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ICHINOSE Masashi
Department Undergraduate School , School of Business Administration Position Professor |
|
| Language | English |
| Publication Date | 2023/10 |
| Type | Academic Journal |
| Peer Review | Peer reviewed |
| Title | Deep-learning-based separation of shallow and deep layer blood flow rates in diffuse correlation spectroscopy. |
| Contribution Type | Co-authored (other than first author) |
| Journal | Biomedical optics express |
| Journal Type | Another Country |
| Volume, Issue, Page | 14(10),pp.5358-5375 |
| International coauthorship | International coauthorship |
| Author and coauthor | Mikie Nakabayashi, Siwei Liu, Nawara Mahmood Broti, Masashi Ichinose, Yumie Ono |
| Details | 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, R2 = 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. |
| DOI | 10.1364/BOE.498693 |
| ISSN | 2156-7085 |
| PMID | 37854549 |