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首页> 外文会议>2010 3rd International Conference on Biomedical Engineering and Informatics >Sparse regularization in MRI iterative reconstruction using GPUs
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Sparse regularization in MRI iterative reconstruction using GPUs

机译：使用GPU进行MRI迭代重建的稀疏正则化

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摘要

Regularization is a common technique used to improve image quality in inverse problems such as MR image reconstruction. In this work, we extend our previous Graphics Processing Unit (GPU) implementation of MR image reconstruction with compensation for susceptibility-induced field inhomogeneity effects by incorporating an additional quadratic regularization term. Regularization techniques commonly impose the prior information that MR images are relatively smooth by penalizing large changes in intensity between neighboring voxels. However, the associated computations often increase data access and the overall computational load, which can lead to slower image reconstruction. This motivates us to adopt a GPU-enabled implementation of spatial regularization using sparse matrices. This implementation enables the computations for the entire reconstruction procedure to be done on the GPU, which avoids the memory bandwidth bottlenecks associated with frequent communications between the GPU and CPU. Both the CPU and GPU code of this implementation will be available for release at the time of the conference.
机译：正则化是用于提高诸如MR图像重建之类的反问题中的图像质量的常用技术。在这项工作中，我们通过合并一个附加的二次正则项，扩展了以前的MR图像重建的图形处理单元（GPU）实施，并通过磁化率引起的场不均匀性效应进行了补偿。正则化技术通常通过惩罚相邻体素之间强度的大变化来强加先验信息，即MR图像相对平滑。但是，相关的计算通常会增加数据访问量和总体计算负荷，这可能会导致图像重建速度变慢。这促使我们采用稀疏矩阵，采用GPU支持的空间正则化实现。此实现使整个重建过程的计算能够在GPU上完成，从而避免了与GPU和CPU之间频繁通信相关的内存带宽瓶颈。会议期间将发布该实现的CPU和GPU代码。

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