深度学习使强大而精确的光线聚焦于治疗需求
Deep Learning Enables Robust and Precise Light Focusing on Treatment Needs
摘要：
如果光穿过身体组织，只聚焦于需要治疗的区域，如肿瘤，将彻底改变许多生物医学成像和治疗技术。因此，如何克服散射使光在不均匀的深层组织中聚焦是生物医学领域的圣杯。在本文中，我们使用深度学习来学习和加速使用波前整形的相位预补偿过程。我们提出了一种方法（LoftGAN，光只关注治疗需求），用于学习相位域X和散斑域Y之间的关系。我们的目标不仅是学习逆映射F：Y->X，以便我们可以像大多数工作一样知道成像Y所需的相应X，而且还通过确保所获得的相位可以前向映射回散斑来使易受干扰影响的聚焦更加鲁棒和精确。因此，我们引入不同的约束来强制F（Y）=X和H（F（Y））=Y，其中传输映射H：X->Y。通过仿真和物理实验验证了该方法的有效性，并通过对比实验验证了该方法在鲁棒性和精度上的显著提高。代码可在https://github.com/ChangchunYang/LoftGAN上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
