title: GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration.
abstract: This paper shows how to significantly accelerate cone-beam CT reconstruction and 3D deformable image registration using the stream-processing model. We describe data-parallel designs for the Feldkamp, Davis and Kress (FDK) reconstruction algorithm, and the demons deformable registration algorithm, suitable for use on a commodity graphics processing unit. The streaming versions of these algorithms are implemented using the Brook programming environment and executed on an NVidia 8800 GPU. Performance results using CT data of a preserved swine lung indicate that the GPU-based implementations of the FDK and demons algorithms achieve a substantial speedup--up to 80 times for FDK and 70 times for demons when compared to an optimized reference implementation on a 2.8 GHz Intel processor. In addition, the accuracy of the GPU-based implementations was found to be excellent. Compared with CPU-based implementations, the RMS differences were less than 0.1 Hounsfield unit for reconstruction and less than 0.1 mm for deformable registration.
authors: Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, USA.
authors: G C
authors: GC
authors: Sharp
authors: N
authors: N
authors: Kandasamy
authors: H
authors: H
authors: Singh
authors: M
authors: M
authors: Folkert
mesh_terms: D000465:Algorithms; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D036542:Tomography, Spiral Computed / Q000295:instrumentation / Q000379:methods*