Chen Liang-Wei, Lu Shang-Yang, Hsu Feng-Chun, Lin Chun-Yu, Chiang Ann-Shyn, Chen Shean-Jen
Opt Express. 2024 Jan 15;32(2):2321-2332. doi: 10.1364/OE.505956.
Deep learning-based computer-generated holography (DeepCGH) has the ability to generate three-dimensional multiphoton stimulation nearly 1,000 times faster than conventional CGH approaches such as the Gerchberg-Saxton (GS) iterative algorithm. However, existing DeepCGH methods cannot achieve axial confinement at the several-micron scale. Moreover, they suffer from an extended inference time as the number of stimulation locations at different depths (i.e., the number of input layers in the neural network) increases. Accordingly, this study proposes an unsupervised U-Net DeepCGH model enhanced with temporal focusing (TF), which currently achieves an axial resolution of around 5 µm. The proposed model employs a digital propagation matrix (DPM) in the data preprocessing stage, which enables stimulation at arbitrary depth locations and reduces the computation time by more than 35%. Through physical constraint learning using an improved loss function related to the TF excitation efficiency, the axial resolution and excitation intensity of the proposed TF-DeepCGH with DPM rival that of the optimal GS with TF method but with a greatly increased computational efficiency.
基于深度学习的计算机生成全息术（DeepCGH）能够生成三维多光子刺激，其速度比诸如格尔奇贝格 - 萨克斯顿（GS）迭代算法等传统CGH方法快近1000倍。然而，现有的DeepCGH方法无法在几微米尺度上实现轴向限制。此外，随着不同深度处刺激位置的数量（即神经网络中输入层的数量）增加，它们的推理时间会延长。因此，本研究提出了一种通过时间聚焦（TF）增强的无监督U-Net DeepCGH模型，该模型目前实现了约5μm的轴向分辨率。所提出的模型在数据预处理阶段采用数字传播矩阵（DPM），这使得能够在任意深度位置进行刺激，并将计算时间减少了35%以上。通过使用与TF激发效率相关的改进损失函数进行物理约束学习，所提出的带有DPM的TF-DeepCGH的轴向分辨率和激发强度可与采用TF方法的最优GS相媲美，但计算效率大大提高。
