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Wide-field fluorescence molecular tomography with compressive sensing based preconditioning.

Ruoyang Yao1, Qi Pian1, Xavier Intes1

  • 1Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180, USA.

Biomedical Optics Express
|December 30, 2015
PubMed
Summary

Compressive sensing preconditioning improves wide-field structured illumination and detection for fluorescence molecular tomography (FMT). This technique enhances image reconstruction performance in both simulations and experiments, optimizing pattern selection for better results.

Keywords:
(070.6120) Spatial light modulators(110.4234) Multispectral and hyperspectral imaging(170.3010) Image reconstruction techniques(170.6920) Time-resolved imaging(170.6960) Tomography

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Area of Science:

  • Biomedical optics
  • Medical imaging
  • Optical tomography

Background:

  • Wide-field optical tomography uses structured light for fast imaging of large tissues.
  • The associated optical inverse problem remains ill-conditioned, limiting reconstruction accuracy.

Purpose of the Study:

  • To investigate compressive sensing preconditioning for wide-field structured illumination and detection.
  • To assess its impact on Fluorescence Molecular Tomography (FMT) performance.
  • To explore pattern selection for optimal reconstruction.

Main Methods:

  • In silico and experimental assessments of FMT using compressive sensing preconditioning.
  • Comparison of reconstruction performance with different pattern subsets.
  • Evaluation of data acquired with normal versus optimally designed bases.

Main Results:

  • Compressive sensing preconditioning significantly improves FMT reconstruction quality.
  • The methodology allows for the selection of optimal pattern subsets.
  • Experimental phantom studies validate the effectiveness of the proposed technique.

Conclusions:

  • Compressive sensing preconditioning is a beneficial strategy for enhancing wide-field optical tomography, particularly FMT.
  • This approach addresses ill-conditioning issues and optimizes data acquisition for improved imaging outcomes.