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 generating future tumor masks and realistic MRIs of how the
tumor will look at any future time points for different treatment plans. Our
model is built upon cutting-edge diffusion probabilistic models and
deep-segmentation neural networks. We extended a diffusion model to include
sequential multi-parametric MRI and treatment information as conditioning input
to guide the generative diffusion process. This allows us to estimate tumor
growth at any given 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 has demonstrated
promising performance across a range of tasks, including the generation of
high-quality synthetic MRIs with tumor masks, time-series tumor segmentations,
and uncertainty estimation. Combined with the treatment-aware generated MRIs,
the tumor growth predictions with uncertainty estimates can provide useful
information for clinical decision-making.

扩散性胶质瘤的成长建模具有挑战性，本论文提出了一种新颖的端到端网络，可生成未来肿瘤掩模和不同治疗计划下肿瘤在任何未来时间点的真实磁共振成像。该模型基于尖端的扩散概率模型和深度分割神经网络，通过扩展扩散模型以包括顺序多参数 MRI 和治疗信息作为条件输入，指导生成扩散过程，以估算给定时间点的肿瘤成长。通过使用真实的手术后纵向 MRI 数据作为训练数据，在各种任务中验证了该模型的优异性能，包括生成带有肿瘤掩模的高质量合成 MRI，时间序列肿瘤分割和不确定性估计。结合治疗感知生成的 MRI，具备不确定性估计的肿瘤成长预测能够为临床决策提供有用的信息。

治疗感知扩散概率模型用于纵向 MRI 生成和弥散性胶质瘤生长预测

Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI  Generation and Diffuse Glioma Growth Prediction

Prognostic tumor growth modeling via volumetric medical imaging observations
can potentially lead to better outcomes of tumor treatment and surgical
planning. Recent advances of convolutional networks have demonstrated higher
accuracy than traditional mathematical models in predicting future tumor
volumes. This indicates that deep learning-based techniques may have great
potentials on addressing such problem. However, current 2D patch-based modeling
approaches cannot make full use of the spatio-temporal imaging context of the
tumor's longitudinal 4D (3D + time) data. Moreover, they are incapable to
predict clinically-relevant tumor properties, other than volumes. In this
paper, we exploit to formulate the tumor growth process through convolutional
Long Short-Term Memory (ConvLSTM) that extract tumor's static imaging
appearances and capture its temporal dynamic changes within a single network.
We extend ConvLSTM into the spatio-temporal domain (ST-ConvLSTM) by jointly
learning the inter-slice 3D contexts and the longitudinal or temporal dynamics
from multiple patient studies. Our approach can incorporate other non-imaging
patient information in an end-to-end trainable manner. Experiments are
conducted on the largest 4D longitudinal tumor dataset of 33 patients to date.
Results validate that the ST-ConvLSTM produces a Dice score of 83.2%+-5.1% and
a RVD of 11.2%+-10.8%, both significantly outperforming (p<0.05) other compared
methods of linear model, ConvLSTM, and generative adversarial network (GAN)
under the metric of predicting future tumor volumes. Additionally, our new
method enables the prediction of both cell density and CT intensity numbers.
Last, we demonstrate the generalizability of ST-ConvLSTM by employing it in 4D
medical image segmentation task, which achieves an averaged Dice score of
86.3+-1.2% for left-ventricle segmentation in 4D ultrasound with 3 seconds per
patient.

通过 ConvLSTM 技术，结合静态图像和时间动态变化，从多个患者研究中提取 3D 和时间维度信息来预测肿瘤体积，同时可实现肿瘤细胞密度和 CT 强度值的预测，并应用于 4D 医学图像分割任务。