DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Objections
Claim 4 and 14 are objected to because of the following informalities:
Claim 4 line 1 – 2, limitation "wherein specifying the location includes" should read "wherein said specifying the location includes".
Claim 14 line 1 – 2, limitation "wherein specifying the location includes" should read "wherein said specifying the location includes".
Appropriate correction is required.
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.
Claim 1 – 8, 11 – 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Muller et al. (US 2009/0299195 A1; published on 12/03/2009) (hereinafter "Muller") in view of Guo et al. (Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study; published on 04/06/2021) (hereinafter "Guo").
Regarding claim 1, Muller teaches an image diagnosis system ("FIG. 1 shows an embodiment of an intravascular catheter system 100 that combines multiple diagnostic modalities." [0027]) comprising:
a catheter insertable into a blood vessel ("… the intravascular catheter 100 is advanced into a blood vessel 18 …" [0028]) and including:
a first sensor configured to transmit ultrasonic waves and receive the waves reflected by the blood vessel while the catheter is inserted in the blood vessel ("The ultrasound transducer system 120 includes one or more transducers that direct ultrasound energy 130 towards the arterial wall 104 and receive ultrasound energy 132 reflected from the arterial wall 104." [0036]; see Fig.1), and
a second sensor configured to emit light and receive the light reflected by the blood vessel while the catheter is inserted in the blood vessel ("… includes an optical bench 118 to transmit and receive light, typically infrared light …" [0030]; "A collection mirror 126 redirects light 127 scattered or reflected from various depths of the arterial wall 104 into a distal end 123 t of the collection fiber 123." [0032]; see Fig.1);
a memory ("… the computer 117 …" [0052]; memory is inherent element of any computer); and
a processor configured to execute a program that is stored in the memory ("… the computer 117 …" [0052]; processor and executable program are inherent elements of any computer) to perform the steps of:
generating an ultrasonic tomographic image of the blood vessel based on the reflected waves received by the first sensor ("At the same time, the IVUS subsystem 312 is enabled to simultaneously generate ultrasound images of the vessel walls 104." [0047]; "… digitized raw IVUS, OCT and NIR data … presents one or more representations of the morphological and chemical structure of the vessel walls to the operator via interface 320, as images ..." [0052]) and an optical coherence tomographic image of the blood vessel based on the reflected light received by the second sensor ("OCT, like IVUS, provides information regarding the structural aspects of the arterial wall but has the capability to generate images …" [0024]; "… digitized raw IVUS, OCT and NIR data … presents one or more representations of the morphological and chemical structure of the vessel walls to the operator via interface 320, as images ..." [0052]),
specifying a location of a lesion in the blood vessel based on the ultrasonic tomographic image and the optical coherence tomographic image ("… by identifying a variety of arterial abnormalities, such as positive remodeling (best detectable by IVUS), thin caps (best detectable by OCT) …" [0026]),
generating first feature data related to the lesion from the ultrasonic tomographic image ("… to the received ultrasound signal 132." [0036]; "… the received signal 132 may be useful for characterizing atherosclerotic plaques, including plaque volume in the blood vessel wall and also the degree of stenosis of the blood vessels." [0037]) and second feature data related to the lesion from the optical coherence tomographic image ("OCT … the thickness of the fibrous cap covering a plaque with a necrotic core can now be measured with high precision." [0024]; "The collected spectral response may be used to determine whether each region of interest of the blood vessel wall 104 comprises a lipid pool or lipid-rich atheroma, a disrupted plaque, a vulnerable plaque or thin-cap fibroatheroma (TCFA), a fibrotic lesion, a calcific lesion, and/or normal tissue ..." [0052]),
inputting the first and second feature data into a computer model ("The computer 117 may combine the structural analysis information from the IVUS subsystem 312 with information from the optical analysis subsystem 310." [0052]).
Muller fails to explicitly teach the processor configured to perform the steps of inputting the first and second feature data into a computer mode to generate risk information related to an onset risk of ischemic heart disease, the computer model having been trained with feature data of different lesions and a plurality of answer information corresponding to the different lesions, each answer information indicating whether the ischemic heart disease has developed from a corresponding one of the lesions, and outputting the risk information related to the onset risk of ischemic heart disease.
However, in the same field of endeavor, Guo teaches the processor configured to perform the steps of inputting the first and second feature data into a computer mode to generate risk information related to an onset risk of ischemic heart disease ("Using ΔLPI, ΔCTI and ΔMPVI as plaque vulnerability change, respectively, 13 key risk factors at baseline were used as predictors to feed four machine learning methods." Page 4), the computer model having been trained with feature data of different lesions ("Then, the new sample after oversampling was used to train and test the four machine learning methods for predicting each plaque vulnerability index ... For each machine learning method, all possible combinations of 13 morphological and mechanical factors at baseline were fit to the method as predictors to determine the prediction accuracies." Page 15 - 16) and a plurality of answer information corresponding to the different lesions, each answer information indicating whether the ischemic heart disease has developed from a corresponding one of the lesions ("For 45 paired slices, the change of LPI, CTI and MPVI from baseline to follow-up were used to measure the change of plaque vulnerability …" Page 14), and
outputting the risk information related to the onset risk of ischemic heart disease ("Four different machine learning methods were employed for the prediction of each plaque vulnerability index …" Page 15; here the vulnerability index is the output).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Regarding claim 2, Muller in view of Guo teaches all claim limitations, as applied in claim 1, and Muller further teaches wherein the first feature data indicates a feature of a plaque volume ("… to the received ultrasound signal 132." [0036]; "… the received signal 132 may be useful for characterizing atherosclerotic plaques, including plaque volume in the blood vessel wall and also the degree of stenosis of the blood vessels." [0037]).
Regarding claim 3, Muller in view of Guo teaches all claim limitations, as applied in claim 1, and Muller further teaches wherein the second feature data indicates a feature of at least one of a thickness of a fibrous cap, a calcified plaque, a lipid plaque ("OCT … the thickness of the fibrous cap covering a plaque with a necrotic core can now be measured with high precision." [0024]; "The collected spectral response may be used to determine whether each region of interest of the blood vessel wall 104 comprises a lipid pool or lipid-rich atheroma, a disrupted plaque, a vulnerable plaque or thin-cap fibroatheroma (TCFA), a fibrotic lesion, a calcific lesion, and/or normal tissue ..." [0052]).
Regarding claim 4, Muller in view of Guo teaches all claim limitations, as applied in claim 1, and Muller further teaches wherein specifying the location includes calculating a plaque burden using the ultrasonic tomographic image ("Also, the boundary between the lumen and the vessel intima as well as that between the media and the adventitia can be visualized with accuracy good enough to calculate lumen dimensions and the area of plaques, or “plaque burden.”" [0005]).
In addition, Guo further teaches wherein specifying the location includes calculating a plaque burden using the ultrasonic tomographic image and determining that the plaque burden exceeds a threshold ("… used IVUS-based CFD models and found that plaque locations with plaque burden > 40% …" Page 6).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Regarding claim 5, Muller in view of Guo teaches all claim limitations, as applied in claim 1, and Muller further teaches a display, wherein the steps further include controlling the display to display the risk information ("... and display information to the user interface 320." [0049], [0050]).
Regarding claim 6, Muller in view of Guo teaches all claim limitations, as applied in claim 5, and Muller further teaches wherein the steps further include controlling the display to display the ultrasonic tomographic image and the optical coherence tomographic image ("The computer 117 may combine the structural analysis information from the IVUS subsystem 312 with information from the optical analysis subsystem 310. For example, information from both systems is combined into hybrid images displayed to the operator on user interface 320." [0052]).
Regarding claim 7, Muller in view of Guo teaches all claim limitations, as applied in claim 1, and Guo further teaches wherein the steps include calculating a value of stress applied to the blood vessel by the lesion, and the calculated value of stress is further input to the computer model ("IVUS + OCT data provided accurate cap thickness and better plaque morphology which led to better stress/strain calculations using IVUS + OCT-based FSI models and more accurate vulnerability prediction using machine learning predictive methods. Combination of 13 morphological and mechanical factors could lead to higher accuracy for vulnerability change predictions." Page 8).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Regarding claim 8, Muller in view of Guo teaches all claim limitations, as applied in claim 1, and Guo further teaches wherein the steps include obtaining blood information about a blood inside the blood vessel ("Morphological and mechanical factors of 45 matched slices (2 × 45 = 90 slices in total) were extracted from IVUS + OCT data and 3D-FSI models, respectively." Page 12; "Combining morphological factors, fluid dynamics factors and structural mechanical factors demonstrated great ability in morphological indices prediction." Page 7), and the obtained blood information is further input to the computer model ("All combinations of baseline 13 risk factors were used as predictors for three vulnerability indices using four machine learning methods." Page 5).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Regarding claim 11, Muller teaches an image diagnosis method performed by an image diagnosis system ("Moreover, by combining at least these three modalities in a single system, apparatus, and method, these multiple informational components used in the diagnosis can be obtained in a single intravascular procedure." [0026]; "FIG. 1 shows an embodiment of an intravascular catheter system 100 that combines multiple diagnostic modalities." [0027]) that includes a catheter insertable into a blood vessel ("… the intravascular catheter 100 is advanced into a blood vessel 18 …" [0028]) and including: a first sensor configured to transmit ultrasonic waves and receive the waves reflected by the blood vessel while the catheter is inserted in the blood vessel ("The ultrasound transducer system 120 includes one or more transducers that direct ultrasound energy 130 towards the arterial wall 104 and receive ultrasound energy 132 reflected from the arterial wall 104." [0036]; see Fig.1), and a second sensor configured to emit light and receive the light reflected by the blood vessel while the catheter is inserted in the blood vessel ("… includes an optical bench 118 to transmit and receive light, typically infrared light …" [0030]; "A collection mirror 126 redirects light 127 scattered or reflected from various depths of the arterial wall 104 into a distal end 123 t of the collection fiber 123." [0032]; see Fig.1),
the image diagnosis method comprising:
generating an ultrasonic tomographic image of the blood vessel based on the reflected waves received by the first sensor ("At the same time, the IVUS subsystem 312 is enabled to simultaneously generate ultrasound images of the vessel walls 104." [0047]; "… digitized raw IVUS, OCT and NIR data … presents one or more representations of the morphological and chemical structure of the vessel walls to the operator via interface 320, as images ..." [0052]) and an optical coherence tomographic image of the blood vessel based on the reflected light received by the second sensor ("OCT, like IVUS, provides information regarding the structural aspects of the arterial wall but has the capability to generate images …" [0024]; "… digitized raw IVUS, OCT and NIR data … presents one or more representations of the morphological and chemical structure of the vessel walls to the operator via interface 320, as images ..." [0052]),
specifying a location of a lesion in the blood vessel based on the ultrasonic tomographic image and the optical coherence tomographic image ("… by identifying a variety of arterial abnormalities, such as positive remodeling (best detectable by IVUS), thin caps (best detectable by OCT) …" [0026]),
generating first feature data related to the lesion from the ultrasonic tomographic image ("… to the received ultrasound signal 132." [0036]; "… the received signal 132 may be useful for characterizing atherosclerotic plaques, including plaque volume in the blood vessel wall and also the degree of stenosis of the blood vessels." [0037]) and second feature data related to the lesion from the optical coherence tomographic image ("OCT … the thickness of the fibrous cap covering a plaque with a necrotic core can now be measured with high precision." [0024]; "The collected spectral response may be used to determine whether each region of interest of the blood vessel wall 104 comprises a lipid pool or lipid-rich atheroma, a disrupted plaque, a vulnerable plaque or thin-cap fibroatheroma (TCFA), a fibrotic lesion, a calcific lesion, and/or normal tissue ..." [0052]),
inputting the first and second feature data into a computer model ("The computer 117 may combine the structural analysis information from the IVUS subsystem 312 with information from the optical analysis subsystem 310." [0052]).
Muller fails to explicitly teach the steps of inputting the first and second feature data into a computer mode to generate risk information related to an onset risk of ischemic heart disease, the computer model having been trained with feature data of different lesions and a plurality of answer information corresponding to the different lesions, each answer information indicating whether the ischemic heart disease has developed from a corresponding one of the lesions, and outputting the risk information related to the onset risk of ischemic heart disease.
However, in the same field of endeavor, Guo teaches the steps of inputting the first and second feature data into a computer mode to generate risk information related to an onset risk of ischemic heart disease ("Using ΔLPI, ΔCTI and ΔMPVI as plaque vulnerability change, respectively, 13 key risk factors at baseline were used as predictors to feed four machine learning methods." Page 4), the computer model having been trained with feature data of different lesions ("Then, the new sample after oversampling was used to train and test the four machine learning methods for predicting each plaque vulnerability index ... For each machine learning method, all possible combinations of 13 morphological and mechanical factors at baseline were fit to the method as predictors to determine the prediction accuracies." Page 15 - 16) and a plurality of answer information corresponding to the different lesions, each answer information indicating whether the ischemic heart disease has developed from a corresponding one of the lesions ("For 45 paired slices, the change of LPI, CTI and MPVI from baseline to follow-up were used to measure the change of plaque vulnerability …" Page 14), and
outputting the risk information related to the onset risk of ischemic heart disease ("Four different machine learning methods were employed for the prediction of each plaque vulnerability index …" Page 15; here the vulnerability index is the output).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Regarding claim 12, Muller in view of Guo teaches all claim limitations, as applied in claim 11, and Muller further teaches wherein the first feature data indicates a feature of a plaque volume ("… to the received ultrasound signal 132." [0036]; "… the received signal 132 may be useful for characterizing atherosclerotic plaques, including plaque volume in the blood vessel wall and also the degree of stenosis of the blood vessels." [0037]).
Regarding claim 13, Muller in view of Guo teaches all claim limitations, as applied in claim 11, and Muller further teaches wherein the second feature data indicates a feature of at least one of a thickness of a fibrous cap, a calcified plaque, a lipid plaque ("OCT … the thickness of the fibrous cap covering a plaque with a necrotic core can now be measured with high precision." [0024]; "The collected spectral response may be used to determine whether each region of interest of the blood vessel wall 104 comprises a lipid pool or lipid-rich atheroma, a disrupted plaque, a vulnerable plaque or thin-cap fibroatheroma (TCFA), a fibrotic lesion, a calcific lesion, and/or normal tissue ..." [0052]).
Regarding claim 14, Muller in view of Guo teaches all claim limitations, as applied in claim 11, and Muller further teaches wherein specifying the location includes calculating a plaque burden using the ultrasonic tomographic image ("Also, the boundary between the lumen and the vessel intima as well as that between the media and the adventitia can be visualized with accuracy good enough to calculate lumen dimensions and the area of plaques, or “plaque burden.”" [0005]).
In addition, Guo further teaches wherein specifying the location includes calculating a plaque burden using the ultrasonic tomographic image and determining that the plaque burden exceeds a threshold ("… used IVUS-based CFD models and found that plaque locations with plaque burden > 40% …" Page 6).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Regarding claim 15, Muller in view of Guo teaches all claim limitations, as applied in claim 11, and Muller further teaches display the risk information ("... and displaying information to the user interface 320." [0049], [0050]).
Regarding claim 16, Muller in view of Guo teaches all claim limitations, as applied in claim 15, and Muller further teaches displaying the ultrasonic tomographic image and the optical coherence tomographic image ("The computer 117 may combine the structural analysis information from the IVUS subsystem 312 with information from the optical analysis subsystem 310. For example, information from both systems is combined into hybrid images displayed to the operator on user interface 320." [0052]).
Regarding claim 17, Muller in view of Guo teaches all claim limitations, as applied in claim 11, and Guo further teaches calculating a value of stress applied to the blood vessel by the lesion, wherein the calculated value of stress is further input to the computer model ("IVUS + OCT data provided accurate cap thickness and better plaque morphology which led to better stress/strain calculations using IVUS + OCT-based FSI models and more accurate vulnerability prediction using machine learning predictive methods. Combination of 13 morphological and mechanical factors could lead to higher accuracy for vulnerability change predictions." Page 8).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Regarding claim 18, Muller in view of Guo teaches all claim limitations, as applied in claim 1, and Guo further teaches obtaining blood information about a blood inside the blood vessel ("Morphological and mechanical factors of 45 matched slices (2 × 45 = 90 slices in total) were extracted from IVUS + OCT data and 3D-FSI models, respectively." Page 12; "Combining morphological factors, fluid dynamics factors and structural mechanical factors demonstrated great ability in morphological indices prediction." Page 7), wherein the obtained blood information is further input to the computer model ("All combinations of baseline 13 risk factors were used as predictors for three vulnerability indices using four machine learning methods." Page 5).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Regarding claim 20, Muller teaches a non-transitory computer readable medium storing a program causing a computer to execute ("… the computer 117 …" [0052]; non-transitory computer readable medium, and executable program are inherent elements of any computer) an image diagnosis method ("Moreover, by combining at least these three modalities in a single system, apparatus, and method, these multiple informational components used in the diagnosis can be obtained in a single intravascular procedure." [0026]) comprising:
generating an ultrasonic tomographic image of the blood vessel based on the reflected waves received by the first sensor ("At the same time, the IVUS subsystem 312 is enabled to simultaneously generate ultrasound images of the vessel walls 104." [0047]; "… digitized raw IVUS, OCT and NIR data … presents one or more representations of the morphological and chemical structure of the vessel walls to the operator via interface 320, as images ..." [0052]) and an optical coherence tomographic image of the blood vessel based on the reflected light received by the second sensor ("OCT, like IVUS, provides information regarding the structural aspects of the arterial wall but has the capability to generate images …" [0024]; "… digitized raw IVUS, OCT and NIR data … presents one or more representations of the morphological and chemical structure of the vessel walls to the operator via interface 320, as images ..." [0052]),
specifying a location of a lesion in the blood vessel based on the ultrasonic tomographic image and the optical coherence tomographic image ("… by identifying a variety of arterial abnormalities, such as positive remodeling (best detectable by IVUS), thin caps (best detectable by OCT) …" [0026]),
generating first feature data related to the lesion from the ultrasonic tomographic image ("… to the received ultrasound signal 132." [0036]; "… the received signal 132 may be useful for characterizing atherosclerotic plaques, including plaque volume in the blood vessel wall and also the degree of stenosis of the blood vessels." [0037]) and second feature data related to the lesion from the optical coherence tomographic image ("OCT … the thickness of the fibrous cap covering a plaque with a necrotic core can now be measured with high precision." [0024]; "The collected spectral response may be used to determine whether each region of interest of the blood vessel wall 104 comprises a lipid pool or lipid-rich atheroma, a disrupted plaque, a vulnerable plaque or thin-cap fibroatheroma (TCFA), a fibrotic lesion, a calcific lesion, and/or normal tissue ..." [0052]),
inputting the first and second feature data into a computer model ("The computer 117 may combine the structural analysis information from the IVUS subsystem 312 with information from the optical analysis subsystem 310." [0052]).
Muller fails to explicitly teach the steps of inputting the first and second feature data into a computer mode to generate risk information related to an onset risk of ischemic heart disease, the computer model having been trained with feature data of different lesions and a plurality of answer information corresponding to the different lesions, each answer information indicating whether the ischemic heart disease has developed from a corresponding one of the lesions, and outputting the risk information related to the onset risk of ischemic heart disease.
However, in the same field of endeavor, Guo teaches the steps of inputting the first and second feature data into a computer mode to generate risk information related to an onset risk of ischemic heart disease ("Using ΔLPI, ΔCTI and ΔMPVI as plaque vulnerability change, respectively, 13 key risk factors at baseline were used as predictors to feed four machine learning methods." Page 4), the computer model having been trained with feature data of different lesions ("Then, the new sample after oversampling was used to train and test the four machine learning methods for predicting each plaque vulnerability index ... For each machine learning method, all possible combinations of 13 morphological and mechanical factors at baseline were fit to the method as predictors to determine the prediction accuracies." Page 15 - 16) and a plurality of answer information corresponding to the different lesions, each answer information indicating whether the ischemic heart disease has developed from a corresponding one of the lesions ("For 45 paired slices, the change of LPI, CTI and MPVI from baseline to follow-up were used to measure the change of plaque vulnerability …" Page 14), and
outputting the risk information related to the onset risk of ischemic heart disease ("Four different machine learning methods were employed for the prediction of each plaque vulnerability index …" Page 15; here the vulnerability index is the output).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to use the multiple information from multiple imaging modalities as taught by Muller with the machine learning based coronary plaque vulnerability prediction as taught by Guo. By combining morphological and mechanical factors extracted from IVUS and OCT data, such model "could lead to higher accuracy for vulnerability change predictions" (see Guo; Page 8).
Claim 9, 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Muller in view of Guo, as applied in claim 1 and 11 respectively, and further in view of Baldauf et al. (US 2018/0310830 A1l published on 11/01/2018) (hereinafter "Baldauf").
Regarding claim 9, Muller in view of Guo teaches all claim limitations, as applied in claim 1, and Muller further teaches an angiography apparatus configured to generate an angiographic image of the blood vessel ("In this way, additional position information is available, beyond that which would typically be provided from X-ray angiogram images." [0042]).
Muller in view of Guo fails to explicitly teach wherein the catheter includes a marker that can be imaged by the angiography apparatus.
However, in the same field of endeavor, Baldauf teaches an angiography apparatus configured to generate an angiographic image of the blood vessel ("… a first angiogram is acquired using a scanner, a gantry supporting the scanner is rotated changing the view point, and a second angiogram acquired using the scanner." [0032]), wherein
the catheter includes a marker ("… wherein the catheter 100 comprises … a first marker 106 (e.g., a lens marker), a second or proximal marker 107, a distal tip marker 108 … The catheter 100 further comprises a geometric array 110, described herein." [0028]) that can be imaged by the angiography apparatus ("Furthermore, the markers 106-108 and 110 can be observed in an angiogram." [0029]).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify the tracking position of IVUS+OCT catheter using angiography image as taught by Muller with additional position marker using angiogram as taught by Baldauf. Doing so would make it possible "for determining the 3D position and orientation of an imaging catheter" and enabling "its composition to be positioned correctly" (see Baldauf; [0027]).
Regarding claim 10, Muller in view of Guo and Baldauf teaches all claim limitations, as applied in claim 9, and Baldauf further teaches wherein the marker is adjacent to the second sensor ("… a sensor 105 (e.g., an imaging sensor) attached to the monitoring body 104, a first marker 106 (e.g., a lens marker) …" [0028]; see positions of 105 and 106 in Fig.1; "… Intravascular Optical Coherence Tomgraphy (OCT), that acquire images of an artery and stenosis from the interior of the artery using an imaging sensor located at the catheter tip." [0005]; here the imaging sensor is the OCT imaging sensor as the second sensor).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify the tracking position of IVUS+OCT catheter using angiography image as taught by Muller with additional position marker using angiogram as taught by Baldauf. Doing so would make it possible "for determining the 3D position and orientation of an imaging catheter" and enabling "its composition to be positioned correctly" (see Baldauf; [0027]).
Regarding claim 19, Muller in view of Guo teaches all claim limitations, as applied in claim 11, and Muller further teaches generating an angiographic image of the blood vessel ("In this way, additional position information is available, beyond that which would typically be provided from X-ray angiogram images." [0042]; by definition, angiogram is a specialized X-ray procedure used to visualize blood flow and detect blockages, aneurysms, or narrowing in arteries and veins)
Muller in view of Guo fails to explicitly teach generating an angiographic image of a marker of the catheter.
However, in the same field of endeavor, Baldauf teaches generating an angiographic image of the blood vessel and a marker of the catheter ("… wherein the catheter 100 comprises … a first marker 106 (e.g., a lens marker), a second or proximal marker 107, a distal tip marker 108 … The catheter 100 further comprises a geometric array 110, described herein." [0028]; "Furthermore, the markers 106-108 and 110 can be observed in an angiogram." [0029]; by definition, angiogram is a specialized X-ray procedure used to visualize blood flow and detect blockages, aneurysms, or narrowing in arteries and veins).
It would have been prima facie obvious to one ordinary skilled in the art before the effective filing date of the invention to modify the tracking position of IVUS+OCT catheter using angiography image as taught by Muller with additional position marker using angiogram as taught by Baldauf. Doing so would make it possible "for determining the 3D position and orientation of an imaging catheter" and enabling "its composition to be positioned correctly" (see Baldauf; [0027]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Fornwalt et al. (US 2021/0145404 A1; published on 05/20/2021) teach a deep neural network to predict risk level of heart disease based on ultrasound video frames.
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/CHAO SHENG/ Primary Examiner, Art Unit 3797