Compressive Sensing Based Algorithms for Limited-View PAT Image Reconstruction

Mary Josy John, Imad Barhumi

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

1 Citation (Scopus)

Abstract

Limited-view sensor arrangement is a major concern in medical imaging as it limits the data that the sensor could acquire. However, this limitation, signal sparsity, can be exploited using compressive sensing (CS) techniques to reconstruct high-resolution images. The objective of this research paper is to develop CS-based algorithms for reconstructing images in limited-view photoacoustic tomography. Various CS reconstruction algorithms and sensor arrangements were assessed to identify the optimal approach for reconstructing images from limited-view sensor data. The results show that the split Bregman total variation (SBTV)-l1 CS algorithm is the most efficient for all sensor arrangements. The study also reveals that the convex sensor array yields the best results among all sensor arrangements. Additionally, the implementation of SBTV-l1 using Cholesky factorization requires less computation time and is 10 to 15 times faster than the direct implementation.

Original languageEnglish
Title of host publication2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1317-1322
Number of pages6
ISBN (Electronic)9798350300673
DOIs
Publication statusPublished - 2023
Event2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023 - Taipei, Taiwan, Province of China
Duration: Oct 31 2023Nov 3 2023

Publication series

Name2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023

Conference

Conference2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Country/TerritoryTaiwan, Province of China
CityTaipei
Period10/31/2311/3/23

Keywords

  • Compressive Sensing
  • Limited view
  • Photoacoustic Tomography
  • Split Bregman

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Artificial Intelligence
  • Computer Science Applications

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