Tight‐frame based iterative image reconstruction for spectral breast CT

Abstract

Purpose: To investigate tight-frame based iterative reconstruction (TFIR) technique for spectral breast computed tomography (CT) using fewer projections while achieving greater image quality. Methods: The experimental data were acquired with a fan-beam breast CT system based on a cadmium zinc telluride photon-counting detector. The images were reconstructed with a varying number of projections using the TFIR and filtered backprojection (FBP) techniques. The image quality between these two techniques was evaluated. The image's spatial resolution was evaluated using a high-resolution phantom, and the contrast to noise ratio (CNR) was evaluated using a postmortem breast sample. The postmortem breast samples were decomposed into water, lipid, and protein contents based on images reconstructed from TFIR with 204 projections and FBP with 614 projections. The volumetric fractions of water, lipid, and protein from the image-based measurements in both TFIR and FBP were compared to the chemical analysis. Results:The spatial resolution and CNR were comparable for the images reconstructed by TFIR with 204 projections and FBP with 614 projections. Both reconstruction techniques provided accurate quantification of water, lipid, and protein composition of the breast tissue when compared with data from the reference standard chemical analysis. Conclusions: Accurate breast tissue decomposition can be done with three fold fewer projection images by the TFIR technique without any reduction in image spatial resolution and CNR. This can result in a two-third reduction of the patient dose in a multislit and multislice spiral CT system in addition to the reduced scanning time in this system.

Breast tissue decomposition with spectral distortion correction: A postmortem study

“…Finally, the petroleum ether was evaporated using heat and vacuum distillation to isolate the lipid content and determine the lipid mass. 60 Skin was also included in the chemical analysis, so that the total volume under investigation was consistent with the image-based measurement. The measured masses of water, lipid, and protein content were converted into volumes through the known densities, respectively.…”

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]. …”

“…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].…”

Tensor-Based Dictionary Learning for Spectral CT Reconstruction

“…In 2013, Zhao et al . [22] developed a tight-frame based iterative reconstruction method for spectral breast CT. All the aforementioned spectral CT reconstruction methods only use the sparsity in the spatial domain. On the other hand, since the projection datasets in different channels are collected from the same object, the resultant images are highly correlated.…”