Skip to main navigation Skip to search Skip to main content

Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method

  • Jinzuo Ye
  • , Chongwei Chi
  • , Zhenwen Xue
  • , Ping Wu
  • , Yu An
  • , Han Xu
  • , Shuang Zhang
  • , Jie Tian*
  • *Corresponding author for this work
  • CAS - Institute of Automation
  • Huawei Technologies Co., Ltd.
  • Beijing Jiaotong University
  • Northeastern University China

Research output: Contribution to journalArticlepeer-review

Abstract

Fluorescence molecular tomography (FMT), as a promising imaging modality, can three-dimensionally locate the specific tumor position in small animals. However, it remains challenging for effective and robust reconstruction of fluorescent probe distribution in animals. In this paper, we present a novel method based on sparsity adaptive subspace pursuit (SASP) for FMT reconstruction. Some innovative strategies including subspace projection, the bottom-up sparsity adaptive approach, and backtracking technique are associated with the SASP method, which guarantees the accuracy, efficiency, and robustness for FMT reconstruction. Three numerical experiments based on a mouse-mimicking heterogeneous phantom have been performed to validate the feasibility of the SASP method. The results show that the proposed SASP method can achieve satisfactory source localization with a bias less than 1mm; the efficiency of the method is much faster than mainstream reconstruction methods; and this approach is robust even under quite ill-posed condition. Furthermore, we have applied this method to an in vivo mouse model, and the results demonstrate the feasibility of the practical FMT application with the SASP method.

Original languageEnglish
Pages (from-to)387-406
Number of pages20
JournalBiomedical Optics Express
Volume5
Issue number2
DOIs
StatePublished - 2014
Externally publishedYes

Fingerprint

Dive into the research topics of 'Fast and robust reconstruction for fluorescence molecular tomography via a sparsity adaptive subspace pursuit method'. Together they form a unique fingerprint.

Cite this