TICMR: Total Image Constrained Material Reconstruction via Nonlocal Total Variation Regularization for Spectral CT

Abstract

This work develops a material reconstruction method for spectral CT, namely Total Image Constrained Material Reconstruction (TICMR), to maximize the utility of projection data in terms of both spectral information and high signal-to-noise ratio (SNR). This is motivated by the following fact: when viewed as a spectrally-integrated measurement, the projection data can be used to reconstruct a total image without spectral information, which however has a relatively high SNR; when viewed as a spectrally-resolved measurement, the projection data can be utilized to reconstruct the material composition, which however has a relatively low SNR. The material reconstruction synergizes material decomposition and image reconstruction, i.e., the direct reconstruction of material compositions instead of a two-step procedure that first reconstructs images and then decomposes images. For material reconstruction with high SNR, we propose TICMR with nonlocal total variation (NLTV) regularization. That is, first we reconstruct a total image using spectrally-integrated measurement without spectral binning, and build the NLTV weights from this image that characterize nonlocal image features; then the NLTV weights are incorporated into a NLTV-based iterative material reconstruction scheme using spectrally-binned projection data, so that these weights serve as a high-SNR reference to regularize material reconstruction. Note that the nonlocal property of NLTV is essential for material reconstruction, since material compositions may have significant local intensity variations although their structural information is often similar. In terms of solution algorithm, TICMR is formulated as an iterative reconstruction method with the NLTV regularization, in which the nonlocal divergence is utilized based on the adjoint relationship. The alternating direction method of multipliers is developed to solve this sparsity optimization problem. The proposed TICMR method was validated using both simulated and experimental data. In comparison with FBP and total-variation-based iterative method, TICMR had improved image quality, e.g., contrast-to-noise ratio and spatial resolution.

Image‐domain multimaterial decomposition for dual‐energy CT based on prior information of material images

“…A natural idea is to use spectral CT that acquires multienergy measurements to achieve multiple basis material images. However, spectral CT requires either multiple scans which result in high radiation to patients and need complex processing (e.g., registration) of CT images at different energies, or specialized scanners which are expensive and not available clinically yet, such as energy‐sensitive photon‐counting detectors …”

A Spectral CT Method to Directly Estimate Basis Material Maps From Experimental Photon-Counting Data

“…Because of the complex factors affecting photon-counting detection, experimental studies are required to effectively evaluate reconstruction algorithms. Several iterative spectral CT reconstruction approaches have been recently investigated on experimental data, for example demonstrating the potential to reduce noise [8], [21], perform material decomposition [22], and improve contrast-to-noise ratio [23]. To our knowledge, this paper presents the first experimental implementation of a spectral CT iterative algorithm that both models the nonlinear polyenergetic X-ray transmission while enforcing convex constraints on the basis maps.…”

Iterative reconstruction for dual energy CT with an average image-induced nonlocal means regularization

“…Up to now, many considerable efforts to suppress noise-induced artifacts in DECT images and material-decomposed images have been reported (Rutherford et al 1976, Kalender et al 1988, Warp et al 2003, Leng et al 2011, Zeng et al 2016a, Niu et al 2014, Clark et al 2014, Dong et al 2014, Sukovic et al 2000, Petrongolo et al 2015, Zhang et al 2014, Long et al 2014, Zhang et al 2016a, Zhang et al 2016b, Szczykutowicz et al 2011, Liu et al 2016). Among them, projection or image domain denoising approaches were proposed to improve low-dose DECT images quality (Rutherford et al 1976, Kalender et al 1988, Warp et al 2003, Leng et al 2011, Zeng et al 2016a, Niu et al 2014, Clark et al 2014).…”

“…Although these approaches can suppress the noise to some extent, they often result in spatial resolution loss because the noise in DECT images does not obey a uniform distribution. By better modeling the projection data and the image geometry in the DECT imaging, statistical iterative reconstruction (SIR) algorithms have shown to be more robust than FBP algorithm in regard to the presence of noise-induced artifacts (Dong et al 2014, Sukovic et al 2000, Petrongolo et al 2015, Zhang et al 2014, Long et al 2014, Zhang et al 2016a, Szczykutowicz et al 2011, Liu et al 2016). Based on the maximum a posterior (MAP) estimation criteria, the SIR algorithms can be mathematically formulated with a cost function.…”