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 .
Prior arts cited in this office action:
Li et al. (US 20210133973 A1, hereinafter “Li”)
Imai (US 20210298697 A1, hereinafter “Imai”)
Polak et al. (US 20210264645 A1, hereinafter “Polak”)
Hu et al (CN 107730567 A, hereinafter “Hu”)
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4-5, 15 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20210133973 A1, hereinafter “Li”) in view of Imai (US 2021029869A1, hereinafter “Imai”).
Regarding claims 1, 15, and 20:
Li teaches a method for medical imaging (Li [0003], where Li teaches Medical imaging is the technique and process of creating visual representations of the interior of a body for clinical analysis and medical intervention, as well as visual representation of the function of some organs or tissues (physiology)), implemented on a computing device having at least one processor and at least one storage device (Li [0004], where Li teaches The method may be implemented on at least one machine each of which has at least one processor and at least one storage device), the method comprising:
the scanning table including N portions corresponding to N bed positions of a target scan, and an ith portion of the N portions corresponding to an ith bed position of the N bed positions (Li figs. 1, 4 and 8, where Li teaches In some embodiments, the target protocol group may include a target general protocol for localizing a plurality of target bed positions and a plurality of target primary protocols for localizing each of the plurality of target bed positions. The target protocol group may be generated by adjusting at least one parameter of the initial protocol group. By using the target general protocol to localize the plurality of target bed positions, a count of localization operations (e.g., generating a reference image as described in FIG. 8, parameters adjusting as described in FIG. 12) to be performed may be reduced by obviating the need to perform a scanning protocol determination followed by a localization/position of an object at each bed position as exemplified in FIG. 4, and the imaging quality of the system may be enhanced. By using the target protocol group, the parameter adjustment of the plurality of target primary protocols and workflow of the scanning may be simplified and/or efficiency may be improved); and
determining at least one scanning parameter or reconstruction parameter corresponding to the ith bed position based on the one or more body parts of the subject (Li [0066], [0078], [0081], [0105], figs. 1, 4 and 8, where Li teaches The processing device 140 may generate one or more scanning protocols to control the imaging device 110 to generate imaging data and process the imaging data generated by the imaging device 110. In some embodiments, the processing device 140 may generate the one or more scanning protocols based on one or more single-bed protocols stored in the processing device 140 or retrieved from the storage device 150 or the imaging device 110 via the network 120. In some embodiments, the processing device 140 may cause the imaging device 110 to conduct one or more operations. For example, the processing device 140 may cause the imaging device 110 to obtain one or more scanning protocols, scan the object according to the one or more scanning protocols, and generate imaging data based on the scanning operations. In some embodiments, the processing device 140 may reconstruct an image based on the imaging data. .
Li fails to teach obtaining a scout image of a subject lying on a scanning table, for the ith bed position, determining, based on the scout image, one or more body parts of the subject located at the ith portion of the scanning table,
identifying, from the scout image, a plurality of feature points of the subject;
obtaining a corresponding relationship between the plurality of feature points and a plurality of body part classifications, wherein each feature point
refers to a landmark point that belongs to a specific body part of the subject
and is used to identify different body parts of the subject from the scout
image;
determining, based on the scout image, a positional relationship between the plurality of feature points and the ith portion of the scanning table;
determining, based on the corresponding relationship and the positional relationship, one or more body parts of the subject located at the i--th portion of the scanning table.
At Step ST23, the processor in the processing apparatus 84 executes the processing of fixing a landmark with reference to the body part to be imaged in the camera image 50. The processor in the processing apparatus 84 executes the processing of fixing a landmark by the landmark fixing section 830 (see FIG. 6). FIG. 11 shows a fixed landmark LM1. The processor in the processing apparatus 84 can fix the landmark LM1 based on, for example, the size, position, shape, and/or the like of the body part to be imaged. According to the present embodiment, the body part to be imaged is the chest, and therefore, the processor in the processing apparatus 84 can fix the landmark LM1 based on the size, position, shape, and/or the like of the chest. The landmark LM1 may be fixed in, for example, the pit of the stomach or its proximity of the patient, although it may be fixed at a position away from the pit of the stomach or its proximity.
The processor in the processing apparatus 84 executes the processing of defining an imaged range by the imaged-range defining section 850 (see FIG. 6). Since according to the present embodiment, the object to be imaged is the chest, an operation for defining an imaged range to include the chest is performed. Specifically, the processing apparatus 84 defines an imaged range on the scout image 70 according to an amount of shift (Δy, Δz) of the table 4 (and cradle 41) from a home position, the height of the table 4 (cradle 41), and landmark data representing the landmark LM1. The landmark data may include, for example, position data representing the position of the landmark with respect to the patient (or the body part to be imaged of the patient), position data representing the position of the landmark relative to the cradle 41, etc. (Imai [0115]-[0126]).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to obtain a scout image and use the scout image to help identify body part of person lying on the scanning table, the position of each body part with regard to the position of the table, in order to properly locate a particular region of interest since implants and other foreign objects can be present and the body part would be in different position or location based on the position of the table.
Regarding claim 4:
Li in view of Imai teaches wherein the determining, based on the corresponding relationship and the positional relationship, the one or more body parts of the subject located at the ith portion of the scanning table includes:
determining, based on the positional relationship, one or more target feature points located at the ith portion of the scanning table;
for each of the plurality of body part classifications, determining, based on the corresponding relationship, a count of target feature points that belong to the body part classification; and
determining, based on the counts corresponding to the plurality of body part classifications, the one or more body parts of the subject located at the ith portion of the scanning table (Li [0118, [0121]-[0130], fig. 10; Imai [0115]-[0126]], where the bed position corresponds to the regions desired to be scanned)
Regarding claim 5:
Li in view of Imai teaches wherein the determining, based on the corresponding relationship and the positional relationship, the one or more body parts of the subject located at the ith portion of the scanning table includes:
determining, based on the positional relationship, one or more target feature points located at the ith portion of the scanning table;
for each of the plurality of body part classifications, determining, based on the corresponding relationship, one or more key feature points that belong to the body part classification from the one or more target feature points; and
determining, based on the one or more key feature points corresponding to the one or more body part classifications, the one or more body parts of the subject located at the ith portion of the scanning table (Li [0118, [0121]-[0130], fig. 10; [0115]-[0126]).
Claims 6, 11, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20210133973 A1, hereinafter “Li”) in view of Imai (US 20210298697A1, hereinafter “Imai”) and in view of Hu et al (CN 107730567 A, hereinafter “Hu”).
Regarding claims 6 and 17:
Li in view of Imai fails to explicitly teach wherein the one or more body parts of the subject located at the it" portion of the scanning table include a body part having physiological motion, and the at least one scanning parameter includes a motion detection parameter.
However, Hu teaches the specific number of the embodiment of medical image scan data corresponding to the pre-set physiological period number not limited in actual use can be according to motion characteristics of the targeted organ or tissue to be detected, or patient physique, or magnetic resonance device parameters, determining magnetic resonance scanning data of pre-set physiological period number needed to be collected, and obtaining pre-set physiological period number of medical image scan data. In one embodiment, the physiological cycle of the region of interest using the monitor region of interest scan monitored during acquisition. Alternatively, the monitor may be monitoring a cardiac cycle of the electrocardiogram device, respiration monitor capable of monitoring the respiration rate, pulse monitor capable of monitoring the pulse jumping (Hu [0051]-[0052]).
Therefore, taking the teaching of Li, Imai and Hu as whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to determine the physiological motion of the organ of interest, in order to compensate for that such that better image can be obtain that would allow for better display or tracking region of interest (target object of the target organ or tissue).
Regarding claims 11 and 19:
Li in view of Imai and in view of Hu teaches further comprising:
obtaining a first image of the subject captured by the target scan;
determining a first target region in the first image;
determining one or more first parameter values of one or more quality parameters of the first target region; and
determining, based on the one or more first parameter values, whether the target scan needs to be re-performed (Li [0056], [0077]; Hu [0051]-[0052], Where Li discloses technique to increase image quality. In other words, performing analysis to determine if the image is good image to obtain the desired information from the target region otherwise perform adjustment to obtain better images).
Claims 7, 9-10, 12, 14, 18, 21, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Li et al. (US 20210133973 A1, hereinafter “Li”) in view of Imai (US 20210298697 A1, hereinafter “Imai”), in view of Polak et al. (US 20210264645 A1, hereinafter “Polak”) and in view of Liu et al. (CN 106204674 A, hereinafter “Liu”).
Regarding claims 7 and 18:
Li in view of Imai teaches wherein the target scan is performed using a first imaging modality, and the method further includes:
obtaining a first image of the subject captured by the target scan and a second image of the subject captured by a reference scan, the reference scan being performed on the subject using a second imaging modality wherein the first imaging modality is positron emission tomography (PET), and the second imaging modality is a computed tomography (CT) or a magnetic resonance (MR); (Li [0029], [0062]; Imai [0039]).
Li in view of Imai fails to explicitly teach generating a third image based on the second image and an image prediction model, the third image being a predicted image of the subject corresponding to the first imaging modality; and determining, based on the first image and the third image, whether the first image includes image artifacts.
However, Polak teaches in general, reference MRI images g.sub.1 to g.sub.N may also be referred to as ground truth images g1 to gN for each MRI protocol, as known in the field of machine learning. Reference MRI images g-1 to gN may represent the desired outcome of a machine learning algorithm, in particular they may be MRI pictures with ideally no image artifacts and distortions, and they may be MRI images with reduced MRI artifacts and/or noise amplification compared to the MRI images u.sub.1.sup.0 to u.sub.n.sup.0. As such, they may be used as reference MRI images to compare the output MRI images of the machine learning algorithms with, in order to determine the quality of the output MRI images. In various embodiments, the reference MRI images g.sub.1 to g.sub.N may be MRI images, which have been reconstructed based on MRI image data, which is based on a respective MRI system setup with a higher resolution, i.e. longer measurement time, and/or in various embodiments, which is fully sampled in k-space. A ML algorithm may include one or more parameters that are trained based on at least one of ground truth images g.sub.1 to g.sub.N, or on the plurality of ground truth images g.sub.1 to g.sub.N (Polak [0040], [0077], [0097], [0169]). Liu further teaches the structure dictionary can study of tissue from the CT/MRI image. Because the CT/MRI image and PET image with the similar boundary structure, the characteristic element linear combination for the boundary region of the PET image, can be learned by CT/MRI image obtained for the smooth region, one element in the structure dictionary representing them is sufficient. method based on sparse expression dictionary structure, which not only can obtain good image detail such as edge characteristics, it can effectively restrain the noise of the smooth region, the imaging quality is improved. However, this algorithm is generally only for PET image information of the same frame, ignoring the on time provided by prior knowledge, it increases the noise in the result to a certain extent (Liu Abstract, [0005], [0032], claim 1).
Therefore, taking the teaching of Li, Imai and Polak and Liu as whole, it would have been obvious to one of ordinary skill in the art before the effective filing date of the application to combine features of more than one image obtain by different modalities and/or to use machine learning to improve the quality of the image taken, in order to better identify and/or detect region of interest and/or to detect and suppress artifact/noise in the desired image.
Regarding claim 9:
Li in view of Imai, in view of Polak and in view of Liu teaches wherein in response to determining that the first image includes the artifact, the method further comprises:
obtaining a plurality of candidate image blocks of the first image by moving a sliding window on the first image;
for each of the plurality of candidate image blocks, determining a similarity degree between the candidate image block and a corresponding image block in the third image; and
determining, based on the similarity degrees of the plurality of candidate image blocks, one or more target image blocks as one or more artifact regions of the first image (Li [0029], [0062]; Imai [0104]-[0112], figs. 8-10; Polak [0040], [0077], [0097]).
Regarding claim 10:
Li in view of Imai, in view of Polak and in view of Liu teaches further comprising:
generating, based on the first image and the one or more target image blocks, one or more incomplete images; and
generating a corrected first image based on the one or more incomplete images and an image recovery model (Li [0029], [0062]; Imai [0104]-[0112], figs. 8-10; Polak [0040], [0077], [0097]).
Regarding claim 12:
Li in view of Imai, in view of Polak and in view of Liu teaches wherein the determining, based on the one or more first parameter values, whether the target scan needs to be re-performed includes:
determining whether the one or more first parameter values satisfy a preset condition;
in response to determining that the one or more first parameter values satisfy the preset condition, determining a second target region in the first image;
the first target region and the second target region include one or more organs or tissues where the uptake of radionuclides is uniform, the first target region is the liver region, and the second target region is a muscle or the brain; determining one or more second parameter values of the one or more quality parameters of the second target region; and
determining, based on the one or more second parameter values, whether the target scan needs to be re-performed (Li [0056], [0077]; Polak [0152]; Liu Abstract, [0005], [0032], claim 1, figs. 2-5, when two regions is to be detected the quality of the image representing both regions would be detected and if one fails the test can be stopped and if it is successful the test can move to analyze the other region).
Regarding claim 14:
Li in view of Imai, in view of Polak and in view of Liu teaches wherein the one or more quality parameters include at least one of a signal noise ratio (SNR) or a proportion of artifact regions in the first target region or the second target region (Polak [0040], [0137], [0153]; Liu Abstract, [0005], [0032], claim 1).
Regarding claim 21:
Li in view of Imai, in view of Polak and in view of Liu teaches wherein the image prediction model is obtained by training an initial image prediction model based on a plurality of training samples, each of the plurality of training samples includes a sample second image of a sample subject as an input of the initial image prediction model, and a sample first image of the sample subject as a first label, the sample first image is obtained by scanning the sample subject using the first imaging modality, and the sample second image is obtained by scanning the sample subject using the second imaging modality (Polak [0040], [0137], [0153]; Liu Abstract, [0005], [0032], claim 1).
Regarding claim 25:
Li in view of Imai and in view of Polak teaches wherein the target scan is divided into a plurality of sub-scans based on at least one of a length of a detector of the imaging device along an axial direction or a field of view of the imaging device, each of the plurality of sub-scans corresponds to one of the N bed positions, and the ith portion of the N portions is located within the field of view of the imaging device when the scanning table is at the ith bed position (Li Abstract, [0003]-[0004])
Allowable Subject Matter
Claims 22-24 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.
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.
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/WEDNEL CADEAU/Primary Examiner, Art Unit 2632 April 29, 2026
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