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International audience; Brain morphometry performed with magnetic resonance (MR) imaging is affected by partial volume (PV) effects when single voxels contain the signal from two different tissues. This paper proposes a generalization of the MP2 RAGE sequence PV estimation model which accounts for transmitted magnetic field (B1 {+}) inhomogeneities at 7T. Our simulation experiments demonstrated that the PV estimation error of the proposed model is significantly lower than the error obtained with the same model neglecting B1 {+} inhomogeneities (p

Multi-tissue partial volume estimation in MRI images is investigated with a viewpoint related to spectral unmixing as used in hyperspectral imaging. The main contribution of this paper is twofold. It firstly proposes a theoretical analysis of the statistical optimality conditions of the proportion estimation problem, which in the context of multi-contrast MRI data acquisition allows to appropriately set the imaging sequence parameters. Secondly, an efficient proportion quantification algorithm based on the minimisation of a penalised least-square criterion incorporating a regularity constraint on the spatial distribution of the proportions is proposed. Furthermore, the resulting developments are discussed using empirical simulations. The practical usefulness of the spectral unmixing approach for partial volume quantification in MRI is illustrated through an application to food analysis on the proving of a Danish pastry.

Tissue-type partial volume modelling is generally an ill-posed problem in single-shell diffusion MRI. On the other hand, T1w images are typically acquired along with the diffusion data, and allow for an accurate estimation of the tissue partial volume fractions (PVFs). We propose in this paper to compare different data driven approach to predict the T1w-derived PVFs from the diffusion data. The aim is to alleviate the within subject mis-registration between the two modalities. Our experiments show that the random forests is the most accurate and scalable method for predicting the tissue partial volume fractions. Additionally, such predictions can be used to inform the fitting of the two-compartment model to retrieve a diffusion tensor that is not biased by partial volume effects or constraints.

The habenula consists of a pair of small epithalamic nuclei located adjacent to the dorsomedial thalamus. Despite increasing interest in imaging the habenula due to its critical role in mediating subcortical reward circuitry, in vivo neuroimaging research targeting the human habenula has been limited by its small size and low anatomical contrast. In this work, we have developed an objective semi-automated habenula segmentation scheme consisting of histogram-based thresholding, region growing, geometric constraints, and partial volume estimation steps. This segmentation scheme was designed around in vivo 3 T myelin-sensitive images, generated by taking the ratio of high-resolution T1w over T2w images. Due to the high myelin content of the habenula, the contrast-to-noise ratio with the thalamus in the in vivo 3T myelin-sensitive images was significantly higher than the T1w or T2w images alone. In addition, in vivo 7 T myelin-sensitive images (T1w over T2*w ratio images) and ex vivo proton density-weighted images, along with histological evidence from the literature, strongly corroborated the in vivo 3 T habenula myelin contrast used in the proposed segmentation scheme. The proposed segmentation scheme represents a step toward a scalable approach for objective segmentation of the habenula suitable for both morphological evaluation and habenula seed region selection in functional and diffusion MRI applications.