Ultrafast and Ultrahigh-Resolution Diffuse Optical Tomography for Brain Imaging with Sensitivity Equation based Noniterative Sparse Optical Reconstruction (SENSOR). (December 2021)

• Record Type: Journal Article
• Authors: Kim, Hyun Keol; Zhao, Yongyi; Raghuram, Ankit; Veeraraghavan, Ashok; Robinson, Jacob; Hielscher, Andreas H.
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 276(2021)
• Publication Date: 2021-12
• Subjects: brain imaging -- diffuse optical tomography -- sensitivity equation -- time domain radiative transfer -- noniterative sparse image reconstruction -- dimensional reduction; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2021.107939
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: We present the novel image reconstruction algorithm to solve the time domain diffuse optical tomography (TD-DOT) problem with high resolution brain imaging. Our new inverse algorithm employs the noniterative inverse formulation based on the nontruncated sensitivity equation, the asymptotic l 0 -norm scheme and the dimensional reduction scheme. We evaluate the performance of the new algorithm in terms of CPU times and spatial resolution. The new algorithm can achieve 1mm spatial resolution in about 20~30 milliseconds on an Intel multicore process with a speedup factor of 17000 as compared to the iterative inverse solver. Abstract: We introduce a novel image reconstruction method for time-resolved diffuse optical tomography (DOT) that yields submillimeter resolution in less than a second. This opens the door to high-resolution real-time DOT in imaging of the brain activity. We call this approach the sensitivity equation based noniterative sparse optical reconstruction (SENSOR) method. The high spatial resolution is achieved by implementing an asymptotic l 0 -norm operator that guarantees to obtain sparsest representation of reconstructed targets. The high computational speed is achieved by employing the nontruncated sensitivity equation based noniterative inverse formulation combined with reduced sensing matrix and parallel computing. We tested the new method with numerical and experimental data. The results demonstrate that the SENSOR algorithm can achieve 1 mm 3 spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.