This paper presents the second-placed solution for task 8 and the participation solution for task 7 of BraTS 2024. The adoption of automated brain analysis algorithms to support clinical practice is increasing. However, many of these algorithms struggle with the presence of brain lesions or the absence of certain MRI modalities. The alterations in the brain's morphology leads to high variability and thus poor performance of predictive models that were trained only on healthy brains. The lack of information that is usually provided by some of the missing MRI modalities also reduces the reliability of the prediction models trained with all modalities. In order to improve the performance of these models, we propose the use of conditional 3D wavelet diffusion models. The wavelet transform enabled full-resolution image training and prediction on a GPU with 48 GB VRAM, without patching or downsampling, preserving all information for prediction. For the inpainting task of BraTS 2024, the use of a large and variable number of healthy masks and the stability and efficiency of the 3D wavelet diffusion model resulted in 0.007, 22.61 and 0.842 in the validation set and 0.07 , 22.8 and 0.91 in the testing set (MSE, PSNR and SSIM respectively). The code for these tasks is available at https://github.com/ShadowTwin41/BraTS_2023_2024_solutions.

翻译：本文介绍了BraTS 2024任务8的第二名解决方案及任务7的参赛方案。支持临床实践的自动化脑部分析算法应用日益广泛，然而许多算法在处理脑部病灶或部分MRI模态缺失时面临困难。脑部形态的改变导致高度变异性，使得仅基于健康大脑训练的预测模型性能不佳。部分缺失MRI模态通常提供的信息缺乏，也降低了使用全部模态训练的预测模型的可靠性。为提升这些模型的性能，我们提出采用条件式3D小波扩散模型。小波变换实现了在48GB显存的GPU上进行全分辨率图像训练与预测，无需分块或下采样，完整保留了预测所需的所有信息。在BraTS 2024的图像修复任务中，通过使用大量可变健康掩码及3D小波扩散模型的稳定性与高效性，验证集获得0.007、22.61和0.842的指标，测试集获得0.07、22.8和0.91的指标（分别为MSE、PSNR和SSIM）。相关任务代码已发布于https://github.com/ShadowTwin41/BraTS_2023_2024_solutions。