This and its companion article address the 10 most frequently asked questions that radiologists face when planning, performing, processing, and interpreting different MR perfusion studies in CNS ...imaging.
Perfusion MRI is a promising tool in assessing stroke, brain tumors, and patients with neurodegenerative diseases. Most of the impediments that have limited the use of perfusion MRI can be overcome to allow integration of these methods into modern neuroimaging protocols.
Neurological and neuroradiological manifestations in patients with COVID-19 have been extensively reported. Available imaging data are, however, very heterogeneous. Hence, there is a growing need to ...standardise clinical indications for neuroimaging, MRI acquisition protocols, and necessity of follow-up examinations. A NeuroCovid working group with experts in the field of neuroimaging in COVID-19 has been constituted under the aegis of the Subspecialty Committee on Diagnostic Neuroradiology of the European Society of Neuroradiology (ESNR). The initial objectives of this NeuroCovid working group are to address the standardisation of the imaging in patients with neurological manifestations of COVID-19 and to give advice based on expert opinion with the aim of improving the quality of patient care and ensure high quality of any future clinical studies.
Key Points
• In patients with COVID-19 and neurological manifestations, neuroimaging should be performed in order to detect underlying causal pathology.
• The basic MRI recommended protocol includes T2-weighted, FLAIR (preferably 3D), and diffusion-weighted images, as well as haemorrhage-sensitive sequence (preferably SWI), and at least for the initial investigation pre and post-contrast T1 weighted-images.
• 3D FLAIR should be acquired after gadolinium administration in order to optimise the detection of leptomeningeal contrast enhancement.
Abstract
Advanced molecular and pathophysiologic characterization of primary central nervous system lymphoma (PCNSL) has revealed insights into promising targeted therapeutic approaches. Medical ...imaging plays a fundamental role in PCNSL diagnosis, staging, and response assessment. Institutional imaging variation and inconsistent clinical trial reporting diminishes the reliability and reproducibility of clinical response assessment. In this context, we aimed to: (1) critically review the use of advanced positron emission tomography (PET) and magnetic resonance imaging (MRI) in the setting of PCNSL; (2) provide results from an international survey of clinical sites describing the current practices for routine and advanced imaging, and (3) provide biologically based recommendations from the International PCNSL Collaborative Group (IPCG) on adaptation of standardized imaging practices. The IPCG provides PET and MRI consensus recommendations built upon previous recommendations for standardized brain tumor imaging protocols (BTIP) in primary and metastatic disease. A biologically integrated approach is provided to addresses the unique challenges associated with the imaging assessment of PCNSL. Detailed imaging parameters facilitate the adoption of these recommendations by researchers and clinicians. To enhance clinical feasibility, we have developed both “ideal” and “minimum standard” protocols at 3T and 1.5T MR systems that will facilitate widespread adoption.
Purpose To evaluate the feasibility of a standardized protocol for acquisition and analysis of dynamic contrast material-enhanced (DCE) and dynamic susceptibility contrast (DSC) magnetic resonance ...(MR) imaging in a multicenter clinical setting and to verify its accuracy in predicting glioma grade according to the new World Health Organization 2016 classification. Materials and Methods The local research ethics committees of all centers approved the study, and informed consent was obtained from patients. One hundred patients with glioma were prospectively examined at 3.0 T in seven centers that performed the same preoperative MR imaging protocol, including DCE and DSC sequences. Two independent readers identified the perfusion hotspots on maps of volume transfer constant (K
), plasma (v
) and extravascular-extracellular space (v
) volumes, initial area under the concentration curve, and relative cerebral blood volume (rCBV). Differences in parameters between grades and molecular subtypes were assessed by using Kruskal-Wallis and Mann-Whitney U tests. Diagnostic accuracy was evaluated by using receiver operating characteristic curve analysis. Results The whole protocol was tolerated in all patients. Perfusion maps were successfully obtained in 94 patients. An excellent interreader reproducibility of DSC- and DCE-derived measures was found. Among DCE-derived parameters, v
and v
had the highest accuracy (are under the receiver operating characteristic curve A
= 0.847 and 0.853) for glioma grading. DSC-derived rCBV had the highest accuracy (A
= 0.894), but the difference was not statistically significant (P > .05). Among lower-grade gliomas, a moderate increase in both v
and rCBV was evident in isocitrate dehydrogenase wild-type tumors, although this was not significant (P > .05). Conclusion A standardized multicenter acquisition and analysis protocol of DCE and DSC MR imaging is feasible and highly reproducible. Both techniques showed a comparable, high diagnostic accuracy for grading gliomas.
RSNA, 2018 Online supplemental material is available for this article.
Background
Early recognition and treatment of autoimmune encephalitis (AE) are crucial for patients, but diagnosis is often difficult and time-consuming. For this purpose, a syndrome-based diagnostic ...approach was published by Graus et al. (Lancet Neurol 15:391–404, 2016), but very little is known in the literature about its application in clinical practice.
Aim
Our aims are to test the feasibility of such approach in a real-world single-centre setting and to analyse the most relevant factors in criteria fulfilment.
Methods
We retrospectively applied these criteria to our cohort of patients discharged from our hospital with diagnosis of autoimmune encephalitis (
n
= 33, 58% antibody-positive).
Results
All the subjects fulfilled criteria for possible AE (pAE), with EEG and MRI playing a central role in diagnosis, while CSF was useful mainly to rule out other conditions. Three patients respected criteria for probable anti-NMDA-R encephalitis (pNMDA). Definite anti-NMDAR encephalitis was diagnosed in 4 patients with detection of the autoantibody but, surprisingly, none of these subjects had fulfilled criteria for pNMDA. 18 patients were diagnosed with definite limbic AE (15 patients were antibody-positive, three antibody-negative). Need for MRI bilateral involvement in antibody-negative limbic AE limited diagnosis. One patient fulfilled criteria for probable antibody-negative AE, while ten patients remained classified as pAE.
Conclusion
From our retrospective analysis, some suggestions for a better definition of the criteria may emerge. Larger studies on prospective cohorts may be more helpful to explore possible important issues.
Among dementia-like diseases, Alzheimer disease (AD) and Vascular Dementia (VD) are two of the most frequent. AD and VD may share multiple neurological symptoms that may lead to controversial ...diagnoses when using conventional clinical and MRI criteria. Therefore, other approaches are needed to overcome this issue. Machine learning (ML) combined with magnetic resonance imaging (MRI) has been shown to improve diagnostic accuracy of several neurodegenerative diseases, including dementia. To this end, in this study we investigated, first, whether different kind of ML algorithms, combined with advanced MRI features, could be supportive in classifying VD from AD; secondly, whether the developed approach might help in predicting the prevalent disease in subjects with an unclear profile of AD or VD. Three ML categories of algorithms were tested: artificial Neural Network (ANN), support vector machine (SVM), and Adaptive Neuro-Fuzzy Inference System (ANFIS). Multiple regional metrics from resting-state fMRI (rs-fMRI) and Diffusion Tensor Imaging (DTI) of 60 subjects (33 AD, 27 VD) were used as input features to train the algorithms and find the best feature pattern to classify VD from AD. We then used the identified VD-AD discriminant feature pattern as input for the most performant ML algorithm to predict the disease prevalence in 15 dementia patients with a “mixed VD-AD dementia” (MXD) clinical profile using their baseline MRI data. ML predictions were compared with the diagnosis evidence from a three years clinical follow-up. ANFIS emerged as the most efficient algorithm in discriminating AD from VD reaching a classification accuracy greater than 84% using a small feature pattern. Moreover, ANFIS showed improved classification accuracy when trained with a multimodal input feature dataset (e.g. DTI + rs-fMRI metrics) rather than a unimodal feature dataset. When applying the best discriminant pattern to the MXD group, ANFIS achieved a correct prediction rate of 77.33%. Overall, results showed that our approach have a high discriminant power to classify AD and VD profiles. Moreover, the same approach showed also potential in predicting earlier the prevalent underlying disease in dementia patients whose clinical profile is uncertain between AD and VD, therefore suggesting its usefulness in supporting physicians’ diagnostic evaluations.
Highlights • In cerebral gliomas, DCE-MRI is able to overcome DSC-MRI shortcomings. • DCE-MRI is as accurate as DSC-MRI for glioma grading. • Hotspot and histogram analyses performed equally for ...glioma grading. • The combination of DCE-derived Vp and Ktrans improves the diagnostic performance.
Abstract Background The widespread use of brain imaging has led to increased recognition of subclinical brain abnormalities, including white matter hyperintensities (WMH) and silent brain infarctions ...(SBI), which have a vascular origin, and have been associated to a high risk of stroke, disability and dementia. Carotid atherosclerosis (CA) may be causative in the development of WMH, SBI and eventually brain atrophy. Aim of the present systematic review and meta-analysis was to assess the existing evidence linking CA to WMH, SBI and brain atrophy. Methods The relation between CA and WMH, SBI and brain atrophy was investigated through the systematic search of online databases up to September 2015 and manual searching of references and related citations. Pooled estimates were calculated by random-effects model, using restricted maximum likelihood method with inverse variance weighting method. Results Of the 3536 records identified, fifteen were included in the systematic review and 9 were found to be eligible for the meta-analysis. CA was significantly associated with the presence of WMH (Odds Ratio, OR 1.42, confidence interval, CI 1.22-1.66, p < 0.0001) and of SBI (OR 1.89, CI 1.46-2.45, p < 0.0001). No meta-analysis could be performed for the relation between CA and brain atrophy due to the lack of suitable studies. Conclusions CA was found to be associated to WMH and SBI. While no causative association can be inferred from the available data, the presence of carotid plaque may be considered a significant risk factor for subclinical cerebral damage.
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