Motion-compensated cone beam computed tomography using a conjugate gradient least-squares algorithm and electrical impedance tomography imaging motion data

Abstract

Cone beam computed tomography (CBCT) is an imaging modality that has been used in image-guided radiation therapy (IGRT). For applications such as lung radiation therapy, CBCT images are greatly affected by the motion artefacts. This is mainly due to low temporal resolution of CBCT. Recently, a dual modality of electrical impedance tomography (EIT) and CBCT has been proposed, in which the high temporal resolution EIT imaging system provides motion data to a motion-compensated algebraic reconstruction technique (ART)-based CBCT reconstruction software. High computational time associated with ART and indeed other variations of ART make it less practical for real applications. This paper develops a motion-compensated conjugate gradient least-squares (CGLS) algorithm for CBCT. A motion-compensated CGLS offers several advantages over ART-based methods, including possibilities for explicit regularization, rapid convergence and parallel computations. This paper for the first time demonstrates motion-compensated CBCT reconstruction using CGLS and reconstruction results are shown in limited data CBCT considering only a quarter of the full dataset. The proposed algorithm is tested using simulated motion data in generic motion-compensated CBCT as well as measured EIT data in dual EIT–CBCT imaging.

Posterior Approximate Clustering-Based Sensitivity Matrix Decomposition for Electrical Impedance Tomography

“…Given this constraint, the solution is extremely sensitive to small perturbations caused by measurement noise and modeling errors, resulting in inherently low spatial resolution and instability in the reconstructed images [ 11 ]. In order to address this problem, many traditional imaging algorithms have been developed, including direct sensitivity coefficient method [ 12 ], Landweber-type algorithms [ 13 , 14 ], gradient algorithms [ 15 , 16 ], Newton algorithms [ 17 , 18 ], regularization algorithms [ 19 , 20 , 21 ], etc. In recent years, owing to the outstanding ability to solve nonlinear problems, deep learning has received widespread attention from academics [ 22 , 23 , 24 ].…”

On Krylov methods for large-scale CBCT reconstruction

“…This class of methods was first introduced in the 1950s (Hestenes and Stiefel 1952), but it is recently getting very popular for solving inverse inverse problems Sabaté Landman 2020, Chung and. Conjugate gradient least squares (CGLS) is the most commonly used Krylov method in applied x-ray CBCT, see, for example (Dabravolski et al 2014, Pengpen andSoleimani 2015, Lohvithee et al 2021). Moreover, it is sometimes also found in combination with Tikhonov regularization, or within more complex minimization schemes tackling different variational regularizers (Kazantsev et al 2015).…”