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Journal article
DrSVision: A Machine Learning Tool for Cortical Region-Specific fNIRS Calibration Based on Cadaveric Head MRI
Sensors, v 25(20), 6340
14 Oct 2025
PMID: 41157394
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
Abstract
Functional Near-Infrared Spectroscopy is (fNIRS) a non-invasive neuroimaging technique that monitors cerebral hemodynamic responses by measuring near-infrared (NIR) light absorption caused by changes in oxygenated and deoxygenated hemoglobin concentrations. While fNIRS has been widely used in cognitive and clinical neuroscience, a key challenge persists: the lack of practical tools required for calibrating source-detector separation (SDS) to maximize sensitivity at depth (SAD) for monitoring specific cortical regions of interest to neuroscience and neuroimaging studies. This study presents DrSVision version 1.0, a standalone software developed to address this limitation. Monte Carlo (MC) simulations were performed using segmented magnetic resonance imaging (MRI) data from eight cadaveric heads to realistically model light attenuation across anatomical layers. SAD of 10–20 mm with SDS of 19–39 mm was computed. The dataset was used to train a Gaussian Process Regression (GPR)-based machine learning (ML) model that recommends optimal SDS for achieving maximal sensitivity at targeted depths. The software operates independently of any third-party platforms and provides users with region-specific calibration outputs tailored for experimental goals, supporting more precise application of fNIRS. Future developments aim to incorporate subject-specific calibration using anatomical data and broaden support for diverse and personalized experimental setups. DrSVision represents a step forward in fNIRS experimentation.
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Details
- Title
- DrSVision: A Machine Learning Tool for Cortical Region-Specific fNIRS Calibration Based on Cadaveric Head MRI
- Creators
- Serhat Ilgaz Yoner - Kent HastanesiMehmet Emin Aksoy - Kent HastanesiHayrettin Can SüdorKurtulus Izzetoglu (Corresponding Author) - Drexel UniversityBaran BozkurtAlp Dinçer
- Publication Details
- Sensors, v 25(20), 6340
- Publisher
- MDPI
- Number of pages
- 25
- Grant note
- Acimath;badem Mehmet Ali Aydimath;nlar University General Research Fund: FBA20232164
The project was supported by Ac & imath;badem Mehmet Ali Ayd & imath;nlar University General Research Fund FBA20232164.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:001602704800001
- Scopus ID
- 2-s2.0-105020240396
- Other Identifier
- 991022120389104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Chemistry, Analytical
- Engineering, Electrical & Electronic
- Instruments & Instrumentation