Fast brain MRI can help patients complete scans more comfortably, but shorter scan time can come with trade-offs in image quality. A 2026 analysis published in European Journal of Radiology evaluated NeuroMix-DL, a deep learning approach designed to improve images from NeuroMix, a rapid multi-contrast brain MRI protocol. The rapid sequence captures several brain MRI contrasts in one short acquisition, while the enhancement model focuses on improving T1-weighted, T2-weighted and FLAIR images. The work used paired fast and conventional brain MRI images from 350 patients scanned during clinical care. The enhanced images showed better overall quality, lower image error and higher clinical image quality scores, although some artefacts remained after processing. The findings place deep learning as a possible adjunct to fast brain MRI rather than a replacement for conventional clinical assessment.
Faster Scanning for Brain Imaging
MRI plays an important role in non-invasive brain imaging, but longer acquisition time can increase discomfort and motion during scanning. Patient anxiety during MRI examinations may also affect tolerance, particularly in those with claustrophobia. Faster protocols aim to reduce these barriers while still producing images suitable for clinical assessment.
The rapid protocol assessed in the analysis generates several unenhanced brain MRI contrasts in a single acquisition. These include T1-weighted, T2-weighted, FLAIR, diffusion-weighted and susceptibility-related imaging. The approach combines motion-robust acquisition methods with fast readouts to reduce total scan time compared with a typical multi-contrast brain MRI examination.
The clinical cohort came from emergency department and inpatient settings. The fast sequence was obtained immediately after scout imaging so that images would be available if a patient could not tolerate a standard protocol. All patients also underwent conventional imaging, which provided higher-resolution reference images for comparison.
The quality evaluation centred on three outputs: T1-weighted, T2-weighted and FLAIR imaging. These were selected because the corresponding conventional sequences had higher resolution and image quality. The rapid acquisition took only a short fraction of the time required for the equivalent conventional sequences, creating a practical setting in which speed and quality could be compared directly.
A Model Designed to Improve Fast MRI
The deep learning framework uses a transformer-based model to convert lower-quality fast images into outputs closer to conventional MRI appearance. Separate models were used for T1-weighted, T2-weighted and FLAIR imaging. Each model received a rapid-acquisition image as input and used the matching conventional image from the same patient as the training target.
The framework was based on SwinUNETR, a neural network architecture that combines transformer components with image reconstruction features. The design allows the model to work with image patterns at different scales. In practice, the system learns from paired fast and conventional images, then generates an enhanced version of the fast image.
Before training, the images were aligned and standardised through preprocessing. Conventional images were resized to match the rapid protocol dimensions, registered to the corresponding fast images and checked visually. Further steps corrected intensity variation, normalised the images and aligned them to a standard brain template.
The model was implemented using Python, MONAI and PyTorch. Training was performed separately for each of the three selected contrasts. Once trained, the system produced an enhanced image volume in around half a minute. This processing speed supports potential near real-time use, although clinical deployment would still require validation in the settings where the tool is used.
Image Quality Improves but Artefacts Remain
The enhanced images showed improved quality across the three assessed MRI contrasts. Quantitative measures showed lower image error and better similarity to conventional imaging after processing. The largest reduction in error occurred in T1-weighted imaging, while T2-weighted and FLAIR images also improved. Measures of image similarity increased across all three sequences.
Clinical review also favoured the enhanced images. A board-certified neuroradiologist scored anonymised fast and enhanced images against the conventional images using a five-point image quality scale. Image quality scores increased for T1-weighted, T2-weighted and FLAIR imaging after processing. Low-quality scores became less common across the assessed cases.
Spatial resolution improved as well. Measurements of a small brain structure showed that the enhanced images moved closer to conventional MRI than the original rapid-acquisition images. Visual assessment also found sharper edges and reduced noise. In some cases, conventional scans showed motion artefacts while the shorter-acquisition images were less affected, consistent with the advantage of faster scanning.
The model did not remove every problem. Artefacts present in the original fast images could persist after processing. One example involved artefact from a ventriculoperitoneal shunt, which limited assessment of nearby brain tissue and was not corrected. Motion during the fast acquisition could also carry through into the output. A grid artefact appeared in some slices, although those images remained interpretable. Susceptibility-related artefacts also remained a limitation.
Deep learning improved the quality of selected images from a fast multi-contrast brain MRI protocol while preserving the central advantage of shorter acquisition. The approach enhanced T1-weighted, T2-weighted and FLAIR images, improved clinical quality scores and brought spatial resolution closer to conventional imaging. The findings also show clear limits, as artefacts in the original fast images can remain after processing. Larger, multi-site evaluation and clinical performance testing are still needed. For now, the approach fits best as an adjunct to established imaging and clinical decision-making rather than a stand-alone replacement.
Source: European Journal of Radiology
Image Credit: iStock
References:
Sanaat A, Decker J, Hussein R et al. (2026) NeuroMix-DL: Improving imaging quality of a fast multiparametric MRI protocol using deep learning. European Journal of Radiology: In Press.
