TY - GEN
T1 - Compressive Sensing Based Algorithms for Limited-View PAT Image Reconstruction
AU - John, Mary Josy
AU - Barhumi, Imad
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Compressive Sensing
KW - Limited view
KW - Photoacoustic Tomography
KW - Split Bregman
UR - http://www.scopus.com/inward/record.url?scp=85180014913&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85180014913&partnerID=8YFLogxK
U2 - 10.1109/APSIPAASC58517.2023.10317590
DO - 10.1109/APSIPAASC58517.2023.10317590
M3 - Conference contribution
AN - SCOPUS:85180014913
T3 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
SP - 1317
EP - 1322
BT - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2023
Y2 - 31 October 2023 through 3 November 2023
ER -