The study presents a deep learning framework aimed at synthesizing 3D MRI
volumes from three-dimensional ultrasound images of the brain utilizing the
Pix2Pix GAN model. The process involves inputting a 3D volume of ultrasounds
into a UNET generator and patch discriminator, generating a corresponding 3D
volume of MRI. Model performance was evaluated using losses on the
discriminator and generator applied to a dataset of 3D ultrasound and MRI
images. The results indicate that the synthesized MRI images exhibit some
similarity to the expected outcomes. Despite challenges related to dataset
size, computational resources, and technical complexities, the method
successfully generated MRI volume with a satisfactory similarity score meant to
serve as a baseline for further research. It underscores the potential of deep
learning-based volume synthesis techniques for ultrasound to MRI conversion,
showcasing their viability for medical applications. Further refinement and
exploration are warranted for enhanced clinical relevance.

本研究提出了一种基于深度学习框架的方法，旨在利用 Pix2Pix GAN 模型从三维超声图像合成三维 MRI 体积。利用 3D 超声卷积数据集输入到 UNET 生成器和修补鉴别器，创建相应的 3D MRI 体积。通过对 3D 超声和 MRI 图像数据集应用鉴别器和生成器的损失函数来评估模型性能。结果表明合成的 MRI 图像与预期结果存在一定的相似性。尽管在数据集大小、计算资源和技术复杂性方面存在挑战，但该方法成功生成了具有满意相似性评分的 MRI 体积，并可作为进一步研究的基线。它突显了基于深度学习的体积合成技术在超声到 MRI 转换中的潜力，展示了其在医学应用中的可行性。需要进一步改进和探索以提高临床相关性。