Search

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Interest in applying artificial intelligence to medical imaging to enhance image quality has grown in both clinical practice and research. Nonetheless, these artificial intelligence models require further validation for use in diagnosing and monitoring disease progression to effectively compare them with traditional techniques. This study aimed to assess the effectiveness of a deep learning-optimized glioma protocol for MRI scans in patients with glioblastoma. The study protocol focuses on improving image quality and shortening scan time, which might be particularly beneficial for critically ill or uncooperative patients. The results showed that deep learning-reconstructed sequences can save scan time and simultaneously increase subjective image quality but with less subjective image noise. In addition, tumor volume measurements and neuro-oncologic response assessments were not affected negatively by deep learning. This study demonstrated the feasibility of the deep learning protocol for MRI of glioblastomas. 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. [ABSTRACT FROM AUTHOR]

A 61-year-old patient was diagnosed with a left-sided falx meningioma. Histopathological analysis following extirpation showed a meningothelial meningioma ZNS WHO grade 1 with sparse mitoses. Over the course of 12 years, the patient received irradiation (54.0 Gy), peptide radio-receptor therapy (177Lu-DOMITATE) and targeted therapy (mTOR inhibitor). Follow-up imaging revealed an increased size of the residual tumor. Due to increased liver function parameters, imaging of the liver was performed, showing widespread space-occupying lesions with atypical appearance. Biopsy revealed metastasis of the meningioma, now with 2.7 mitoses/mm2, necrosis and homozygous CDKN2A/B deletion, corresponding to an anaplastic CNS meningioma WHO grade 3. A second small meningioma on the left petroclival side has been consistent in size over 12 years. Metastatic meningiomas pose a pertinent clinical challenge due to poor prognosis. The lung, bone, liver and cervical lymph nodes are the most common sites of extracranial metastasis. According to the World Health Organization criteria, the most important predictive factor for recurrence and metastasis is the tumor grade. [ABSTRACT FROM AUTHOR]

Purpose To prospectively compare the image quality and diagnostic performance of orbital MR images obtained by using a dual-source parallel transmission (pTX) 3D sequence (Sampling Perfection with Application optimized Contrasts using different flip angle Evolution, SPACE) with the image quality of conventional high-resolution standard protocol for clinical use in patients at 3T. Materials and methods After obtaining institutional review board approval and patient consent, 32 patients with clinical indication for orbital MRI were examined using a high-resolution conventional sequences and 3D pTX SPACE sequences. Quantitative measurements, image quality of the healthy orbit, incidence of artifacts, and the subjective diagnostic performance to establish diagnosis was rated. Statistical significance was calculated by using a Student's t -test and nonparametric Wilcoxon signed rank test. Results Length measurements were comparable in the two techniques, 3D pTX SPACE resulted in significant faster image acquisition with higher spatial resolution and less motion artifacts as well as better delineation of the optic nerve sheath. However, estimated contrast-to-noise and signal-to-noise and overall image quality as well as subjective scores of the conventional TSE imaging were rated significantly higher. The conventional MR sequences were the preferred techniques by the readers. Conclusion This study demonstrates the feasibility of 3D pTX SPACE of the orbit resulting in a rapid acquisition of isotropic high-resolution images. Although no pathology was missed in 3D pTX SPACE, conventional MRI techniques showed the higher diagnostic confidence in our study, presumably due to the higher signal-to-noise and contrast-to-noise ratios. We observed high-resolution TSE imaging to be the preferred technique, 3D pTX SPACE cannot replace conventional MRI so far. [ABSTRACT FROM AUTHOR]