论文标题
3D复发卷积自动编码器的扩散MRI中的角度超分辨率
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional Autoencoder
论文作者
论文摘要
高分辨率扩散MRI（DMRI）数据通常受到临床环境中扫描时间有限的限制，从而限制了下游分析技术的使用。在这项工作中，我们开发了一个3D复发性卷积神经网络（RCNN），能够在角（Q-Space）域中超级分辨DMRI体积。我们的方法使用以目标B向量为条件的3D自动编码器制定了角度超分辨率作为贴剂回归的任务。在网络中，我们使用卷积长的短期内存（ConvlstM）单元格对Q空间样本之间的关系进行建模。我们将模型性能与基线球形谐波插值和模型体系结构的1D变体进行了比较。我们表明，在不同的子采样方案和B值之间，3D模型的错误率最低。 3D RCNN的相对性能在非常低的角度分辨率域中最出色。该项目的代码可在https://github.com/m-lyon/dmri-rcnn上找到。
High resolution diffusion MRI (dMRI) data is often constrained by limited scanning time in clinical settings, thus restricting the use of downstream analysis techniques that would otherwise be available. In this work we develop a 3D recurrent convolutional neural network (RCNN) capable of super-resolving dMRI volumes in the angular (q-space) domain. Our approach formulates the task of angular super-resolution as a patch-wise regression using a 3D autoencoder conditioned on target b-vectors. Within the network we use a convolutional long short term memory (ConvLSTM) cell to model the relationship between q-space samples. We compare model performance against a baseline spherical harmonic interpolation and a 1D variant of the model architecture. We show that the 3D model has the lowest error rates across different subsampling schemes and b-values. The relative performance of the 3D RCNN is greatest in the very low angular resolution domain. Code for this project is available at https://github.com/m-lyon/dMRI-RCNN.
