Introduction: Low back pain is a global health issue causing disability and missed work days. Commonly used MRI scans including T1-weighted and T2-weighted images provide detailed information of the spine and surrounding tissues. Artificial intelligence showed promise in improving image quality and simultaneously reducing scan time. This study evaluates the performance of deep learning (DL)-based T2 turbo spin-echo (TSE, T2 _DLR ) and T1 TSE (T1 _DLR ) in lumbar spine imaging regarding acquisition time, image quality, artifact resistance, and diagnostic confidence. Material and methods: This retrospective monocentric study included 60 patients with lower back pain who underwent lumbar spinal MRI between February and April 2023. MRI parameters and DL reconstruction (DLR) techniques were utilized to acquire images. Two neuroradiologists independently evaluated image datasets based on various parameters using a 4-point Likert scale. Results: Accelerated imaging showed significantly less image noise and artifacts, as well as better image sharpness, compared to standard imaging. Overall image quality and diagnostic confidence were higher in accelerated imaging. Relevant disk herniations and spinal fractures were detected in both DLR and conventional images. Both readers favored accelerated imaging in the majority of examinations. The lumbar spine examination time was cut by 61% in accelerated imaging compared to standard imaging. Conclusion: In conclusion, the utilization of deep learning-based image reconstruction techniques in lumbar spinal imaging resulted in significant time savings of up to 61% compared to standard imaging, while also improving image quality and diagnostic confidence. These findings highlight the potential of these techniques to enhance efficiency and accuracy in clinical practice for patients with lower back pain.

Purpose: Patients with Moyamoya Angiopathy (MMA) require hemodynamic evaluation to assess the risk of stroke. Assessment of cerebral blood flow with [¹⁵O]water PET and acetazolamide challenge is the diagnostic standard for the evaluation of the cerebral perfusion reserve (CPR). Estimation of the cerebrovascular reactivity (CVR) by use of breath-hold-triggered fMRI (bh-fMRI) as an index of CPR has been proposed as a reliable and more readily available approach. Recent findings suggest the use of resting-state fMRI (rs-fMRI) which requires minimum patient compliance. The aim of this study was to compare rs-fMRI to bh-fMRI and [¹⁵O]water PET in patients with MMA. Methods: Patients with MMA underwent rs-fMRI and bh-fMRI in the same MRI session. Maps of the CVR gained by both modalities were compared retrospectively by calculating the correlation between the mean CVR of 12 volumes of interest. Additionally, the rs-maps of a subgroup of patients were compared to CPR-maps gained by [¹⁵O]water PET. Results: The comparison of the rs-maps and the bh-maps of 24 patients revealed a good correlation (Pearson’s r = 0.71 ± 0.13; preoperative patients: Pearson’s r = 0.71 ± 0.17; postoperative patients: Pearson’s r = 0.71 ± 0.11). The comparison of 7 rs-fMRI data sets to the corresponding [¹⁵O]water PET data sets also revealed a high level of agreement (Pearson’s r = 0.80 ± 0.19). Conclusion: The present analysis indicates that rs-fMRI might be a promising non-invasive method with almost no patient cooperation needed to evaluate the CVR. Further prospective studies are required.

Purpose: Patients with Moyamoya Angiopathy (MMA) require hemodynamic assessment to evaluate the risk of stroke. Hemodynamic evaluation by use of breath-hold-triggered fMRI (bh-fMRI) was proposed as a readily available alternative to the diagnostic standard [¹⁵O]water PET. Recent studies suggest voxel-wise hemodynamic delay correction in hypercapnia-triggered fMRI. The aim of this study was to evaluate the effect of delay correction of bh-fMRI in patients with MMA and to compare the results with [¹⁵O]water PET. Methods: bh-fMRI data sets of 22 patients with MMA were evaluated without and with voxel-wise delay correction within different shift ranges and compared to the corresponding [¹⁵O]water PET data sets. The effects were evaluated combined and in subgroups of data sets with most severely impaired CVR (apparent steal phenomenon), data sets with territorial time delay, and data sets with neither steal phenomenon nor delay between vascular territories. Results: The study revealed a high mean cross-correlation (r = 0.79, p < 0.001) between bh-fMRI and [¹⁵O]water PET. The correlation was strongly dependent on the choice of the shift range. Overall, no shift range revealed a significantly improved correlation between bh-fMRI and [¹⁵O]water PET compared to the correlation without delay correction. Delay correction within shift ranges with positive high high cutoff revealed a lower agreement between bh-fMRI and PET overall and in all subgroups. Conclusion: Voxel-wise delay correction, in particular with shift ranges with high cutoff, should be used critically as it can lead to false-negative results in regions with impaired CVR and a lower correlation to the diagnostic standard [¹⁵O]water PET.

For the prognosis of stroke, patients with moyamoya disease (MMD) require the estimation of remaining cerebrovascular reactivity. For this purpose, CO₂-triggered BOLD fMRI by use of short breath-hold periods seems to be a highly available alternative to nuclear medicine methods. Too long breath-hold periods are difficult to perform, too short breath-hold periods do not lead to sufficient BOLD signal changes. We aimed to investigate the required minimum breath-hold duration to detect distinct BOLD signals in the tissue of healthy subjects to find out how long the minimum breath-hold duration in clinical diagnostics of MMD should be. A prospective study was performed. Fourteen healthy subjects underwent fMRI during end-expiration breath-hold periods of different duration (3, 6, 9, and 12 s). Additionally, we compared the influence of paced and self-paced breathing altering the breath-hold periods. Data of a patient with MMD was evaluated to investigate whether the tested procedure is suitable for clinical use. Significant global BOLD signal increases were detected after breath-hold periods of 6, 9, and 12 s. The signals were significantly higher after breath-hold periods of 9 s than after 6 s, while not when the duration was extended from 9 to 12 s. Furthermore, we found additional BOLD signal changes before the expected signal increases, which could be avoided by paced respiratory instructions. This investigation indicates that end-expiration breath-hold period of at least 9 s might be used to measure the cerebrovascular reactivity. This time period resulted in distinct BOLD signal changes and could be performed easily.

Before revascularization, moyamoya patients require hemodynamic evaluation. In this study, we evaluated the scoring system Prior Infarcts, Reactivity and Angiography in Moyamoya Disease (PIRAMID). We also devised a new scoring system, MRI-Based Assessment of Risk for Stroke in Moyamoya Angiopathy (MARS-MMA), and compared the scoring systems with respect to the capability to predict impaired [¹⁵O]water PET cerebral perfusion reserve capacity (CPR). We evaluated 69 MRI, 69 DSA and 38 [¹⁵O]water PET data sets. The PIRAMID system was validated by ROC curve analysis with neurological symptomatology as a dependent variable. The components of the MARS-MMA system and their weightings were determined by binary logistic regression analysis. The comparison of PIRAMID and MARS-MMA was performed by ROC curve analysis. The PIRAMID score correlated well with the symptomatology (AUC = 0.784). The MARS-MMA system, including impaired breath-hold-fMRI, the presence of the Ivy sign and arterial wall contrast enhancement, correlated slightly better with CPR impairment than the PIRAMID system (AUC = 0.859 vs. 0.827, Akaike information criterion 140 vs. 146). For simplified clinical use, we determined three MARS-MMA grades without loss of diagnostic performance (AUC = 0.855). The entirely MRI-based MARS-MMA scoring system might be a promising tool to predict the risk of stroke.

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.