论文标题
深度学习使专注于治疗需求的强大而精确的光
Deep Learning Enables Robust and Precise Light Focusing on Treatment Needs
论文作者
论文摘要
如果光线通过身体组织，只专注于治疗需求（例如肿瘤）的区域，将彻底改变许多生物医学成像和治疗技术。因此，如何通过深层的不均匀组织聚焦光线是生物医学区域的圣杯。在本文中，我们使用深度学习来学习和加速使用波前塑形的相位预补偿过程。我们提出了一种方法（Loftgan，仅关注治疗需求），以了解相域X和Speckle域y之间的关系。我们的目标不仅是学习一个逆映射f：y-> x，以便我们可以知道像大多数工作一样对y进行成像所需的相应x，而且还要使焦点更容易受到干扰，并通过确保可以将获得的阶段转发回到斑点，从而更加可靠和精确。因此，我们引入了不同的约束，以强制使用传输映射H：X-> Y，强制执行F（y）= X和H（f（y））= y。进行了模拟和物理实验，以研究焦点的效果，以证明我们的方法的有效性和比较实验证明了鲁棒性和精度的关键改善。代码可在https://github.com/changchunyang/loftgan上找到。
If light passes through the body tissues, focusing only on areas where treatment needs, such as tumors, will revolutionize many biomedical imaging and therapy technologies. So how to focus light through deep inhomogeneous tissues overcoming scattering is Holy Grail in biomedical areas. In this paper, we use deep learning to learn and accelerate the process of phase pre-compensation using wavefront shaping. We present an approach (LoftGAN, light only focuses on treatment needs) for learning the relationship between phase domain X and speckle domain Y . Our goal is not just to learn an inverse mapping F:Y->X such that we can know the corresponding X needed for imaging Y like most work, but also to make focusing that is susceptible to disturbances more robust and precise by ensuring that the phase obtained can be forward mapped back to speckle. So we introduce different constraints to enforce F(Y)=X and H(F(Y))=Y with the transmission mapping H:X->Y. Both simulation and physical experiments are performed to investigate the effects of light focusing to demonstrate the effectiveness of our method and comparative experiments prove the crucial improvement of robustness and precision. Codes are available at https://github.com/ChangchunYang/LoftGAN.
