Single-Shot Echo-Planar-Imaging (SS-EPI) is the most common method to acquire Diffusion-Weighted-Imaging (DWI) data in clinic due to its immunity to patient motion. However, its image quality is impacted by geometric distortions and poor spatial resolution. While Multi-Shot EPI (MS-EPI) has the potential to achieve high spatial resolution, it suffers from significant motion-induced artifacts. Partially Parallel Imaging (PPI) reconstruction techniques such as Sensitivity-Encoding (SENSE) has shown its ability to improve the image quality of MRI. In this paper we proposed a SENSE based model to reconstruct DW images from MS-EPI data by solving a minimization problem. Under the condition when the motion is not significantly large, We assume the images reconstructed from different shots are low rank except for sparse errors and our model is solved by an accelerated Alternating direction method of multipliers (AADMM) scheme.
Image quality of Four Dimensional Cone-Beam Computer-Tomography (4DCBCT) is severely impaired by highly insufficient amount of projection data available for each phase. Therefore, making good use of limited projection data is crucial to solve this problem. Noticing that usually only a portion of the images is affected by motion, we separate the moving part (different between phases) of the images from the static part (identical among all phases) with the help of prior image reconstructed using all projection data. Then we update the moving part and the static part of images alternatively through solving minimization problems based on a global (use full projection data) and several local (use projection data for respective phase) linear systems. In the other word, we rebuild a large over-determined linear system for static part from the original under-determined systems and we reduce the number of unknowns in the original system for each phase as well. As a result, image quality for both static part and moving part are greatly improved and reliable 4D CBCT images are then reconstructed.
In clinical exams, multi-contrast images from conventional MRI are scanned with the same field of view (FOV) for complementary diagnostic information, such as proton density- (PD-), T1- and T2-weighted images. Their sharable information can be utilized for more robust and accurate image reconstruction. In this work, we propose a novel model and an efficient algorithm for joint image reconstruction and coil sensitivity estimation in multi-contrast partially parallel imaging (PPI) in MRI. Our algorithm restores the multi-contrast images by minimizing an energy function consisting of an L2-norm fidelity term to reduce construction errors caused by motion, a regularization term of underlying images to preserve common anatomical features by using vectorial total variation (VTV) regularizer, and updating sensitivity maps by Tikhonov smoothness based on their physical property. We present the numerical results including T1- and T2-weighted MR images recovered from partially scanned k-space data and provide the comparisons between our results and those obtained from the related existing works. Our numerical results indicate that the proposed method using vectorial TV and penalties on sensitivities can be made promising and widely used for multi-contrast multi-channel MR image reconstruction.