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Free water fraction (FWF) represents the amount of water per unit volume of brain parenchyma, which is not bound to macromolecules. Its excess in multiple sclerosis (MS) is related to increased tissue loss. The use of mcDESPOT (multicomponent driven single pulse observation of T1 and T2), a 3D imaging method which exploits both the T1 and T2 contrasts, allows FWF to be derived in clinically feasible times. However, this method has not been used to quantify changes of FWF and their potential clinical impact in MS. The aim of this study is to investigate the changes in FWF in MS patients and their relationship with tissue damage and cognition, under the hypothesis that FWF is a proxy of clinically meaningful tissue loss. To this aim, we tested the relationship between FWF, MS lesion burden and information processing speed, evaluated via the Symbol Digit Modalities Test (SDMT). In addition to standard sequences, used for T1‐ and T2‐weighted lesion delineation, the mcDESPOT sequence with 1.7 mm isotropic resolution and a diffusion weighted imaging protocol (b = 0, 1200 s/mm2, 40 diffusion directions) were employed at 3 T. The fractional anisotropy map derived from diffusion data was used to define a subject‐specific white matter (WM) atlas. Brain parenchyma segmentation returned masks of gray matter (GM) and WM, and normal‐appearing WM (NAWM), in addition to the T1 and T2 lesion masks (T1L and T2L, respectively). Ninety‐nine relapsing–remitting MS patients (age = 43.3 ± 9.9 years, disease duration 12.3 ± 7.7 years) were studied, together with twenty‐five healthy controls (HC, age = 38.8 ± 11.0 years). FWF was higher in GM and NAWM of MS patients, compared to GM and WM of HC (both p <.001). In MS patients, FWF was the highest in the T1L and GM, followed by T2L and NAWM, respectively. FWF increased significantly with T1L and T2L volume (ρ ranging from 0.40 to 0.58, p <.001). FWF in T2L was strongly related to both T1L volume and the volume ratio T1L/T2L (ρ = 0.73, p <.001). MS patients performed worse than HC in the processing speed test (mean ± SD: 54.1 ± 10.3 for MS, 63.8 ± 10.8 for HC). FWF in GM, T2L, perilesional tissue and NAWM increased with SDMT score reduction (ρ = −0.30, −0.29, −0.33 respectively and r = −.30 for T2L, all with p <.005). A regional analysis, conducted to determine which NAWM regions were of particular importance to explain the relationship between FWF and cognitive impairment, revealed that FWF spatial variance was negatively related to SDMT score in the corpus callosum and the superior longitudinal fasciculus, WM structures known to be associated with cognitive impairment, in addition to the left corticospinal tract, the sagittal stratum, the right anterior limb of internal capsule. In conclusion, we found excess free water in brain parenchyma of MS patients, an alteration that involved not only MS lesions, but also the GM and NAWM, impinging on brain function and negatively associated with cognitive processing speed. We suggest that the FWF metric, derived from noninvasive, rapid MRI acquisitions and bearing good biological interpretability, may prove valuable as an MRI biomarker of tissue damage and associated cognitive impairment in MS. [ABSTRACT FROM AUTHOR]

Neurovascular alterations are increasingly recognized as a key feature of many brain diseases. They can manifest as a reduction in resting cerebral blood flow or cerebrovascular reactivity (CVR) in the whole brain or in specific regions, depending on the underlying condition. Neurovascular impairment is observed in hypertension, Alzheimer's disease, stroke, multiple sclerosis and cerebral small vessel disease. Magnetic resonance imaging (MRI)-derived CVR mapping is a reliable marker of vascular dysfunction and has been performed mainly at standard functional MRI (fMRI) resolutions of 2-3 mm using the blood oxygen level dependent (BOLD) contrast. However, vascular alterations may occur at a finer scale (i.e., in the capillary bed) which would be better characterized with smaller voxel sizes. Capillaries in gray matter deliver oxygen and glucose to neural tissue and are arranged in a mesh structure, with variable density across the cortical depth. Given that the human cortex is, on average, 2.5 mm thick, submillimetric voxel sizes are effective in increasing the spatial specificity of measurements of hemodynamic and metabolic changes. Novel MRI sequences offer the possibility to map physiological parameters at high resolution with relatively simple experimental setups. In particular, pairing the BOLD acquisition with a contrast sensitive to blood volume changes, while administering a mild hypercapnic challenge, allows for simultaneous mapping of CVR, cerebral metabolic rate of oxygen consumption and other relevant parameters at a high resolution and can be performed at the clinical field strength of 3 T. We propose that this approach will help provide crucial insights into vascular impairment. [ABSTRACT FROM AUTHOR]

Myelin is the protective sheath wrapped around axons, consisting of a phospholipid bilayer with water between the wraps. The measurement of damage to the myelin sheaths, the evaluation of the efficacy of therapies aiming to promote remyelination as well as monitoring the degree of brain maturation in children require non-invasive quantitative myelin imaging methods. To date, various myelin imaging techniques have been developed. Five different MRI approaches can be distinguished based on their biophysical principles: (1) imaging of the water between the lipid bilayers directly (e.g. myelin water imaging), (2) imaging the non-aqueous protons of the phospholipid bilayer directly with ultrashort echo time techniques, (3) indirect imaging of the macromolecular content (e.g. magnetization transfer; inhomogeneous magnetization transfer), (4) mapping of the effects of the myelin sheath's magnetic susceptibility on the MRI signal (e.g., quantitative susceptibility mapping), and (5) mapping of the effects of the myelin sheath on water diffusion. Myelin imaging with PET uses radioactive molecules with high affinity to specific myelin components, in particular myelin basic protein. This review aims to give an overview of the various myelin imaging techniques, their biophysical principles, image acquisition, data analysis, and their validation status. [ABSTRACT FROM AUTHOR]

One promising approach for mapping CMRO2 is dual-calibrated functional MRI (dc-fMRI). This method exploits the Fick Principle to combine estimates of CBF from ASL, and OEF derived from BOLD-ASL measurements during arterial O2 and CO2 modulations. Multiple gas modulations are required to decouple OEF and deoxyhemoglobin-sensitive blood volume. We propose an alternative single gas calibrated fMRI framework, integrating a model of oxygen transport, that links blood volume and CBF to OEF and creates a mapping between the maximum BOLD signal, CBF and OEF (and CMRO2). Simulations demonstrated the method's viability within physiological ranges of mitochondrial oxygen pressure, PmO2, and mean capillary transit time. A dc-fMRI experiment, performed on 20 healthy subjects using O2 and CO2 challenges, was used to validate the approach. The validation conveyed expected estimates of model parameters (e.g., low PmO2), with spatially uniform OEF maps (grey matter, GM, OEF spatial standard deviation ≈ 0.13). GM OEF estimates obtained with hypercapnia calibrated fMRI correlated with dc-fMRI (r = 0.65, p = 2·10−3). For 12 subjects, OEF measured with dc-fMRI and the single gas calibration method were correlated with whole-brain OEF derived from phase measures in the superior sagittal sinus (r = 0.58, p = 0.048; r = 0.64, p = 0.025 respectively). Simplified calibrated fMRI using hypercapnia holds promise for clinical application. [ABSTRACT FROM AUTHOR]

Intra-arterial Digital Subtraction Angiography (DSA) is the gold standard technique for radiosurgery target delineation in brain Arterio-Venous Malformations (AVMs). This study aims to evaluate whether a combination of three Magnetic Resonance Angiography sequences (triple-MRA) could be used for delineation of brain AVMs for Gamma Knife Radiosurgery (GKR). Fifteen patients undergoing DSA for GKR targeting of brain AVMs also underwent triple-MRA: 4D Arterial Spin Labelling based angiography (ASL-MRA), Contrast-Enhanced Time-Resolved MRA (CE-MRA) and High Definition post-contrast Time-Of-Flight angiography (HD-TOF). The arterial phase of the AVM nidus was delineated on triple-MRA by an interventional neuroradiologist and a consultant neurosurgeon (triple-MRA volume). Triple-MRA volumes were compared to AVM targets delineated by the clinical team for delivery of GKR using the current planning paradigm, i.e., stereotactic DSA and volumetric MRI (DSA volume). Difference in size, degree of inclusion (DI) and concordance index (CcI) between DSA and triple-MRA volumes are reported. AVM target volumes delineated on triple-MRA were on average 9.8% smaller than DSA volumes (95%CI:5.6–13.9%; SD:7.14%; p =.003). DI of DSA volume in triple-MRA volume was on average 73.5% (95%CI:71.2–76; range: 65–80%). The mean percentage of triple-MRA volume not included on DSA volume was 18% (95%CI:14.7–21.3; range: 7–30%). The technical feasibility of using triple-MRA for visualisation and delineation of brain AVMs for GKR planning has been demonstrated. Tighter and more precise delineation of AVM target volumes could be achieved by using triple-MRA for radiosurgery targeting. However, further research is required to ascertain the impact this may have in obliteration rates and side effects. [ABSTRACT FROM AUTHOR]

On behalf of the International Society for Magnetic Resonance in Medicine (ISMRM) Quantitative MR Study Group, this article provides an overview of considerations for the development, validation, qualification, and dissemination of quantitative MR (qMR) methods. This process is framed in terms of two central technical performance properties, i.e., bias and precision. Although qMR is confounded by undesired effects, methods with low bias and high precision can be iteratively developed and validated. For illustration, two distinct qMR methods are discussed throughout the manuscript: quantification of liver proton‐density fat fraction, and cardiac T1. These examples demonstrate the expansion of qMR methods from research centers toward widespread clinical dissemination. The overall goal of this article is to provide trainees, researchers, and clinicians with essential guidelines for the development and validation of qMR methods, as well as an understanding of necessary steps and potential pitfalls for the dissemination of quantitative MR in research and in the clinic. [ABSTRACT FROM AUTHOR]

This document is a corrigendum for an article titled "Towards high-resolution quantitative assessment of vascular dysfunction." The corrigendum addresses an error in the funding statement of the published article. The correct funding statement is provided, acknowledging the funding sources for the research. The authors apologize for the error but state that it does not affect the scientific conclusions of the article. The document also includes a note from the publisher stating that the claims expressed in the article are solely those of the authors and do not necessarily represent the views of their affiliated organizations or the publisher. [Extracted from the article]

Background: Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective: The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods: Three hundred twenty‐two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsonian variant (MSA‐P), and 23 with MSA of the cerebellar variant (MSA‐C), were recruited. They were divided into a training cohort (n = 179) scanned in a research environment and a replication cohort (n = 143) examined in clinical practice on different MRI systems. Volumes and diffusion tensor imaging (DTI) metrics in 13 brain regions were used as input for a supervised machine learning algorithm. To harmonize data across scanners and reduce scanner‐dependent effects, we tested two types of normalizations using patient data or healthy control data. Results: In the replication cohort, high accuracies were achieved using volumetry in the classification of PD–PSP, PD–MSA‐C, PSP–MSA‐C, and PD‐atypical parkinsonism (balanced accuracies: 0.840–0.983, area under the receiver operating characteristic curves: 0.907–0.995). Performances were lower for the classification of PD–MSA‐P, MSA‐C–MSA‐P (balanced accuracies: 0.765–0.784, area under the receiver operating characteristic curve: 0.839–0.871) and PD–PSP–MSA (balanced accuracies: 0.773). Performance using DTI was improved when normalizing by controls, but remained lower than that using volumetry alone or combined with DTI. Conclusions: A machine learning approach based on volumetry enabled accurate classification of subjects with early‐stage parkinsonism, examined on different MRI systems, as part of their clinical assessment. © 2020 International Parkinson and Movement Disorder Society [ABSTRACT FROM AUTHOR]

Myelin is the protective sheath wrapped around axons, consisting of a phospholipid bilayer with water between the wraps. The measurement of damage to the myelin sheaths, the evaluation of the efficacy of therapies aiming to promote remyelination and monitoring the degree of brain maturation in children all require non-invasive quantitative myelin imaging methods. To date, various myelin imaging techniques have been developed. Five different MRI approaches can be distinguished based on their biophysical principles: (i) imaging of the water between the lipid bilayers directly (e.g. myelin water imaging); (ii) imaging the non-aqueous protons of the phospholipid bilayer directly with ultra-short echo-time techniques; (iii) indirect imaging of the macromolecular content (e.g. magnetization transfer; inhomogeneous magnetization transfer); (iv) mapping of the effects of the myelin sheath's magnetic susceptibility on the MRI signal (e.g. quantitative susceptibility mapping); and (v) mapping of the effects of the myelin sheath on water diffusion. Myelin imaging with PET uses radioactive molecules with high affinity to specific myelin components, in particular myelin basic protein. This review aims to give an overview of the various myelin imaging techniques, their biophysical principles, image acquisition, data analysis and their validation status., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

One promising approach for mapping CMRO 2 is dual-calibrated functional MRI (dc-fMRI). This method exploits the Fick Principle to combine estimates of CBF from ASL, and OEF derived from BOLD-ASL measurements during arterial O 2 and CO 2 modulations. Multiple gas modulations are required to decouple OEF and deoxyhemoglobin-sensitive blood volume. We propose an alternative single gas calibrated fMRI framework, integrating a model of oxygen transport, that links blood volume and CBF to OEF and creates a mapping between the maximum BOLD signal, CBF and OEF (and CMRO 2 ). Simulations demonstrated the method's viability within physiological ranges of mitochondrial oxygen pressure, P m O 2 , and mean capillary transit time. A dc-fMRI experiment, performed on 20 healthy subjects using O 2 and CO 2 challenges, was used to validate the approach. The validation conveyed expected estimates of model parameters (e.g., low P m O 2 ), with spatially uniform OEF maps (grey matter, GM, OEF spatial standard deviation ≈ 0.13). GM OEF estimates obtained with hypercapnia calibrated fMRI correlated with dc-fMRI (r = 0.65, p = 2·10 -3 ). For 12 subjects, OEF measured with dc-fMRI and the single gas calibration method were correlated with whole-brain OEF derived from phase measures in the superior sagittal sinus (r = 0.58, p = 0.048; r = 0.64, p = 0.025 respectively). Simplified calibrated fMRI using hypercapnia holds promise for clinical application.