Iterative reconstruction for photon-counting CT using prior image constrained total generalized variation

Abstract

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Photon-counting x-ray detectors for CT

“…Three of these, including one mobile head CT system, utilize cadmium-based sensors (Yu et al 2016c, Si-Mohamed et al 2017a, Han-soo 2017, and one uses silicon-based sensors (da Silva et al 2019). In particular, one cadmium-based system has generated a large number of publications (Yu et al 2016a, 2016c, Symons et al 2018a, 2018b. Furthermore, a CdTe-based photon-counting system limited to breast CT (Kalender et al 2017) has been evaluated on patients.…”

“…Typical photopeak widths reported for detectors designed for high-flux x-ray imaging are 3.5-5.4 keV full width at half maximum (FWHM) for Si (Xu et al 2013a) and 5-10 keV FWHM for CdTe/CZT (Iwanczyk et al 2009, Brambilla et al 2013, Barber et al 2015. Spectral response models incorporating physical effects and electronic properties have been published for silicon (Liu et al 2015a(Liu et al , 2015b and CdTe/CZT (Schlomka et al 2008, Cammin et al 2014, 2018b.…”

“…Other, more sophisticated regularization methods that exploit redundancy between the bin images have also been studied, including total nuclear variation (Rigie and La Rivière 2015), total generalized variation (Niu et al 2018b), and dictionary learning (Zhang et al 2017). Denoising based on non-local means filtering where the broad-spectrum image is used to weight pixels according to similarity was studied by Li et al (2017) and Zhang et al (2016 combined non-local-means-like regularization in the spectral dimension with SPICCS.…”

Partial Differential Equations-Based Iterative Denoising Algorithm for Movie Images

“…The above noise level is usually expressed in terms of signal-to-noise ratio (SNR), and if the SNR is below a certain level, the noise gradually becomes visible in the shape of particles and even obscures the detailed information of the image in high-speed video, resulting in the degradation of image quality, and increases the entropy of the image, thus hindering the effective compression of the video, and the more typical noise is white noise and impulse noise, etc. [4].…”

Improved Material Decomposition With a Two-Step Regularization for Spectral CT

“…Here , many other algorithms were developed, including the patch-based low-rank reconstruction [27], tensor dictionary learning (TDL) [28] and its modified version (L 0 TDL) [29], spatial-spectral cube matching frame (SSCMF) [30], nonlocal low-rank cube tensor factorization (NLCTF) [31] and aided by self-similarity in image-spectral tensors (ASSIST) [32]. To further improve the accuracy of material images, it is natural to incorporate high quality prior into the reconstruction model, such as the spectral prior image constraint compressed sensing (SPICCS) [33], the averageimage-incorporated block-matching and 3D (aiiBM3D) filtering [34], nonlocal spectral similarity model [35], prior image constrained total generalized variation [36], the total image constrained diffusion tensor (TICDT) [37].…”