Deep learning model for ultrafast multifrequency optical property extractions for spatial frequency domain imaging
| Autor: | Hannah M. Peterson, Yue Deng, Raeef Istfan, Feng Bao, Yanyu Zhao, Darren Roblyer |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Phantoms Imaging Computer science business.industry Deep learning Optical Imaging Inverse Inverse problem 01 natural sciences Atomic and Molecular Physics and Optics Bottleneck 030218 nuclear medicine & medical imaging Machine Learning 010309 optics 03 medical and health sciences 0302 clinical medicine Optics 0103 physical sciences Image Processing Computer-Assisted Medical imaging Spatial frequency Artificial intelligence business Ultrashort pulse Algorithm |
| Zdroj: | Optics Letters. 43:5669 |
| ISSN: | 1539-4794 0146-9592 |
| DOI: | 10.1364/ol.43.005669 |
| Popis: | Spatial frequency domain imaging (SFDI) is emerging as an important new method in biomedical imaging due to its ability to provide label-free, wide-field tissue optical property maps. Most prior SFDI studies have utilized two spatial frequencies (2−fx) for optical property extractions. The use of more than two frequencies (multi−fx) can vastly improve the accuracy and reduce uncertainties in optical property estimates for some tissue types, but it has been limited in practice due to the slow speed of available inversion algorithms. We present a deep learning solution that eliminates this bottleneck by solving the multi−fx inverse problem 300× to 100,000× faster, with equivalent or improved accuracy compared to competing methods. The proposed deep learning inverse model will help to enable real-time and highly accurate tissue measurements with SFDI. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|