Florentin Spadin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Florentin Spadin has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Aleksandr V. Khilov
Institute of Applied Physics RAS, 603950 Nizhny Novgorod, Russia
1 shared publication - Alexey Kurnikov
Institute of Applied Physics RAS, 603950 Nizhny Novgorod, Russia
1 shared publication - Andrey Kovalchuk
Institute of Applied Physics RAS, 603950 Nizhny Novgorod, Russia
1 shared publication - Ksenia Pavlova
Institute of Applied Physics RAS, 603950 Nizhny Novgorod, Russia
1 shared publication - Martin Frenz
Institute of Applied Physics, University of Bern, Sidlerstrasse 5, 3012 Bern, Switzerland
1 shared publication - Michael Jaeger
University of Bern
1 shared publication
Latest Publications
- Open Access
Toward Real-Time Giga-Voxel Optoacoustic/Photoacoustic Microscopy: GPU-Accelerated Fourier Reconstruction with Quasi-3D Implementation
We propose a GPU-accelerated implementation of frequency-domain synthetic aperture focusing technique (SAFT) employing truncated regularized inverse k-space interpolation. Our implementation achieves sub-1s reconstruction time for data sizes of up to 100 M voxels, providing more than a tenfold decrease in reconstruction time as compared to CPU-based SAFT. We provide an empirical model that can be used to predict the execution time of quasi-3D reconstruction for any data size given the specifications of the computing system.
Open AccessToward Real-Time Giga-Voxel Optoacoustic/Photoacoustic Microscopy: GPU-Accelerated Fourier Reconstruction with Quasi-3D Implementation
We propose a GPU-accelerated implementation of frequency-domain synthetic aperture focusing technique (SAFT) employing truncated regularized inverse k-space interpolation. Our implementation achieves sub-1s reconstruction time for data sizes of up to 100 M voxels, providing more than a tenfold decrease in reconstruction time as compared to CPU-based SAFT. We provide an empirical model that can be used to predict the execution time of quasi-3D reconstruction for any data size given the specifications of the computing system.