GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography

摘要

As today s standard screening metnods frequently tan to detect Breast cancer Derore metastases have developed, early diagnosis is still a major challenge. With the promise of high-quality volume images, three-dimensional ultrasound computer tomography is likely to improve this situation, but has high computational needs. In this work, we investigate the acceleration of the ray-based transmission reconstruction by a GPU-based implementation of the iterative numerical optimization algorithm TVAL3. We identified the regular and transposed sparse-matrix-vector multiply as the performance limiting operations. For accelerated reconstruction we propose two different concepts and devise a hybrid scheme as optimal configuration. In addition we investigate multi-GPU scalability and derive the optimal number of devices for our two primary use-cases: a fast preview mode and a high-resolution mode. In order to achieve a fair estimation of the speedup, we compare our implementation to an optimized CPU version of the algorithm. Using our accelerated implementation we reconstructed a preview 3D volume with 24,576 unknowns, a voxel size of (8 mm)~3 and approximately 200,000 equations in 0.5 s. A high-resolution volume with 1,572,864 unknowns, a voxel size of (2mm)~3 and approximately 1.6 million equations was reconstructed in 23 s. This constitutes an acceleration of over one order of magnitude in comparison to the optimized CPU version.