 13. The combination teaches the method according to claim 12, wherein said context parameter(s) is (are) physiological parameter(s) and/or acquisition parameter(s) (see Sharma, paragraph 65, arterial geometries of the patient are determined based on the medical imaging data that is associated with physiological qualities data that most closely matches the physiological qualities of the patient. In some embodiments, the physiological qualities of the patient are input directly to the computational model to determine the arterial geometries of the patient).
Regarding claims 18 and 19. The combination teaches the method according to claim 11, wherein said prediction model comprises a Convolutional Neural Network, CNN (see Sharma, paragraph 98, a data-drive machine learning model is trained to predict the measures of interest based on the extracted features. the machine learning approaches may include, e.g., regression, instance-based methods, regularization methods, decision tree learning, Bayesian, kernel methods, clustering methods, association rule learning, artificial neural networks).
Regarding claim 22. The combination teaches a non-transitory computer medium comprising code instructions that, when executed by a computer, cause the computer to execute a method according to claim 19 (see claim 11 and 19 above).
Allowable Subject Matter
Claims 14-17 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.
Response to Arguments
Applicant’s arguments with respect to claim(s) 11 and 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.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to XIN JIA whose telephone number is (571)270-5536. The examiner can normally be reached 9:00 am-7:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at (571)272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/XIN JIA/Primary Examiner, Art Unit 2663