纵向MRI生成和弥漫性胶质瘤生长预测的治疗感知扩散概率模型

Qinghui Liu;Elies Fuster-Garcia;Ivar Thokle Hovden;Bradley J. MacIntosh;Edvard O. S. Grødem;Petter Brandal;Carles Lopez-Mateu;Donatas Sederevičius;Karoline Skogen;Till Schellhorn;Atle Bjørnerud;Kyrre Eeg Emblem
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摘要

弥漫性神经胶质瘤是一种恶性脑肿瘤,在大脑中广泛生长。肿瘤细胞和正常组织之间复杂的相互作用,以及经常遇到的治疗诱导的变化,使胶质瘤肿瘤生长模型具有挑战性。在本文中,我们提出了一种新颖的端到端网络,能够预测肿瘤掩膜和多参数磁共振图像(MRI)在不同治疗计划的任何未来时间点的肿瘤外观。我们的方法是基于尖端的扩散概率模型和深度分割神经网络。我们将顺序的多参数MRI和治疗信息作为条件输入来指导生成扩散过程以及联合分割过程。这允许在任何给定的治疗和时间点进行肿瘤生长估计和真实的MRI生成。我们使用真实世界的术后纵向MRI数据训练模型,其中胶质瘤肿瘤生长轨迹随时间的变化表示为肿瘤分割图。该模型在各种任务中表现出良好的性能,包括使用肿瘤掩膜生成高质量的多参数MRI,执行时间序列肿瘤分割,以及提供不确定性估计。结合治疗感知生成的MRI,不确定性估计的肿瘤生长预测可以为临床决策提供有用的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Treatment-Aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Diffuse gliomas are malignant brain tumors that grow widespread through the brain. The complex interactions between neoplastic cells and normal tissue, as well as the treatment-induced changes often encountered, make glioma tumor growth modeling challenging. In this paper, we present a novel end-to-end network capable of future predictions of tumor masks and multi-parametric magnetic resonance images (MRI) of how the tumor will look at any future time points for different treatment plans. Our approach is based on cutting-edge diffusion probabilistic models and deep-segmentation neural networks. We included sequential multi-parametric MRI and treatment information as conditioning inputs to guide the generative diffusion process as well as a joint segmentation process. This allows for tumor growth estimates and realistic MRI generation at any given treatment and time point. We trained the model using real-world postoperative longitudinal MRI data with glioma tumor growth trajectories represented as tumor segmentation maps over time. The model demonstrates promising performance across various tasks, including generating high-quality multi-parametric MRI with tumor masks, performing time-series tumor segmentations, and providing uncertainty estimates. Combined with the treatment-aware generated MRI, the tumor growth predictions with uncertainty estimates can provide useful information for clinical decision-making.
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