A learned filtered backprojection method for use with half-time circular radon transform data

Refik Mert Cam; Umberto Villa; Mark A. Anastasio

doi:10.1117/12.2612941

4 April 2022 A learned filtered backprojection method for use with half-time circular radon transform data

Refik Mert Cam, Umberto Villa, Mark A. Anastasio

Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 1203134 (2022) https://doi.org/10.1117/12.2612941
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

The circular Radon transform (CRT) is widely employed as an imaging model for wave-based tomographic bioimaging modalities like ultrasound reflectivity tomography. A complete set of CRT data function is known to have redundancies. However, no explicit non-iterative image reconstruction method is known for inverting temporally-truncated data. To address this, a learning-based approach is proposed to establish a filtered backprojection (FBP) method for use with the half-time CRT data function. The proposed method approximates a mapping that is known to exist in theory; therefore, it is fundamentally different than many deep-learning based reconstruction methods that seek to establish a non-existent mapping. Thus, the proposed method performs well on unforeseen data. The learned half-time FBP achieves image quality comparable to a conventional full-time FBP method although it uses half of the complete data.

Conference Presentation

Citation Download Citation

Refik Mert Cam, Umberto Villa, and Mark A. Anastasio "A learned filtered backprojection method for use with half-time circular radon transform data", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 1203134 (4 April 2022); https://doi.org/10.1117/12.2612941

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