Nakabayashi Mikie, Liu Siwei, Broti Nawara Mahmood, Ichinose Masashi, Ono Yumie
Electrical Engineering Program, Graduate School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 2148571, Japan.
Human Integrative Physiology Laboratory, School of Business Administration, Meiji University,1-1 Surugadai, Kanda, Chiyoda-ku, Tokyo,1018301, Japan.
Biomed Opt Express. 2023 Sep 21;14(10):5358-5375. doi: 10.1364/BOE.498693. eCollection 2023 Oct 1.
Diffuse correlation spectroscopy faces challenges concerning the contamination of cutaneous and deep tissue blood flow. We propose a long short-term memory network to directly quantify the flow rates of shallow and deep-layer tissues. By exploiting the different contributions of shallow and deep-layer flow rates to auto-correlation functions, we accurately predict the shallow and deep-layer flow rates (RMSE = 0.047 and 0.034 ml/min/100 g of simulated tissue, R = 0.99 and 0.99, respectively) in a two-layer flow phantom experiment. This approach is useful in evaluating the blood flow responses of active muscles, where both cutaneous and deep-muscle blood flow increase with exercise.
扩散相关光谱技术在皮肤和深部组织血流污染方面面临挑战。我们提出了一种长短期记忆网络，以直接量化浅层和深层组织的血流速度。通过利用浅层和深层血流速度对自相关函数的不同贡献，我们在双层血流模型实验中准确预测了浅层和深层血流速度（均方根误差分别为0.047和0.034毫升/分钟/100克模拟组织，相关系数分别为0.99和0.99）。这种方法在评估活跃肌肉的血流反应方面很有用，在这种情况下，皮肤和深部肌肉的血流都会随着运动而增加。
