Thomas Leblanc1,2, Florent Lalys3, Quentin Tollenaere4, Adrien Kaladji5,6, Antoine Lucas5,6, Antoine Simon5. 1. Therenva, 35000, Rennes, France. thomas.leblanc@therenva.com. 2. Univ Rennes, CHU Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, F-35000, Rennes, France. thomas.leblanc@therenva.com. 3. Therenva, 35000, Rennes, France. 4. Vascular Medicine Unit, CHU Rennes, 35033, Rennes, France. 5. Univ Rennes, CHU Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, F-35000, Rennes, France. 6. Department of Cardiothoracic and Vascular Surgery, CHU Rennes, 35033, Rennes, France.
Abstract
PURPOSE: Endovascular revascularization is becoming the established first-line treatment of peripheral artery disease (PAD). Ultrasound (US) imaging is used pre-operatively to make the first diagnosis and is often followed by a CT angiography (CTA). US provides a non-invasive and non-ionizing method for the visualization of arteries and lesion(s). This paper proposes to generate a 3D stretched reconstruction of the femoral artery from a sequence of 2D US B-mode frames. METHODS: The proposed method is solely image-based. A Mask-RCNN is used to segment the femoral artery on the 2D US frames. In-plane registration is achieved by aligning the artery segmentation masks. Subsequently, a convolutional neural network (CNN) predicts the out-of-plane translation. After processing all input frames and re-sampling the volume according to the vessel's centerline, the whole femoral artery can be visualized on a single slice of the resulting stretched view. RESULTS: 111 tracked US sequences of the left or right femoral arteries have been acquired on 18 healthy volunteers. fivefold cross-validation was used to validate our method and achieve an absolute mean error of 0.28 ± 0.28 mm and a median drift error of 8.98%. CONCLUSION: This study demonstrates the feasibility of freehand US stretched reconstruction following a deep learning strategy for imaging the femoral artery. Stretched views are generated and can give rich diagnosis information in the pre-operative planning of PAD procedures. This visualization could replace traditional 3D imaging in the pre-operative planning process, and during the pre-operative diagnosis phase, to identify, locate, and size stenosis/thrombosis lesions.
PURPOSE: Endovascular revascularization is becoming the established first-line treatment of peripheral artery disease (PAD). Ultrasound (US) imaging is used pre-operatively to make the first diagnosis and is often followed by a CT angiography (CTA). US provides a non-invasive and non-ionizing method for the visualization of arteries and lesion(s). This paper proposes to generate a 3D stretched reconstruction of the femoral artery from a sequence of 2D US B-mode frames. METHODS: The proposed method is solely image-based. A Mask-RCNN is used to segment the femoral artery on the 2D US frames. In-plane registration is achieved by aligning the artery segmentation masks. Subsequently, a convolutional neural network (CNN) predicts the out-of-plane translation. After processing all input frames and re-sampling the volume according to the vessel's centerline, the whole femoral artery can be visualized on a single slice of the resulting stretched view. RESULTS: 111 tracked US sequences of the left or right femoral arteries have been acquired on 18 healthy volunteers. fivefold cross-validation was used to validate our method and achieve an absolute mean error of 0.28 ± 0.28 mm and a median drift error of 8.98%. CONCLUSION: This study demonstrates the feasibility of freehand US stretched reconstruction following a deep learning strategy for imaging the femoral artery. Stretched views are generated and can give rich diagnosis information in the pre-operative planning of PAD procedures. This visualization could replace traditional 3D imaging in the pre-operative planning process, and during the pre-operative diagnosis phase, to identify, locate, and size stenosis/thrombosis lesions.
Authors: Raphael Prevost; Mehrdad Salehi; Simon Jagoda; Navneet Kumar; Julian Sprung; Alexander Ladikos; Robert Bauer; Oliver Zettinig; Wolfgang Wein Journal: Med Image Anal Date: 2018-06-15 Impact factor: 8.545
Authors: Alexander H Zielinski; Kim Kargaard Bredahl; Qasam Ghulam; Laurence Rouet; Cecile Dufour; Henrik Hegaard Sillesen; Jonas Peter Eiberg Journal: Ultrasound Med Biol Date: 2020-09-28 Impact factor: 2.998