Abstract
Various diseases are relevant to the abnormal blood flow in tissue. Diffuse correlation spectroscopy (DCS) is an emerging technology to extract the blood flow index (BFI) from light electric field temporal autocorrelation data. To account for tissue heterogeneity and irregular geometry, we developed an innovative DCS algorithm (i.e., the Nth order linear algorithm, or simply the NL algorithm) previously, in which the DCS signals are fully utilized through iterative linear regressions. Under the framework of NL algorithm, the BFI to be extracted is significantly influenced by the linear regression approach adopted. In this study, three approaches were proposed and evaluated for performing the iterative linear regressions, in order to understand what are the appropriate regression methods for BFI estimation. The three methods are least-squared minimization (L2 norm), least-absolute minimization (L1 norm) and support vector regression (SVR), where L2 norm is a conventional approach to perform linear regression. L1 norm and SVR are the approaches newly introduced here to process the DCS data. Computer simulations and the autocorrelation data collected from liquid phantom and human tissues are utilized to evaluate the three approaches. The results show that the best performance is achieved by the SVR approach in extracting the BFI values, with an error rate of 2.23% at 3.0 cm source-detector separation. The L1 norm method gives a medium error of 2.81%. In contrast, the L2 norm method leads to the largest error (3.93%) in extracting the BFI values. The outcomes derived from this study will be very helpful for the tissue blood flow measurements, which is critical for translating the DCS technology to the clinic.
Highlights
Monitoring tissue hemodynamics is important for early disease diagnosis and treatment evaluation, since many diseases, such as tumors, ischemic stroke and peripheral artery disease, are associated with abnormal blood flow, blood oxygenation or oxygen metabolism
We extended the Nth-order linear (NL) algorithm for use in diffuse correlation tomography (DCT) [27], and the computer simulations on a realistic human head verified its capability in accurate and robust reconstruction of blood flow imaging
The RMSE was found to be positively influenced by the S-D separation, which is anticipated, because the larger S-D separation leads to a lower signal-to-noise ratio (SNR)
Summary
Monitoring tissue hemodynamics is important for early disease diagnosis and treatment evaluation, since many diseases, such as tumors, ischemic stroke and peripheral artery disease, are associated with abnormal blood flow, blood oxygenation or oxygen metabolism. The tissue blood flow measurement by DCS technology has been validated through comparing with other modalities [12,13,14,15]. This technology has been applied in clinic for early diagnosis and therapeutic monitoring of various diseases, such as ischemic stroke [1,8,13,16,17,18], tumor [2,3,7,19,20,21], obstructive sleep apnea [4], and surgical flap [22]. The extension of DCS technology to the blood flow imaging, i.e., diffuse correlation tomography (DCT), has been developed over years [25,26,27]
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