A fast reconstruction algorithm for fluorescence optical diffusion tomography based on preiteration

Xiaolei Song, Xiaoyun Xiong, Jing Bai

Research output: Contribution to journalArticlepeer-review

Abstract

Fluorescence optical diffusion tomography in the near-infrared (NIR) bandwidth is considered to be one of the most promising ways for noninvasive molecular-based imaging. Many reconstructive approaches to it utilize iterative methods for data inversion. However, they are time-consuming and they are far from meeting the real-time imaging demands. In this work, a fast preiteration algorithm based on the generalized inverse matrix is proposed. This method needs only one step of matrix-vector multiplication online, by pushing the iteration process to be executed offline. In the preiteration process, the second-order iterative format is employed to exponentially accelerate the convergence. Simulations based on an analytical diffusion model show that the distribution of fluorescent yield can be well estimated by this algorithm and the reconstructed speed is remarkably increased.£.

Original languageEnglish (US)
Article number23219
JournalInternational Journal of Biomedical Imaging
Volume2007
DOIs
StatePublished - 2007

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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