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High-Speed Time-Domain Diffuse Optical Tomography With a Sensitivity Equation-Based Neural Network

Wang, Fay; Kim, Stephen H.; Zhao, Yongyi; Raghuram, Ankit; Veeraraghavan, Ashok; Robinson, Jacob; Hielscher, Andreas H.
Steady progress in time-domain diffuse optical tomography (TD-DOT) technology is allowing for the first time the design of low-cost, compact, and high-performance systems, thus promising more widespread clinical TD-DOT use, such as for recording brain tissue hemodynamics. TD-DOT is known to provide more accurate values of optical properties and physiological parameters compared to its frequency-domain or steady-state counterparts. However, achieving high temporal resolution is still difficult, as solving the inverse problem is computationally demanding, leading to relatively long reconstruction times. The runtime is further compromised by processes that involve ‘nontrivial’ empirical tuning of reconstruction parameters, which increases complexity and inefficiency. To address these challenges, we present a new reconstruction algorithm that combines a deep-learning approach with our previously introduced sensitivity-equation-based, non-iterative sparse optical reconstruction (SENSOR) code. The new algorithm (called SENSOR-NET) unfolds the iterations of SENSOR into a deep neural network. In this way, we achieve high-resolution sparse reconstruction using only learned parameters, thus eliminating the need to tune parameters prior to reconstruction empirically. Furthermore, once trained, the reconstruction time is not dependent on the number of sources or wavelengths used. We validate our method with numerical and experimental data and show that accurate reconstructions with 1 mm spatial resolution can be obtained in under 20 milliseconds regardless of the number of sources used in the setup. This opens the door for real-time brain monitoring and other high-speed DOT applications.
ISSN: 2334-0118
CID: 5502082

Prediction of breast cancer response to neoadjuvant chemotherapy in different biological breast cancer subtypes using diffuse optical tomography [Meeting Abstract]

Altoe, Mirella L.; Kalinsky, Kevin M.; Guo, Hua; Hibshoosh, Hanina; Tejada, Mariella; Crew, Katherine D.; Accordino, Melissa K.; Trivedi, Meghna S.; Marone, Alessandro; Kim, Hyun K.; Hielscher, Andreas H.; Hershman, Dawn L.
ISSN: 0008-5472
CID: 5390482

Prediction of pathologic complete response in breast cancer neoadjuvant chemotherapy based on pretreatment data obtained with dynamic diffuse optical tomography [Meeting Abstract]

Ghosh, S.; Altoe, M. L.; Marone, A.; Kim, H. K.; Kalinsky, K.; Guo, H.; Hibshoosh, H.; Tejada, M.; Crew, K. D.; Accordino, M. K.; Trivedi, M. S.; Hershman, D. L.; Hielscher, A. H.
ISSN: 0277-786x
CID: 5390492

Early treatment window for predicting breast cancer response to neoadjuvant chemotherapy in the HR+/HER2-subgroup using diffuse optical tomography [Meeting Abstract]

Altoe, Mirella L.; Marone, Alessandro; Kim, Hyun K.; Guo, Hua; Hibshoosh, Haninna; Tejada, Mariella; Crew, Katherine D.; Accordino, Melissa K.; Trivedi, Meghna S.; Kalinsky, Kevin; Hershman, Dawn L.; Hielscher, Andreas H.
ISSN: 0277-786x
CID: 5390502

Initial Evaluation of the Use of Dynamic Vascular Optical Imaging for Predicting Outcome of Surgical Intervention to Improve Blood Perfusion in Feet of Diabetic Patients [Meeting Abstract]

Marone, A.; Maheshwari, N.; Kim, H. K.; Bajakian, D. R.; Hielscher, A. H.
ISSN: 0277-786x
CID: 5390512

Pilot study on monitoring ulcer healing with diffuse optical imaging in a patient cohort affected by peripheral arterial disease (PAD) [Meeting Abstract]

Maheshwari, N.; Marone, A.; Altoe, M.; Kim, H. K.; Bajakian, D. R.; Hielscher, A. H.
ISSN: 0277-786x
CID: 5390522

Employing a Neural Network Approach for Reducing the Convergence Speed of Diffuse Optical Image Reconstruction Algorithms [Meeting Abstract]

Wang, Fay; Hielscher, Andreas H.; Kim, Hyun K.
ISSN: 0277-786x
CID: 5390532

Ultrahigh spatiotemporal resolution fluorescence molecular tomography with a sparsity constrained dimensional reduction reconstruction model [Meeting Abstract]

Kim, Hyun K.; Raghuram, Ankit; Zhao, Yongyi; Veeraraghavan, Ashok; Robinson, Jacob; Hielscher, Andreas H.
ISSN: 0277-786x
CID: 5390542

The Role of Amyloid, Tau, and APOE Genotype on the Relationship Between Informant-Reported Sleep Disturbance and Alzheimer's Disease Risks

Kim, Hyun; Levine, Alina; Cohen, Daniel; Gehrman, Philip; Zhu, Xi; Devanand, Davangere P; Lee, Seonjoo; Goldberg, Terry E
BACKGROUND:The association between sleep and Alzheimer's disease (AD) biomarkers are well-established, but little is known about how they interact to change the course of AD. OBJECTIVE:To determine the potential interaction between sleep disturbance and Aβ, tau, and APOE4 on brain atrophy and cognitive decline. METHODS:Sample included 351 participants (mean age 72.01 ± 6.67, 50.4%female) who were followed for approximately 5 years as part of the Alzheimer's Disease Neuroimaging Initiative. Informant-reported sleep disturbance (IRSD) was measured using the Neuropsychiatric Inventory (NPI). Changes in magnetic resonance imaging (MRI)-measured AD signature brain regions and cognitive performance and IRSD's interaction with cerebrospinal fluid amyloid-β (Aβ42) and p-Tau depositions and APOE4 status were examined using the linear mixed models. RESULTS:Baseline IRSD was not significantly associated with the rate of atrophy after adjusting for covariates (age, sex, education, total NPI severity score, and sleep medications). However, there was a significant interaction between IRSD and AD biomarkers on faster atrophy rates in multiple brain regions, including the cortical and middle temporal volumes. Post-hoc analyses indicated that Aβ and p-Tau/Aβ predicted a faster decline in these regions/domains in IRSD, compared with biomarker-negative individuals with IRSD (ps≤0.001). There was a significant IRSD*APOE4 interaction for brain atrophy rate (ps≤0.02) but not for cognition. CONCLUSION:IRSD may increase the future risk of AD by contributing to faster brain atrophy and cognitive decline when combined with the presence of AD biomarkers and APOE4. Early intervention for sleep disturbance could help reduce the risk of developing AD.
PMID: 35491776
ISSN: 1875-8908
CID: 5402232

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

Kim, Hyun Keol; Zhao, Yongyi; Raghuram, Ankit; Veeraraghavan, Ashok; Robinson, Jacob; Hielscher, Andreas H
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 mm3 spatial-resolution optical tomographic imaging at depth of ∼60 mean free paths (MFPs) in 20∼30 milliseconds on an Intel Core i9 processor.
PMID: 34966190
ISSN: 0022-4073
CID: 5389802