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1 September 2005 Image quality improvement via spatial deconvolution in optical tomography: time-series imaging
Yong Xu, Yaling Pei, Harry L. Graber, Randall Locke Barbour
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Journal of Biomedical Optics, Vol. 10, Issue 5, 051701 (September 2005). https://doi.org/10.1117/1.2103747
Abstract
We present the fourth in a series of studies devoted to the issue of improving image quality in diffuse optical tomography (DOT) by using a spatial deconvolution operation that seeks to compensate for the information-blurring property of first-order perturbation algorithms. Our earlier reports consider only static target media. Here we report spatial deconvolution applied to media with time-varying optical properties, as a model of tissue dynamics resulting from varying metabolic demand and modulation of the vascular bed. Issues under study include the influence of deconvolution on the accuracy of the recovered temporal and spatial information. The impact of noise is also explored, and techniques for ameliorating its information-degrading effects are examined. At low noise levels (i.e, ≤5% of the time-varying signal amplitude), spatial deconvolution markedly improves the accuracy of recovered information. Temporal information is more seriously degraded by noise than is spatial information, and the impact of noise increases with the complexity of the time-varying signal. These effects, however, can be significantly reduced using simple noise suppression techniques (e.g., low-pass filtering). Results suggest that the deconvolution scheme should provide considerable enhancement of the quality of spatiotemporal information recovered from dynamic DOT techniques applied to tissue studies.
©(2005) Society of Photo-Optical Instrumentation Engineers (SPIE)
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Yong Xu, Yaling Pei, Harry L. Graber, and Randall Locke Barbour "Image quality improvement via spatial deconvolution in optical tomography: time-series imaging," Journal of Biomedical Optics 10(5), 051701 (1 September 2005). https://doi.org/10.1117/1.2103747
Published: 1 September 2005
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Cited by 11 scholarly publications.
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KEYWORDS
Deconvolution
Image quality
Sensors
Image restoration
Denoising
Reconstruction algorithms
Image filtering
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