Region-Based Iterative Reconstruction of Structurally Changing Objects in CT

Abstract

Abstract-X-ray computed tomography (CT) is a powerful tool for noninvasive imaging of time-varying objects. In the past, methods have been proposed to reconstruct images from continuously changing objects. For discretely or structurally changing objects, however, such methods fail to reconstruct high quality images, mainly because assumptions about continuity are no longer valid. In this paper, we propose a method to reconstruct structurally changing objects. Starting from the observation that there exist regions within the scanned object that remain unchanged over time, we introduce an iterative optimization routine that can automatically determine these regions and incorporate this knowledge in an algebraic reconstruction method. The proposed algorithm was validated on simulation data and experimental μCT data, illustrating its capability to reconstruct structurally changing objects more accurately in comparison to current techniques.

Region based 4D tomographic image reconstruction: Application to cardiac x-ray CT

“…As a validation measure, the relative root mean squared error (RRMSE) is utilized. The rSIRT-4D reconstruction is compared to the standard SIRT and FBP reconstruction (calculated independently for each phase bin), to the rSIRT reconstruction [16] and to the SIRT reconstruction where at each n filt iterations the BM4D filter is applied to the entire reconstruction domain. The latter is referred to as SIRT-BM4D.…”

“…The dynamic region, for which a mask is needed in rSIRT and rSIRT-4D, was indicated manually. For automatic detection methods the reader is referred to [16].…”

“…From the flowchart in Fig. 1 and code line 5 in Algorithm 1, one can notice that the inner most loop calculates two updates: A traditional SIRT update for the stationary region using all available projection data p and an update for the dynamic region using only the projection data corresponding to the relevant phase bin [16]. Since the update for the stationary region is based on all available projection data p, image quality in the stationary region greatly improves.…”

An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging

“…A more statistical approach, i.e., a maximum a posteriori probability (MAP) estimation method, was presented in [17]. Other approaches do not rely on a prior high quality image, such as the spatialtemporal regularization approach based on non-local means proposed by Kazantsev et al [18], [19] and the region-based SIRT (rSIRT) method, which assumes only prior knowledge about the location of stationary voxels and dynamic voxels [20].…”

“…The proposed algorithm exploits two types of prior knowledge. Firstly, following the approach of [20], a dynamic reconstruction is generated assuming the presence of stationary regions (the solid matter) and dynamic regions (the fluid flow) throughout the reconstruction domain. Secondly, corresponding to the actual physical advancing fluid/air boundary, the attenuation of a particular voxel over time in the dynamic region can typically be described by a piecewise constant (PWC) function.…”