Depth Discrimination in Diffuse Optical Transmission Imaging by Planar Scanning Off-Axis Fibers: INITIAL Applications to Optical Mammography

Jana M Kainerstorfer, Yang Yu, Geethika Weliwitigoda, Pamela G Anderson, Angelo Sassaroli and Sergio Fantini

PLOS ONE, 2013, vol. 8, issue 3, 1-12

Abstract: We present a method for depth discrimination in parallel-plate, transmission mode, diffuse optical imaging. The method is based on scanning a set of detector pairs, where the two detectors in each pair are separated by a distance δDi along direction δDi within the x-y scanning plane. A given optical inhomogeneity appears shifted by αiδDi (with 0≤ αi ≤1) in the images collected with the two detection fibers of the i-th pair. Such a spatial shift can be translated into a measurement of the depth z of the inhomogeneity, and the depth measurements based on each detector pair are combined into a specially designed weighted average. This depth assessment is demonstrated on tissue-like phantoms for simple inhomogeneities such as straight rods in single-rod or multiple-rod configurations, and for more complex curved structures which mimic blood vessels in the female breast. In these phantom tests, the method has recovered the depth of single inhomogeneities in the central position of the phantom to within 4 mm of their actual value, and within 7 mm for more superficial inhomogeneities, where the thickness of the phantom was 65 mm. The application of this method to more complex images, such as optical mammograms, requires a robust approach to identify corresponding structures in the images collected with the two detectors of a given pair. To this aim, we propose an approach based on the inner product of the skeleton images collected with the two detectors of each pair, and we present an application of this approach to optical in vivo images of the female breast. This depth discrimination method can enhance the spatial information content of 2D projection images of the breast by assessing the depth of detected structures, and by allowing for 3D localization of breast tumors.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0058510

DOI: 10.1371/journal.pone.0058510

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