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

As today's standard screening methods frequently fail to detect breast cancer before 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. We accelerate a ray-based 3D ultrasound CT reconstruction by GPU processing.By use-case optimized SpMV variants a speedup of one order of magnitude is obtained.We derive the optimal number of GPUs for reconstructions that do not fit on one GPU.A 3D-preview is reconstructed in 0.5 s, a high-resolution volume in 23 s.