Model-based Three-dimensional X-ray Induced Acoustic Computerized Tomography

X-ray-induced acoustic (XA) computerized tomography (XACT) is an evolving imaging technique that aims to reconstruct the X-ray energy deposition from XA measurements. Main challenges in XACT are the poor signal-to-noise ratio and limited field-of-view, which cause artifacts in the images. We demonst...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on radiation and plasma medical sciences 2023-01, p.1-1
Main Authors: Pandey, Prabodh Kumar, Wang, Siqi, Sun, Leshan, Xing, Lei, Xiang, Liangzhong
Format: Article
Language:English
Subjects:
Acoustics
Biomedical imaging
Computational modeling
Image reconstruction
least-squares problem
model back-projection
model-based image reconstruction
regularization
Three-dimensional displays
Tomography
X-ray imaging
X-ray induced acoustic tomography (XACT)
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:X-ray-induced acoustic (XA) computerized tomography (XACT) is an evolving imaging technique that aims to reconstruct the X-ray energy deposition from XA measurements. Main challenges in XACT are the poor signal-to-noise ratio and limited field-of-view, which cause artifacts in the images. We demonstrate the efficacy of model-based (MB) algorithms for three-dimensional XACT and compare with the traditional algorithms. The MB algorithm is based on iterative, matrix-free approach for regularized-least-squares minimization corresponding to XACT. The matrix-free-LSQR (MF-LSQR) and the non-iterative model-backprojection (MBP) reconstructions were evaluated and compared with universal backprojection (UBP), time-reversal (TR) and fast-Fourier transform (FFT)-based reconstructions for numerical and experimental XACT datasets. The results demonstrate the capability of MF-LSQR algorithm to reduce noisy artifacts thus yielding better reconstructions. MBP and MF-LSQR algorithms perform particularly well with the experimental XACT dataset, where noise in signals significantly affects the reconstruction of the target in UBP and FFT-based reconstructions. The TR reconstruction for experimental XACT are comparable to MF-LSQR, but takes thrice as much time and filters the frequency components greater than maximum frequency supported by the grid, resulting loss of resolution. The MB algorithms are able to overcome the challenges in XACT and hence are vital for the clinical translation of XACT.
ISSN:2469-7311
DOI:10.1109/TRPMS.2023.3238017