Image quality improvement via spatial deconvolution in optical tomography: time-series imaging

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, < or = 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.

Spatial deconvolution of 3-D diffuse optical tomographic image time series: Influence of background medium heterogeneity

“…Here we build upon previous demonstrations that the relatively low spatial resolution and quantitative accuracy of recovered optical parameters, in diffuse optical tomographic images reconstructed by linear perturbation approaches is primarily a result of linear convolution of spatial information [1][2][3][4]. A deconvolution algorithm, based on temporal encoding of spatial information, was developed that was shown to significantly improve qualitative and quantitative image accuracy, with a computational effort far lower than that required for recursive iterative reconstruction techniques [1].…”

“…A deconvolution algorithm, based on temporal encoding of spatial information, was developed that was shown to significantly improve qualitative and quantitative image accuracy, with a computational effort far lower than that required for recursive iterative reconstruction techniques [1]. Subsequent refinements of the deconvolution procedure have proved capable of performing equally well for 3-D imaging problems [2], and in restricted-view cases [3], and it has been shown that the tradeoff between enhancement of spatial information and degradation of temporal accuracy can be contained within acceptable bounds [4].…”

Systems and Strategies for Accessing the Information Content of fNIRS Imaging in Support of Noninvasive BCI Applications

“…The latter methods, however, have severe practical limitations when applied to image time-series studies. To this end, we have implemented alternative image correction methods that have good performance and efficiency [15][16][17][18][19][20].…”

“…In the case of NIRS, information is convolved spatially, on a macroscopic scale, because of scattering, and temporally because of coincident phenomenology affecting different elements of the vascular tree. Compared to topographic imaging methods, image reconstruction using model-based techniques provides an objective basis for effectively reducing the blurred paths of light in tissue caused by scattering [1][2][3][4][5][12][13][14][15][16][17][18][19][20].…”

Somatosensory activation of two fingers can be discriminated with ultrahigh-density diffuse optical tomography

“…Second, the inverse problem of recovering interior optical properties from measured surface data is solved. Image reconstruction is an ill-posed and under-determined problem and many research groups have focused on one or both parts of the problem (Arridge and Hebden, 1997; Arridge and Schweiger, 1997; Boas et al, 2002; Dehghani et al, 2008; Dehghani et al, 2009; Fang, 2010; Fang and Boas, 2009; Gibson and Dehghani, 2009; Gibson et al, 2005; Xu et al, 2005). …”