Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting

Magnetic Resonance Fingerprinting (MRF) enables the simultaneous quantification of multiple properties of biological tissues. It relies on a pseudo-random acquisition and the matching of acquired signal evolutions to a precomputed dictionary. However, the dictionary is not scalable to higher-parametric spaces, limiting MRF to the simultaneous mapping of only a small number of parameters (proton density, T1 and T2 in general). Inspired by diffusion-weighted SSFP imaging, we present a proof-of-concept of a novel MRF sequence with embedded diffusion-encoding gradients along all three axes to efficiently encode orientational diffusion and T1 and T2 relaxation. We take advantage of a convolutional neural network (CNN) to reconstruct multiple quantitative maps from this single, highly undersampled acquisition. We bypass expensive dictionary matching by learning the implicit physical relationships between the spatiotemporal MRF data and the T1, T2 and diffusion tensor parameters. The predicted parameter maps and the derived scalar diffusion metrics agree well with state-of-the-art reference protocols. Orientational diffusion information is captured as seen from the estimated primary diffusion directions. In addition to this, the joint acquisition and reconstruction framework proves capable of preserving tissue abnormalities in multiple sclerosis lesions

In vivo myelin water quantification using diffusion–relaxation correlation MRI: A comparison of 1D and 2D methods

Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion–relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion

Der sozialarbeiterische Auftrag bei der Inklusion von Menschen mit Behinderung bezogen auf die Eingliederung in das deutsche Schulsystem.

nicht vorhande

Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging

Magnetic resonance imaging is a versatile diagnostic tool with numerous clinical applications. However, despite advances towards higher resolutions, it cannot resolve images on a cellular level. To nevertheless probe tissue microstructure, multidimensional correlation imaging emerges as a promising method. It takes advantage of the fact that each tissue compartment has a unique signal. Usually, these multi-compartmental characteristics are averaged over a macroscopic voxel. In contrast, correlation imaging aims to probe the true, heterogeneous nature of tissue. Based on image series acquired with varying inversion time T I and echo time T E, multiparametric spectra of T1 and T2 relaxation times in every voxel can be reconstructed, revealing sub-voxel tissue classes. However, even with impractically long acquisition times spent on dense sampling of the image (3D) and T IT E-space (2D), the inverse problem of retrieving these components from measured signal curves remains highly ill-conditioned and requires expensive regularized approaches. We formulate multiparametric correlation imaging as a classification problem and propose a flexible physics informed deep learning framework comprising a multilayer perceptron. This way, we efficiently reconstruct voxel-wise T1-T2-spectra with increased robustness to noise and undersampling in the T I-T E-space compared to state-of-the-art regression. Our results show feasibility of further acceleration of the acquisition by a factor of 4. After training on synthetic data that is not constraint by pre-defined tissue classes and independent of annotated data, we test our method on in-vivo brain data, revealing sub-voxel compartments in white and gray matter. This allows us to quantify tissue microstructure and will potentially lead to novel biomarkers

Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging

Purpose: Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T1 and T2 values. Methods: To demonstrate the viability of the proposed 3D QTI technique, nine glioma patients (grade II-IV), with a variety of disease states and treatment histories, were included in this study. First, we investigated the feasibility of 3D QTI (6:25 min scan time) for its use in clinical routine imaging, focusing on image reconstruction, parameter estimation, and contrast-weighted image synthesis. Second, for an initial assessment of 3D QTI-based quantitative MR biomarkers, we performed a ROI-based analysis to characterize T1 and T2 components in tumor and peritumoral tissue. Results: The 3D acquisition combined with a compressed sensing reconstruction and neural network-based parameter inference produced parametric maps with high isotropic resolution (1.125 × 1.125 × 1.125 mm3 voxel size) and whole-brain coverage (22.5 × 22.5 × 22.5 cm3 FOV), enabling the synthesis of clinically relevant T1-weighted, T2-weighted, and FLAIR contrasts without any extra scan time. Our study revealed increased T1 and T2 values in tumor and peritumoral regions compared to contralateral white matter, good agreement with healthy volunteer data, and high inter-subject consistency. Conclusion: 3D QTI demonstrated comprehensive tissue assessment of tumor substructures captured in T1 and T2 parameters. Aiming for fast acquisition of quantitative MR biomarkers, 3D QTI has potential to improve disease characterization in brain tumor patients under tight clinical time-constraints. Keywords: Glioma imaging; Image-based biomarkers; MRI; Multiparametric imaging; Neural networks