Iterative 4D cardiac micro-CT image reconstruction using an adaptive spatio-temporal sparsity prior

Abstract

Temporal-correlated image reconstruction, also known as 4D CT image reconstruction, is a big challenge in computed tomography. The reasons for incorporating the temporal domain into the reconstruction are motions of the scanned object, which would otherwise lead to motion artifacts. The standard method for 4D CT image reconstruction is extracting single motion phases and reconstructing them separately. These reconstructions can suffer from undersampling artifacts due to the low number of used projections in each phase. There are different iterative methods which try to incorporate some a priori knowledge to compensate for these artifacts. In this paper we want to follow this strategy. The cost function we use is a higher dimensional cost function which accounts for the sparseness of the measured signal in the spatial and temporal directions. This leads to the definition of a higher dimensional total variation. The method is validated using in vivo cardiac micro-CT mouse data. Additionally, we compare the results to phase-correlated reconstructions using the FDK algorithm and a total variation constrained reconstruction, where the total variation term is only defined in the spatial domain. The reconstructed datasets show strong improvements in terms of artifact reduction and low-contrast resolution compared to other methods. Thereby the temporal resolution of the reconstructed signal is not affected.

Constrained <formula formulatype="inline"><tex Notation="TeX">${\rm T}p{\rm V}$</tex> </formula> Minimization for Enhanced Exploitation of Gradient Sparsity: Application to CT Image Reconstruction

“…Several theoretical investigations have demonstrated accurate CT image reconstruction from reduced data sampling employing various convex optimization problems involving total variation (TV) minimization [1]–[6]. Many of these algorithms have been adapted to use on actual scanner data for sparse-view CT [7]–[12] or gated/dynamic CT [7], [13]–[17]. While the volume of work on this topic speaks to the success of the idea of exploiting GMI sparsity, TV minimization is not the most direct method for taking advantage of this prior.…”

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

“…A standard approach consists of minimizing the total variation in the spatial and/or temporal domain [8,9,10] or with respect to an a priori high quality reconstruction [11]. Other examples include the incorporation of a nonlocal means filter in the reconstruction algorithm [12].…”

“…This is a realistic assumption for breath hold scanning protocols or in small animal imaging, where only the heart motion is relevant and the lung associated motion can often be neglected. This assumption leads to accurate reconstruction quality in the stationary region, without having to make any assumptions about its sparsity like the methods proposed in [8,9,11,12]. Secondly, the dynamic region is assumed to have sparse structures after a proper sparsifying space-time transformation.…”

Sinogram smoothing techniques for myocardial blood flow estimation from dose-reduced dynamic computed tomography

“…Sawall et al 10,11 used a variant of the McKinnon–Bates image reconstruction algorithm along with bilateral filtering in multiple dimensions (three spatial, three temporal: cardiac, respiratory, and perfusion) to achieve low-dose phase-correlated imaging in small animals. Ritschl et al 12 used a method for spatial and temporal regularizations for temporal-correlated CT image reconstruction. Their method utilized a total variation constraint in both the spatial and temporal dimensions.…”