===affil2:

===firstname:
Veronica
===firstname4:

===firstname3:

===lastname2:

===lastname:
Mejia Bustamante
===firstname5:

===affil6:

===lastname3:

===email:
vmejia@emory.edu
===lastname6:

===affil5:

===otherauths:

===lastname4:

===affil4:

===lastname7:

===affil7:

===firstname7:

===postal:
Dept. of Math & CS
Emory University
400 Dowman Dr., W401
Atlanta, GA 30322
===firstname6:

===ABSTRACT:
Tomosynthesis imaging provides a viable alternative to computed tomography (CT) and has obtained significant interest from the medical community as a means for diagnostic radiology and radiation therapy. In digital tomosynthesis imaging, multiple projections of an object are obtained along a small range of different incident angles in order to reconstruct a 3D representation of the object. In this paper we discuss the implementation details of the polyenergetic digital breast tomosynthesis reconstruction algorithm in a GPU using OpenCL. We describe three different algorithm implementations: a serial implementation, a GPU implementation threaded by functionality of the model, and a GPU fused kernel implementation which is threaded to increase performance, throughput, and GPU utilization in the application. We show that the explicit kernel fusion achieves significant speed-up in the reconstruction process of a clinical size patient data set, from running over 100X faster than the version threaded by functionality to 200X faster than the serial approach.

===affil3:

===lastname5:

===affilother:

===title:
Implementation of Iterative Solvers for the Digital Tomosynthesis Problem in GPUs
===firstname2: