Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany.
Magn Reson Med. 2022 Dec;88(6):2679-2693. doi: 10.1002/mrm.29380. Epub 2022 Aug 2.
To develop an algorithm for the retrospective correction of signal dropout artifacts in abdominal DWI resulting from cardiac motion.
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.
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.
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.
开发一种用于校正腹部 DWI 中因心脏运动导致的信号丢失伪影的回溯校正算法。
对于一个切片的一组图像重复，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，该算法分析了来自补丁参考的信号偏差。为了确保稳健参考的计算，通过分类器对重复项进行过滤，该分类器经过训练可检测到被信号丢失损坏的图像。该方法名为深度学习引导自适应加权平均（DLAWA），根据抑制丢失能力、ADC 中的偏差减少和噪声特性进行评估。
在均匀平均的情况下，运动相关的丢失会导致肝脏部分信号衰减和 ADC 高估，尤其是左叶。DLAWA 可以大大减轻这两种影响，同时防止由于局部信号抑制而导致 SNR 全局降低。通过对患者数据进行评估，还证明了该方法能够恢复因信号丢失而隐藏的病变。此外，DLAWA 通过几个超参数允许对 SNR 和信号丢失抑制之间的权衡进行透明控制。
本工作提出了一种用于校正运动和脉动引起的信号丢失的有效且灵活的局部补偿方法。由于 DLAWA 采用回溯方法，因此不需要对采集进行更改。
