Abstract
Conventional reconstruction of diffuse optical tomography (DOT) is based on the Tikhonov regularization and the white Gaussian noise assumption. Consequently, the reconstructed DOT images usually have a low spatial resolution. In this work, we have derived a novel quantification method for noise variance based on the linear Rytov approximation of the photon diffusion equation. Specifically, we have implemented this quantification of noise variance to normalize the measurement signals from all source-detector channels along with sparsity regularization to provide high-quality DOT images. Multiple experiments from computer simulations and laboratory phantoms were performed to validate and support the newly developed algorithm. The reconstructed images demonstrate that quantification and normalization of noise variance with sparsity regularization (QNNVSR) is an effective reconstruction approach to greatly enhance the spatial resolution and the shape fidelity for DOT images. Since noise variance can be estimated by our derived expression with relatively limited resources available, this approach is practically useful for many DOT applications.
Highlights
Diffuse optical tomography (DOT) is a non-invasive medical imaging technique, which uses low power light in the near infrared (NIR) range to detect hemodynamic changes inside the brain
In order to verify the noise quantification method just derived in Section 3.2, two sets of data from two spherical objects were simulated and collected: Φ0 readings were related to the base line measurements from all S-D channels in the absence of the absorbers
Note that since the noise term here corresponds to relative changes in light absorption in a log scale [see Eq (2)], it is derived from a fraction of two raw measurements
Summary
Diffuse optical tomography (DOT) is a non-invasive medical imaging technique, which uses low power light in the near infrared (NIR) range to detect hemodynamic changes inside the brain. The spatial resolution and depth penetration of DOT are limited compared to functional magnetic resonance imaging (fMRI). A variety of solutions have been proposed and explored to improve the spatial resolution of DOT by (1) optimally selecting optode arrangements [7,8,9], (2) applying different regularization terms in image reconstruction algorithms [9,10,11], and (3) reducing the measurement noise after noise information is calculated and used to weight/balance the measurements from different channels [12,13]. Acquisition of the noise information usually requires a large amount of measurement samples, which might be difficult to collect in many DOT applications
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