Simulating rigid head motion artifacts on brain magnitude MRI data–Outcome on image quality and segmentation of the cerebral cortex

• Published in: PloS one Vol. 19; no. 4; p. e0301132
• Main Authors: Olsson, Hampus, Millward, Jason Michael, Starke, Ludger, Gladytz, Thomas, Klein, Tobias, Fehr, Jana, Lai, Wei-Chang, Lippert, Christoph, Niendorf, Thoralf, Waiczies, Sonia
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.04.2024; Public Library of Science (PLoS)
• Subjects: Analysis; Biology and Life Sciences; Brain; Brain - diagnostic imaging; Cerebral Cortex; Computer and Information Sciences; Engineering and Technology; Epidemiology; Health aspects; Human mechanics; Humans; Image Processing, Computer-Assisted - methods; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging equipment; Medicine and Health Sciences; Methods; Motion; Prevalence studies (Epidemiology); Research and Analysis Methods; Simulation methods; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0301132

Magnetic Resonance Imaging (MRI) datasets from epidemiological studies often show a lower prevalence of motion artifacts than what is encountered in clinical practice. These artifacts can be unevenly distributed between subject groups and studies which introduces a bias that needs addressing when augmenting data for machine learning purposes. Since unreconstructed multi-channel k-space data is typically not available for population-based MRI datasets, motion simulations must be performed using signal magnitude data. There is thus a need to systematically evaluate how realistic such magnitude-based simulations are. We performed magnitude-based motion simulations on a dataset (MR-ART) from 148 subjects in which real motion-corrupted reference data was also available. The similarity of real and simulated motion was assessed by using image quality metrics (IQMs) including Coefficient of Joint Variation (CJV), Signal-to-Noise-Ratio (SNR), and Contrast-to-Noise-Ratio (CNR). An additional comparison was made by investigating the decrease in the Dice-Sørensen Coefficient (DSC) of automated segmentations with increasing motion severity. Segmentation of the cerebral cortex was performed with 6 freely available tools: FreeSurfer, BrainSuite, ANTs, SAMSEG, FastSurfer, and SynthSeg+. To better mimic the real subject motion, the original motion simulation within an existing data augmentation framework (TorchIO), was modified. This allowed a non-random motion paradigm and phase encoding direction. The mean difference in CJV/SNR/CNR between the real motion-corrupted images and our modified simulations (0.004±0.054/-0.7±1.8/-0.09±0.55) was lower than that of the original simulations (0.015±0.061/0.2±2.0/-0.29±0.62). Further, the mean difference in the DSC between the real motion-corrupted images was lower for our modified simulations (0.03±0.06) compared to the original simulations (-0.15±0.09). SynthSeg+ showed the highest robustness towards all forms of motion, real and simulated. In conclusion, reasonably realistic synthetic motion artifacts can be induced on a large-scale when only magnitude MR images are available to obtain unbiased data sets for the training of machine learning based models.