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Purpose: To develop an algorithm for the retrospective correction of signaldropout artifacts in abdominal diffusion-weighted imaging (DWI) resulting fromcardiac motion.  Methods: Given a set of image repetitions for a slice, a locally adaptiveweighted averaging is proposed which aims to suppress the contribution of imageregions affected by signal dropouts. Corresponding weight maps were estimatedby a sliding-window algorithm which analyzed signal deviations from apatch-wise reference. In order to ensure the computation of a robust reference,repetitions were filtered by a classifier that was trained to detect imagescorrupted by signal dropouts. The proposed method, termed Deep Learning-guidedAdaptive Weighted Averaging (DLAWA), was evaluated in terms of dropoutsuppression capability, bias reduction in the Apparent Diffusion Coefficient(ADC) and noise characteristics.  Results: In the case of uniform averaging, motion-related dropouts causedsignal attenuation and ADC overestimation in parts of the liver with the leftlobe being affected particularly. Both effects could be substantially mitigatedby DLAWA while preventing global penalties with respect to signal-to-noiseratio (SNR) due to local signal suppression. Performing evaluations on patientdata, the capability to recover lesions concealed by signal dropouts wasdemonstrated as well. Further, DLAWA allowed for transparent control of thetrade-off between SNR and signal dropout suppression by means of a fewhyperparameters.  Conclusion: This work presents an effective and flexible method for the localcompensation of signal dropouts resulting from motion and pulsation. SinceDLAWA follows a retrospective approach, no changes to the acquisition arerequired.

Deep learning-guided weighted averaging for signal dropout compensation in diffusion-weighted imaging of the liver