{"id":86,"date":"2016-04-01T00:00:43","date_gmt":"2016-04-01T00:00:43","guid":{"rendered":"https:\/\/live-optics-wp.pantheonsite.io\/cgri\/?page_id=86"},"modified":"2017-02-16T17:27:02","modified_gmt":"2017-02-16T17:27:02","slug":"gpu-accelerated-data-reconstruction-on-fastspect-ii","status":"publish","type":"page","link":"https:\/\/wp.optics.arizona.edu\/cgri\/objectives\/gpu-accelerated-data-reconstruction-on-fastspect-ii\/","title":{"rendered":"GPU-ACCELERATED DATA RECONSTRUCTION"},"content":{"rendered":"

FastSPECT II is a SPECT imaging system developed at CGRI for small-animal imaging. It consists of sixteen stationary (i.e., non-moving) modular gamma-ray cameras. Each camera has an input face measuring about 120×120 mm^2.\u00a0 GPU code was developed to speed up the pre-processing of the acquired data and the actual reconstruction via the expectation maximization maximum likelihood (MLEM) algorithm. Pre-processing of the data consisted in 2D maximum likelihood estimation of position of interaction for each detected gamma-ray photon.<\/p>\n

The one-page highlight can be download here<\/a>.<\/p>\n

The manual for this toolkit can be downloaded here<\/a>.<\/p>\n

For further details and to obtain the FastSPECT II Image Reconstruction Toolbox, please contact Luca Caucci at caucci@email.arizona.edu<\/a>.<\/p>\n

References<\/h3>\n

FastSPECT II: A Second Generation High-Resolution Dynamic SPECT Imager, Lars R. Furenlid, Donald W Wilson, Yi-chun Chen, Hyunki Kim, Philip J. Pietraski, Michael J. Crawford, Harrison H. Barrett, IEEE Trans Nucl Science, 51(3): 631-635, 2004.<\/a><\/p>\n

Maximum-likelihood estimation with a contracting-grid search algorithm, J. Y. Hesterman, L. Caucci, M. A. Kupinski, H. H. Barrett, and L. R. Furenlid<\/a>, IEEE Trans. Nucl. Sci., 57(3):1077-1084, 2010. \u00a0PMC293245.<\/p>\n","protected":false},"excerpt":{"rendered":"

FastSPECT II is a SPECT imaging system developed at CGRI for small-animal imaging. It consists of sixteen stationary (i.e., non-moving) modular gamma-ray cameras. Each camera has an input face measuring about 120×120 mm^2.\u00a0 GPU code was developed to speed up the pre-processing of the acquired data and the actual reconstruction via the expectation maximization maximum likelihood (MLEM) algorithm. Pre-processing of<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":74,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-86","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/pages\/86","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/comments?post=86"}],"version-history":[{"count":6,"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/pages\/86\/revisions"}],"predecessor-version":[{"id":625,"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/pages\/86\/revisions\/625"}],"up":[{"embeddable":true,"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/pages\/74"}],"wp:attachment":[{"href":"https:\/\/wp.optics.arizona.edu\/cgri\/wp-json\/wp\/v2\/media?parent=86"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}