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Diffusion magnetic resonance imaging (dMRI) is pivotal for probing the microstructure of the rapidly-developing fetal brain. However, fetal motion during scans and its interaction with magnetic field inhomogeneities result in artifacts and data scattering across spatial and angular domains. The effects of those artifacts are more pronounced in high-angular resolution fetal dMRI, where signal-to-noise ratio is very low. Those effects lead to biased estimates and compromise the consistency and reliability of dMRI analysis. This work presents HAITCH, the first and the only publicly available tool to correct and reconstruct multi-shell high-angular resolution fetal dMRI data. HAITCH offers several technical advances that include a blip-reversed dual-echo acquisition for dynamic distortion correction, advanced motion correction for model-free and robust reconstruction, optimized multi-shell design for enhanced information capture and increased tolerance to motion, and outlier detection for improved reconstruction fidelity. The framework is open-source, flexible, and can be used to process any type of fetal dMRI data including single-echo or single-shell acquisitions, but is most effective when used with multi-shell multi-echo fetal dMRI data that cannot be processed with any of the existing tools. Validation experiments on real fetal dMRI scans demonstrate significant improvements and accurate correction across diverse fetal ages and motion levels. HAITCH successfully removes artifacts and reconstructs high-fidelity fetal dMRI data suitable for advanced diffusion modeling, including fiber orientation distribution function estimation. These advancements pave the way for more reliable analysis of the fetal brain microstructure and tractography under challenging imaging conditions.
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Over the last 20 years, there have been remarkable developments in fetal brain MR imaging analysis methods. This article delves into the specifics of structural imaging, diffusion imaging, functional MR imaging, and spectroscopy, highlighting the latest advancements in motion correction, fetal brain development atlases, and the challenges and innovations. Furthermore, this article explores the clinical applications of these advanced imaging techniques in comprehending and diagnosing fetal brain development and abnormalities.
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Encéfalo , Imagen por Resonancia Magnética , Diagnóstico Prenatal , Humanos , Encéfalo/diagnóstico por imagen , Encéfalo/embriología , Embarazo , Imagen por Resonancia Magnética/métodos , Diagnóstico Prenatal/métodos , Femenino , Neuroimagen/métodos , Feto/diagnóstico por imagenRESUMEN
INTRODUCTION: Multiple sclerosis (MS) is an inflammatory disorder of the central nervous system. Although conventional magnetic resonance imaging (MRI) is widely used for MS diagnosis and clinical follow-up, quantitative MRI has the potential to provide valuable intrinsic values of tissue properties that can enhance accuracy. In this study, we investigate the efficacy of diffusion MRI in distinguishing MS lesions within the cervical spinal cord, using a combination of metrics extracted from diffusion tensor imaging and Ball-and-Stick models. METHODS: We analyzed spinal cord data acquired from multiple hospitals and extracted average diffusion MRI metrics per vertebral level using a collection of image processing methods and an atlas-based approach. We then performed a statistical analysis to evaluate the feasibility of these metrics for detecting lesions, exploring the usefulness of combining different metrics to improve accuracy. RESULTS: Our study demonstrates the sensitivity of each metric to underlying microstructure changes in MS patients. We show that selecting a specific subset of metrics, which provide complementary information, significantly improves the prediction score of lesion presence in the cervical spinal cord. Furthermore, the Ball-and-Stick model has the potential to provide novel information about the microstructure of damaged tissue. CONCLUSION: Our results suggest that diffusion measures, particularly combined measures, are sensitive in discriminating abnormal from healthy cervical vertebral levels in patients. This information could aid in improving MS diagnosis and clinical follow-up. Our study highlights the potential of the Ball-and-Stick model in providing additional insights into the microstructure of the damaged tissue.
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Médula Cervical , Esclerosis Múltiple , Humanos , Médula Cervical/diagnóstico por imagen , Médula Cervical/patología , Esclerosis Múltiple/diagnóstico por imagen , Esclerosis Múltiple/patología , Imagen de Difusión Tensora/métodos , Médula Espinal/diagnóstico por imagen , Imagen de Difusión por Resonancia Magnética/métodos , Imagen por Resonancia Magnética/métodosRESUMEN
Introduction: Neuroimaging heterogeneity in dementia has been examined using single modalities. We evaluated the associations of magnetic resonance imaging (MRI) atrophy and flortaucipir positron emission tomography (PET) clusters across the Alzheimer's disease (AD) spectrum. Methods: We included 496 Alzheimer's Disease Neuroimaging Initiative participants with brain MRI, flortaucipir PET scan, and amyloid beta biomarker measures obtained. We applied a novel robust collaborative clustering (RCC) approach on the MRI and flortaucipir PET scans. We derived indices for AD-like (SPARE-AD index) and brain age (SPARE-BA) atrophy. Results: We identified four tau (I-IV) and three atrophy clusters. Tau clusters were associated with the apolipoprotein E genotype. Atrophy clusters were associated with white matter hyperintensity volumes. Only the hippocampal sparing atrophy cluster showed a specific association with brain aging imaging index. Tau clusters presented stronger clinical associations than atrophy clusters. Tau and atrophy clusters were partially associated. Conclusions: Each neuroimaging modality captured different aspects of brain aging, genetics, vascular changes, and neurodegeneration leading to individual multimodal phenotyping.
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Introduction: To describe the protocol and findings of the instrumental validation of three imaging-based biomarker kits selected by the MarkVCID consortium: free water (FW) and peak width of skeletonized mean diffusivity (PSMD), both derived from diffusion tensor imaging (DTI), and white matter hyperintensity (WMH) volume derived from fluid attenuation inversion recovery and T1-weighted imaging. Methods: The instrumental validation of imaging-based biomarker kits included inter-rater reliability among participating sites, test-retest repeatability, and inter-scanner reproducibility across three types of magnetic resonance imaging (MRI) scanners using intra-class correlation coefficients (ICC). Results: The three biomarkers demonstrated excellent inter-rater reliability (ICC >0.94, P-values < .001), very high agreement between test and retest sessions (ICC >0.98, P-values < .001), and were extremely consistent across the three scanners (ICC >0.98, P-values < .001). Discussion: The three biomarker kits demonstrated very high inter-rater reliability, test-retest repeatability, and inter-scanner reproducibility, offering robust biomarkers suitable for future multi-site observational studies and clinical trials in the context of vascular cognitive impairment and dementia (VCID).
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BACKGROUND: In patients with MS, the effect of structural damage to the corticospinal tract (CST) has been separately evaluated in the brain and spinal cord (SC), even though a cumulative impact is suspected. OBJECTIVE: To evaluate CST damages on both the cortex and cervical SC, and examine their relative associations with motor function, measured both clinically and by electrophysiology. METHODS: We included 43 patients with early relapsing-remitting MS. Lesions were manually segmented on SC (axial T2*) and brain (3D FLAIR) scans. The CST was automatically segmented using an atlas (SC) or tractography (brain). Lesion volume fractions and diffusion parameters were calculated for SC, brain and CST. Central motor conduction time (CMCT) and triple stimulation technique amplitude ratio were measured for 42 upper limbs, from 22 patients. RESULTS: Mean lesion volume fractions were 5.2% in the SC portion of the CST and 0.9% in the brain portion. We did not find a significant correlation between brain and SC lesion volume fraction (r = 0.06, p = 0.68). The pyramidal EDSS score and CMCT were both significantly correlated with the lesion fraction in the SC CST (r = 0.39, p = 0.01 and r = 0.33, p = 0.03), but not in the brain CST. CONCLUSION: Our results highlight the major contribution of SC lesions to CST damage and motor function abnormalities.