Synthetic MRI in subarachnoid haemorrhage

Technical aspects and clinical applications of synthetic MRI: a scoping review

INTRODUCTION

Synthetic magnetic resonance imaging (SyMRI) is a non-invasive, robust MRI technique that generates multiple contrast-weighted images by acquiring a single MRI sequence within a few minutes, along with quantitative maps, automatic brain segmentation, and volumetry. Since its inception, it has undergone technical advancements and has also been tested for feasibility in various organs and pathological conditions. This scoping review comprehensively pinpoints the critical technical aspects and maps the wide range of clinical applications/benefits of SyMRI.

CONTENT

A comprehensive search was conducted across five databases, PubMed, Scopus, Web of Science, Embase, and CINAHL Ultimate, using appropriate keywords related to SyMRI. A total of 99 studies were included after a 2-step screening process. Data related to the technical factors and clinical application was charted.

SUMMARY

SyMRI provides quantitative maps and segmentation techniques comparable to conventional MRI and has demonstrated feasibility and applications across neuroimaging, musculoskeletal, abdominal and breast pathologies spanning the entire human lifespan, from prenatal development to advanced age. Certain drawbacks related to image quality have been encountered that can be overcome with technical advances, especially AI-based algorithms.

OUTLOOK

SyMRI has immense potential for being incorporated into routine imaging for various pathologies due to its added advantage of providing quantitative measurements for more robust diagnostic and prognostic work-up with faster acquisitions and greater post-processing options.

Synthetic MR: Clinical applications in neuroradiology

PURPOSE

Synthetic MR is a quantitative MRI method that measures tissue relaxation times and generates multiple contrast-weighted images using suitable algorithms. The present article principally discusses the multiple dynamic multiple echo (MDME) technique of synthetic MR and briefly describes other quantitative MR sequences.

METHODS

Using illustrative cases, various applications of the MDME sequence in neuroradiology are explained. The MDME sequence allows rapid quantification of tissue relaxation times in a scan duration of 5-7 minutes for full brain coverage. It also has the additional advantages of myelin quantification and automatic segmentation of brain volumes.

RESULTS

Applications including reducing scan time, improved detection of demyelinating plaques in Multiple Sclerosis (MS), objective assessment and follow-up for brain atrophy in neurodegenerative MS and dementia cases, and applications in stroke imaging and neuro-oncology are discussed. Uses in the pediatric population, including assessment of brain development and progression of myelination in children, evaluation of white matter disorders, and evaluation of pediatric and adult epilepsy, are elaborated. Quantitative evaluation by synthetic MR is discussed, which allows homogenization and objectification of the radiology data and can serve as a valuable source for artificial intelligence and future multicentre studies. A brief discussion on the technique, other quantitative MR methods, and limitations of the MDME sequence is also presented.

CONCLUSION

The article intends to provide an explicit and comprehensive review of the applications of synthetic MR in neuroradiology, exploring its potential as a routine sequence in daily neuroimaging practice.

Multi-Dynamic-Multi-Echo-based MRI for the Pre-Surgical Determination of Sellar Tumor Consistency: a Quantitative Approach for Predicting Lesion Resectability

PURPOSE

Pre-surgical information about tumor consistency could facilitate neurosurgical planning. This study used multi-dynamic-multi-echo (MDME)-based relaxometry for the quantitative determination of pituitary tumor consistency, with the aim of predicting lesion resectability.

METHODS

Seventy-two patients with suspected pituitary adenomas, who underwent preoperative 3 T MRI between January 2020 and January 2022, were included in this prospective study. Lesion-specific T1-/T2-relaxation times (T1R/T2R) and proton density (PD) metrics were determined. During surgery, data about tumor resectability were collected. A Receiver Operating Characteristic (ROC) curve analysis was performed to investigate the diagnostic performance (sensitivity/specificity) for discriminating between easy- and hard-to-remove by aspiration (eRAsp and hRAsp) lesions. A Mann-Whitney-U-test was done for group comparison.

RESULTS

A total of 65 participants (mean age, 54 years ± 15, 33 women) were enrolled in the quantitative analysis. Twenty-four lesions were classified as hRAsp, while 41 lesions were assessed as eRAsp. There were significant differences in T1R (hRAsp: 1221.0 ms ± 211.9; eRAsp: 1500.2 ms ± 496.4; p = 0.003) and T2R (hRAsp: 88.8 ms ± 14.5; eRAsp: 137.2 ms ± 166.6; p = 0.03) between both groups. The ROC analysis revealed an area under the curve of 0.72 (95% CI: 0.60-0.85) at p = 0.003 for T1R (cutoff value: 1248 ms; sensitivity/specificity: 78%/58%) and 0.66 (95% CI: 0.53-0.79) at p = 0.03 for T2R (cutoff value: 110 ms; sensitivity/specificity: 39%/96%).

CONCLUSION

MDME-based relaxometry enables a non-invasive, pre-surgical characterization of lesion consistency and, therefore, provides a modality with which to predict tumor resectability.

Highly accelerated 3D MPRAGE using deep neural network-based reconstruction for brain imaging in children and young adults

OBJECTIVES

This study aimed to accelerate the 3D magnetization-prepared rapid gradient-echo (MPRAGE) sequence for brain imaging through the deep neural network (DNN).

METHODS

This retrospective study used the k-space data of 240 scans (160 for the training set, mean ± standard deviation age, 93 ± 80 months, 94 males; 80 for the test set, 106 ± 83 months, 44 males) of conventional MPRAGE (C-MPRAGE) and 102 scans (77 ± 74 months, 52 males) of both C-MPRAGE and accelerated MPRAGE. All scans were acquired with 3T scanners. DNN was developed with simulated-acceleration data generated by under-sampling. Quantitative error metrics were compared between images reconstructed with DNN, GRAPPA, and E-SPIRIT using the paired t-test. Qualitative image quality was compared between C-MPRAGE and accelerated MPRAGE reconstructed with DNN (DNN-MPRAGE) by two readers. Lesions were segmented and the agreement between C-MPRAGE and DNN-MPRAGE was assessed using linear regression.

RESULTS

Accelerated MPRAGE reduced scan times by 38% compared to C-MPRAGE (142 s vs. 320 s). For quantitative error metrics, DNN showed better performance than GRAPPA and E-SPIRIT (p < 0.001). For qualitative evaluation, overall image quality of DNN-MPRAGE was comparable (p > 0.999) or better (p = 0.025) than C-MPRAGE, depending on the reader. Pixelation was reduced in DNN-MPRAGE (p < 0.001). Other qualitative parameters were comparable (p > 0.05). Lesions in C-MPRAGE and DNN-MPRAGE showed good agreement for the dice similarity coefficient (= 0.68) and linear regression (R² = 0.97; p < 0.001).

CONCLUSIONS

DNN-MPRAGE reduced acquisition time by 38% and revealed comparable image quality to C-MPRAGE.

KEY POINTS

• DNN-MPRAGE reduced acquisition times by 38%. • DNN-MPRAGE outperformed conventional reconstruction on accelerated scans (SSIM of DNN-MPRAGE = 0.96, GRAPPA = 0.43, E-SPIRIT = 0.88; p < 0.001). • Compared to C-MPRAGE scans, DNN-MPRAGE showed improved mean scores for overall image quality (2.46 vs. 2.52; p < 0.001) or comparable perceived SNR (2.56 vs. 2.58; p = 0.08).