title: A LSQR-type method provides a computationally efficient automated optimal choice of regularization parameter in diffuse optical tomography.
abstract: OBJECTIVE
  Developing a computationally efficient automated method for the optimal choice of regularization parameter in diffuse optical tomography.
  
  
  METHODS
  The least-squares QR (LSQR)-type method that uses Lanczos bidiagonalization is known to be computationally efficient in performing the reconstruction procedure in diffuse optical tomography. The same is effectively deployed via an optimization procedure that uses the simplex method to find the optimal regularization parameter. The proposed LSQR-type method is compared with the traditional methods such as L-curve, generalized cross-validation (GCV), and recently proposed minimal residual method (MRM)-based choice of regularization parameter using numerical and experimental phantom data.
  
  
  RESULTS
  The results indicate that the proposed LSQR-type and MRM-based methods performance in terms of reconstructed image quality is similar and superior compared to L-curve and GCV-based methods. The proposed method computational complexity is at least five times lower compared to MRM-based method, making it an optimal technique.
  
  
  CONCLUSIONS
  The LSQR-type method was able to overcome the inherent limitation of computationally expensive nature of MRM-based automated way finding the optimal regularization parameter in diffuse optical tomographic imaging, making this method more suitable to be deployed in real-time.
authors: Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore 560012, India.
authors: Jaya
authors: J
authors: Prakash
authors: Phaneendra K
authors: PK
authors: Yalavarthy
mesh_terms: D000042:Absorption; D000465:Algorithms; D001331:Automation; D005780:Gelatin; D016018:Least-Squares Analysis; D019047:Phantoms, Imaging; D041622:Tomography, Optical / Q000379:methods*
chemical_list: D005780:Gelatin