Model-resolution-based basis pursuit deconvolution improves diffuse optical tomographic imaging

Jaya Prakash, Hamid Dehghani, Brian W Pogue, Phaneendra K Yalavarthy

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)


The image reconstruction problem encountered in diffuse optical tomographic imaging is ill-posed in nature, necessitating the usage of regularization to result in stable solutions. This regularization also results in loss of resolution in the reconstructed images. A frame work, that is attributed by model-resolution, to improve the reconstructed image characteristics using the basis pursuit deconvolution method is proposed here. The proposed method performs this deconvolution as an additional step in the image reconstruction scheme. It is shown, both in numerical and experimental gelatin phantom cases, that the proposed method yields better recovery of the target shapes compared to traditional method, without the loss of quantitativeness of the results.

Original languageEnglish
Pages (from-to)891-901
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number4
Publication statusPublished - Apr 2014


  • Algorithms
  • Breast
  • Female
  • Humans
  • Image Processing, Computer-Assisted
  • Models, Biological
  • Phantoms, Imaging
  • Tomography, Optical


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