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Journal of Biomedical Optics • Open Access

Indocyanine green enhanced co-registered diffuse optical tomography and photoacoustic tomography

Paper Abstract

To overcome the intensive light scattering in biological tissue, diffuse optical tomography (DOT) in the near-infrared range for breast lesion detection is usually combined with other imaging modalities, such as ultrasound, x-ray, and magnetic resonance imaging, to provide guidance. However, these guiding imaging modalities may depend on different contrast mechanisms compared to the optical contrast in the DOT. As a result, they cannot provide reliable guidance for DOT because some lesions may not be detectable by a nonoptical modality but may have a high optical contrast. An imaging modality that relies on optical contrast to provide guidance is desirable for DOT. We present a system that combines a frequency-domain DOT and real-time photoacoustic tomography (PAT) systems to detect and characterize deeply seated targets embedded in a turbid medium. To further improve the contrast, the exogenous contrast agent, indocyanine green (ICG), is used. Our experimental results show that the combined system can detect a tumor-mimicking phantom, which is immersed in intralipid solution with the concentrations ranging from 100 to 10 μM and with the dimensions of 0.8  cm×0.8  cm×0.6  cm , up to 2.5 cm in depth. Mice experiments also confirmed that the combined system can detect tumors and monitor the ICG uptake and washout in the tumor region. This method can potentially improve the accuracy to detect small breast lesions as well as lesions that are sensitive to background tissue changes, such as the lesions located just above the chest wall.

Paper Details

Date Published: 16 December 2013
PDF: 9 pages
J. Biomed. Opt. 18(12) 126006 doi: 10.1117/1.JBO.18.12.126006
Published in: Journal of Biomedical Optics Volume 18, Issue 12
Show Author Affiliations
Chen Xu, Univ. of Connecticut (United States)
Patrick D. Kumavor, Univ. of Connecticut (United States)
Umar S. Alqasemi, Univ. of Connecticut (Saudi Arabia)
Hai Li, Univ. of Connecticut (United States)
Yan Xu, Univ. of Connecticut (United States)
Saeid Zanganeh, Univ. of Connecticut (United States)
Quing Zhu, Univ. of Connecticut (United States)

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