标题： 通过多任务学习的MRI同时分割和松弛率分析 显示英文标题

标题： Simultaneous Segmentation and Relaxometry for MRI through Multitask Learning

摘要： 目的：本研究展示了一种用于人体脑组织三维同时分割和弛豫时间测量的MR信号多任务学习方法。 材料和方法：使用了3D反转准备平衡稳态自由进动序列来获取体内多对比度脑部图像。 深度神经网络包含3个残差块，每个块具有8个全连接层，每个层具有Sigmoid激活函数、层归一化和256个神经元。 在线合成的MR信号演化和标签被用来逐批训练神经网络。 经验定义的正常灰质、白质和脑脊液（CSF）的T1和T2值范围被用作先验知识。 在3名健康志愿者以及动物（N=6）和前列腺患者（N=1）实验中进行了MRI脑部实验。 结果：在动物验证实验中，从所提出的方法估计的T1和T2值与真实值之间的差异/误差（均值差异$\pm$标准差差异）分别为T1的113$\pm$486和154$\pm$512毫秒，以及T2的5$\pm$33和7$\pm$41毫秒。 在健康志愿者实验（N=3）中，整个大脑的分割和弛豫时间测量在~5秒内完成。 估计的表观T1和T2图与已知的大脑解剖结构一致，并且不受线圈灵敏度变化的影响。 灰质、白质和CSF成功地被分割出来。 深度神经网络还可以生成合成的T1和T2加权图像。 结论：所提出的多任务学习方法可以结合灰质、白质和CSF的分割，直接生成大脑表观T1和T2图以及合成的T1和T2加权图像。 显示英文摘要

摘要： Purpose: This study demonstrated an MR signal multitask learning method for 3D simultaneous segmentation and relaxometry of human brain tissues. Materials and Methods: A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multi-contrast brain images. The deep neural network contained 3 residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on 3 healthy volunteers as well as animal (N=6) and prostate patient (N=1) experiments. Results: In animal validation experiment, the differences/errors (mean difference $\pm$ standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113 $\pm$ 486 and 154 $\pm$ 512 ms for T1, and 5 $\pm$ 33 and 7 $\pm$ 41 ms for T2, respectively. In healthy volunteer experiments (N=3), whole brain segmentation and relaxometry were finished within ~5 seconds. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1 and T2 weighted images. Conclusion: The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1 and T2 weighted images, in conjunction with segmentation of gray matter, white matter and CSF.