A geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction
Autor: | Liyue Shen, Wei Zhao, Dante Capaldi, John Pauly, Lei Xing |
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Rok vydání: | 2022 |
Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Health Informatics Electrical Engineering and Systems Science - Image and Video Processing Cone-Beam Computed Tomography Machine Learning (cs.LG) Computer Science Applications Deep Learning Imaging Three-Dimensional FOS: Electrical engineering electronic engineering information engineering Image Processing Computer-Assisted Algorithms |
Zdroj: | Computers in biology and medicine. 148 |
ISSN: | 1879-0534 |
Popis: | Deep learning affords enormous opportunities to augment the armamentarium of biomedical imaging, albeit its design and implementation have potential flaws. Fundamentally, most deep learning models are driven entirely by data without consideration of any prior knowledge, which dramatically increases the complexity of neural networks and limits the application scope and model generalizability. Here we establish a geometry-informed deep learning framework for ultra-sparse 3D tomographic image reconstruction. We introduce a novel mechanism for integrating geometric priors of the imaging system. We demonstrate that the seamless inclusion of known priors is essential to enhance the performance of 3D volumetric computed tomography imaging with ultra-sparse sampling. The study opens new avenues for data-driven biomedical imaging and promises to provide substantially improved imaging tools for various clinical imaging and image-guided interventions. |
Databáze: | OpenAIRE |
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