Near-infrared spectroscopy (NIRS) has emerged as a low-cost, portable approach for rapid, point-of-care detection of hematomas caused by traumatic brain injury. As a new technology, there is a need to develop standardized test methods for objective, quantitative performance evaluation of these devices. Towards this goal, we have developed and studied two types of phantom-based testing approaches. The first involves 3D-printed phantoms incorporating hemoglobin-filled inclusions. Phantom layers representing specific cerebral tissues were printed using photopolymers doped with varying levels of titanium oxide and black resin. The accuracy, precision and spectral dependence of printed phantom optical properties were validated using spectrophotometry. The phantom also includes a hematoma inclusion insert which was filled with a hemoglobin solution. Oxygen saturation levels were modified by adding sodium dithionite at calibrated concentrations. The second phantom approach involves molded silicone layers with a superficial region – simulating the scalp and skull – comprised of removable layers to vary hematoma size and depth, and a bottom layer representing brain matter. These phantoms were tested with both a commercial hematoma detector and a custom NIRS system to optimize their designs and validate their utility in performing inter-device comparisons. The effects of hematoma depth, diameter, and height, as well as tissue optical properties and biological variables including hemoglobin saturation level and scalp/skull thickness were studied. Results demonstrate the ability to quantitatively compare NIRS device performance and indicate the promise of using 3D printing to achieve phantoms with realistic variations in tissue optical properties for evaluating biophotonic device performance.
Jianting Wang, Stanley Huang, Yu Chen, Cristin G. Welle, and T. Joshua Pfefer, "Cerebral NIRS performance testing with molded and 3D-printed phantoms (Conference Presentation)," Proc. SPIE 10056, Design and Quality for Biomedical Technologies X, 1005608 (Presented at SPIE BiOS: January 28, 2017; Published: 20 April 2017); https://doi.org/10.1117/12.2254901.5371820532001.
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