2022-07-15 ワシントン大学セントルイス
Song Hu invented multi-parametric photoacoustic microscopy, which enables simultaneous imaging of blood perfusion, oxygenation and flow at the microscopic level. (Whitney Curtis photo)
組織内を回折せずに伝播するベッセルビームと条件付き生成敵対ネットワークベースの深層学習モデルを組み合わせ、約600ミクロンの焦点深度を拡張してマウス脳内のヘモグロビン濃度、血液酸素化、血流に関する高解像度光音響画像を取得することに成功しました。
従来のガウシアンビーム励起による光音響顕微鏡装置と比較し、本複合手法は有意に高い定量精度を示した。
＜関連情報＞
- https://source.wustl.edu/2022/07/a-one-two-punch-for-photoacoustic-imaging/
- https://ieeexplore.ieee.org/document/9815293
ディープラーニングを用いたベッセルビーム型マルチパラメトリック光音響顕微鏡の開発 Deep Learning-powered Bessel-beam Multi-parametric Photoacoustic Microscopy
Yifeng Zhou, Naidi Sun, Song Hu
IEEE Transactions on Medical Imaging Published:05 July 2022
DOI:10.1109/TMI.2022.3188739
Abstract
Enabling simultaneous and high-resolution quantification of the total concentration of hemoglobin (CHb), oxygen saturation of hemoglobin (sO2), and cerebral blood flow (CBF), multi-parametric photoacoustic microscopy (PAM) has emerged as a promising tool for functional and metabolic imaging of the live mouse brain. However, due to the limited depth of focus imposed by the Gaussian-beam excitation, the quantitative measurements become inaccurate when the imaging object is out of focus. To address this problem, we have developed a hardware-software combined approach by integrating Bessel-beam excitation and conditional generative adversarial network (cGAN)-based deep learning. Side-by-side comparison of the new cGAN-powered Bessel-beam multi-parametric PAM against the conventional Gaussian-beam multi-parametric PAM shows that the new system enables high-resolution, quantitative imaging of CHb, sO2, and CBF over a depth range of ~600 μm in the live mouse brain, with errors 13–58 times lower than those of the conventional system. Better fulfilling the rigid requirement of light focusing for accurate hemodynamic measurements, the deep learning-powered Bessel-beam multi-parametric PAM may find applications in large-field functional recording across the uneven brain surface and beyond (e.g., tumor imaging).
