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Background: Assessments of subclinical connective tissue disorders depend on complex approaches, emphasizing the need for more accessible methods applicable to clinical routine. Therefore, we aimed to establish a reliable approach assessing cervical vessel tortuosity, which is known to be associated with such disorders., Methods: Magnetic resonance angiography (MRA) images of ReSect study participants [single-center prospective cohort of spontaneous cervical artery dissection (sCeAD) patients] were used. Each patient underwent the same magnetic resonance imaging (MRI) protocol. The segmentation procedure was done using MATrix LABoratory 9.4 [up-sampling of raw MRA images, distance metric (DM) calculation], ITK-SNAP [region of interest (ROI) determination, vessel segmentation] and Vascular Modelling ToolKit (centerline determination). To assess inter-user variability and validity, we (I) had two blinded independent users segment all arteries and we (II) compared the results of our method to visual appraisal of vessel tortuosity done by two blinded expert neuro-radiologists., Results: A total of 526 extracranial cervical arteries were available for analysis. The inter-user variability of our method users was below 0.5% throughout. Overall, our method outperformed the visual tortuosity appraisal, as the visual grading underestimated the DM in 38.8% subjects when tasked to assess overall cervical artery tortuosity (both vertebral and internal carotid arteries) and in 16.6% and 33.3% respectively if tasked to grade anterior or posterior circulation separately., Conclusions: We present a reliable method to assess cervical artery tortuosity derived from MRA images applicable in clinical routine and future research investigating the potential correlation of sCeAD and connective tissue disorder., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1057/coif). All authors report that this study was supported by VASCage-Research Centre on Clinical Stroke Research. VASCage is a COMET Centre within the Competence Centers for Excellent Technologies (COMET) program and funded by the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology, the Federal Ministry of Labour and Economy, and the federal states of Tyrol, Salzburg and Vienna. COMET is managed by the Austrian Research Promotion Agency (Österreichische Forschungsförderungsgesellschaft), FFG Project number: 898252. S.P. Jr reports the support from the Austrian Science Fund (FWF): project P 29514-N32. The authors have no other conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objectives: We aimed to evaluate the ability of feed-forward neural networks (fNNs) to predict the neurodevelopmental outcome (NDO) of very preterm neonates (VPIs) at 12 months corrected age by using biomarkers of cerebral MR proton spectroscopy ( 1 H-MRS) and diffusion tensor imaging (DTI) at term-equivalent age (TEA)., Methods: In this prospective study, 300 VPIs born before 32 gestational weeks received an MRI scan at TEA between September 2013 and December 2017. Due to missing or poor-quality spectroscopy data and missing neurodevelopmental tests, 173 VPIs were excluded. Data sets consisting of 103 and 115 VPIs were considered for prediction of motor and cognitive developmental delay, respectively. Five metabolite ratios and two DTI characteristics in six different areas of the brain were evaluated. A feature selection algorithm was developed for receiving a subset of characteristics prevalent for the VPIs with a developmental delay. Finally, the predictors were constructed employing multiple fNNs and fourfold cross-validation., Results: By employing the constructed fNN predictors, we were able to predict cognitive delays of VPIs with 85.7% sensitivity, 100% specificity, 100% positive predictive value (PPV) and 99.1% negative predictive value (NPV). For the prediction of motor delay, we achieved a sensitivity of 76.9%, a specificity of 98.9%, a PPV of 90.9% and an NPV of 96.7%., Conclusion: FNNs might be able to predict motor and cognitive development of VPIs at 12 months corrected age when employing biomarkers of cerebral 1 H-MRS and DTI quantified at TEA., Key Points: • A feed-forward neuronal network is a promising tool for outcome prediction in premature infants. • Cerebral proton magnetic resonance spectroscopy and diffusion tensor imaging can be used for the construction of early prognostic biomarkers. • Premature infants that would most benefit from early intervention services can be spotted at the time of optimal neuroplasticity.

Introduction: To determine an exploratory multimodal approach including serum NFL and MR planimetric measures to discriminate Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)., Methods: MR planimetric measurements and NFL serum levels, with a mean time interval of 60 months relative to symptom onset, were assessed in a retrospective cohort of 11 progressive supranuclear palsy (PSP), 22 Parkinson's disease (PD), 16 multiple system atrophy (MSA) patients and 42 healthy controls (HC). A decision tree model to discriminate PD, PSP, and MSA was constructed using receiver operating characteristic curve analysis and Classification and Regression Trees algorithm., Results: Our multimodal decision tree provided accurate differentiation of PD versus MSA and PSP patients using a serum NFL cut-off of 14.66 ng/L. The pontine-to-midbrain-diameter-ratio (P d /M d ) discriminated MSA from PSP at a cut-off value of 2.06. The combined overall diagnostic yield was an accuracy of 83.7% (95% CI 69.8-90.8%)., Conclusion: We provide a clinically feasible decision algorithm which combines serum NFL levels and a planimetric MRI marker to differentiate PD, MSA and PSP with high diagnostic accuracy., Classification of Evidence: This study provides Class III evidence that the combination of serum NFL levels und MR planimetric measurements discriminates between PD, PSP and MSA., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Determination of the distribution function of relaxation times (DFRT) is an approach that gives us more detailed insight into system processes, which are not observable by simple electrochemical impedance spectroscopy (EIS) measurements. DFRT maps EIS data into a function containing the timescale characteristics of the system under consideration. The extraction of such characteristics from noisy EIS measurements can be described by Fredholm integral equation of the first kind that is known to be ill-posed and can be treated only with regularization techniques. Moreover, since only a finite number of EIS data may actually be obtained, the above-mentioned equation appears as after application of a collocation method that needs to be combined with the regularization. In the present study, we discuss how a regularized collocation of DFRT problem can be implemented such that all appearing quantities allow symbolic computations as sums of table integrals. The proposed implementation of the regularized collocation is treated as a multi-parameter regularization. Another contribution of the present work is the adjustment of the previously proposed multiple parameter choice strategy to the context of DFRT problem. The resulting strategy is based on the aggregation of all computed regularized approximants, and can be in principle used in synergy with other methods for solving DFRT problem. We also report the results from the experiments that apply the synthetic data showing that the proposed technique successfully reproduced known exact DFRT. The data obtained by our techniques is also compared to data obtained by well-known DFRT software (DRTtools)., (© The Author(s) 2019.)