Cerebral blood flow monitoring using a deep learning implementation of the two-layer DCS analytical model with a 512x512 SPAD array
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866908598263611392 |
|---|---|
| author | Pan, Mingliang Li, Chenxu Zhang, Yuanzhe Mollins, Alan Wang, Quan Erdogan, Ahmet T. Hua, Yuanyuan Zang, Zhenya Finlayson, Neil Henderson, Robert K. Li, David Day-Uei |
| author_facet | Pan, Mingliang Li, Chenxu Zhang, Yuanzhe Mollins, Alan Wang, Quan Erdogan, Ahmet T. Hua, Yuanyuan Zang, Zhenya Finlayson, Neil Henderson, Robert K. Li, David Day-Uei |
| contents | Diffuse correlation spectroscopy (DCS) analyzes the autocorrelation function of photons scattered by red blood cells, enabling non-invasive, continuous measurement of deep tissue blood flow at the bedside. Multi-layer DCS models (two- and three-layer) enhance cerebral blood flow index (CBFi) sensitivity and mitigate interference from extracerebral tissues. However, these models require multiple predefined parameters and are computationally intensive, making them impractical for real-time bedside monitoring. To address this challenge, we integrate a single-photon avalanche diode (SPAD) array with a deep learning (DL)-based approach trained on data generated by the two-layer analytical model. This method bypasses traditional model fitting, enabling real-time CBFi monitoring while minimizing superficial tissue contamination. We first validate our approach using Monte Carlo-simulated test datasets, demonstrating superior accuracy in relative CBFi estimation (5.8% error vs. 19.1% for conventional fitting) and enhanced CBFi sensitivity (87.1% vs. 55.4%). Additionally, our method effectively isolates shallow blood flow changes and 750-fold faster than single-exponential fitting in a realistic scenario. We further evaluate the system in a healthy adult, achieving real-time CBFi monitoring and pulsatile waveform recovery during a brain activity test using a 512 512 SPAD array sensor. These results highlight the potential of our approach for real-time brain activity monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06997 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cerebral blood flow monitoring using a deep learning implementation of the two-layer DCS analytical model with a 512x512 SPAD array Pan, Mingliang Li, Chenxu Zhang, Yuanzhe Mollins, Alan Wang, Quan Erdogan, Ahmet T. Hua, Yuanyuan Zang, Zhenya Finlayson, Neil Henderson, Robert K. Li, David Day-Uei Medical Physics Biological Physics Diffuse correlation spectroscopy (DCS) analyzes the autocorrelation function of photons scattered by red blood cells, enabling non-invasive, continuous measurement of deep tissue blood flow at the bedside. Multi-layer DCS models (two- and three-layer) enhance cerebral blood flow index (CBFi) sensitivity and mitigate interference from extracerebral tissues. However, these models require multiple predefined parameters and are computationally intensive, making them impractical for real-time bedside monitoring. To address this challenge, we integrate a single-photon avalanche diode (SPAD) array with a deep learning (DL)-based approach trained on data generated by the two-layer analytical model. This method bypasses traditional model fitting, enabling real-time CBFi monitoring while minimizing superficial tissue contamination. We first validate our approach using Monte Carlo-simulated test datasets, demonstrating superior accuracy in relative CBFi estimation (5.8% error vs. 19.1% for conventional fitting) and enhanced CBFi sensitivity (87.1% vs. 55.4%). Additionally, our method effectively isolates shallow blood flow changes and 750-fold faster than single-exponential fitting in a realistic scenario. We further evaluate the system in a healthy adult, achieving real-time CBFi monitoring and pulsatile waveform recovery during a brain activity test using a 512 512 SPAD array sensor. These results highlight the potential of our approach for real-time brain activity monitoring. |
| title | Cerebral blood flow monitoring using a deep learning implementation of the two-layer DCS analytical model with a 512x512 SPAD array |
| topic | Medical Physics Biological Physics |
| url | https://arxiv.org/abs/2504.06997 |