Vishnu S. Emani, Caglar Ozturk, Manisha Singh, Carly Long, Summer Duffy, Danielle Gottlieb Sen, Ellen T. Roche, Wesley B. Baker
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Finite Element Modeling of Abdominal Near-Infrared Spectroscopy for Infant Splanchnic Oximetry
Abdominal near-infrared spectroscopy (NIRS) holds promise for early detection of necrotizing enterocolitis and other infant pathologies prior to irreversible injury, but the optimal NIRS sensor design is not well defined. In this study, we develop and demonstrate a computational method to evaluate NIRS sensor designs for infant splanchnic oximetry. We used a finite element (FE) approach to simulate near-infrared light transport through a 3D model of the infant abdomen constructed from computed tomography (CT) images. The simulations enable the measurement of the contrast-to-noise ratio (CNR) for splanchnic oximetry, given a specific NIRS sensor design. A key design criterion is the sensor's source–detector distance (SDD). We calculated the CNR as a function of SDD for two sensor positions near the umbilicus. Contrast-to-noise was maximal at SDDs between 4 and 5 cm, and comparable between sensor positions. Sensitivity to intestinal tissue also exceeded sensitivity to superficial adipose tissue in the 4–5 cm range. FE modeling of abdominal NIRS signals provides a means for rapid and thorough evaluation of sensor designs for infant splanchnic oximetry. By informing optimal NIRS sensor design, the computational methods presented here can improve the reliability and applicability of infant splanchnic oximetry.
期刊介绍:
All differential equation based models for biomedical applications and their novel solutions (using either established numerical methods such as finite difference, finite element and finite volume methods or new numerical methods) are within the scope of this journal. Manuscripts with experimental and analytical themes are also welcome if a component of the paper deals with numerical methods. Special cases that may not involve differential equations such as image processing, meshing and artificial intelligence are within the scope. Any research that is broadly linked to the wellbeing of the human body, either directly or indirectly, is also within the scope of this journal.