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

Refik Mert Cam, Umberto Villa, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationPhysics of Medical Imaging
EditorsWei Zhao, Lifeng Yu
PublisherSPIE
ISBN (Electronic)9781510649378
DOIs
StatePublished - 2022
Externally publishedYes
EventMedical Imaging 2022: Physics of Medical Imaging - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12031
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Physics of Medical Imaging
CityVirtual, Online
Period3/21/223/27/22

Keywords

  • circular Radon transform
  • deep learning
  • image reconstruction

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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