Iterative Reconstruction for X-Ray Computed Tomography Using Prior-Image Induced Nonlocal Regularization

Abstract

Repeated x-ray computed tomography (CT) scans are often required in several specific applications such as perfusion imaging, image-guided biopsy needle, image-guided intervention, and radiotherapy with noticeable benefits. However, the associated cumulative radiation dose significantly increases as comparison with that used in the conventional CT scan, which has raised major concerns in patients. In this study, to realize radiation dose reduction by reducing the x-ray tube current and exposure time (mAs) in repeated CT scans, we propose a prior-image induced nonlocal (PINL) regularization for statistical iterative reconstruction via the penalized weighted least-squares (PWLS) criteria, which we refer to as “PWLS-PINL”. Specifically, the PINL regularization utilizes the redundant information in the prior image and the weighted least-squares term considers a data-dependent variance estimation, aiming to improve current low-dose image quality. Subsequently, a modified iterative successive over-relaxation algorithm is adopted to optimize the associative objective function. Experimental results on both phantom and patient data show that the present PWLS-PINL method can achieve promising gains over the other existing methods in terms of the noise reduction, low-contrast object detection and edge detail preservation.

Extracting Information From Previous Full-Dose CT Scan for Knowledge-Based Bayesian Reconstruction of Current Low-Dose CT Images

“…While utilizing previous full-dose image (or high-quality image) to improve the quality of follow-up LdCT images has become a research endeavor [9–16, 28], one major limitation therein is that the previous full-dose image usually needs to be registered to the current low-dose images. In practice, severe patient motion, tissue or organ deformation, fluid flow, etc.…”

“…More recently, noticeable research efforts have been devoted to take advantage of previously-available FdCT scan, in addition to the above assumption, for the purpose of improving the piece-wise smooth image reconstruction of low-dose CT (LdCT) images [9–16]. For instance, Nett et al [9] incorporated a registered FdCT image into their prior image constrained compressed sensing (PICCS) cost function [17] for iterative reconstruction of subsequent LdCT images.…”

“…Stayman et al [14, 15] presented a PICCS-type penalty term, but the high-quality prior image was formulated into a joint estimation framework for both image registration and image reconstruction in order to better capturing the anatomical motion among different scans. Moreover, Ma et al [11, 16] proposed previous FdCT image induced nonlocal means penalties to improve the following LdCT image reconstruction for perfusion and interventional imaging, wherein the previous FdCT image was also pre-registered with the LdCT scans. These efforts share the common idea of registering the FdCT image structure with the LdCT image to ensure piece-wise regional smoothness and edge-sharpness image reconstructions.…”

Patch-based models and algorithms for image processing: a review of the basic principles and methods, and their application in computed tomography

“…The patch similarity weights were computed between patches of the reconstructed image (from a low-dose scan) and those of the prior image. A similar approach was suggested in [89]. A more general algorithm that computes the patch similarity weights from the reconstructed image itself was proposed in [90].…”

Direct Reconstruction of CT-Based Attenuation Correction Images for PET With Cluster-Based Penalties

“…It is necessary to use noise suppression techniques to prevent the deterioration of signal-to-noise ratio and the introduction of bias and artifacts in CT images from low-dose acquisitions [18–20]. With decreasing tube current, some groups investigated the effects of denoising techniques in the sinogram [21–25] or image domains [26–28], or statistical image reconstruction techniques with more accurate noise models of the detection process [29–31] or different prior/regularization functions [32, 33]. Additional dose reduction can be achieved with sparse-view acquisition and compressed sensing recovery techniques based on dictionary learning or total variation in the CT images [34–37].…”