Streaking Artifact

Abstract: Recently, compressed sensing (CS) computed tomography (CT) using sparse projection views has been extensively investigated to reduce the potential risk of radiation to patient. However, due to the insufficient number of projection views, an analytic reconstruction approach results in severe streaking artifacts and CS-based iterative approach is computationally very expensive. To address this issue, here we propose a novel deep residual learning approach for sparse view CT reconstruction. Specifically, based on a novel persistent homology analysis showing that the manifold of streaking artifacts is topologically simpler than original ones, a deep residual learning architecture that estimates the streaking artifacts is developed. Once a streaking artifact image is estimated, an artifact-free image can be obtained by subtracting the streaking artifacts from the input image. Using extensive experiments with real patient data set, we confirm that the proposed residual learning provides significantly better image reconstruction performance with several orders of magnitude faster computational speed.

Abstract: Quantitative susceptibility mapping (QSM) is a novel MRI technique for the measurement of tissue magnetic susceptibility in three dimensions. Although numerous algorithms have been developed to solve this ill-posed inverse problem, the estimation of susceptibility maps with a wide range of values is still problematic. In cases such as large veins, contrast agent uptake and intracranial hemorrhages, extreme susceptibility values in focal areas cause severe streaking artifacts. To enable the reduction of these artifacts, whilst preserving subtle susceptibility contrast, a two-level QSM reconstruction algorithm (streaking artifact reduction for QSM, STAR-QSM) was developed in this study by tuning a regularization parameter to automatically reconstruct both large and small susceptibility values. Compared with current state-of-the-art QSM methods, such as the improved sparse linear equation and least-squares (iLSQR) algorithm, STAR-QSM significantly reduced the streaking artifacts, whilst preserving the sharp boundaries for blood vessels of mouse brains in vivo and fine anatomical details of high-resolution mouse brains ex vivo. Brain image data from patients with cerebral hematoma and multiple sclerosis further illustrated the superiority of this method in reducing streaking artifacts caused by large susceptibility sources, whilst maintaining sharp anatomical details. STAR-QSM is implemented in STI Suite, a comprehensive shareware for susceptibility imaging and quantification.

Abstract: A new method is presented for the removal of streaking artifact in computed tomography. The method uses a nearest-neighbor pattern recognition technique to redetermine ray sums that intersect the foreign object responsible for the streaking. When the method is applied to the removal of streaking artifact caused by lead in a skull phantom, excellent results are obtained.

Abstract: This invention generally relates to the removal of streaking artifacts and periodic noise from tomographic images. The method comprises obtaining an A-scan from an imaging data set. The A-scan having a signal and the signal defining an amplitude. Noise specific to the A-scan is estimated. The amplitude of the A-scan is scaled based on its specific estimated noise floor. In another aspect, a plurality of A-scans is obtained from an imaging data set. Each of the plurality of A-scans has a signal and the signal defines an amplitude. Noise specific to each A-scan of the plurality of A-scans is estimated. Each A-scan of the plurality of A-scans is scaled by the A-scan's specific estimated noise floor.