Introduction

Existing approaches to assess motion artifact severity in MR images rely on navigators^1,2, optical tracking cameras³, analyzing the air background⁴ or sequences with special motion-sensitive sampling patterns⁵. With the rise of machine learning based solutions, it becomes appealing to simply learn a function that matches radiologists’ quality opinions by training end-to-end. Nevertheless, motion-corrupted data is barely available and even more rarely annotated. We therefore simulated motion-corrupted images to compute associated metrics and quantify the corresponding severity of motion. We finally train a neural network to regress to those metrics and ultimately detect whether the image is of acceptable quality or not.

Methods

Motion corrupted images are generated from subsets of the publicly available dataset HCP⁶. The motion model assumes 2D in-plane translational motion for each slice, where the static object is shifted in the image space and then transformed into k-space to mimic data acquisition with motion. The k-space readout is assumed fast enough to freeze any patient motion and only inter-readout motion are simulated. To mimic real MR acquisitions where k-space data may not be collected sequentially, random numbers of motion-affected readout lines are used and these lines are selected randomly for each 2D k-space. The direction and the magnitude of the displacement are also randomly assigned to each affected lines. After replacing all motion-affected readout lines, the k-space data is transformed back into image space. For each slice, we compute the normalized L₂-distance between the simulated and the original image (NRMSE) and the MSSIM⁷ that we use as quality metrics. Each metric is then used as target of a regression task. A fully convolutional Dense-Net⁸ architecture depicted in figure 1 was trained without fully connected layers. As motion artifacts in MR images propagate into the background, an object mask computed via histogram analysis was used to separate the foreground (FG) to the background (BG). We trained four models that would take either the full image (IMG-model), FG only (FG-model), BG only (BG-model) or both FG and BG in two channels as input (FGBG-model). The training set included 230,104 2D slices from 90 T2 weighted volumes including all three orientations. Data augmentation included random transformations, such as identity, horizontal and vertical mirroring, and 90, 180 and 270 degrees rotations. The data split is 90% for training and 10% for validation. Inputs are normalized to zero mean and unit standard deviation. We train end-to-end with a batch size of 20 and the Adam optimizer⁹ with a learning rate of 10-4 to minimize the L₁-distance. In a real world scenario, an image is either of an acceptable quality or not. We establish an acceptable / not acceptable ground truth by setting a threshold on the previously mentioned metrics from figure 2. Any threshold within a reasonable range could be a valid choice, depending on the level of tolerable motion. Therefore, we calculate the accuracy for a range of thresholds across the range of scores that we obtained for each metric by thresholding targets and predictions.

Results

We define a range of potential thresholds for cases with very little motion, close to the optimal metric value (figure 3). For both metrics used, BG-models perform better than FGBG-models. Models that have background input seem to converge to the same performance when moving the threshold away from the optimum. We test the sensitivity of the models for a fixed ground truth threshold and plot the ROC-curve in figure 4. The threshold is chosen to be 0.01 for the NRMSE and 0.99 for the MSSIM. Models including background information share AUC-ROC values above 0.99 for both metrics. FG-only models perform slightly worse (0.9826 for NRMSE and 0.989 for MSSIM). We visualize the network activation in a guided backpropagation map¹⁰ in figure 5.

Conclusions

We present a regression approach for image quality assessment of simulated motion-corrupted MR-images. With simulated images, we use a reference metric to establish a motion related image quality metric and compare the importance of foreground and background information for two different image metrics, the NRMSE and the MSSIM. We present a network that is capable of regressing to both metrics with high accuracy and can be trained on arbitrary input dimensions without the need of padding. Since metric functions for clinical image quality is still an active field of research, this framework can be adapted for any metric that is to be found in future research¹¹.

Acknowledgements

No acknowledgement found.

References

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