Detailed Action
1. Claims 1-11 are pending in this Application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
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 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.
3. Claims 1-2,4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Qing-Yang Yao et al., (hereafter Qing), “Image-based visualization of stents in mechanical thrombectomy for acute ischemic stroke: Preliminary findings from a series of cases”, pub.,
Jul 26, 2023, in view of GUNNING et al., (hereafter GUNNING), WO 2023150730 A2, pub. 08/10/2023.
As to claim 1, Qing teaches A medical image processing device comprising processing circuitry (Abstract, Image-based visualization of stents in mechanical thrombectomy for acute ischemic stroke)configured to:
acquire a medical image in which at least blood vessels of a patient are captured and patient information regarding the patient (Fig. 1, Abstract, Image-based visualization of stents in mechanical thrombectomy for acute ischemic stroke, Fig.1 Illustrates a Digital subtraction angiography (DSA) revealing proximal basilar artery (BA), Stent deployed at the left posterior cerebral artery (LPCA)-P1 of patient 1, where the patient 1 is a 64-year-old man. He had a history of smoking, and his neurological examination on admission showed mild coma, contraction of bilateral pupils)
estimate a state of a thrombus captured in the medical image based on the medical image and the patient information, and output an estimation result relating to the estimated thrombus (page 2nd par., Four patients with acute cerebral large-vessel occlusion presented the localization of the stent after release, where a thrombus was captured in mechanical thrombosis. Patient 2: A 74-year-old man with a middle cerebral artery occlusion had a deployed stent that captured a thrombus. Patient 3: A 74-year-old woman with a middle cerebral artery occlusion had a stent deployed that captured a large thrombus. Patient 4: An 82-year-old man with a middle cerebral artery occlusion had a stent placed that captured a large thrombus and fragmented thrombi.);
generate a display image for presenting any one or a plurality of the medical image, the estimation result, and Figs.1-3, Fig. 1 the display images of cerebral artery which has thrombus, while Fig.3 the captured display images of cerebral artery after removing thrombus);
However, it is noted that Qing does not specifically teach “determine a thrombus removal method for removing the thrombus based on the medical image, the patient information, and the estimation result, and output a result of determination of the thrombus removal method, and the determination result and cause a display device to display the display image.” Although Qing teaches image of cerebral artery which has thrombus and image of cerebral artery after thrombus are removed).
On the other hand, in the same field of endeavor a systems and methods for removing a thrombus from a blood vessel of a patient disclosed by GUNNING teaches determine a thrombus removal method for removing the thrombus based on the medical image, the patient information, and the estimation result, and output a result of determination of the thrombus removal method( [0169], [01175], [0117], A method for removing thrombus from a blood vessel of a patient with a thrombus removal device is provided, the method
Comprising: obtaining a pretreatment image representative of a thrombus; introducing a distal portion of an elongate catheter in a blood vessel to a target location near the thrombus; operating an aspiration source of the elongate
catheter; removing the thrombus from the patient with the aspiration source through the thrombus removal device; and determining the volume of the thrombus removed from the patient. The method comprises calculating a pre-treatment volume of the thrombus from the pre-treatment image; comparing the estimated or calculated volume of thrombus removed to the pre-treatment volume (see [0175]);and
the determination result and cause a display device to display the display ([0177], In one aspect, the method further comprises generating an indicator that sufficient thrombus has been removed; and displaying a representation of the indicator)
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to combine the image visualization insights of Qing with the physical removal techniques of Gunning, because integrating real-time visual feedback of stent deployment and clot interaction with mechanical or fluid-jet removal mechanisms provides a predictable and optimized way to monitor, adjust, and confirm the effectiveness of the physical thrombus removal (clot-removal) process during a procedure
As to claim 2, Qing teaches the processing circuitry estimates a region of the thrombus in the medical image outputs a thrombus region image of the estimated region of the thrombus as the estimation result, estimates a disease type of the thrombus (Figs.1-4, for example, Postoperatively, the patient was in a mild coma (NIHSS score, 30). On day 2, magnetic resonance imaging revealed acute infarcts in the brainstem, left cerebellum, and left basal ganglia region, with minor brainstem hemorrhage) outputs thrombotic type information representing the estimated disease type of the thrombus as the estimation result, estimates constituents of the thrombus (page 2 2nd par., for example , Patient 1 was a 64-year-old man admitted after 5 h of confusion; angiography revealed basilar artery occlusion and, Patient 2 was a 74-year-oldman admitted with confusion, which lasted approximately 3 h. Angiography revealed a left middle cerebral artery (MCA)-M1 segment occlusion ), and outputs thrombus constituent composition information representing the estimated constituents of the thrombus as the estimation result (Figs.1-4; page 2, For example Patient 3 was a 74-year old woman admitted after experiencing left hemiplegia for 3 h. We deployed a stent at the distal right MCA-M2segment, and the developing stent captured a large thrombus. Patient 4 was an 82-year-old man who presented with confusion for 3 h. A developing stent was placed in the distal left MCA-M1segment, which captured a large thrombus and several fragmented thrombi).
As to claim 4, Qing teaches the thrombus region image, the thrombotic type of information, and the thrombus constituent composition information (see Figs1-4 and page 2)
However, it is noted that Qing does not specifically teach “the processing circuitry determines the thrombus removal method based on a combination of the thrombus region image,
On the other hand GUNNING teaches the processing circuitry determines the thrombus removal method based on a combination of the thrombus region image0169], [01175], [0117], A method for removing thrombus from a blood vessel of a patient with a thrombus removal device is provided, the method Comprising: obtaining a pretreatment image representative of a thrombus; introducing a distal portion of an elongate catheter in a blood vessel to a target location near the thrombus; operating an aspiration source of the elongate catheter; removing the thrombus from the patient with the aspiration source through the thrombus removal device; and determining a volume of the thrombus removed from the patient. The method further comprises calculating a pre-treatment volume of the thrombus from the pre-treatment image; comparing the estimated or calculated volume of thrombus removed to the pre-treatment volume (see [0175]).
Claim 11 is rejected the same as claim 1 except claim 11 is directed to a method claim. The rejection of claim 1 includes all the limitations of claim 11. Thus, argument analogous to that presented above for claim 1 is applicable to claim 11.
4. Claims 5-8 are rejected under 35 U.S.C. 103 as being unpatentable over Qing, “Image-based visualization of stents in mechanical thrombectomy for acute ischemic stroke: Preliminary findings from a series of cases”, in view of GUNNING, WO 2023150730 A2, further in view of Chao et al., (hereafter Chao) “A Novel Approach for Assessing the Progression of Deep Venous Thrombosis by Area of Venous Thrombus in Ultrasonic Elastography ” Clinical and Applied Thrombosis/Hemostasis 2014, Vol. 20(3) 311-317,
As to claim 5, the combination of Qing and GUNNING teaches the processing circuitry determines a segmentation method of segmenting the region of the thrombus represented by the thrombus region image based on the combination of the thrombus region image (Qing: Figs. 1-4, page 7, for example G: Stent deployed at the LPCA-P2 segment and stent imaging presenting a grid-form shadow; H: Stent capturing three fragmented thrombi; I: Repeat DSA revealing good recanalization.), the thrombotic type information, and the thrombus constituent composition information (Qing: Figs1-4 and page 2, Figs.1-4; page 2, For example Patient 3 was a 74-year old woman admitted after experiencing left hemiplegia for 3 h. We deployed a stent at the distal right MCA-M2segment, and the developing stent captured a large thrombus. Patient 4 was an 82-year-old man who presented with confusion for 3 h. A developing stent was placed in the distal left MCA-M1segment, which captured a large thrombus and several fragmented thrombi ), and
the thrombus removal method (GUNNING: [0169], [01175], [0117], A method for
removing thrombus from a blood vessel of a patient with a thrombus removal device is provided);
It is noted that the combination of Qing and GUNNING does not specifically teach
“ outputs a result of determination of the segmentation method, performs image processing for segmenting the thrombus region image into a plurality of regions according to the determination result and the result of determination of the segmentation method
On the other hand Chao teaches outputs a result of determination of the segmentation method, performs image processing for segmenting the thrombus region image into a plurality of regions according to the determination result and the result of determination of the segmentation method (Figs. 1-4, The operation panel of area measurement of hardness region(AMEHR) program. This program could contour the blood vessel in strain image with yellow line and the blood thrombus with red line. It could provide an explicit percentage of thrombus in the lumen. For example in Figure2. Blood clot hardness region distribution in strain image of modelno.4.B mode(left), elastography (middle),and segmented blood clot image(right).Thrombosis
processondays1,3,6,and 9 was displayed as A, B, C, and D. It indicated that the area of thrombus increased with time significantly while the strain values inside the thrombus changed little. ), and outputs region segmentation information
representing feature values including constituents of the thrombus in the segmented regions(Figure2. Blood clot hardness region distribution in strain image of modelno.4.B-mode(left), elastography (middle), and segmented blood clot image(right).Thrombosis process ondays1,3,6, and 9 was displayed as A ,B, C, and D. It indicated that the area of thrombus increased with time significantly while the strain values inside the thrombus changed little).
It would have been obvious to a person of ordinary skill in the art, before the effective filing date of the claimed invention to incorporate the thrombus hardness classification method of Chao into the image-based stent visualization and thrombectomy evaluation techniques of modified Qing because combining a reliable way to categorize clot consistency or hardness with direct fluoroscopic visualization of the stent-clot interaction would provide predictable procedural optimization, allowing clinicians to better tailor retrieval strategies, assess mechanical resistance, and improve recanalization success rates during mechanical thrombectomy.
As to claim 6, Chao teaches the thrombus removal method includes information representing the hardness of the thrombus, and the processing circuitry performs the image processing for segmenting the thrombus region image into the regions depending on the hardness of the thrombus (Fig.2, Figure2 showed the blood clot hardness region distribution in strain images of modelno.4 during the process of thrombosis on days 1,3,6,and 9. With increasing age, the distribution of blood clots (red part) in strain images also increased0.
The motivation applied to claim applied to claim 5 above equally apply to claim 6
As to claim 7, Qing the thrombus removal method includes information representing the disease type of the thrombus, and the processing circuitry performs the image processing for segmenting the thrombus region image into the regions depending on the disease type of the thrombus(page 2 2nd par., for example , Patient 1 was a 64-year-old man admitted after 5 h of confusion; angiography revealed basilar artery occlusion and, Patient 2 was a 74-year-oldman admitted with confusion, which lasted approximately 3 h. Angiography revealed a left middle cerebral artery (MCA)-M1 segment occlusion).
As to claim 8, the combination of GUNNING and Chao teaches the thrombus removal method includes a procedure for removing the thrombus using a thrombus removal device (GUNNING: [0169], [01175], [0117], A method for removing thrombus from a blood vessel of a patient with a thrombus removal device is provided) and the processing circuitry performs the image processing for segmenting the thrombus region image into the regions (Chao: Figs. 1-4, The operation panel of area measurement of hardness region(AMEHR) program. This program could contour the blood vessel in strain image with yellow line and the blood thrombus with red line) according to the thrombus removal device used in the procedure for removing the thrombus when the determination result is a determination to remove the thrombus through the procedure using the thrombus removal device(GUNNING: [0169], [01175], [0117], a thrombus removal device is provided. removing the thrombus
from the patient with the aspiration source through the thrombus removal device
Motivation applied to claim 1 above equally applied to claim 8.
Allowable Subject Matter
5 Claims 3, 9 and 10 are objected to as being dependent upon a rejected base claim1 but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
6. Regarding claim 3 no prior art is found to anticipate or render the following limitation obvious:
“ wherein the processing circuitry outputs the thrombus region image separated from the medical image as the estimation result using a trained model trained to output an image in which a region showing signs of thrombus that is able to be confirmed on the medical image is determined as the region of the thrombus when the medical image is input, outputs the thrombotic type information estimated from the medical image before thrombus removal as the estimation result using a trained model trained to output the disease type of the thrombus identified after thrombus collection when image feature amounts of the medical image before thrombus removal are input, and outputs the thrombus constituent composition information estimated from the medical image before thrombus removal as the estimation result using a trained model trained to output an analysis result after thrombus collection when image feature amounts of the medical image before thrombus removal are input.
7. Regarding claim 9 no prior art is found to anticipate or render the following limitation obvious:
“wherein the processing circuitry calculates constituent proportions corresponding to the feature values using a trained model trained to output a constituent composition including proportions of constituents constituting the thrombus represented by the feature values when the feature values are input, and outputs the calculated constituent proportions as the region segmentation information”
Claim 10 is objected to because it depends on the objected claim 9.
Prior arts are not used in rejections but pertinent to the claims or disclosure.
a.. “Deep-Learning-Based Thrombus Localization and Segmentation in Patients with Posterior Circulation Stroke”, Published: 6 June 2022, by Riaan Zoetmulder et al., disclosed:
Abstract: Thrombus volume in posterior circulation stroke (PCS) has been associated with outcome, through recanalization. Manual thrombus segmentation is impractical for large scale analysis of image characteristics. Hence, in this study we develop the first automatic method for thrombus localization and segmentation on CT in patients with PCS.
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Figure 2. Examples of automatic segmentation results obtained by BL-UNet and Polar-UNet. From
the left to right column: The original scan with a bounding box indicating the zoom location, the
ground truth segmentation map, the results obtained from the BL-UNet without volume-based
removal (VBR), the results obtained from the Polar-UNet without VBR, and the results obtained from
the Polar-UNet with VBR. The top three rows display NCCT scans; the bottom row shows a CTA scan.
The top row shows the difficulty all CNN methods have with segmenting a thrombus in the vertebral
arteries. The second row from the top shows an example of small false positives removed by the
VBR step. The third row from the top row shows false positives that are removed by restricting the
volume-of-interest to the posterior circulation with Polar-UNet. The bottom row shows an example
of a scan without a hyperdense artery sign. The segmentation maps show the ground truth (pink),
true positive (green), false negative (orange) and false positive (blue). The NCCT scans were plotted
using a window center level of 35, with a window width of 30. The CTA scan was plotted using a
window center level of 300, with a window width of 600.
b. “Cancer Prediction With Machine Learning of Thrombi From Thrombectomy in Stroke:
Multicenter Development and Validation”, CLINICAL AND POPULATION SCIENCES Published: 2023 , by JoonNyung Heo et al., ., disclosed:
This study showed that machine learning models can identify patients with cancer by analyzing histopathologic images of thrombi obtained through EVT. The best performing model was based on immunohistochemistry staining for platelets and had an AUC of 0.954 on internal validation and 0.949 on external validation. When applied to patients with undiagnosed cancer, the machine learning model accurately detected the presence of cancer, with a mean classification probability of over 88.5%.
Our study suggests that machine learning could be an automated decision support tool in predicting the presence of occult cancer as the cause of ischemic stroke, particularly for those with cryptogenic stroke. We showed that occult cancer can be predicted with high accuracy by using machine learning models of thrombi (see Discussion section pages 2111-2112)
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/MEKONEN T BEKELE/Primary Examiner, Art Unit 2699
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