Iterative Deblurring of Large 3D Datasets from Cryomicrotome Imaging Using an Array of GPUs

The aim was to enhance vessel like features of large 3D datasets (\(4000 \times 4000 \times 4000\) pixels) resulting from cryomicrotome images using a system specific point spread function (PSF). An iterative (Gauss-Seidel) spatial convolution strategy for GPU arrays was developed to enhance the vessels. The PSF is small and spatially invariant and resides in fast constant memory of the GPU while the unfiltered data reside in slower global memory but are prefetched by blocks of threads in shared GPU memory. Filtering is achieved by a series of unrolled loops in shared memory. Between iterations the filtered data is stored to disk using asynchronous MPI-IO effectively hiding the IO overhead with the kernel execution time. Our implementation reduces computational time up to 350 times on four GPU’s in parallel compared to a single core CPU implementation and outperforms FFT based filtering strategies on GPU’s. Although developed for filtering the complete arterial system of the heart, the method is general applicable.