Compressive computed tomography image reconstruction with denoising message passing algorithms

TY - GEN

T1 - Compressive computed tomography image reconstruction with denoising message passing algorithms

AU - Perelli, Alessandro

AU - Davies, Mike E.

PY - 2015/9/4

Y1 - 2015/9/4

N2 - In this paper we address the compressive reconstruction of images from a limited number of projections in order to reduce the X-ray radiation dose in Computed Tomography (CT) while achieving high diagnostic performances. Our objective is to study the feasibility of applying message passing Compressive Sensing (CS) imaging algorithms to CT image reconstruction extending the algorithm from its theoretical domain of i.i.d. random matrices. Exploiting the intuition described in [1] of employing a generic denoiser in a CS reconstruction algorithm, we propose a denoising-based Turbo CS algorithm (D-Turbo) and we extend the application of the de-noising approximate message passing (D-AMP) algorithm to partial Radon Projection data with a Gaussian approximation of the Poisson noise model. The proposed CS message passing approaches have been tested on simulated CT data using the BM3D denoiser [2] yielding an improvement in the reconstruction quality compared to existing direct and iterative methods. The promising results show the effectiveness of the idea to employ a generic denoiser Turbo CS or message passing algorithm for reduced number of views CT reconstruction.

AB - In this paper we address the compressive reconstruction of images from a limited number of projections in order to reduce the X-ray radiation dose in Computed Tomography (CT) while achieving high diagnostic performances. Our objective is to study the feasibility of applying message passing Compressive Sensing (CS) imaging algorithms to CT image reconstruction extending the algorithm from its theoretical domain of i.i.d. random matrices. Exploiting the intuition described in [1] of employing a generic denoiser in a CS reconstruction algorithm, we propose a denoising-based Turbo CS algorithm (D-Turbo) and we extend the application of the de-noising approximate message passing (D-AMP) algorithm to partial Radon Projection data with a Gaussian approximation of the Poisson noise model. The proposed CS message passing approaches have been tested on simulated CT data using the BM3D denoiser [2] yielding an improvement in the reconstruction quality compared to existing direct and iterative methods. The promising results show the effectiveness of the idea to employ a generic denoiser Turbo CS or message passing algorithm for reduced number of views CT reconstruction.

KW - Computed tomography

KW - Signal processing algorithms

KW - Radon

KW - Message passing

KW - Image reconstruction

KW - X-ray imaging

KW - Approximation algorithms

UR - https://ieeexplore.ieee.org/document/7362896/

U2 - 10.1109/EUSIPCO.2015.7362896

DO - 10.1109/EUSIPCO.2015.7362896

M3 - Conference contribution

SN - 978-0-9928-6263-3

SP - 2806

EP - 2810

BT - 2015 23rd European Signal Processing Conference (EUSIPCO)

PB - IEEE

T2 - 2015 23rd European Signal Processing Conference (EUSIPCO)

Y2 - 31 August 2015 through 4 September 2015

ER -