Multi-frequency diffuse optical tomography for cancer detection

Previous work has validated that the accuracy of absorption coefficient can be improved using frequency-domain (FD) DOT measurements with multiple modulation frequencies. In this paper, we investigate the use of multi-frequency FD-DOT to improve the recovery accuracy of scattering coefficient, which is of great interest to cancer study. A new method called the clustered sparsity reconstruction (CSR) is proposed to reconstruct the absorption and scattering coefficients jointly. We conduct numerical simulations for FD-DOT image reconstruction with multi-modulation frequencies. The numerical results show that the recovery accuracy of scattering coefficient can be significantly improved using multi-frequency data and the proposed CSR method. It is interesting to demonstrate that the combination of two modulation frequencies results in the best reconstruction accuracy in terms of contrast-to-noise ratio (CNR) and root-mean-square error (RMSE), while more number of modulation frequencies does not improve the image quality much.

[1]  Ozlem Birgul,et al.  Diffuse optical tomographic reconstruction using multifrequency data. , 2006, Journal of biomedical optics.

[2]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[3]  Junzhou Huang,et al.  Efficient MR Image Reconstruction for Compressed MR Imaging , 2010, MICCAI.

[4]  Hamid Dehghani,et al.  Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction. , 2009, Communications in numerical methods in engineering.

[5]  Junzhou Huang,et al.  Learning with structured sparsity , 2009, ICML '09.

[6]  A. Yodh,et al.  Diffuse optics for tissue monitoring and tomography , 2010, Reports on progress in physics. Physical Society.

[7]  Turgut Durduran,et al.  Compressed sensing in diffuse optical tomography. , 2010, Optics express.

[8]  Jong Chul Ye,et al.  Joint sparsity-driven non-iterative simultaneous reconstruction of absorption and scattering in diffuse optical tomography. , 2013, Optics express.

[9]  Jean-Philippe Vert,et al.  Group lasso with overlap and graph lasso , 2009, ICML '09.

[10]  M. Yuan,et al.  Model selection and estimation in regression with grouped variables , 2006 .

[11]  Phaneendra K. Yalavarthy,et al.  Sparse Recovery Methods Hold Promise for Diffuse Optical Tomographic Image Reconstruction , 2014, IEEE Journal of Selected Topics in Quantum Electronics.

[12]  Junzhou Huang,et al.  Diffuse Optical Tomography Enhanced by Clustered Sparsity for Functional Brain Imaging , 2014, IEEE Transactions on Medical Imaging.

[13]  Junzhou Huang,et al.  Preconditioning for Accelerated Iteratively Reweighted Least Squares in Structured Sparsity Reconstruction , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[15]  Hanli Liu,et al.  Prostate cancer detection using combined auto-fluorescence and light reflectance spectroscopy: ex vivo study of human prostates. , 2014, Biomedical optics express.

[16]  Jürgen Beuthan,et al.  Optimal source-modulation frequencies for transport-theory-based optical tomography of small-tissue volumes. , 2008, Optics express.

[17]  Jarod C Finlay,et al.  Determination of the distribution of light, optical properties, drug concentration, and tissue oxygenation in-vivo in human prostate during motexafin lutetium-mediated photodynamic therapy. , 2005, Journal of photochemistry and photobiology. B, Biology.