Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review

Application of Anti-Motion Ultra-Fast Quantitative MRI in Neurological Disorder Imaging: Insights From Huntington's Disease

BACKGROUND

Conventional quantitative MRI (qMRI) scan is time-consuming and highly sensitive to movements, posing great challenges for quantitative images of individuals with involuntary movements, such as Huntington's disease (HD).

PURPOSE

To evaluate the potential of our developed ultra-fast qMRI technique, multiple overlapping-echo detachment (MOLED), in overcoming involuntary head motion and its capacity to quantitatively assess tissue changes in HD.

STUDY TYPE

Prospective.

PHANTOM/SUBJECTS

A phantom comprising 13 tubes of MnCl2 at varying concentrations, 5 healthy volunteers (male/female: 1/4), 22 HD patients (male/female: 14/8) and 27 healthy controls (male/female: 15/12).

FIELD STRENGTH/SEQUENCE

3.0 T. MOLED-T2 sequence, MOLED-T2* sequence, T2-weighted spin-echo sequence, T1-weighted gradient echo sequence, and T2-dark-fluid sequence.

ASSESSMENT

T1-weighted images were reconstructed into high-resolution images, followed by segmentation to delineate regions of interest (ROIs). Subsequently, the MOLED T2 and T2* maps were aligned with the high-resolution images, and the ROIs were transformed into the MOLED image space using the transformation matrix and warp field. Finally, T2 and T2* values were extracted from the MOLED relaxation maps.

STATISTICAL TESTS

Bland-Altman analysis, independent t test, Mann-Whitney U test, Pearson correlation analysis, and Spearman correlation analysis, P < 0.05 was considered statistically significant.

RESULTS

MOLED-T2 and MOLED-T2* sequences demonstrated good accuracy (Meandiff = - 0.20%, SDdiff = 1.05%, and Meandiff = -1.73%, SDdiff = 10.98%, respectively), and good repeatability (average intraclass correlation coefficient: 0.856 and 0.853, respectively). More important, MOLED T2 and T2* maps remained artifact-free across all HD patients, even in the presence of apparent head motions. Moreover, there were significant differences in T2 and T2* values across multiple ROIs between HD and controls.

DATA CONCLUSION

The ultra-fast scanning capabilities of MOLED effectively mitigate the impact of head movements, offering a robust solution for quantitative imaging in HD. Moreover, T2 and T2* values derived from MOLED provide powerful capabilities for quantifying tissue changes.

PLAIN LANGUAGE SUMMARY

Quantitative MRI scan is time-consuming and sensitive to movements. Consequently, obtaining quantitative images is challenging for patients with involuntary movements, such as those with Huntington's Disease (HD). In response, a newly developed MOLED technique has been introduced, promising to resist motion through ultra-fast scan. This technique has demonstrated excellent accuracy and reproducibility and importantly all HD patient's MOLED maps remained artifacts-free. Additionally, there were significant differences in T2 and T2∗ values across ROIs between HD and controls. The robust resistance of MOLED to motion makes it particularly suitable for quantitative assessments in patients prone to involuntary movements.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Motion and magnetic field inhomogeneity correction techniques for chemical exchange saturation transfer (CEST) MRI: A contemporary review

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) has emerged as a powerful imaging technique sensitive to tissue molecular composition, pH, and metabolic processes in situ. CEST MRI uniquely probes the physical exchange of protons between water and specific molecules within tissues, providing a window into physiological phenomena that remain invisible to standard MRI. However, given the very low concentration (millimolar range) of CEST compounds, the effects measured are generally only on the order of a few percent of the water signal. Consequently, a few critical challenges, including correction of motion artifacts and magnetic field (B0 and B1 +) inhomogeneities, have to be addressed in order to unlock the full potential of CEST MRI. Motion, whether from patient movement or inherent physiological pulsations, can distort the CEST signal, hindering accurate quantification. B0 and B1 + inhomogeneities, arising from scanner hardware imperfections, further complicate data interpretation by introducing spurious variations in the signal intensity. Without proper correction of these confounding factors, reliable analysis and clinical translation of CEST MRI remain challenging. Motion correction methods aim to compensate for patient movement during (prospective) or after (retrospective) image acquisition, reducing artifacts and preserving data quality. Similarly, B0 and B1 + inhomogeneity correction techniques enhance the spatial and spectral accuracy of CEST MRI. This paper aims to provide a comprehensive review of the current landscape of motion and magnetic field inhomogeneity correction methods in CEST MRI. The methods discussed apply to saturation transfer (ST) MRI in general, including semisolid magnetization transfer contrast (MTC) and relayed nuclear Overhauser enhancement (rNOE) studies.

Clean Self-Supervised MRI Reconstruction from Noisy, Sub-Sampled Training Data with Robust SSDU

Most existing methods for magnetic resonance imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a fully sampled dataset with a high signal-to-noise ratio (SNR) is available for training. In many circumstances, however, such a dataset is highly impractical or even technically infeasible to acquire. Recently, a number of self-supervised methods for MRI reconstruction have been proposed, which use sub-sampled data only. However, the majority of such methods, such as Self-Supervised Learning via Data Undersampling (SSDU), are susceptible to reconstruction errors arising from noise in the measured data. In response, we propose Robust SSDU, which provably recovers clean images from noisy, sub-sampled training data by simultaneously estimating missing k-space samples and denoising the available samples. Robust SSDU trains the reconstruction network to map from a further noisy and sub-sampled version of the data to the original, singly noisy, and sub-sampled data and applies an additive Noisier2Noise correction term upon inference. We also present a related method, Noiser2Full, that recovers clean images when noisy, fully sampled data are available for training. Both proposed methods are applicable to any network architecture, are straightforward to implement, and have a similar computational cost to standard training. We evaluate our methods on the multi-coil fastMRI brain dataset with novel denoising-specific architecture and find that it performs competitively with a benchmark trained on clean, fully sampled data.

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation

PURPOSE

The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat-navigator-based PMC in improving perivascular space (PVS) visibility in T2-weighted MRI.

METHODS

MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2-weighted NoPMC and PMC images. To simulate motion artifacts, k-space data at motion-perturbed positions were calculated from artifact-free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion-induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility.

RESULTS

In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC.

CONCLUSIONS

The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.

Deep learning for accelerated and robust MRI reconstruction

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides a comprehensive overview of recent advances in DL for MRI reconstruction, and focuses on various DL approaches and architectures designed to improve image quality, accelerate scans, and address data-related challenges. It explores end-to-end neural networks, pre-trained and generative models, and self-supervised methods, and highlights their contributions to overcoming traditional MRI limitations. It also discusses the role of DL in optimizing acquisition protocols, enhancing robustness against distribution shifts, and tackling biases. Drawing on the extensive literature and practical insights, it outlines current successes, limitations, and future directions for leveraging DL in MRI reconstruction, while emphasizing the potential of DL to significantly impact clinical imaging practices.

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

BACKGROUND

Magnetic 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.

PURPOSE

To 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.

METHODS

We 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.

RESULTS

Training 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.

CONCLUSIONS

The 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.

Sharing Data Is Essential for the Future of AI in Medical Imaging

If we want artificial intelligence to succeed in radiology, we must share data and learn how to share data.