Magnetic resonance imaging
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Randomized Controlled Trial Multicenter Study
Are multi-contrast magnetic resonance images necessary for segmenting multiple sclerosis brains? A large cohort study based on deep learning.
Magnetic resonance images with multiple contrasts or sequences are commonly used for segmenting brain tissues, including lesions, in multiple sclerosis (MS). However, acquisition of images with multiple contrasts increases the scan time and complexity of the analysis, possibly introducing factors that could compromise segmentation quality. ⋯ Best performance for segmented tissue volumes was obtained with all four image contrasts as the input, and comparable performance was attainable with FLAIR only as the input, albeit with a moderate increase in FPR for small lesions. This implies that acquisition of only FLAIR images provides satisfactory tissue segmentation. Lesion segmentation was poor for very small lesions and improved rapidly with lesion size.
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Multicenter Study Comparative Study
Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.
Advanced imaging analysis for the prediction of tumor biology and modelling of clinically relevant parameters using computed imaging features is part of the emerging field of radiomics research. Here we test the hypothesis that a machine learning approach can distinguish grade 1 from higher gradings in meningioma patients using radiomics features derived from a heterogenous multicenter dataset of multi-paramedic MRI. ⋯ Machine learning using radiomics features derived from multi-parametric MRI is capable of high AUC scores with high sensitivity and specificity in classifying meningiomas between low and higher gradings despite heterogeneous protocols across different centers. Feature selection can be performed effectively even when extracting a large amount of data for radiomics fingerprinting.
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Multicenter Study
Scan-rescan repeatability and cross-scanner comparability of DTI metrics in healthy subjects in the SPRINT-MS multicenter trial.
To assess intrascanner repeatability and cross-scanner comparability for diffusion tensor imaging (DTI) metrics in a multicenter clinical trial. ⋯ The good repeatability of the DTI metrics among the 27 scanners used in this study confirms the feasibility of combining DTI data from multiple centers using high angular resolution sequences. Our observations support the feasibility of longitudinal multicenter clinical trials using DTI outcome measures. The noise floor level and SNFR are important parameters that must be assessed when comparing studies that used different scanner models.
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A phantom-based quality assurance (QA) protocol was developed for a multicenter clinical trial including high angular resolution diffusion imaging (HARDI). A total of 27 3T MR scanners from 2 major manufacturers, GE (Discovery and Signa scanners) and Siemens (Trio and Skyra scanners), were included in this trial. With this protocol, agar phantoms doped to mimic relaxation properties of brain tissue are scanned on a monthly basis, and quantitative procedures are used to detect spiking and to evaluate eddy current and Nyquist ghosting artifacts. ⋯ Software upgrades and hardware replacement sometimes affected SNR substantially but sometimes did not. In light of these results, it is important to monitor longitudinal SNR with phantom QA to help interpret potential effects on in vivo measurements. Our phantom QA procedure for HARDI scans was successful in tracking scanner performance and detecting unwanted artifacts.
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To report MRI spinal changes after surgical infusion of bone marrow stem cells (BMSc) in ALS patients and assess their correlation with clinical events and functional performance. ⋯ Infusion of BMSc produces a variety of spinal changes apparently unrelated with clinical events and disease worsening.