Abstract
Near-infrared diffuse correlation tomography (DCT) is an emerging technology for non-invasive imaging of the tissue blood flow. The flow imaging quality relies on the image reconstruction algorithm, which, however, is little studied thus far. In this study, we conducted the first investigation of reconstruction algorithm impact on DCT blood flow imaging. Two reconstruction algorithms, i.e., the finite element method (FEM) representing the imaging framework of partial differential equation, and the Nth-order linear (NL) approach, representing the imaging framework of integral equation that was recently proposed by us to incorporate the tissue morphological information, were compared. Both computer simulations and phantom experiment outcomes show that the NL approach performs much better in image accuracy and homogeneity over anomaly or background, when compared with the FEM at the same source-detector configuration and spatial resolution. This study demonstrates that the DCT blood flow imaging is substantially influenced by the reconstruction algorithm, thus it has great potential in future algorithm design and optimization.
Highlights
Many diseases are associated with abnormal tissue hemodynamics such as blood flow, blood oxygen or oxygen metabolism [1]–[3]
Near-infrared diffuse optical spectroscopy (NIRS) is a noninvasive, portable and inexpensive modality that has been developed since 1970s [8]
The NIRS and diffuse correlation spectroscopy (DCS) are integrated to form a hybrid optical instrument, allowing for extraction of the tissue metabolic rate [16]. Both the NIRS and DCS have been extended from spectroscopy to tomography, namely diffuse optical tomography (DOT) and diffuse correlation tomography (DCT), respectively, offering spatial contrast of the oxygenation and blood flow at microvasculature level [1], [3]
Summary
Many diseases are associated with abnormal tissue hemodynamics such as blood flow, blood oxygen or oxygen metabolism [1]–[3]. DCS measures the microvasculature blood flow by using the temporal autocorrelation (g1(τ )) of the light electric field to quantify the movement of red blood cells, offering a direct approach to probe tissue blood flow [11] Both NIRS and DCS have been translated to numerous physiological or clinical studies, providing a diagnostic basis for a variety of diseases such as cerebral ischemia, tumors, muscle ischemia or skin wounds [12]–[15]. The NIRS and DCS are integrated to form a hybrid optical instrument, allowing for extraction of the tissue metabolic rate [16] Both the NIRS and DCS have been extended from spectroscopy to tomography, namely diffuse optical tomography (DOT) and diffuse correlation tomography (DCT), respectively, offering spatial contrast of the oxygenation and blood flow at microvasculature level [1], [3]
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