Multi-energy computed tomography reconstruction using a nonlocal spectral similarity model

Abstract

Multi-energy computed tomography (MECT) is able to acquire simultaneous multi-energy measurements from one scan. In addition, it allows material differentiation and quantification effectively. However, due to the limited energy bin width, the number of photons detected in an energy-specific channel is smaller than that in traditional CT, which results in image quality degradation. To address this issue, in this work, we develop a statistical iterative reconstruction algorithm to acquire high-quality MECT images and high-accuracy material-specific images. Specifically, this algorithm fully incorporates redundant self-similarities within nonlocal regions in the MECT image at one bin and rich spectral similarities among MECT images at all bins. For simplicity, the presented algorithm is referred to as ‘MECT-NSS’. Moreover, an efficient optimization algorithm is developed to solve the MECT-NSS objective function. Then, a comprehensive evaluation of parameter selection for the MECT-NSS algorithm is conducted. In the experiment, the datasets include images from three phantoms and one patient to validate and evaluate the MECT-NSS reconstruction performance. The qualitative and quantitative results demonstrate that the presented MECT-NSS can successfully yield better MECT image quality and more accurate material estimation than the competing algorithms.

Contrast-Medium Anisotropy-Aware Tensor Total Variation Model for Robust Cerebral Perfusion CT Reconstruction With Low-Dose Scans

“…The proposed CMAA-TTV model and CMAA-TTV algorithm can be used in other medical imaging tasks such as dynamic myocardial perfusion CT imaging [52], spectral CT imaging [53], dynamic positron emission tomography (PET) imaging [54], and dynamic magnetic resonance (MR) imaging [55]. This will be another research focus in our future work.…”

Artificial intelligence in image reconstruction: The change is here

“…Considering sparsity and low-rank properties in the spatialspectral domain, multiple dedicated algorithms were developed using tensor-based nuclear norm [148], prior rank, intensity and sparsity model [149,150], total nuclear variation [151], patch-based low-rank [152], structure tensor TV [153], and tensor dictionary learning [154,155]. To further improve the reconstructed image quality, the nonlocal image similarity was explored in spectral CT reconstruction, including nonlocal low-rank and sparse matrix decomposition [156], spatial-spectral non-local means [157], nonlocal spectral similarity [158], spatial-spectral cube matching frame (SSCMF) [159], non-local low-rank cube-based tensor factorization (NLCTF) [160], and so on. All these methods were designed for two-dimensional cases.…”

Refined Locally Linear Transform-Based Spectral-Domain Gradient Sparsity and Its Applications in Spectral CT Reconstruction

“…The spatial feature can be ascribed to a sparsity in the spatial domain itself [ 11 ], an appropriate transform domain [ 12 ], [ 13 ], or a high-dimensional space [ 14 ]–[ 16 ]. The spectral feature is a correlation among channel images, more specifically, a structural similarity [ 17 ]–[ 20 ]. Most existing methods for spectral CT employ different measurements to directly describe both or either the aforementioned features.…”