Unsupervised deep learning is a promising method in brain MRI registration to reduce the reliance on anatomical labels, while still achieving anatomically accurate transformations. For the Learn2Reg2024 LUMIR challenge, we propose fine-tuning of the pre-trained TransMorph model to improve the convergence stability as well as the deformation smoothness. The former is achieved through the FAdam optimizer, and consistency in structural changes is incorporated through the addition of gradient correlation in the similarity measure, improving anatomical alignment. The results show slight improvements in the Dice and HdDist95 scores, and a notable reduction in the NDV compared to the baseline TransMorph model. These are also confirmed by inspecting the boundaries of the tissue. Our proposed method highlights the effectiveness of including Gradient Correlation to achieve smoother and structurally consistent deformations for interpatient brain MRI registration.

本研究解决了无监督深度学习在脑MRI配准中对解剖标签依赖性的问题，提出通过使用FAdam优化器精细调整预训练的TransMorph模型，从而提高收敛稳定性和变形平滑性。结果表明，与基线TransMorph模型相比，Dice和HdDist95评分有所提高，并且NDV显著降低，强调了梯度相关性的有效性，以实现患者间脑MRI配准的平滑和结构一致性变形。

使用梯度相关的精细调整TransMorph进行解剖对齐