Abstract：
OBJECTIVE: To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion.
METHODS: Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed that aims to suppress the contribution of image regions affected by signal dropouts. Corresponding weight maps were estimated by a sliding-window algorithm, which analyzed signal deviations from a patch-wise reference. In order to ensure the computation of a robust reference, repetitions were filtered by a classifier that was trained to detect images corrupted by signal dropouts. The proposed method, named Deep Learning-guided Adaptive Weighted Averaging (DLAWA), was evaluated in terms of dropout suppression capability, bias reduction in the ADC, and noise characteristics.
RESULTS: In the case of uniform averaging, motion-related dropouts caused signal attenuation and ADC overestimation in parts of the liver, with the left lobe being affected particularly. Both effects could be substantially mitigated by DLAWA while preventing global penalties with respect to SNR due to local signal suppression. Performing evaluations on patient data, the capability to recover lesions concealed by signal dropouts was demonstrated as well. Further, DLAWA allowed for transparent control of the trade-off between SNR and signal dropout suppression by means of a few hyperparameters.
CONCLUSIONS: This work presents an effective and flexible method for the local compensation of signal dropouts resulting from motion and pulsation. Because DLAWA follows a retrospective approach, no changes to the acquisition are required.
METHODS: Given a set of image repetitions for a slice, a locally adaptive weighted averaging is proposed that aims to suppress the contribution of image regions affected by signal dropouts. Corresponding weight maps were estimated by a sliding-window algorithm, which analyzed signal deviations from a patch-wise reference. In order to ensure the computation of a robust reference, repetitions were filtered by a classifier that was trained to detect images corrupted by signal dropouts. The proposed method, named Deep Learning-guided Adaptive Weighted Averaging (DLAWA), was evaluated in terms of dropout suppression capability, bias reduction in the ADC, and noise characteristics.
RESULTS: In the case of uniform averaging, motion-related dropouts caused signal attenuation and ADC overestimation in parts of the liver, with the left lobe being affected particularly. Both effects could be substantially mitigated by DLAWA while preventing global penalties with respect to SNR due to local signal suppression. Performing evaluations on patient data, the capability to recover lesions concealed by signal dropouts was demonstrated as well. Further, DLAWA allowed for transparent control of the trade-off between SNR and signal dropout suppression by means of a few hyperparameters.
CONCLUSIONS: This work presents an effective and flexible method for the local compensation of signal dropouts resulting from motion and pulsation. Because DLAWA follows a retrospective approach, no changes to the acquisition are required.
摘要：
目的：开发一种算法，用于回顾性校正由心脏运动引起的腹部DWI中的信号丢失伪影。
方法：给定一组切片的图像重复，提出了一种局部自适应加权平均，旨在抑制受信号丢失影响的图像区域的贡献。通过滑动窗口算法估计相应的权重图，它分析了信号与逐片参考的偏差。为了确保可靠的参考计算，重复由分类器过滤,该分类器被训练以检测被信号丢失损坏的图像.所提出的方法，名为深度学习引导的自适应加权平均(DLAWA)，根据跌落抑制能力进行了评估，ADC中的偏置减少，和噪声特性。
结果：在均匀平均的情况下，运动相关的脱落导致肝脏部分的信号衰减和ADC高估，左叶尤其受到影响。DLAWA可以基本上减轻这两种影响,同时防止由于局部信号抑制而导致的关于SNR的全局损失。对患者数据进行评估，还证明了恢复信号丢失隐藏的病变的能力。Further,DLAWA允许通过一些超参数对SNR和信号丢失抑制之间的权衡进行透明控制。
结论：这项工作提出了一种有效且灵活的方法，用于对运动和脉动引起的信号丢失进行局部补偿。由于DLAWA遵循回顾性方法，不需要对收购进行任何更改。
方法：给定一组切片的图像重复，提出了一种局部自适应加权平均，旨在抑制受信号丢失影响的图像区域的贡献。通过滑动窗口算法估计相应的权重图，它分析了信号与逐片参考的偏差。为了确保可靠的参考计算，重复由分类器过滤,该分类器被训练以检测被信号丢失损坏的图像.所提出的方法，名为深度学习引导的自适应加权平均(DLAWA)，根据跌落抑制能力进行了评估，ADC中的偏置减少，和噪声特性。
结果：在均匀平均的情况下，运动相关的脱落导致肝脏部分的信号衰减和ADC高估，左叶尤其受到影响。DLAWA可以基本上减轻这两种影响,同时防止由于局部信号抑制而导致的关于SNR的全局损失。对患者数据进行评估，还证明了恢复信号丢失隐藏的病变的能力。Further,DLAWA允许通过一些超参数对SNR和信号丢失抑制之间的权衡进行透明控制。
结论：这项工作提出了一种有效且灵活的方法，用于对运动和脉动引起的信号丢失进行局部补偿。由于DLAWA遵循回顾性方法，不需要对收购进行任何更改。
