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
Overview
This is a first action on the merits (FAOM) to this instant application in which claims 1-20 are pending. Claims 1 and 15 are independent and claims 2-14 and 16-20 are dependent.
Independent claim 1 is directed to a method of image processing. This claimed image processing method appears to be a post-processing method and not a part of a step of raw data acquisition process.
In lines 2-4, the independent claim 1 describes k-space data to comprise two kinds of data:
1) motion corrupted k-space data and
2) data that are different from motion-corrupted data1.
In lines 5-6 of the claim (claim 1), the Applicant describes how to generate corrected MRI data from the motion-corrupted MRI data by using “information”2 indicating whether the object (i.e., patient) has moved while (during) scanning the object. In dependent claim 143, the Applicant has clarified/detailed that the claimed “information” is a piece of information which is detected by using navigator signals4. The claim does not further detail the “information” beyond just mentioning the “information” being obtained from navigator signals.
On line 7 of the claim (claim 1), an [MRI] image is described to be formed from corrected MRI data, which are obtained from k-space data corrupted by motion, and the second set of k-space data (i.e., data that are not corrupted by motion).
Examiner’s search has found reference(s)5 that can be used against independent claim 1, see claim rejections elsewhere in this Office action.
Rejection under 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.
Claims 1-2, 5-10, 12-16 and 19-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang (US-2022/0187406-A1).
Claim No
Claim feature
Prior art
Wang (US-2022/0187406-A1)
1
A method of image processing comprising:
Wang meets the preamble of the claim as it discloses receiving or acquiring MRI data and generating an image from the MRI data, see flowchart in Fig. 2.
receiving k-space data which is acquired by scanning an object by a magnetic resonance imaging apparatus, the k-space data including a first set of motion corrupted k-space data and a second set of k-space data, different from the first set of motion corrupted k-space data;
Wang meets this receiving k-space data step of the claim, see steps 210, 220 and 230 in the flowchart in Fig. 2 and also see Fig. 3 in Wang.
First set of data (314), see Fig. 3.
Second set of data (312 and 314), see Fig. 3.
generating motion correction data based on the first set of motion corrupted k-space data and information indicating whether the object moved while scanning the object; and
Wang meets this generating motion correction data, see step 240 in the flowchart in Fig. 2 in Wang.
generating an image based on the second set of k-space data and the motion correction data.
Wang meets this generating an image as it discloses steps 250 and 260 in Fig. 2 in Wang.
2
The method as claimed in claim 1, wherein the k-space data corresponding to a movement of the object among the first set of motion corrupted k-space data is not used for generating the motion correction data.
Wang meets claim 2 as it does not correct uncorrupted data (312 and 316), see Fig. 2 and 3.
5
The method as claimed in claim 1, wherein the first set of motion corrupted k-space data is at least one of undersampled k-space data and data acquired by parallel imaging.
Wang meets claim 5 as it discloses the MRI data acquisition may include under-sampling and parallel imaging, see various places in Wang, e.g., claim 16 in Wang.
6
The method as claimed in claim 1, wherein the first set of motion corrupted k-space data is auto-calibration signal (ACS) data.
Wang meets claim as it discloses GRAPPA that can be used for correcting motion corrupted data and GRAPPA includes ACS.
7
The method as claimed in claim 1, wherein generating the image based on the second set of k-space data and the motion correction data comprises generating the image based on the motion correction data and data in the second set of k-space data that is not motion corrupted.
Wang meets claim 7, see Fig, 2 and 3 in Wang.
8
The method as claimed in claim 7, wherein the motion correction data is sensitivity information indicating a sensitivity of each of a plurality of coils which receive magnetic resonance signals from the object, and wherein generating the image based on the second set of k-space data and the motion correction data comprises generating the image based on the sensitivity information and the data in the second set of k-space data that is not motion corrupted.
Claim 8 is met by Wang when it discloses that coil sensitivity of an RF coil can be used to compensate for motion-corrupted k-space data, see para [0010]6.
9
The method as claimed in claim 7, wherein the motion correction data is a set of GRAPPA weights, and wherein generating the image based on the second set of k-space data and the motion correction data comprises generating the image based on the set of GRAPPA weights and data in the second set of k-space data that is not motion corrupted.
Claim 9 is met by Wang.
Wang discloses GRAPPA process can be used to correct motion-corrupted k-space data.
10
The method as claimed in claim 1, wherein the second set of k-space data is undersampled k-space data.
Claim 10 is met by Wang as it discloses K-space data can be undersampled, this includes data that is not motion-corrupted.
12
The method as claimed in claim 10, wherein the motion correction data is a set of GRAPPA weights, and wherein generating the image based on the second set of k-space data and the motion correction data comprises generating the image based on k-space data interpolated by the set of GRAPPA weights and the second set of undersampled k-space data.
Claim 12 is met by Wang which discloses GRAPPA operators for correcting motion corrupted MRI data.
Wang also discloses MRI data acquisition using undersampling techniques.
13
The method as claimed in claim 10, wherein the motion correction data is sensitivity information indicating a sensitivity of each of a plurality of coils which receive magnetic resonance signals from the object, and wherein generating the image based on the second set of k-space data and the motion correction data comprises generating the image based on the sensitivity information and the second set of undersampled k-space data.
Claim 13 is met by Wang.
Wang discloses k-space data can be undersampled data.
Wang discloses coil sensitivity can be used to compensate for motion-corrupted k-space data.
14
The method as claimed in claim 1, wherein the information indicating whether the object moved while scanning the object is detected by using navigator signals.
Wang meets claim 14 as it discloses navigator signals for determining for determining whether or not the subject has moved.
15
An apparatus for performing image processing, comprising:
Wang meets preamble of claim 15 as it performs image processing.
processing circuitry configured to:
receive k-space data which is acquired by scanning an object by a magnetic resonance imaging apparatus, the k-space data including a first set of motion corrupted k-space data and a second set of k-space data, different from the first set of motion corrupted k-space data; generate motion correction data based on the first set of motion corrupted k-space data and information indicating whether the object moved while scanning the object; and generate an image based on the second set of k-space data and the motion correction data.
Wang discloses a processing circuit (computing device 100).
The functional features of the claim are the same features set forth in independent claim 1.
See treatment of claim 1 to see how the functional features are met by Wang.
16
The apparatus as claimed in claim 15, wherein the k-space data corresponding to a movement of the object among the first set of motion corrupted k-space data is not used for generating the motion correction data.
Wang meets claim 16 as it discloses motion-corrupted data are not used in forming MRI image. It corrects motion-corrupted data and generates new set of data for forming MRI image.
19
The apparatus as claimed in claim 15, wherein the first set of motion corrupted k-space data is at least one of undersampled k-space data and data acquired by parallel imaging.
Wang meets claim 19, as it discloses MRI data acquisition may include undersampling acquisition.
20
The apparatus as claimed in claim 15, wherein the first set of motion corrupted k-space data is auto-calibration signal (ACS) data.
Wang meets claim 20 as it discloses GRAPPA operators and the GRAPPA is known to include auto-calibration signals.
Allowable Subject Matter
Claims 3-4, 11, and 17-18 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 an examiner’s statement of reasons for allowance:
As to claim 3, the claim would be allowable because the applied prior art (Wang) neither disclose nor reasonably suggest application of iterative GRAPPA process even though it discusses application of GRAPPA process for correcting motion-corrupted k-space data.
As to claim 4, the claim would be allowable because the applied prior art (Wang) neither disclose nor reasonably suggest application of iterative GRAPPA process or iterative RAKI process, even though it discusses application of GRAPPA process for correcting motion-corrupted k-space data.
As to claim 11, the claim would be allowable if written in independent form because the applied reference Wang neither discloses nor reasonably suggests use of ESPIRiT map to correct motion-corrupted k-space date.
As to claims 17 and 18, each of these claims contains subject matter like that of claim 3 and 4, see reasons for allowance for claims 3 and 4 respectively for claims 17 and 18.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to G.M. HYDER whose telephone number is (571)270-3896. The examiner can normally be reached on M-F 9 AM- 5 PM.
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G.M. HYDER
Primary Examiner
Art Unit 2852
/G.M. A HYDER/Primary Examiner, Art Unit 2852
1 Examiner comment: These data that are different from the motion corrupted data can be understood to comprise data that are not corrupted by motion as these data are not corrected when used in image reconstruction, see line 7 of claim 1.
2 Examiner comment: The independent claim 1 does not describe what constitutes the “information” that indicates the whether the object (patient) has moved.
3 Examiner comment: the dependent claim 14 depends from independent claim 1. Dependent claim 14 sets forth that the information indicating whether the object has moved is information that is detected by a navigator signal.
4 Examiner comment: A person having ordinary skill in the art recognizes that navigator signals are signals that track movement of a patient during an MRI imaging operation. The claim does not further detail the “information” beyond just mentioning the “information” being obtained from navigator signals.
5 Examiner comment: A reference USPG Pub US-2022/0187406-A1 credited to Wang can be applied against independent claim 1. The same reference, coincidentally, is also found in the IDS filed by the Applicant on 17 January 2025.
6 [0010] U.S. Pat. No. 9,354,289 B2 to Wilfried Landschuetz and Peter Speier discloses a method and apparatus to reduce movement artifacts in MM images. The method teaches that the motion reduction can be conducted by weighted factors of two images. One image of unmoving area of a region is acquired by a first group coils, and another image of a moving area is acquired by a second group coils. The weighting factors are determined so as to reduce gradient of the weighted, combined, spatially dependent coil sensitivity of the second group coils.