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We present a novel method for higher order reconstruction of fetal diffusion MRI signal that enables detection of fiber crossings. We combine data-driven motion and intensity correction with super-resolution reconstruction and spherical harmonic parametris ...

Earth observation is taking more and more importance in our society. Indeed, recent technologies march allows for satellite images to get ground resolution up to less than one meter. Thus, most of satellite image information obtained begins now to be widely used for multiple purposes ranging from cartography and mapping to agricultural management and security applications. Among all the satellite imaging systems, Synthetic Aperture Radar (or SAR) are very interesting for global earth surveillance and monito

Damage to the myelin sheath and the neuroaxonal unit is a cardinal feature of multiple sclerosis; however, a detailed characterization of the interaction between myelin and axon damage in vivo remains challenging. ...

Quality assessment (QA) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where unpredictable fetal motion can lead to substantial artifacts in the acquired images. Mu ...

In this paper the problem of intensity inhomogeneity at high magnetic field on magnetic resonance images is addressed. Specifically, rat brain images at 9.4T acquired with a surface coil are bias corrected. We propose a low- pass frequency model that takes ...

This paper presents a validation study on statistical non-supervised brain tissue classification techniques in MR images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of cla ...

Inference of Markov random field images segmentation models is usually performed using iterative methods which adapt the well-known expectation-maximization (EM) algorithm for independent mixture models. However, some of these adaptations are ad-hoc and ma ...

Purpose: To investigate the efficacy of Fluid and White Matter Suppression (FLAWS) MRI sequence in improving Deep Learning (DL)-based detection and segmentation of cortical lesions in Multiple Sclerosis (MS) patients even, and to develop models that can ge ...

Conventional magnetic resonance imaging (cMRI) in multiple sclerosis (MS) patients provides measures of focal brain damage and activity, which are fundamental for disease diagnosis, prognosis, and the evaluation of response to therapy. However, cMRI is ins ...

Magnetic Resonance Imaging (MRI) is nowadays a widely used medical tool, as it is a non-invasive and non-harmful way to study inner soft tissues. One of the characteristics of this method is the bias field, also called Intensity In-homogeneity (IIH), which is an artifact that affects quantitative image analysis consisting in a low frequency variation of the brightness through all the image acquired. This undesired effect makes difficult medical functions such as visual inspection or also intensity-based seg

The thalamus has a key role in mediating cortical–subcortical interactions but is often neglected in neuroimaging studies, which mostly focus on changes in cortical structure and activity. One of the main reasons for the thalamus being overlooked is that t ...

Uncertainty quantification (UQ) has become critical for evaluating the reliability of artificial intelligence systems, especially in medical image segmentation. This study addresses the interpretability of instance-wise uncertainty values in deep learning models for focal lesion segmentation in magnetic resonance imaging, specifically cortical lesion (CL) segmentation in multiple sclerosis. CL segmentation presents several challenges, including the complexity of manual segmentation, high variability in anno

Children with congenital heart disease (CHD) who survive surgery often present impaired neurodevelopment and qualitative brain anomalies. However, the impact of CHD on total or regional brain volumes only received little attention. We address this question ...

Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ a ...

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable accurate automated classification of AD patients, mild cognitive impaired (MCI) patients and elderly ...

Diffusion Magnetic Resonance Imaging (dMRI) is a non-invasive method for depicting brain microstructure in vivo. Fiber orientation distributions (FODs) are mathematical representations extensively used to map white matter fiber configurations. Recently, FO ...

Trustworthy AI is critical for effectively adopting AI systems in medical imaging and broader healthcare contexts. While the Trustworthy AI framework defines seven core principles —ranging from technical robustness to societal well-being— these are often addressed in isolation, lacking a coherent integration strategy. In this perspective paper, we propose a unified, layered framework that organizes these principles across three tiers of increasing trust: core operations, feedback, and explainability. Each l

Lesion load (LL), deep gray matter (DGM) and normal-appearing white matter (NAWM) susceptibility and morphometry may help in monitoring brain changes in multiple sclerosis (MS) patients. We aimed at evaluating the feasibility of a fully automated segmentat ...

Accurate segmentation of lymph nodes in head and neck (H&N) CT images is essential for the radiotherapy planning of the H&N cancer. Atlas-based segmentation methods are widely used for the automated segmentation of such structures. Multi-atlas approaches a ...

Meritxell Bach Cuadra

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Higher Order Spherical Harmonics Reconstruction of Fetal Diffusion MRI With Intensity Correction

Exploiting Multi-Temporal Information for SAR Image Segmentation

Myelin and axon pathology in multiple sclerosis assessed by myelin water and multi-shell diffusion imaging

Assessing Data Quality on Fetal Brain MRI Reconstruction: A Multi-site and Multi-rater Study

Fast Bias Field Correction for 9.4 Tesla Magnetic Resonance Imaging

Comparison and Validation of Tissue Modelization and Statistical Classification Methods in T1-weighted MR Brain Images

On the Convergence of EM-Like Algorithms for Image Segmentation using Markov Random Fields

Fluid and White Matter Suppression contrasts MRI improves Deep Learning detection of Multiple Sclerosis Cortical Lesions

GAMER-MRI in Multiple Sclerosis Identifies the Diffusion-Based Microstructural Measures That Are Most Sensitive to Focal Damage: A Deep-Learning-Based Analysis and Clinico-Biological Validation

Bias Field Correction in Magnetic Resonance Images of a Rat Brain

A roadmap towards standardized neuroimaging approaches for human thalamic nuclei

Interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis

Regional cortical volumes and congenital heart disease: a MRI study in 22q11.2 deletion syndrome

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Volume-based vs. voxel-based brain morphometry in Alzheimer's disease prediction

Ground-Truth Effects in Learning-Based Fiber Orientation Distribution Estimation in Neonatal Brains

Trustworthy AI in medical image analysis: A unified perspective built on robustness and layers of trust

Editorial: Computational Neuroimage Analysis Tools for Brain (Diseases) Biomarkers

Automated Quantitative Susceptibility and Morphometry MR Study: Feasibility and Interrelation Between Clinical Score, Lesion Load, Deep Grey Matter and Normal-Appearing White Matter in Multiple Sclerosis

Evaluation of Atlas Fusion Strategies for Segmentation of Head and Neck Lymph Nodes for Radiotherapy Planning