Parallel programming of gradient-based iterative image reconstruction schemes for optical tomography.
Optical tomography (OT) is a fast developing novel imaging modality that uses near-infrared (NIR) light to obtain cross-sectional views of optical properties inside the human body. A major challenge remains the time-consuming, computational-intensive image reconstruction problem that converts NIR transmission measurements into cross-sectional images. To increase the speed of iterative image reconstruction schemes that are commonly applied for OT, we have developed and implemented several parallel algorithms on a cluster of workstations. Static process distribution as well as dynamic load balancing schemes suitable for heterogeneous clusters and varying machine performances are introduced and tested. The resulting algorithms are shown to accelerate the reconstruction process to various degrees, substantially reducing the computation times for clinically relevant problems.
Hielscher
Andreas H
AH
Departments of Biomedical Engineering and Radiology, Columbia University, 500 West 120th Street, MC 8904, New York, NY 10027, USA. ahh2004@columbia.edu
Bartel
Sebastian
S
D000465:Algorithms; D003195:Computer Communication Networks; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical; D011381:Programming Languages; D041622:Tomography, Optical