Hyperspectral imaging combined with blood oxygen saturation for in vivo analysis of small intestinal necrosis tissue.

[Display omitted] • We apply a linear transformation method based on measurements at three isosbestic points in combination with hyperspectral imaging technology for detection of blood oxygen saturation in small intestine issues. • We visualize the detection results using pseudo-color images, which is expected to be an effective auxiliary detection method for the diagnosis of acute mesenteric ischemia. Acute mesenteric ischemia (AMI) is a clinically significant vascular and gastrointestinal condition, which is closely related to the blood supply of the small intestine. Unfortunately, it is still challenging to properly discriminate small intestinal tissues with different degrees of ischemia. In this study, hyperspectral imaging (HSI) was used to construct pseudo-color images of oxygen saturation about small intestinal tissues and to discriminate different degrees of ischemia. First, several small intestine tissue models of New Zealand white rabbits were prepared and collected their hyperspectral data. Then, a set of isosbestic points were used to linearly transform the measurement data twice to match the reference spectra of oxyhemoglobin and deoxyhemoglobin, respectively. The oxygen saturation was measured at the characteristic peak band of oxyhemoglobin (560 nm). Ultimately, using the oxygenated hemoglobin reflectance spectrum as the benchmark, we obtained the relative amount of median oxygen saturation in normal tissues was 70.0 %, the IQR was 10.1 %, the relative amount of median oxygen saturation in ischemic tissues was 49.6 %, and the IQR was 14.6 %. The results demonstrate that HSI combined with the oxygen saturation computation method can efficiently differentiate between normal and ischemic regions of the small intestinal tissues. This technique provides a powerful support for internist to discriminate small bowel tissues with different degrees of ischemia, and also provides a new way of thinking for the diagnosis of AMI. [ABSTRACT FROM AUTHOR]