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SUMMARY:Acceleration of an image reconstruction algorithm for Positron Emi
 ssion\nTomography using a Graphics Processing Unit
DTSTART;VALUE=DATE-TIME:20090225T110000Z
DTEND;VALUE=DATE-TIME:20090225T120000Z
DTSTAMP;VALUE=DATE-TIME:20261110T003244Z
UID:indico-event-1680@indico.ific.uv.es
DESCRIPTION:In this work we applied an iterative image reconstruction algo
 rithm to measured data of a Siemens Biograph16 PET scanner. Furthermore th
 e reconstruction algorithm was also implemented on a graphics processing u
 nit (GPU) to reduce the calculation time. Positron emission tomography (PE
 T) is a medical imaging modality to study metabolic processes in a human o
 r animal body. To reconstruct images from measurement data of a Siemens Bi
 ograph16 PET scanner we applied the maximum likelihood - expectation maxim
 ization (ML-EM) algorithm. The system matrix\, which is needed for the ML-
 EM\, is calculated using the Siddon's ray-tracing algorithm. Basically two
  approaches were implemented\; the online version saves the pre-calculated
  system matrix to the hard disk and reads the values during computation of
  the reconstruction process. The online version computes the elements of t
 he system matrix during the reconstruction. The online version was impleme
 nted on the graphic card and on the CPU. We evaluated if a medium range gr
 aphic card can already accelerate a reconstruction algorithm versus a CPU 
 implementation. The same algorithms were applied to the GPU and the CPU\, 
 however\, due to the limited and specific hardware the implementation on t
 he graphic card had to be adapted and parallelized. For the GPU applicatio
 ns NVIDIA's programming language compute unified device architecture (CUDA
 ) was used. The reconstructed images showed artifacts that are typical for
  iterative reconstruction algorithms\, as well as background noise. Apart 
 from these effects the images were good in quality. In the GPU application
  we detected a loss of data due to simultaneous memory accesses in the bac
 k-projection process. The error depended\, among others\, on the resolutio
 n of the image. We took various measures that reduced the error\, so that 
 we finally achieved images for several resolutions that are comparable in 
 quality to the images calculated by the CPU. We implemented two GPU applic
 ations\, both run faster than the CPU approaches\; the maximum acceleratio
 n factor that we achieved is 3.\n\nhttps://indico.ific.uv.es/event/1680/
LOCATION:Edf. Institutos de Investigación Sala Seminarios IFIC
URL:https://indico.ific.uv.es/event/1680/
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