On the R2⁎ relaxometry in complex multi-peak multi-Echo chemical shift-based water-fat quantification: Applications to the neuromuscular diseases

Magnetism of materials: theory and practice in magnetic resonance imaging

All substances exert magnetic properties in some extent when placed in an external magnetic field. Magnetic susceptibility represents a measure of the magnitude of magnetization of a certain substance when the external magnetic field is applied. Depending on the tendency to be repelled or attracted by the magnetic field and in the latter case on the magnitude of this effect, materials can be classified as diamagnetic or paramagnetic, superparamagnetic and ferromagnetic, respectively. Knowledge of type and extent of susceptibility of common endogenous and exogenous substances and how their magnetic properties affect the conventional sequences used in magnetic resonance imaging (MRI) can help recognize them and exalt or minimize their presence in the acquired images, so as to improve diagnosis in a wide variety of benign and malignant diseases. Furthermore, in the context of diamagnetic susceptibility, chemical shift imaging enables to assess the intra-voxel ratio between water and fat content, analyzing the tissue composition of various organs and allowing a precise fat quantification. The following article reviews the fundamental physical principles of magnetic susceptibility and examines the magnetic properties of the principal endogenous and exogenous substances of interest in MRI, providing potential through representative cases for improved diagnosis in daily clinical routine.

Evaluation of principal component analysis image denoising on multi-exponential MRI relaxometry

PURPOSE

Multi-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures.

METHODS

PCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared.

RESULTS

Denoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of ≈3×, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution.

CONCLUSION

Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.

A data-oriented self-calibration and robust chemical-shift encoding by using clusterization (OSCAR): Theory, optimization and clinical validation in neuromuscular disorders

Multi-echo Chemical Shift-Encoded (CSE) methods for Fat-Water quantification are growing in clinical use due to their ability to estimate and correct some confounding effects. State of the art CSE water/fat separation approaches rely on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept constant over the entire MRI dataset. However, the latter approximation introduces a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are significant (such as for neuromuscular disorders) because of the spatial dependence of the peak amplitudes. The present work aims to overcome this limitation by taking advantage of an unsupervised clusterization-based approach offering a reliable criterion to carry out a data-driven segmentation of the input MRI dataset into multiple regions. Results established that the presented algorithm is able to identify at least 4 different partitions from MRI dataset under which to perform independent self-calibration routines and was found robust in NMD imaging studies (as evaluated on a cohort of 24 subjects) against latest CSE techniques with either calibrated or non-calibrated approaches. Particularly, the PDFF of the thigh was more reproducible for the quantitative estimation of pathological muscular fat infiltrations, which may be promising to evaluate disease progression in clinical practice.