AI‐based motion artifact severity estimation in undersampled MRI allowing for selection of appropriate reconstruction models

Abstract

BackgroundMagnetic Resonance acquisition is a time consuming process, making it susceptible to patient motion during scanning. Even motion in the order of a millimeter can introduce severe blurring and ghosting artifacts, potentially necessitating re‐acquisition. Magnetic Resonance Imaging (MRI) can be accelerated by acquiring only a fraction of k‐space, combined with advanced reconstruction techniques leveraging coil sensitivity profiles and prior knowledge. Artificial intelligence (AI)‐based reconstruction techniques have recently been popularized, but generally assume an ideal setting without intra‐scan motion.PurposeTo retrospectively detect and quantify the severity of motion artifacts in undersampled MRI data. This may prove valuable as a safety mechanism for AI‐based approaches, provide useful information to the reconstruction method, or prompt for re‐acquisition while the patient is still in the scanner.MethodsWe developed a deep learning approach that detects and quantifies motion artifacts in undersampled brain MRI. We demonstrate that synthetically motion‐corrupted data can be leveraged to train the convolutional neural network (CNN)‐based motion artifact estimator, generalizing well to real‐world data. Additionally, we leverage the motion artifact estimator by using it as a selector for a motion‐robust reconstruction model in case a considerable amount of motion was detected, and a high data consistency model otherwise.ResultsTraining and validation were performed on 4387 and 1304 synthetically motion‐corrupted images and their uncorrupted counterparts, respectively. Testing was performed on undersampled in vivo motion‐corrupted data from 28 volunteers, where our model distinguished head motion from motion‐free scans with 91% and 96% accuracy when trained on synthetic and on real data, respectively. It predicted a manually defined quality label (‘Good’, ‘Medium’ or ‘Bad’ quality) correctly in 76% and 85% of the time when trained on synthetic and real data, respectively. When used as a selector it selected the appropriate reconstruction network 93% of the time, achieving near optimal SSIM values.ConclusionsThe proposed method quantified motion artifact severity in undersampled MRI data with high accuracy, enabling real‐time motion artifact detection that can help improve the safety and quality of AI‐based reconstructions.

Motion Correction for Brain MRI Using Deep Learning and a Novel Hybrid Loss Function

“…In [19], a retrospective motion correction method that combined the advantages of classical model-driven and data-consistency-preserving methods for fast and robust motion correction was proposed and evaluated. In a recent study, a DCNN was also used for estimating the severity of motion artifacts in under-sampled MRI data, providing useful information for use in the reconstruction method [20]. Finally, an encoder-decoder network was able to suppress motion artifacts with motion simulation augmentation [2].…”

“…Although great progress has been made by applying DCNNs to the motion correction problem, there is still much room for improvement. To the best of our knowledge, the source codes of most studies [1,9,[18][19][20] are not publicly available, making it difficult for researchers to build and improve upon prior studies. Also, many previous approaches directly utilized existing DCNN models for motion correction.…”