Prospective Quality Metric Assessment of SyntheticCT Images via a Learnable Framework

Motivation: Prospective quality assessment of synthetic CT images by predicting an accuracy metric. Such a score can be an indication of confidence of model prediction or be used as a feedback for performance of the model. Goal(s): Prediction of mean absolute value of synthetic CT image without a reference CT imageApproach: A deep learning framework which is trained to predict MAE metric of a given image. Results: The proposed QMetNet model learns to predict the MAE metric on unseen data in a reliable manner without a reference image. Impact: This novel framework makes it possible to train models to predict a choice of metrics as suitable for different applications. It could be a potential solution to provide confidence of prediction of a model to ease adoption of AI solutions.