A geometric-sensitivity-difference based algorithm improves object depth-localization for diffuse optical tomography in a circular-array outward-imaging geometry

• Published in: Medical physics (Lancaster) Vol. 40; no. 1; pp. 013101 - n/a
• Main Authors: Xu, Guan, Piao, Daqing
• Format: Journal Article
• Language: English
• Published: United States American Association of Physicists in Medicine 01.01.2013
• Subjects: Algorithms; biomedical optical imaging; diffuse optical tomography; Digital computing or data processing equipment or methods, specially adapted for specific applications; Image data processing or generation, in general; Image Processing, Computer-Assisted - methods; image reconstruction; Image reconstruction; tomography; Image sensors; Jacobians; medical image processing; Medical image reconstruction; Medical imaging; Numerical optimization; Optical arrays; Optical properties; optical tomography; optimisation; photon migration in tissue; Photons; Position sensitive detectors; Reconstruction; sensitivity; Tomography; Tomography, Optical - methods; image reconstruction; sensitivity; diffuse optical tomography; photon migration in tissue
• ISSN: 0094-2405; 2473-4209; 1522-8541; 2473-4209
• DOI: 10.1118/1.4771957

Purpose: To improve object depth-localization for diffuse optical tomography (DOT) in a circular-array outward-imaging geometry that is subjected to strong sensitivity variation with respect to imaging depth. Methods: The authors introduce an alternative DOT image reconstruction approach that optimizes the data-model fit based on thepaired measurements corresponding to two pairs of source-detector that share either the source or the detector, in comparison to the conventional method that optimizes the data-model fit based on the unpaired measurements corresponding to individual pairs of source-detector. This alternative approach, namely, geometric-sensitivity-difference (GSD) method, effectively reduces the variation of the reconstruction sensitivity with respect to imaging depth. The DOT image reconstruction based on GSD-scheme applied to same-source source-detector pairs is demonstrated using simulated and experimental continuous-wave measurements in a circular-array outward-imaging geometry, of which the native sensitivity varies strongly with respect to the depth. The outcomes of GSD-based image reconstruction are compared to those of two other methods: one is the conventional baseline method that utilizes the native sensitivity but does not involve depth-compensating scheme; and the other is a reference-compensation approach that employs active and depth-adapted compensation scheme to counteract the dependence of the reconstruction sensitivity with respect to imaging depth. Results: The GSD method generally outperforms the other two methods in localizing the depth of single object, resolving two objects that are azimuthally separated, and estimating the optical property of single object or azimuthally separated dual objects. The GSD method, however, demands more computations due to an increase of the element size of the resulted sensitivity matrix and more matrix multiplications. Conclusions: The GSD method improves the depth localization in the circular-array outward-imaging geometry, by taking advantage of the paired measurements of two source-sharing source-detector-pairs to passively and effectively homogenize the sensitivity of the reconstruction with respect to imaging depth.