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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/27/2025, 08/13/2025, 11/03/2025, and 16/30/2026 is/are compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim(s) 18 is/are objected to because of the following informalities:
In claim 18, line 5, “displaying information of the planed interventional […]” should read “displaying information of the planned interventional […]”.
Appropriate correction is required.
Office Action Summary
Claim(s) 20 and 22-23 is/are canceled.
Claim(s) 1, 14, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dai et al (CN 112163987 A; See translation provided by Examiner).
Claim(s) 2-8, 15, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Becker et al (US 2013/0039552 A1).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner), further in view of Durand et al (Computer assisted electromagnetic navigation improves accuracy in CT guided interventions: A prospective randomized clinical trial).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner), further in view of Lang et al (US 2021/0137634 A1).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner), further in view of Holsing et al (US 2013/0225942 A1).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner), further in view of Komp et al (US 2020/0188032 A1).
Claim(s) 9-10 and 12 is/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.
Claim Rejections - 35 USC § 102
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.
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.
(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.
Claim(s) 1, 14, and 19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dai et al (CN 112163987 A; See translation provided by Examiner).
Regarding claim(s) 1 and 19, Dai teaches a medical image processing system for an interventional procedure, comprising:
a control system including at least one processor and at least one storage medium, the at least one storage medium storing operating instructions, wherein when executing the operating instructions (Paragraph [0064]: “The puncture path system […]”), the at least one processor is directed to cause the system to perform operations including:
obtaining a first medical image (read as “preoperative MRI images”) of a target object before the interventional procedure and a second medical image (read as “intraoperative images”) of the target object during the interventional procedure (Paragraph [0066]: “Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound), but the tumor and its boundary can be clearly displayed in preoperative MRI images. However, there is no correspondence between the spatial positions of preoperative images and intraoperative images”);
registering the second medical image and the first medical image to obtain a registration result (Paragraph [0028] – Paragraph [0031]: “Select the marked points with one-to-one correspondence on the segmented 3D image model and the reference 3D image model […] Use the point set registration method to register the marked points to obtain the spatial transformation matrix Tf […] Use the space transformation matrix Tf to spatially transform the segmented three-dimensional image model to obtain the three-dimensional image model registered by the three-dimensional mark point registration method […] voxel registration of the three-dimensional image model lfr using a three-dimensional voxel registration method, and finally a three-dimensional image model after the three-dimensional voxel registration is obtained”; and Paragraph [0066]: “Different images can be unified into the same coordinate system through registration to achieve image alignment”); and
determining interventional procedure planning information of the target object at least based on the registration result (Paragraph [0009] – Paragraph [0011]: “registering the segmented 3D image model to obtain the registered 3D image model […] The 3D image display module can display the registered 3D image model […] The two-dimensional image display module can display two-dimensional images formed by projecting the registered three-dimensional image model […]”; Paragraph [0012]: “The puncture planning path interactive selection module can select the puncture starting point and the puncture end point in the two-dimensional image display module and/or the three-dimensional image display module, and calculate the initial puncture planning path”; and Paragraph [0025]: “The puncture path interactive selection module selects the puncture start point and the puncture end point in the two-dimensional image display module and/or the three-dimensional image display module, and the puncture path interactive selection module calculates an initial puncture planned path”), performing an interventional procedure risk assessment based on the interventional procedure planning information (Paragraph [0039]: “the steps of the automatic puncture planning and risk assessment module in step 4) performing risk assessment and path optimization on the adjusted puncture planning path include: calculating puncture feasible region, calculating puncture depth and tissue path length Calculate the distance from the puncture path to the organ that needs to be avoided, and calculate other constraint items”), and obtaining a risk assessment result corresponding to the interventional procedure planning information (Paragraph [0055] – Paragraph [0057]: “4-4-1) Construct a risk factor model, the risk factor is recorded as Ri, and its weight is factor ϒi […] 4-4-2) Its equivalent risk factor is recorded as L3 […] 4-5) Calculate the comprehensive risk F of the puncture path, F = L1 + L3 - L2, and minimize F through the optimization algorithm, and then obtain the optimal puncture planning path Popt […]”; and Paragraph [0059]: “the puncture risk assessment and further optimization can be performed through the puncture automatic planning and risk assessment module, thereby reducing the risk”).
Regarding claim(s) 14, Dai teaches the system of claim 1, wherein the operations further include:
in response to the risk assessment result corresponding to the interventional procedure planning information satisfying a predetermined condition, guiding the interventional procedure based on the interventional procedure planning information satisfying the predetermined condition (Paragraph [0013]: “The automatic puncture planning and risk assessment module performs risk assessment and path optimization on the adjusted puncture planning path, and finally obtains the optimal puncture planning path”; Paragraph [0096]: “Perform risk assessment and path optimization on the initial puncture planning path through the automatic puncture planning and risk assessment module, and finally obtain the optimal puncture planning path, including: calculating puncture feasible region, calculating puncture depth and tissue path length, and calculating [the distance from the] puncture path to the organ that needs to be avoided and other constraints [...]”; Paragraph [0097] – Paragraph [0100]: “4-1) Calculate the feasible region of puncture: 4-1-1) With the adjusted puncture planning path as the center and the cone angle ϴ as the search range, construct a three-dimensional space R for puncture path optimization search; 4-1-2) Perform segmentation modeling of the organs that need to be avoided […] 4-1-3) Calculate the feasible region of puncture R' […] (wherein, the feasible region R' defines a spatial range, that is, all subsequent adjustment ranges of puncture needles need to be within R')”; Paragraph [0057]: “4-5) Calculate the comprehensive risk F of the puncture path, F = L1 + L3 - L2, and minimize F through the optimization algorithm, and then obtain the optimal puncture planning path Popt […]”; and Paragraph [0017]: “the two-dimensional image display module can display the projection line […] where the planned puncture path is located, which is called the puncture guide line […]”).
Claim Rejections - 35 USC § 103
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.
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.
Claim(s) 2-8, 15, and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner).
Regarding claim(s) 2, Dai teaches the system of claim 1, wherein the obtaining a first medical image of a target object before the interventional procedure and a second medical image of the target object during the interventional procedure includes:
obtaining a preoperative enhanced image (Paragraph [0065] – Paragraph [0066]: “The original three-dimensional image may include multi-modal images, such as one or more of CT, ultrasound, and MRI images […] Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound), but the tumor and its boundary can be clearly displayed in preoperative MRI images”);
obtaining a first medical image of a first target structure set by segmenting the first target structure set from the preoperative enhanced image (Paragraph [0065] – Paragraph [0066]: “the 3D image segmentation module is used to segment the input original 3D image of the puncture object to obtain a segmented 3D image model. The original three-dimensional image may include multi-modal images, such as one or more of CT, ultrasound, and MRI images […] the tumor and its boundary can be clearly displayed in preoperative MRI images”);
obtaining an intraoperative scanning image (Paragraph [0066]: “Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound) […]”); and
obtaining a second medical image of a second target structure set by segmenting the second target structure set from the intraoperative scanning image (Paragraph [0065] – Paragraph [0066]: “the 3D image segmentation module is used to segment the input original 3D image […] to obtain a segmented 3D image model. The original three-dimensional image may include multi-modal images, such as one or more of CT, ultrasound, and MRI images […] Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound) […] the 3D image registration module is used to register the segmented 3D image model to obtain the registered 3D image model. Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound), but the tumor and its boundary can be clearly displayed in preoperative MRI images”).
Dai fails to teach wherein the first target structure set has an intersection with the second target structure set. However, Chang teaches wherein the first target structure set has an intersection with the second target structure set (Paragraph [0114] – Paragraph [0117]: “Performing three-dimensional modeling on the preoperative medical image to obtain a preoperative three-dimensional medical image […] Performing three-dimensional modeling on the intraoperative real-time medical image to obtain the intraoperative real-time three-dimensional medical image […] The preoperative three-dimensional medical image is registered to the intraoperative real-time three-dimensional medical image […] For medical images, this kind of matching refers to the same […] anatomical points have the same spatial position on the two matched medical images”; Paragraph [0038] – Paragraph [0039]: “Segmenting the pre-operative three-dimensional medical image by using a pre-trained deep neural network model to obtain segmented images […] Fusing the segmented image with the first real-time registration image to obtain a second real-time fusion image”; and Paragraph [0042]: “According to the fourth real-time fusion image, real-time lesion information is acquired”).
Dai teaches obtaining preoperative and intraoperative medical images, segmenting medical images to obtain segmented three-dimensional image models, and registering the segmented three-dimensional image models for puncture path planning (Paragraph [0065] – Paragraph [0066]). Chang teaches acquiring a preoperative medical image and an intraoperative real-time medical image, performing three-dimensional modeling and registration of the preoperative and intraoperative images, and matching corresponding anatomical points such that the same anatomical points have the same spatial position in the registered images (Paragraph [0114] – Paragraph [0117]). Chang further teaches segmenting the preoperative three-dimensional medical image to obtain segmented images and fusing the segmented image with the real-time registered image to obtain real-time lesion information (Paragraph [0038] – Paragraph [0042]).
Therefore, 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 system of Dai in view of Chang such that the target structures obtained from the preoperative and intraoperative medical images include corresponding anatomical structures having an intersection. The motivation for this combination of references would have been to improve the accuracy of lesion recognition and provide more accurate anatomical information for subsequent surgical path planning by registering corresponding anatomical structures of the preoperative and intraoperative medical images. This motivation for the combination of Dai and Chang is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 3, Dai as modified by Chang teaches the system of claim 2, where Dai teaches wherein:
the registration result includes a spatial position of a third target structure set in the interventional procedure (Paragraph [0065] – Paragraph [0067]: “the 3D image segmentation module is used to segment the input original 3D image of the puncture object to obtain a segmented 3D image model […] the 3D image registration module is used to register the segmented 3D image model to obtain the registered 3D image model […] Different images can be unified into the same coordinate system through registration to achieve image alignment […] the 3D image display module can display the registered 3D image model”; and Paragraph [0069]: “body surface puncture point is interactively selected in the 3D display module, the puncture planning path interactive selection module will automatically calculate the interactive position in the display window coordinate system, and project it to the 3D image model coordinate system to calculate The position of the projection point in the three-dimensional image model”), and elements of the third target structure set are determined based on a mode of planning an interventional path (Paragraph [0012] – Paragraph [0013]: “The puncture planning path interactive selection module can select the puncture starting point and the puncture end point […] and calculate the initial puncture planning path[…] The automatic puncture planning and risk assessment module performs risk assessment and path optimization on the adjusted puncture planning path”; and Paragraph [0039]: “[…] calculating puncture feasible region, calculating puncture depth and tissue path length Calculate the distance from the puncture path to the organ that needs to be avoided, and calculate other constraint items”); and
where Chang teaches at least one element in the third target structure set is included in the first target structure set (Paragraph [0012]: “The automatic planning module is used to plan a surgical path according to the real-time lesion information, so as to obtain a target surgical path”; and Paragraph [0156]: “segmented images including the lesion, the organ where the lesion is located, and the surrounding organs at risk can be obtained”) and at least one element in the third target structure set is excluded from the second target structure set (where Dai teaches in Paragraph [0066]: “Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound), but the tumor and its boundary can be clearly displayed in preoperative MRI images […] Different images can be unified into the same coordinate system through registration to achieve image alignment”; and where Chang teaches in Paragraph [0038] – Paragraph [0042]: “Segmenting the pre-operative three-dimensional medical image by using a pre-trained deep neural network model to obtain segmented images […] Fusing the segmented image with the […] third real-time fusion image to obtain a fourth real-time fusion image […] According to the fourth real-time fusion image, real-time lesion information is acquired”; and Paragraph [0156]: “[…] segmented images including the lesion, the organ where the lesion is located, and the surrounding organs at risk can be obtained”).
Regarding claim(s) 4, Dai as modified by Chang teaches the system of claim 3, where Dai teaches wherein the performing an interventional procedure risk assessment based on the interventional procedure planning information includes:
determining intervention risk values of one or more elements of the third target structure set, each of the intervention risk values corresponding to one of the one or more elements (Paragraph [0047] – Paragraph [0053]: “4-2-3) Perform segmentation modeling of the organs that need to be passed on the adjusted puncture planning path, and the obtained three-dimensional closed model is denoted as mi […] According to the importance of the tissue, each three-dimensional closed model mi Assign weight […] 4-2-4) Calculate the Euclidean distance of the adjusted puncture planning path through the three-dimensional closed model mi […] 4-2-5) Calculate the puncture risk distance L 1 of the adjusted puncture planning path through the human tissue […] 4-3-1) Calculate the Euclidean distance […] from the adjusted puncture planning path to Mi […] 4-3-2) Assign a weight […] to each three-dimensional closed model Mi according to the importance of the organization […] 4-3-3) Calculate the equivalent risk distance from the puncture path to the organ that needs to be avoided L2 […]”); and
performing the interventional procedure risk assessment based on the intervention risk values (Paragraph [0054] – Paragraph [0055]: “4-4) Calculate other risk constraints […] 4-4-1) Construct a risk factor model, the risk factor is recorded as Ri, and its weight is factor ϒi […] 4-4-2) Its equivalent risk factor is recorded as L3 […] 4-5) Calculate the comprehensive risk F of the puncture path, F = L1 + L3 - L2, and minimize F through the optimization algorithm, and then obtain the optimal puncture planning path Popt […]”).
Regarding claim(s) 5, Dai as modified by Chang teaches the system of claim 4, where Dai teaches wherein the performing an interventional procedure risk assessment based on the interventional procedure planning information further includes:
determining whether a planned interventional path in the interventional procedure planning information crosses a predetermined element in the third target structure set (Paragraph [0045] – Paragraph [0047]: “4-2-1) Calculate the intersection p between the adjusted puncture planning path and the registered 3D image model of the puncture object […] 4-2-3) Perform segmentation modeling of the organs that need to be passed on the adjusted puncture planning path, and the obtained three-dimensional closed model is denoted as mi, and its internal space is denoted as si”); and
in response to a determination that the planned interventional path in the interventional procedure planning information crosses the predetermined element in the third target structure set, determining the intervention risk value of a predetermined risk object in the third target structure set (Paragraph [0047] – Paragraph [0053]: “According to the importance of the tissue, each three-dimensional closed model mi Assign weight […] 4-2-4) Calculate the Euclidean distance of the adjusted puncture planning path through the three-dimensional closed model mi […] 4-2-5) Calculate the puncture risk distance L 1 of the adjusted puncture planning path through the human tissue […] 4-3) Calculate the risk distance from the puncture path to the organ that needs to be avoided […] 4-3-1) Calculate the Euclidean distance […] from the adjusted puncture planning path to Mi […] 4-3-2) Assign a weight […] to each three-dimensional closed model Mi according to the importance of the organization […] 4-3-3) Calculate the equivalent risk distance from the puncture path to the organ that needs to be avoided L2 […]”).
Regarding claim(s) 6, Dai as modified by Chang teaches the system of claim 4, where Dai teaches wherein the determining intervention risk values of one or more elements of the third target structure set includes:
determining a risk level of each element of the one or more elements based on a shortest distance between the element and the planned interventional path (Paragraph [0050] – Paragraph [0053]: “4-3) Calculate the risk distance from the puncture path to the organ that needs to be avoided […] 4-3-1) Calculate the Euclidean distance […] from the adjusted puncture planning path to Mi […] 4-3-2) Assign a weight […] to each three-dimensional closed model Mi according to the importance of the organization […] 4-3-3) Calculate the equivalent risk distance from the puncture path to the organ that needs to be avoided L2 […]”); and
determining an intervention risk value of each element based on the risk level (Paragraph [0048]: “4-2-4) Calculate the Euclidean distance of the adjusted puncture planning path through the three-dimensional closed model mi”; Paragraph [0050] – Paragraph [0053]: “4-3-1) Calculate the Euclidean distance […] from the adjusted puncture planning path to Mi […] 4-3-2) Assign a weight […] to each three-dimensional closed model Mi according to the importance of the organization […] 4-3-3) Calculate the equivalent risk distance from the puncture path to the organ that needs to be avoided L2 […]”; Paragraph [0055]: “4-4-1) Construct a risk factor model, the risk factor is recorded as Ri, and its weight is factor ϒi […]”; and Paragraph [0057]: “4-5) Calculate the comprehensive risk F of the puncture path, F = L1 + L3 - L2, and minimize F through the optimization algorithm, and then obtain the optimal puncture planning path Popt […]”).
Regarding claim(s) 7, Dai as modified by Chang teaches the system of claim 4, where Dai teaches wherein the determining intervention risk values of one or more elements of the third target structure set includes:
determining a risk level of each element of the one or more elements based on a shortest distance between the element and the planned interventional path (Paragraph [0050] – Paragraph [0053]: “4-3) Calculate the risk distance from the puncture path to the organ that needs to be avoided […] 4-3-1) Calculate the Euclidean distance […] from the adjusted puncture planning path to Mi […] 4-3-2) Assign a weight […] to each three-dimensional closed model Mi according to the importance of the organization […] 4-3-3) Calculate the equivalent risk distance from the puncture path to the organ that needs to be avoided L2 […]”);
determining an intervention risk value of each element based on the risk level (Paragraph [0052] – Paragraph [0053]: “4-3-2) Assign a weight […] to each three-dimensional closed model Mi according to the importance of the organization […] 4-3-3) Calculate the equivalent risk distance from the puncture path to the organ that needs to be avoided L2 […]”); and
determining a priority based on a predetermined rule associated with each element (Paragraph [0047]: “According to the importance of the tissue, each three-dimensional closed model mi Assign weight […]”; and Paragraph [0052]: “4-3-2) Assign a weight […] to each three-dimensional closed model Mi according to the importance of the organization”), and setting a corresponding predetermined weight for the intervention risk value (Paragraph [0052]: “4-3-2) Assign a weight […] to each three-dimensional closed model Mi according to the importance of the organization”; Paragraph [0055] – Paragraph [0056]: “4-4-1) Construct a risk factor model, the risk factor is recorded as Ri, and its weight is factor ϒi […] 4-4-2) Its equivalent risk factor is recorded as L3 […]”; and Paragraph [0057]: “4-5) Calculate the comprehensive risk F of the puncture path, F=L 1 +L3-L2, and minimize F through the optimization algorithm […]”).
Regarding claim(s) 8, Dai as modified by Chang teaches the system of claim 4, where Dai teaches wherein the performing the interventional procedure risk assessment based on the intervention risk values includes:
determining a total risk value of at least one interventional path (Paragraph [0053]: “4-3-3) Calculate the equivalent risk distance from the puncture path to the organ that needs to be avoided L2 […]”; Paragraph [0055]: “4-4-1) Construct a risk factor model, the risk factor is recorded as Ri, and its weight is factor ϒi […]”; Paragraph [0056]: “4-4-2) Its equivalent risk factor is recorded as L3 […]”; and Paragraph [0057]: “4-5) Calculate the comprehensive risk F of the puncture path, F = L1 + L3 - L2, and minimize F through the optimization algorithm, and then obtain the optimal puncture planning path Popt […]”); and
determining an interventional path with a smallest total risk value as an optimal interventional path (Paragraph [0057]: “4-5) Calculate the comprehensive risk F of the puncture path, F = L1 + L3 - L2, and minimize F through the optimization algorithm, and then obtain the optimal puncture planning path Popt […]”).
Regarding claim(s) 15, Dai teaches the system of claim 1, but do not specifically teach wherein the operations further include: obtaining a third medical image of the target object in the interventional procedure; and mapping the registration result to the third medical image to guide the interventional procedure.
However, Chang teaches wherein the operations further include:
obtaining a third medical image of the target object in the interventional procedure (Paragraph [0014]: “the first image acquisition device is in communication connection with the control device, and is used to acquire real-time intraoperative medical images”; and Paragraph [0016] – Paragraph [0017]: “The image acquisition unit is used to acquire preoperative medical images and intraoperative real-time medical images […] The image registration unit is configured to register the preoperative medical image and the intraoperative real-time medical image to obtain a real-time registered image”); and
mapping the registration result to the third medical image to guide the interventional procedure (Paragraph [0134] – Paragraph [0135]: “Registration and fusion of the pre-operative three-dimensional medical image and the intra-operative real-time three-dimensional medical image to obtain a first real-time fusion image […] The first real-time fusion image is registered to the intraoperative real-time human body model image to obtain a real-time registered image”; and Paragraph [0136]: “[…] register the first real-time fusion image obtained by registering and fusing the preoperative three-dimensional medical image and the intraoperative real-time three-dimensional medical image to the intraoperative real-time human body model image, so as to obtain A three-dimensional real-time registration image containing the contour of the human body […] the actual position of the lesion in the human body can be directly output, which is more conducive to follow-up The planning of the surgical path and the execution of related operations”).
Therefore, 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 system of Dai in view of Chang to obtain an additional medical image of the target object during the interventional procedure and map the registration result to the additional medical image for guiding the interventional procedure. The motivation for this combination of references would have been to provide more accurate real-time anatomical and lesion position information during the interventional procedure, thereby facilitating subsequent surgical path planning and execution of the interventional procedure. This motivation for the combination of Dai and Chang is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Regarding claim(s) 21, Dai teaches a guiding system for an interventional procedure, comprising:
a control system including at least one processor and at least one storage medium, wherein the at least one storage medium storing operating instructions, wherein when executing the operating instructions (Paragraph [0064]: “The puncture path system […]”), the at least one processor is directed to cause the system to perform operations including:
obtaining a first medical image, a second medical image, (Paragraph [0066]: “Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound), but the tumor and its boundary can be clearly displayed in preoperative MRI images. However, there is no correspondence between the spatial positions of preoperative images and intraoperative images […] Different images can be unified into the same coordinate system through registration to achieve image alignment”);
registering the first medical image and the second medical image to obtain a fourth medical image, wherein the fourth medical image includes registered interventional procedure planning information (Paragraph [0012] – Paragraph [0013]: “The puncture planning path interactive selection module can select the puncture starting point and the puncture end point […] and calculate the initial puncture planning path[…] The automatic puncture planning and risk assessment module performs risk assessment and path optimization on the adjusted puncture planning path”; Paragraph [0039]: “[…] calculating puncture feasible region, calculating puncture depth and tissue path length Calculate the distance from the puncture path to the organ that needs to be avoided, and calculate other constraint items”; Paragraph [0065] – Paragraph [0067]: “the 3D image segmentation module is used to segment the input original 3D image of the puncture object to obtain a segmented 3D image model […] the 3D image registration module is used to register the segmented 3D image model to obtain the registered 3D image model […] Different images can be unified into the same coordinate system through registration to achieve image alignment […] the 3D image display module can display the registered 3D image model”; and Paragraph [0069]: “body surface puncture point is interactively selected in the 3D display module, the puncture planning path interactive selection module will automatically calculate the interactive position in the display window coordinate system, and project it to the 3D image model coordinate system to calculate The position of the projection point in the three-dimensional image model”).
Dai fails to teach a third medical image of a target object; and mapping the fourth medical image to the third medical image to guide the interventional procedure. However, Chang teaches a third medical image of a target object (Paragraph [0197]: “the real-time intraoperative medical images collected by the first image acquisition device 100 can be acquired by the image acquisition unit 211. The image registration unit 212 can perform real-time registration of preoperative medical images with intraoperative real-time medical images to obtain real-time registered images”); and
mapping the fourth medical image to the third medical image to guide the interventional procedure (Paragraph [0197]: “The image registration unit 212 can perform real-time registration of preoperative medical images with intraoperative real-time medical images to obtain real-time registered images, and the human-computer interaction module 600 can display the real-time registered images In this way, the movement trajectory (such as puncture trajectory) of the surgical device 300 can be displayed in real time”; and Paragraph [0194]: “the functional safety module 240 is configured to perform according to the image registration unit The real-time registration image output by 212 monitors the real-time motion trajectory of the surgical device 300. When the real-time motion trajectory of the surgical device 300 deviates too much from the planned path, an alarm message is output”).
Therefore, 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 system of Dai in view of Chang to obtain an additional intraoperative medical image at a subsequent time and map the registered medical image and associated interventional procedure planning information to the additional intraoperative medical image, thereby providing real-time guidance of the interventional procedure. The motivation for this combination of references would have been to account for changes in the patient's anatomy and the position of the surgical device during the interventional procedure and to maintain correspondence between the planned interventional path and the real-time intraoperative anatomy, thereby improving the accuracy and safety of image-guided intervention. This motivation for the combination of Dai and Chang is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Becker et al (US 2013/0039552 A1).
Regarding claim(s) 11, Dai teaches the system of claim 1, wherein the operations further include:
obtaining an intraoperative scanning image (Paragraph [0066]: “Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound), but the tumor and its boundary can be clearly displayed in preoperative MRI images”).
Dai fails to teach to detecting an image abnormality for the intraoperative scanning image; determining an image abnormality type based on the detected image abnormality; determining whether to perform a quantitative calculation based on the image abnormality type; and determining an image abnormality degree based on a result of determining whether to perform the quantitative calculation.
However, Becker teaches to detecting an image abnormality for the intraoperative scanning image (Paragraph [0041]: “determining whether the one or more images show any abnormalities with respect to the patient's condition, for example, free pleural air (pneumothorax) or fluid, aortic dissection, intracranial hemorrhage, liver metastases etc”; and Paragraph [0045]: “the PAPVR system is configured to automatically detect and quantify various physiological features or abnormalities […]”);
determining an image abnormality type based on the detected image abnormality (Paragraph [0051]: “the PAPVR system can automatically detect and quantify various physiological features or abnormalities, such as, e.g., pneumothorax, tension pneumothorax, pleural effusion, ascending and descending aortic caliber and aortic dissections”);
determining whether to perform a quantitative calculation based on the image abnormality type (Paragraph [0041]: “In some cases, further the system may perform additional analysis, including but not limited to providing quantitative measurements, as well as, in some cases, the indication of the localization […] where the measurement has been performed […] For example, When we detect blood in the pleural effusion (hemothorax), we can highlight the areas where blood was detected into the pleural effusion. This can save time in some cases since if blood is detected correctly somewhere in the pleural effusion and the radiologist is brought automatically to that place for verification, the radiologist can diagnose the hemothorax without further measuring liquid intensity in other slices“; Paragraph [0042]: “the PAPVR system can perform quantitative measurements and calculations (e.g., distances, cross-sectional areas, volumes), that is relevant to the patient's condition, for example, measuring the volume of air in a pneumothorax by doing image analysis […]”; and Paragraph [0045]: “by using image processing algorithms that identify the pneumothorax condition, and other image processing algorithms that can segment the area of the pneumothorax and calculate its volume”); and
determining an image abnormality degree based on a result of determining whether to perform the quantitative calculation (Paragraph [0042]: “the PAPVR system can perform quantitative measurements and calculations (e.g., distances, cross-sectional areas, volumes), that is relevant to the patient's condition […] can compare findings and quantify changes such as increased pleural fluid or increased dilatation of an aortic aneurysm which has significant clinical implications.”).
Therefore, 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 system of Dai in view of Becker to analyze the intraoperative scanning image for abnormalities, identify the type of detected abnormality, selectively perform quantitative analysis appropriate to the detected abnormality, and determine an extent or degree of the abnormality based on the resulting quantitative information. The motivation for this combination of references would have been to improve the accuracy and efficiency of detecting and evaluating abnormal medical conditions during an interventional procedure, thereby providing more useful quantitative information for clinical decision-making and improving patient safety. This motivation for the combination of Dai and Becker is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner), further in view of Durand et al (Computer assisted electromagnetic navigation improves accuracy in CT guided interventions: A prospective randomized clinical trial).
Regarding claim(s) 13, Dai as modified by Chang teaches the system of claim 3, Where Dai teaches wherein the operations further include:
registering the planned interventional path obtained based on the preoperative enhanced image and the intraoperative scanning image(Paragraph [0009] – Paragraph [0011]: “registering the segmented 3D image model to obtain the registered 3D image model […] The 3D image display module can display the registered 3D image model […] The two-dimensional image display module can display two-dimensional images formed by projecting the registered three-dimensional image model […]”; Paragraph [0012]: “The puncture planning path interactive selection module can select the puncture starting point and the puncture end point in the two-dimensional image display module and/or the three-dimensional image display module, and calculate the initial puncture planning path”; and Paragraph [0065] – Paragraph [0066]: “the 3D image segmentation module is used to segment the input original 3D image […] to obtain a segmented 3D image model. The original three-dimensional image may include multi-modal images, such as one or more of CT, ultrasound, and MRI images […] Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound) […] the 3D image registration module is used to register the segmented 3D image model to obtain the registered 3D image model. Part of the puncture target (such as tumor) cannot be clearly displayed in intraoperative images (usually CT or ultrasound), but the tumor and its boundary can be clearly displayed in preoperative MRI images”); and
where Chang teaches determining postoperative feedback information based on a determination result of whether a deviation of the actual interventional path from the planned interventional path has an intersection with a particular element in the third target structure set of the intraoperative scanning image (Paragraph [0194]: “When the real-time motion trajectory of the surgical device 300 deviates too much from the planned path, an alarm message is output […] by setting a deviation threshold, when it is monitored that the deviation between the real-time motion trajectory and the planned path is greater than the threshold, an alarm message is output”; and Paragraph [0195] – Paragraph [0197]: “the functional safety module 240 also obtains the real-time lesion information through the lesion recognition unit 213 to generate safe operation boundary information, for example, based on key organ tissue information […] When the real-time motion track of the surgical device 300 touches or exceeds the safe operation boundary area, an alarm message is output […] the control module 230 can control the surgical device 300 to perform the operation according to the acquired surgical operation parameters, the target surgical path, and the safe operation boundary information, and the functional safety module 240 can perform the operation on the real-time motion trajectory of the surgical device 300 is monitored […] for puncture operations, when the puncture needle hits the operating boundary, the system will automatically stop the operation and automatically withdraw the needle […] the functional safety module 240 can determine in real time whether the movement trajectory (such as puncture trajectory) of the surgical device 300 deviates from the target surgical path, and Whether the motion trajectory […] exceeds the safe operation boundary […]”).
Dai and Chang fail to teach an actual interventional path obtained based on a postoperative scanning image. However, Durand teaches an actual interventional path obtained based on a postoperative scanning image (Figure 2; Figure 3; Figure 7: “After biopsy, the last control acquisition showed a small periadrenal hematoma without the need for additional treatment. Pathologic findings on the biopsy sample showed adrenal tissues with metastasis from a malignant lung tumor”; Page 4, Outcomes, 1st Paragraph: “The main outcome was the accuracy of the initial needle placement, defined as the maximum distance and angle between: the planned (expected) trajectory chosen by the operator (saved in the Navigation system log for the NAV group or saved in the CT scan console for the CT group), and the achieved needle trajectory, shown by the control acquisition performed immediately after the initial needle placement”; and Page 5, 1st Paragraph: “During CT-guided interventions, the final distance between the needle tip and the target is the result of an iterative process, as the operator will frequently: evaluate the position of the needle relative to the target using a CT control image, adjust the needle position and orientation and move the needle forward (or even backwards if the trajectory is excessively incorrect)”).
Therefore, 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 system of Dai and Chang in view of Durand to obtain an actual interventional path from a scanning image acquired following an intervention, register and compare the actual interventional path with the planned interventional path, and provide feedback based on whether a deviation of the actual interventional path from the planned interventional path intersects an anatomical structure or safety region. The motivation for this combination of references would have been to improve the accuracy and safety of image-guided interventional procedures by enabling verification of the actual interventional trajectory relative to the planned trajectory and identifying potentially unsafe deviations involving critical anatomical structures. This motivation for the combination of Dai, Chang, and Durand is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner), further in view of Lang et al (US 2021/0137634 A1).
Regarding claim(s) 16, Dai as modified by Chang teaches the system of claim 15, but do not specifically teach wherein: the first medical image is obtained when the target object is at a first respiratory amplitude point before the interventional procedure, the second medical image is obtained when the target object is at a second respiratory amplitude point during the interventional procedure and before a puncture procedure, and the third medical image is obtained when the target object is at a third respiratory amplitude point during the puncture procedure; and a deviation of the second respiratory amplitude point from the first respiratory amplitude point is less than a predetermined value, and a deviation of the third respiratory amplitude point from the first respiratory amplitude point and/or the second respiratory amplitude point is less than the predetermined value.
However, Lang teaches wherein: the first medical image is obtained when the target object is at a first respiratory amplitude point before the interventional procedure (Paragraph [1257] – Paragraph [1259]: “Pre-operative 3D imaging data […] can be acquired using respiratory and/or cardiac gating, which can synchronize the acquisition of the image data with the respiratory and/or cardiac cycle […] Respiratory gating can measure the patient's respiratory pattern and motion, […] the frequency of the respiratory cycle, and/or the phase of the respiratory cycle […] and/or the direction of respiratory movement or excursion, and/or the speed of respiratory movement or excursion, and/or the amount of excursion during the respiratory cycle, and can display them, for example, as one or more cyclic waveforms […] Image data can be acquired at the same phase, e.g. expiration, in different periods of the cyclic waveform. Image data can also be acquired at the same phase in the same period of the cyclic waveform, e.g. with multi-detector CT scanners or MRI scanners”), the second medical image is obtained when the target object is at a second respiratory amplitude point during the interventional procedure and before a puncture procedure (Paragraph [1241] – Paragraph [1242]: “a respiratory gating system can measure one or more of the frequency of the respiratory cycle, and/or the phase of the respiratory cycle […] and/or the direction of respiratory movement or excursion, and/or the speed of respiratory movement or excursion and/or the amount of excursion of the respiratory cycle in one or more directions […] The measured data can be used to compute a synchronized movement of the virtual data […] to maintain superimposition and/or alignment […] through the entire respiratory cycle”), and the third medical image is obtained when the target object is at a third respiratory amplitude point during the puncture procedure (Paragraph [1228] – Paragraph [1229]: “[…] following the full or a partial amount of respiratory excursion, e.g. in mm or cm, e.g. by 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 50 mm or any other amount of excursion in one or more directions […] the movement of the display of the virtual data […] can be performed to follow or mirror or to be synchronized with diaphragmatic movement […] By synchronizing the display of the virtual data […] with the frequency of the respiratory cycle, and/or the phase of the respiratory cycle […] and/or the direction of respiratory movement […] and/or the speed of respiratory movement or excursion of the diaphragm […] and/or the amount of movement or excursion of the diaphragm […]”); and
a deviation of the second respiratory amplitude point from the first respiratory amplitude point is less than a predetermined value, and a deviation of the third respiratory amplitude point from the first respiratory amplitude point and/or the second respiratory amplitude point is less than the predetermined value (Paragraph [1228] – Paragraph [1229]: “[…] following the full or a partial amount of respiratory excursion, e.g. in mm or cm, e.g. by 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 50 mm or any other amount of excursion in one or more directions […] the movement of the display of the virtual data […] can be performed to follow or mirror or to be synchronized with diaphragmatic movement […] By synchronizing the display of the virtual data […] with the frequency of the respiratory cycle, and/or the phase of the respiratory cycle […] and/or the direction of respiratory movement […] and/or the speed of respiratory movement or excursion of the diaphragm […] and/or the amount of movement or excursion of the diaphragm […]”; Paragraph [1241] – Paragraph [1242]: “a respiratory gating system can measure one or more of the frequency of the respiratory cycle, and/or the phase of the respiratory cycle […] and/or the direction of respiratory movement or excursion, and/or the speed of respiratory movement or excursion and/or the amount of excursion of the respiratory cycle in one or more directions […] The measured data can be used to compute a synchronized movement of the virtual data […] to maintain superimposition and/or alignment […] through the entire respiratory cycle”; and Paragraph [1259]: “Image data can be acquired at the same phase, e.g. expiration, in different periods of the cyclic waveform. Image data can also be acquired at the same phase in the same period of the cyclic waveform, e.g. with multi-detector CT scanners or MRI scanners”).
Therefore, 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 system of Dai and Chang in view of Lang to acquire the preoperative, intraoperative, and puncture-procedure medical images at corresponding respiratory amplitude points and to maintain the respiratory amplitude points within a predetermined deviation, such that images obtained at different stages of the procedure correspond to substantially the same respiratory state. Determining correspondence between measured respiratory amplitude points using a predetermined allowable deviation would have been an obvious implementation of Lang's respiratory phase matching and synchronization, because Lang expressly measures respiratory excursion and matches image data corresponding to the same or corresponding respiratory states. The motivation for this combination of references would have been to compensate for respiratory motion and maintain registration and alignment of the image data with the patient's moving anatomy during the interventional procedure, thereby improving the accuracy of image-guided puncture and surgical guidance. This motivation for the combination of Dai, Chang, and Lang is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Holsing et al (US 2013/0225942 A1).
Regarding claim(s) 17, Dai teaches the system of claim 1, where Dai teaches wherein the registering the second medical image and the first medical image to obtain a registration result includes:
obtaining an interventional procedure planning information image based on the first medical image (Paragraph [0012] – Paragraph [0013]: “The puncture planning path interactive selection module can select the puncture starting point and the puncture end point […] and calculate the initial puncture planning path […] The automatic puncture planning and risk assessment module performs risk assessment and path optimization on the adjusted puncture planning path”);
Dai fails to teach performing a first registration on the first medical image and the second medical image to obtain first deformation information; and applying the first deformation information to the interventional procedure planning information image to obtain the registration result, wherein the interventional procedure planning information in the registration result is interventional procedure planning information obtained after the first registration.
However, Holsing teaches obtaining an interventional procedure planning information image based on the first medical image (Paragraph [0116]: “a user could complete planning and pathway segmentation on an inspiration scan of the patient”);
performing a first registration on the first medical image and the second medical image to obtain first deformation information (Paragraph [0105]: “A deformation vector field can be calculated between a first set of points […] that correspond to inspiration and a second set of points […] that correspond to expiration”; and Paragraph [0106]: “The image dataset from a first time interval may then be modified or deformed by the deformation vector field to match the anatomy of the patient during the second time interval”); and
applying the first deformation information to the interventional procedure planning information image to obtain the registration result, wherein the interventional procedure planning information in the registration result is interventional procedure planning information obtained after the first registration (Paragraph [0106]: “The image dataset from a first time interval may then be modified or deformed by the deformation vector field to match the anatomy of the patient during the second time interval”; and Paragraph [0115] – Paragraph [0116]: “Deformation of a complete 3D volume would be time consuming so methods to create deformation matrices for regions may be preferred […] a user could complete planning and pathway segmentation on an inspiration scan of the patient. Preferably, a deformation vector field is created between at least two datasets. The deformation vector field may then be applied to the segmented vessels and/or airways and the user's planned path and target”).
Therefore, 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 system of Dai in view of Holsing to obtain deformation information from registration of medical image datasets and apply the deformation information to the interventional procedure planning information, including the planned puncture path and target, thereby obtaining registered planning information corresponding to the anatomy after registration. The motivation for this combination of references would have been to maintain the planned interventional path and target in correspondence with changes in the patient's anatomy between medical image datasets, thereby providing more accurate and reliable image-guided interventional planning and navigation. This motivation for the combination of Dai and Holsing is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dai et al (CN 112163987 A) in view of Chang et al (CN 113057734 A; See translation provided by Examiner), further in view of Komp et al (US 2020/0188032 A1).
Regarding claim(s) 18, Dai as modified by Chang teaches the system of claim 15, but do not specifically teach wherein the operations further include at least one of: displaying, outside a display range of the third medical image, image information of the registration result that lies outside the display range of the third medical image; displaying information of the planed interventional path of the interventional procedure outside the display range of the third medical image; or displaying image information within and outside the display range of the third medical image in different ways.
However, Komp teaches wherein the operations further include at least one of:
displaying, outside a display range of the third medical image, image information of the registration result that lies outside the display range of the third medical image (Wherein clause is claimed as an alternative “or”);
displaying information of the planed interventional path of the interventional procedure outside the display range of the third medical image (Wherein clause is claimed as an alternative “or”); or
displaying image information within and outside the display range of the third medical image in different ways (Figure 12; Paragraph [0067]: “Previously scanned structures completely outside of the current view may change as well […] system 100 indicates to the user that all items outside the current field of view may have changed […] system 100 may modify the displayed image of all elements outside the current field of view via blurring, removing 3D effects (e.g., flattening the image), and removal of color or fading of color […] the items within the field of view may continue to be updated in real-time by system”).
Therefore, 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 system of Dai and Chang in view of Komp to display image information within the display range of the intraoperative medical image differently from image information outside the display range of the intraoperative medical image. The motivation for this combination of references would have been to visually distinguish currently viewed and updated image information from previously acquired image information outside the current field of view, thereby alerting the clinician that anatomical structures outside the current field of view may have changed and improve the reliability of image information presented during surgical navigation. This motivation for the combination of Dai, Chang, and Komp is/are supported by KSR exemplary rationale (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention. MPEP 2141 (III).
Allowable Subject Matter
Claim(s) 9-10 and 12 is/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.
Relevant Prior Art Directed to State of Art
Geraats et al (US 2019/0060666 A1) are relevant prior art not applied in the rejection(s) above. Geraats discloses a system for assisting in performing an interventional procedure, the system comprising: a first subsystem comprising a) a first imaging device for generating a first image of a subject while an interventional device is introduced into the subject a position determination device for determining the position of the interventional device within the subject relative to the first image, and c) a storage for storing the determined position of the interventional device within the subject, after the interventional device has been introduced into the subject, a second subsystem comprising a) a second imaging device for generating a second image of the subject with the introduced interventional device, wherein the first and second imaging devices are different imaging modalities, and b) a planning and/or monitoring device for planning a treatment to be performed by using the interventional device and/or for monitoring a treatment performed by using the interventional device based on the stored position of the interventional device and the second image, wherein the first subsystem and the second subsystem are located at different places.
Izadyyazdanabadi et al (US 2022/0051400 A1) are relevant prior art not applied in the rejection(s) above. Izadyyazdanabadi discloses a method for transforming a digital image generated by an endomicroscopy device into a simulated pathology image, the method comprising: (a) receiving a first image depicting in vivo tissue of a first subject; (b) generating a first plurality of features indicative of content of the first image using a first hidden layer of a pre-trained convolutional neural network trained to recognize at least a multitude of classes of common objects; (c) receiving a second plurality of features indicative of style of a second image corresponding to features generated using a second hidden layer of the pre-trained convolutional neural network, wherein the second image depicts a histopathology slide prepared using tissue of a second subject; (d) generating a third image; (e) generating a third plurality of features indicative of content of the third image using the first hidden layer; (f) generating a fourth plurality of features indicative of a style of the third image using the second hidden layer; (g) generating a loss value based on a loss function using the first plurality of features, the second plurality of features, the third plurality of features, and the fourth plurality of features; (h) modifying the third image based on the loss value; (i) repeating (e) through (h) until a criterion is satisfied; and (j) causing a final version of the third image to be presented in response to the criterion being satisfied.
Conclusion
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/JONGBONG NAH/Examiner, Art Unit 2674
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