Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI
Zero-Echo-Time and Dixon Deep Pseudo-CT (ZeDD CT): Direct Generation of Pseudo-CT Images for Pelvic PET/MRI Attenuation Correction Using Deep Convolutional Neural Networks with Multiparametric MRI
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DOI：
10.2967/jnumed.117.198051
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发表时间：
2018-05-01
影响因子：
9.3
通讯作者：
Larson, Peder E. Z.
中科院分区：
文献类型：
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作者：
Leynes, Andrew P.;Yang, Jaewon;Larson, Peder E. Z.
PET图像上摄取的准确定量取决于重建中准确的衰减校正。当前用于身体PET的基于MR的衰减校正方法使用从2回波狄克逊MRI序列导出的脂肪和水图，其中忽略了骨骼。超短回波时间或零回波时间（ZTE）脉冲序列可以捕获骨骼信息。我们建议使用由狄克逊MRI和质子密度加权ZTE MRI组成的患者特定多参数MRI，通过深度学习模型直接合成伪CT图像：我们将这种方法称为ZTE和狄克逊深度伪CT（ZeDD CT）。方法：26例患者使用集成的3-T飞行时间PET/MRI系统进行扫描。分别采集患者的螺旋CT图像。训练深度卷积神经网络将ZTE和狄克逊MR图像转换为伪CT图像。10名患者用于模型训练，16名患者用于评估。确定骨和软组织病变，并测量SUVmax。均方根误差（RMSE）用于比较基于MR的衰减校正与地面真实CT衰减校正。结果：共评价了30处骨病变和60处软组织病变。对于骨病变，PET定量的RMSE降低了4倍（狄克逊PET为10.24%，ZeDD PET为2.68%），对于软组织病变，降低了1.5倍（狄克逊PET为6.24%，ZeDD PET为4.07%）。结论：与标准方法相比，ZeDD CT可生成外观自然且定量准确的伪CT图像，并减少骨盆PET/MRI衰减校正的误差。
Accurate quantification of uptake on PET images depends on accurate attenuation correction in reconstruction. Current MR-based attenuation correction methods for body PET use a fat and water map derived from a 2-echo Dixon MRI sequence in which bone is neglected. Ultrashort-echo-time or zero-echo-time (ZTE) pulse sequences can capture bone information. We propose the use of patient-specific multiparametric MRI consisting of Dixon MRI and proton-density-weighted ZTE MRI to directly synthesize pseudo-CT images with a deep learning model: we call this method ZTE and Dixon deep pseudo-CT (ZeDD CT). Methods: Twenty-six patients were scanned using an integrated 3-T time-of-flight PET/MRI system. Helical CT images of the patients were acquired separately. A deep convolutional neural network was trained to transform ZTE and Dixon MR images into pseudo-CT images. Ten patients were used for model training, and 16 patients were used for evaluation. Bone and soft-tissue lesions were identified, and the SUVmax was measured. The root-mean-squared error (RMSE) was used to compare the MR-based attenuation correction with the ground-truth CT attenuation correction. Results: In total, 30 bone lesions and 60 soft-tissue lesions were evaluated. The RMSE in PET quantification was reduced by a factor of 4 for bone lesions (10.24% for Dixon PET and 2.68% for ZeDD PET) and by a factor of 1.5 for soft-tissue lesions (6.24% for Dixon PET and 4.07% for ZeDD PET). Conclusion: ZeDD CT produces natural-looking and quantitatively accurate pseudo-CT images and reduces error in pelvic PET/MRI attenuation correction compared with standard methods.