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首页> 美国卫生研究院文献>Frontiers in Oncology >Generation of Synthetic CT Images From MRI for Treatment Planning and Patient Positioning Using a 3-Channel U-Net Trained on Sagittal Images
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Generation of Synthetic CT Images From MRI for Treatment Planning and Patient Positioning Using a 3-Channel U-Net Trained on Sagittal Images

机译：使用在矢状面图像上训练的3通道U网，从MRI生成用于治疗计划和患者定位的MRI合成CT图像

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A novel deep learning architecture was explored to create synthetic CT (MRCT) images that preserve soft tissue contrast necessary for support of patient positioning in Radiation therapy. A U-Net architecture was applied to learn the correspondence between input T1-weighted MRI and spatially aligned corresponding CT images. The network was trained on sagittal images, taking advantage of the left-right symmetry of the brain to increase the amount of training data for similar anatomic positions. The output CT images were divided into three channels, representing Hounsfield Unit (HU) ranges of voxels containing air, soft tissue, and bone, respectively, and simultaneously trained using a combined Mean Absolute Error (MAE) and Mean Squared Error (MSE) loss function equally weighted for each channel. Training on 9192 image pairs yielded resulting synthetic CT images on 13 test patients with MAE of 17.6+/−3.4 HU (range 14–26.5 HU) in soft tissue. Varying the amount of training data demonstrated a general decrease in MAE values with more data, with the lack of a plateau indicating that additional training data could further improve correspondence between MRCT and CT tissue intensities. Treatment plans optimized on MRCT-derived density grids using this network for 7 radiosurgical targets had doses recalculated using the corresponding CT-derived density grids, yielding a systematic mean target dose difference of 2.3% due to the lack of the immobilization mask on the MRCT images, and a standard deviation of 0.1%, indicating the consistency of this correctable difference. Alignment of MRCT and cone beam CT (CBCT) images used for patient positioning demonstrated excellent preservation of dominant soft tissue features, and alignment comparisons of treatment planning CT scans to CBCT images vs. MRCT to CBCT alignment demonstrated differences of −0.1 (σ 0.2) mm, −0.1 (σ 0.3) mm, and −0.2 (σ 0.3) mm about the left-right, anterior-posterior and cranial-caudal axes, respectively.
机译：探索了一种新型的深度学习体系结构，以创建合成的CT（MRCT）图像，该图像保留了支持放射治疗中患者定位所必需的软组织对比度。应用U-Net架构学习输入的T1加权MRI与空间对齐的相应CT图像之间的对应关系。利用矢状面图像对网络进行训练，利用大脑的左右对称性来增加类似解剖位置的训练数据量。输出的CT图像分为三个通道，分别代表包含空气，软组织和骨骼的体素的Hounsfield单位（HU）范围，并使用组合的平均绝对误差（MAE）和均方误差（MSE）损失同时进行训练每个通道的权重均等。对9192个图像对进行训练后，在软组织中的13位MAE为17.6 +/- 3.4 HU（范围为14-26.5 HU）的测试患者上产生了合成的CT图像。训练数据量的变化表明，随着数据的增加，MAE值普遍下降，而缺乏平稳状态则表明其他训练数据可以进一步改善MRCT和CT组织强度之间的对应关系。使用该网络针对7个放射外科目标在MRCT衍生的密度网格上优化的治疗计划已使用相应的CT衍生的密度网格重新计算了剂量，由于MRCT图像上缺少固定罩，导致系统平均目标剂量差异为2.3％ ，标准偏差为0.1％，表明此可校正差异的一致性。用于患者定位的MRCT和锥束CT（CBCT）图像的对齐方式显示了出色的优势软组织特征保留，并且治疗计划CT扫描与CBCT图像对比以及MRCT到CBCT对齐方式的对比比较表明，差异为-0.1（σ0.2）分别围绕左右，前后和颅尾轴分别为mm，-0.1（σ0.3）mm和-0.2（σ0.3）mm。

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