Abstract
Reconstructing digital brain phantoms in the form of multi-channeled brain tissue probability maps for individual subjects is essential for capturing brain anatomical variability, understanding neurological diseases, as well as for testing image processing methods. We demonstrate the first framework that optimizes brain tissue probability maps (Gray Matter - GM, White Matter - WM, and Cerebrospinal fluid - CSF) with the help of a Physics-based differentiable MRI simulator that models the magnetization signal at each voxel in the image. Given an observed $T_1$/$T_2$-weighted MRI scan, the corresponding clinical MRI sequence, and the MRI differentiable simulator, we optimize the simulator's input probability maps by back-propagating the L2 loss between the simulator's output and the $T_1$/$T_2$-weighted scan. This approach has the significant advantage of not relying on any training data, and instead uses the strong inductive bias of the MRI simulator. We tested the model on 20 scans from the BrainWeb database and demonstrate a highly accurate reconstruction of GM, WM, and CSF.
Abstract (translated)
重建数字脑图在个体 subjects 形式为多通道脑组织概率图对于捕捉脑解剖变异、理解神经系统疾病以及测试图像处理方法来说至关重要。我们证明了第一个利用基于物理的差分 MRI 模拟器优化脑组织概率图（灰质 - GM，白质 - WM 和蛛网膜腔 - CSF）的框架。在观察到 $T_1$/$T_2$ 加权 MRI 扫描、相应临床 MRI 序列和 MRI 差分模拟器的基础上，我们通过反向传播模拟器输出与 $T_1$/$T_2$ 加权扫描之间的 L2 损失来优化模拟器的输入概率图。这种方法具有不依赖于任何训练数据的优势，而是利用了 MRI 模拟器的强大归纳偏差。我们在 BrainWeb 数据库的 20 个扫描上测试了该模型，并证明了 GM、WM 和 CSF 的重建高度准确。
URL
https://arxiv.org/abs/2404.14739
