TY - JOUR
T1 - Ultrafast and Ultrahigh-Resolution Diffuse Optical Tomography for Brain Imaging with Sensitivity Equation based Noniterative Sparse Optical Reconstruction (SENSOR)
AU - Kim, Hyun Keol
AU - Zhao, Yongyi
AU - Raghuram, Ankit
AU - Veeraraghavan, Ashok
AU - Robinson, Jacob
AU - Hielscher, Andreas H.
N1 - Funding Information:
This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA), Contract No. N66001-19-C-4020. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. This project was also funded partially by the NSF Expeditions in Computing Grant #1730147. Additionally, author Y. Zhao was supported by a training fellowship from the NLM Training Program (T15LM007093).
Funding Information:
This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA), Contract No. N66001-19-C-4020. The views, opinions and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government. This project was also funded partially by the NSF Expeditions in Computing Grant # 1730147 . Additionally, author Y. Zhao was supported by a training fellowship from the NLM Training Program (T15LM007093).
Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/12
Y1 - 2021/12
N2 - 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 l0-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 mm3 spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.
AB - 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 l0-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 mm3 spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.
KW - brain imaging
KW - diffuse optical tomography
KW - dimensional reduction
KW - noniterative sparse image reconstruction
KW - sensitivity equation
KW - time domain radiative transfer
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U2 - 10.1016/j.jqsrt.2021.107939
DO - 10.1016/j.jqsrt.2021.107939
M3 - Article
AN - SCOPUS:85115922897
SN - 0022-4073
VL - 276
JO - Journal of Quantitative Spectroscopy and Radiative Transfer
JF - Journal of Quantitative Spectroscopy and Radiative Transfer
M1 - 107939
ER -