Methods of producing three-dimensional arterial morphological geometries using duplex-ultrasound brightness-mode images
AssigneeThe Trustees of Indiana University
InventorsHuidan Yu, Alan P. Sawchuk, David M. Rollins
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Abstract
Methods of constructing three-dimensional arterial morphological geometries from two-dimensional images produced by duplex-ultrasound brightness-mode processes. Ultrasound cine looping is used in order to produce multiple images from multiple directions, including longitudinal and transverse directions of an artery, and three-dimensional geometries are produced from the aforementioned images by an image-based deep-learning model.
Claims17
A method of acquiring duplex-ultrasound (DUS) cine loop images and producing three-dimensional arterial morphological geometries from duplex-ultrasound brightness-mode images extracted from the duplex-ultrasound cine loop images, the method comprising:
- acquiring two-dimensional brightness-mode images of arterial systems along both longitudinal (coronal and sagittal) and transverse (axial) directions of an artery via ultrasound cine looping;
- and
- constructing three-dimensional arterial morphological geometries from the two-dimensional brightness-mode images via an image-based deep-learning technique;
- wherein the three-dimensional arterial morphological geometries are sufficiently detailed for use in image-based computational hemodynamics applications.
A method of any of the preceding claims, wherein the method is performed by a system integrated into an ultrasound machine, a three-dimensional printer, and/or a computational solver.
A system for producing three-dimensional arterial morphological geometries from duplex-ultrasound brightness-mode images, the system comprising:
- means for acquiring two-dimensional brightness-mode images of arterial systems along both longitudinal and transverse directions of an artery via ultrasound cine looping;
- and
- means for constructing three-dimensional arterial morphological geometries from the two-dimensional brightness-mode images via an image-based deep-learning model;
- wherein the three-dimensional arterial morphological geometries are sufficiently detailed for use in image-based computational hemodynamics applications.
Dependencies are inferred from the claim text.