A high-resolution photon-counting breast CT system with tensor-framelet based iterative image reconstruction for radiation dose reduction

Abstract

Both computer simulations and experimental phantom studies were carried out to investigate the radiation dose reduction with tensor framelet based iterative image reconstruction (TFIR) for a dedicated high-resolution spectral breast computed tomography (CT) based on a silicon strip photon-counting detector. The simulation was performed with a 10 cm-diameter water phantom including three contrast materials (polyethylene, 8 mg/ml iodine and B-100 bone-equivalent plastic). In the experimental study, the data were acquired with a 1.3 cm-diameter polymethylmethacrylate (PMMA) phantom containing iodine in three concentrations (8, 16 and 32 mg/ml) at various radiation doses (1.2, 2.4 and 3.6 mGy) and then CT images were reconstructed using filtered-back-projection (FBP) technique and TFIR technique, respectively. The image quality between these two techniques was evaluated by the quantitative analysis on contrast-to-noise ratio (CNR) and spatial resolution that was evaluated using the tasked-based modulation transfer function (MTF). Both simulation and experimental results indicated that the task-based MTF obtained from TFIR reconstruction with one-third of the radiation dose was comparable to that from FBP reconstruction for low contrast target. For high contrast target, TFIR was substantially superior to FBP reconstruction in term of spatial resolution. In addition, TFIR was able to achieve a factor of 1.6 to 1.8 increase in CNR depending on the target contrast level. This study demonstrates that TFIR can reduce the required radiation dose by a factor of two-third for a CT image reconstruction compared to FBP technique. It achieves much better CNR and spatial resolution for high contrast target in addition to retaining similar spatial resolution for low contrast target. This TFIR technique has been implemented with a graphic processing unit (GPU) system and it takes approximately 10 seconds to reconstruct a single-slice CT image, which can be potentially used in a future multi-slit multi-slice (MSMS) spiral CT system.

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

“…In the result section, we compare TICMR with FBP and the following TV based material reconstruction $$Z^{*}=\text{arg }\underset{Z}{\text{min}}\frac{1}{2}{false\Vert AZB-Pfalse\Vert }_W^2+\lambda {false|\nabla Zfalse|}_1.$$ $$\mathrm{normals}.\mathrm{normalt}.ZC=D,L\le Z\le U.$$ Note that in terms of the regularization in (17), the alternative strategies can be used, such as tensor framelet transform (as a natural high-order generalization of isotropic TV) [3], [11], [29]–[31], and low-rank models [7], [10], [15], [32], [33]. …”

“…Spectral CT based on energy-resolved photon-counting detector (PCD) has been recently introduced for medical imaging, such as spectral breast CT [1]–[3] and k-edge imaging [4], [5]. Conventional CT imaging scans at a fixed tube voltage and reconstructs a single image, which represents the effective x-ray attenuation coefficient of the object for the input spectrum.…”

“…It can be determined in a two-step procedure, i.e., image reconstruction for spectral images and then material decomposition from these spectral images to material compositions [3], [6]–[16], or alternatively material-specific sinogram decomposition and then material reconstruction [4], [17]–[19]. Various iterative reconstruction models have been developed [20], with energy-by-energy reconstruction [3], [4], [9], [11], [17]–[19] and joint reconstruction [7], [10], [15], [16], such as total variation (TV) sparsity [14], [16], HYPR algorithm [8], tight frame sparsity [3], [11], bilateral filtration [12], [13], patch-based low-rank model [15], rank-and-sparsity decomposition model [7] and its tensor version [10]. In order to fully utilize the image similarity in the spectral dimension, the joint reconstruction is a natural formulation [7], [10], [15], [16].…”

Multi-contrast brain magnetic resonance image super-resolution using the local weight similarity

“…High-resolution (HR) images appear favorable to perform subsequent posterior image processing and visualization [5]. Super-resolution methods are widely utilized to improve image resolution [6–10]. Typical methods include sparse representations [6–8], projection onto convex sets (POCS) [9], tensor frames [10], etc.…”

“…Super-resolution methods are widely utilized to improve image resolution [6–10]. Typical methods include sparse representations [6–8], projection onto convex sets (POCS) [9], tensor frames [10], etc. However, these methods need numerous iterations to accomplish super-resolution, thus they inevitably lead to high computational costs.…”