Magnetic Resonance Fingerprinting (MRF) is an emerging technology with the potential to revolutionize radiology and medical diagnostics. In comparison to traditional magnetic resonance imaging (MRI), MRF enables the rapid, simultaneous, noninvasive acquisition and reconstruction of multiple tissue parameters, paving the way for novel diagnostic techniques. In the original matching approach, reconstruction is based on the search for the best matches between in vivo acquired signals and a dictionary of high-dimensional simulated signals (fingerprints) with known tissue properties. A critical and limiting challenge is that the size of the simulated dictionary increases exponentially with the number of parameters, leading to an extremely costly matching. In this work we propose to address this scalability issue by considering probabilistic mixtures of high-dimensional elliptical distributions to learn more efficient dictionary representations. Mixture components are modelled as flexible elliptical shapes in low-dimensional subspaces. They are exploited to cluster similar signals and reduce their dimension locally cluster-wise limiting information loss. To estimate such a mixture model, we provide a new incremental algorithm capable of handling large numbers of signals, allowing us to go far beyond the hardware limitations encountered by standard implementations. We demonstrate, on simulated and real data, that our method effectively manages large volumes of MRF data with maintained accuracy. It offers a more efficient solution for accurate tissue characterization and significantly reduces the computational burden, making the clinical application of MRF more practical and accessible.

PURPOSE:This study proposes a novel, contrast-free Magnetic Resonance Fingerprinting (MRF) method using balanced Steady-State Free Precession (bSSFP) sequences for the quantification of cerebral blood volume (CBV), vessel radius (R), and relaxometry parameters (T   1 $$ {}_1 $$ , T   2 $$ {}_2 $$ , T   2 $$ {}_2 $$ *) in the brain. METHODS:The technique leverages the sensitivity of bSSFP sequences to intra-voxel frequency distributions in both transient and steady-state regimes. A dictionary-matching process is employed, using simulations of realistic mouse microvascular networks to generate the MRF dictionary. The method is validated through in silico and in vivo experiments on six healthy subjects, comparing results with standard MRF methods and literature values. RESULTS:The proposed method shows strong correlation and agreement with standard MRF methods for T   1 $$ {}_1 $$ and T   2 $$ {}_2 $$ values. High-resolution maps provide detailed visualizations of CBV and microvascular structures, highlighting differences in white matter (WM) and gray matter (GM) regions. The measured GM/WM ratio for CBV is 1.91, consistent with literature values. CONCLUSION:This contrast-free bSSFP-based MRF method offers an new approach for quantifying CBV, vessel radius, and relaxometry parameters. Further validation against DSC imaging and clinical studies in pathological conditions is warranted to confirm its clinical utility.

With the rapid advancements in medical data acquisition and production, increasingly richer representations exist to characterize medical information. However, such large-scale data do not usually meet computing resource constraints or algorithmic complexity, and can only be processed after compression or reduction, at the potential loss of information. In this work, we consider specific Gaussian mixture models (HD-GMM), tailored to deal with high dimensional data and to limit information loss by providing component-specific lower dimensional representations. We also design an incremental algorithm to compute such representations for large data sets, overcoming hardware limitations of standard methods. Our procedure is illustrated in a magnetic resonance fingerprinting study, where it achieves a 97% dictionary compression for faster and more accurate map reconstructions.

The Magnetic Resonance Fingerprinting (MRF) approach aims to estimate multiple MR or physiological parameters simultaneously with a single fast acquisition sequence. Most of the MRF studies proposed so far have used simple MR sequence types to measure relaxation times (T-1, T-2). In that case, deep learning algorithms have been successfully used to speed up the reconstruction process. In theory, the MRF concept could be used with a variety of other MR sequence types and should be able to provide more information about the tissue microstructures. Yet, increasing the complexity of the numerical models often leads to prohibited simulation times, and estimating multiple parameters from one sequence implies new dictionary dimensions whose sizes become too large for standard computers and DL architectures. In this paper, we propose to analyze the MRF signal coming from a complex balanced Steady-State Free Precession (bSSFP) type sequence to simultaneously estimate relaxometry maps (T-1, T-2), Field maps (B-1, B-0) as well as microvascular properties such as the local Cerebral Blood Volume (CBV) or the averaged vessel Radius (R). To bypass the curse of dimensionality, we propose an efficient way to simulate the MR signal coming from numerical voxels containing realistic microvascular networks as well as a Bidirectional Long Short-Term Memory network that replaces the matching process. On top of standard MRF maps, our results on 3 human volunteers suggest that our approach can quickly produce high-quality quantitative maps of microvascular parameters that are otherwise obtained using longer dedicated sequences and intravenous injection of a contrast agent. This approach could be used for the management of multiple pathologies and could be tuned to provide other types of microstructural information.

Motivation: Most MR methods for quantifying microvascular properties involve the injection of exogenous contrast agents (CA). Goal(s): We propose an innovative MRI method that simultaneously maps the deoxygenated cerebral blood volume (CBV), microvascular geometry (averaged vessel radius), and relaxometry (T1&T2) without CA injection. Approach: Acquisitions are made using a multi-echo, phase-cycled, bSSFP-sequence acquired in transient and pseudo-steady-state regimes. Reconstruction of the maps is made under the MRFingerprinting framework using realistic 3D microvessel representations. Magnetic field distributions (B1&B0) are also taken into account. Results: Preliminary results on five healthy volunteers are in line with previous measurements made with MRF and PWI with gadolinium injection. Impact: We propose a contrast-free quantification technique of microvascular properties and relaxation times. It could be useful for functional experiments as well as clinical investigations of several cerebrovascular pathologies including stroke and cancer.

MR vascular Fingerprinting proposes to use the MR Fingerprinting framework to quantitatively and simultaneously map several microvascular characteristics at a sub-voxel scale. The initial implementation assessed the local blood oxygenation saturation (SO 2), blood volume fraction (BVf) and vessel averaged radius (R) in humans and rodent brains using simple 2D representations of the vascular network during dictionary generation. In order to improve the results and possibly extend the approach to pathological environments and other biomarkers, we propose in this study to use 3D realistic vascular geometries in the numerical simulations. 28,000 different synthetic voxels containing vascular networks segmented from whole brain healthy mice microscopy images were created. A Bayesian-based regression model was used for map reconstruction. We show on 8 healthy and 9 tumor bearing rats that realistic vascular representations yield microvascular estimates in better agreement with the literature than 2D or 3D cylindrical models. Furthermore, tumoral blood oxygenation estimates obtained with the proposed approach are the only ones correlating with in vivo optic-fiber measurements performed in the same animals.

The MR vascular fingerprinting (MRvF) approach extends the concept of MR fingerprinting to the study of microvascular properties and functions. Encouraging results have been obtained in healthy human volunteers as well as in stroke and tumor models in rats. However, it has been suggested that the method has a low sensitivity to blood oxygenation measurements. We improved the MRvF approach by using simulations with 3D realistic blood vessels from animal microscopy, new fingerprint-pattern organization and machine learning tools. The method was tested in retrospective data acquired in healthy-human volunteers while breathing different gas mixtures (Hyperoxia (100%O2), Normoxia (21%O2), hypoxia (14%O2)).

We propose to use numerical simulations and Monte Carlo approaches to test 40 MR fingerprinting sequences for their ability to quantify the BOLD effect and provide maps of baseline blood oxygen saturation without the need for contrast agent injection. Our results suggest that several sequences can produce SO2 maps with less that 3% error on average even in the presence of B1 and B0 inhomogeneities. T2 and blood vessel radius could also be estimated with the same acquisitions.