Accelerating reconstruction of reference digital tomosynthesis using graphics hardware.

Published

Journal Article

The successful implementation of digital tomosynthesis (DTS) for on-board image guided radiation therapy (IGRT) requires fast DTS image reconstruction. Both target and reference DTS image sets are required to support an image registration application for IGRT. Target images are usually DTS image sets reconstructed from on-board projections, which can be accomplished quickly using the conventional filtered backprojection algorithm. Reference images are DTS image sets reconstructed from digitally reconstructed radiographs (DRRs) previously generated from conventional planning CT data. Generating a set of DRRs from planning CT is relatively slow using the conventional ray-casting algorithm. In order to facilitate DTS reconstruction within a clinically acceptable period of time, we implemented a high performance DRR reconstruction algorithm on a graphics processing unit of commercial PC graphics hardware. The performance of this new algorithm was evaluated and compared with that which is achieved using the conventional software-based ray-casting algorithm. DTS images were reconstructed from DRRs previously generated by both hardware and software algorithms. On average, the DRR reconstruction efficiency using the hardware method is improved by a factor of 67 over the software method. The image quality of the DRRs was comparable to those generated using the software-based ray-casting algorithm. Accelerated DRR reconstruction significantly reduces the overall time required to produce a set of reference DTS images from planning CT and makes this technique clinically practical for target localization for radiation therapy.

Full Text

Duke Authors

Cited Authors

  • Yan, H; Ren, L; Godfrey, DJ; Yin, F-F

Published Date

  • October 2007

Published In

Volume / Issue

  • 34 / 10

Start / End Page

  • 3768 - 3776

PubMed ID

  • 17985622

Pubmed Central ID

  • 17985622

International Standard Serial Number (ISSN)

  • 0094-2405

Digital Object Identifier (DOI)

  • 10.1118/1.2779945

Language

  • eng

Conference Location

  • United States