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.

Purpose: To evaluate the value of dual-phase parenchymal blood volume (PBV) C-arm mounted cone-beam-CT (CBCT) to enable assessment of radiopaque, doxorubicin-loaded drug-eluting embolics (rDEE) based on the visual degree of embolization, embolic density and residual tumor perfusion as early predictors for tumor recurrence after transarterial chemoembolization (TACE) of hepatocellular carcinoma (HCC). Material and Methods: Thirty patients (50 HCCs) were prospectively enrolled, underwent cross-sectional imaging before and after TACE using 100–300 µm rDEE and had regular follow-up examinations. Directly before and after the TACE procedure, PBV-CBCT was acquired. The response was evaluated and compared to visual degree of embolization (DE) and embolic density (ED) of rDEE deposits, as well as the presence of residual tumor perfusion (RTP) derived from PBV-CBCT. Outcome was assessed by mid-term tumor response applying mRECIST and patient survival after 12 months. Results: RTP was detected in 16 HCCs and correlated negatively with DE (p = .03*) and ED (p = .0009*). The absence of RTP significantly improved lesion-based mid-term response rates regarding complete response (CR, 30/34 (88%) vs 2/16 (12.5%), p = .0002*), lesion-based complete response rate was 75% (21/28) for DE ≥ 50% vs. 50% (11/22) for DE < 50% (p = .08) and 82% (27/33) for ED ≥ 2 vs. 29% for ED < 2 (5/17), p = .005*). Thirteen patients were treated with re-TACE within 12 months, 11 of which had shown RTP. 12-month survival rate was 93%. Conclusion: Residual tumor perfusions as assessed by PBV-CBCT during rDEE-TACE proved to be the best parameter to predict mid-term response. “Level of Evidence: Level 3”

In neuroimaging, there is no equivalent alternative to magnetic resonance imaging (MRI). However, image acquisitions are generally time-consuming, which may limit utilization in some cases, e.g., in patients who cannot remain motionless for long or suffer from claustrophobia, or in the event of extensive waiting times. For multiple sclerosis (MS) patients, MRI plays a major role in drug therapy decision-making. The purpose of this study was to evaluate whether an ultrafast, T2-weighted (T2w), deep learning-enhanced (DL), echo-planar-imaging-based (EPI) fluid-attenuated inversion recovery (FLAIR) sequence (FLAIR_UF) that has targeted neurological emergencies so far might even be an option to detect MS lesions of the brain compared to conventional FLAIR sequences. Therefore, 17 MS patients were enrolled prospectively in this exploratory study. Standard MRI protocols and ultrafast acquisitions were conducted at 3 tesla (T), including three-dimensional (3D)-FLAIR, turbo/fast spin-echo (TSE)-FLAIR, and FLAIR_UF. Inflammatory lesions were grouped by size and location. Lesion conspicuity and image quality were rated on an ordinal five-point Likert scale, and lesion detection rates were calculated. Statistical analyses were performed to compare results. Altogether, 568 different lesions were found. Data indicated no significant differences in lesion detection (sensitivity and positive predictive value [PPV]) between FLAIR_UF and axially reconstructed 3D-FLAIR (lesion size ≥3 mm × ≥2 mm) and no differences in sensitivity between FLAIR_UF and TSE-FLAIR (lesion size ≥3 mm total). Lesion conspicuity in FLAIR_UF was similar in all brain regions except for superior conspicuity in the occipital lobe and inferior conspicuity in the central brain regions. Further findings include location-dependent limitations of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) as well as artifacts such as spatial distortions in FLAIR_UF. In conclusion, FLAIR_UF could potentially be an expedient alternative to conventional methods for brain imaging in MS patients since the acquisition can be performed in a fraction of time while maintaining good image quality.

Background/Objectives: Postoperative MRI is crucial for detecting residual tumor, identifying complications, and planning subsequent therapy. This study evaluates accelerated deep learning reconstruction (DLR) versus standard clinical protocols for early postoperative MRI following tumor resection. Methods: This study uses a multidisciplinary approach involving a neuroradiologist, neurosurgeon, neuro-oncologist, and radiotherapist to evaluate qualitative aspects using a 5-point Likert scale, the preferred reconstruction variant and potential residual tumor of DLR and conventional reconstruction (CR) of FLAIR, T1-weighted non-contrast and contrast-enhanced (T1), and coronal T2-weighted (T2) sequences for 1.5 and 3 T MRI. Quantitative analysis included the image quality metrics Structural Similarity Index (SSIM), Multi-Scale SSIM (MS-SSIM), Feature Similarity Index (FSIM), Noise Quality Metric (NQM), signal-to-noise ratio (SNR), and Peak SNR (PSNR) with CR as a reference. Results: All raters strongly preferred DLR over CR. This was most pronounced for FLAIR images at 1.5 and 3 T (91% at 1.5 T and 97% at 3 T) and least pronounced for T1 at 1.5 T (79% for non-contrast-enhanced and 84% for contrast-enhanced sequences) and for T2 at 3 T (69%). DLR demonstrated superior qualitative image quality for all sequences and field strengths (p < 0.001), except for T2 at 3 T, which was observed across all raters (p = 0.670). Diagnostic confidence was similar at 3 T with better but non-significant differences for T2 (p = 0.134) and at 1.5 T with better but non-significant differences for non-contrast-enhanced T1 (p = 0.083) and only marginally significant results for FLAIR (p = 0.033). Both the SSIM and MS-SSIM indicated near-perfect similarity between CR and DLR. FSIM performs worse in terms of consistency between CR and DLR. The image quality metrics NQM, SNR, and PSNR showed better results for DLR. Visual assessment of residual tumor was similar at 3 T but differed at 1.5 T, with more residual tumor detected with DLR, especially by the neurosurgeon (n = 4). Conclusions: An accelerated DLR protocol demonstrates clinical feasibility, enabling high-quality reconstructions in challenging postoperative MRIs. DLR sequences received strong multidisciplinary preference, underscoring their potential to improve neuro-oncologic decision making and suitability for clinical implementation.