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Su, J., Klibanov, M.V., Liu, Y., Lin, Z., Pantong, N. and Liu, H. (2013) Optical Imaging of Phantoms from Real Data by an Approximately Globally Convergent Inverse Algorithm. Inverse Problems in Science and Engineering, 21, 1125-1150. http://dx.doi.org/10.1080/17415977.2012.743126

has been cited by the following article:

• TITLE: Reconstructions for Continuous-Wave Diffuse Optical Tomography by a Globally Convergent Method

  AUTHORS: Jianzhong Su, Yueming Liu, Zi-Jing Lin, Steven Teng, Aubrey Rhoden, Natee Pantong, Hanli Liu

  KEYWORDS: Inverse Problems, Diffuse Optical Tomography, Medical and Biological Imaging, Globally Convergence Method

  JOURNAL NAME: Journal of Applied Mathematics and Physics, Vol.2 No.5, April 25, 2014

  ABSTRACT: In this paper, a novel reconstruction method is presented for Near Infrared (NIR) 2-D imaging to recover optical absorption coefficients from laboratory phantom data. The main body of this work validates a new generation of highly efficient reconstruction algorithms called “Globally Convergent Method” (GCM) based upon actual measurements taken from brain-shape phantoms. It has been demonstrated in earlier studies using computer-simulated data that this type of reconstructions is stable for imaging complex distributions of optical absorption. The results in this paper demonstrate the excellent capability of GCM in working with experimental data measured from optical phantoms mimicking a rat brain with stroke.