Optimal estimation of T2 maps from magnitude MR images (original) (raw)
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Parameter estimation from magnitude MR images
International Journal of Imaging Systems and Technology, 1999
This article deals with the estimation of model-based parameters, such as the noise variance and signal components, from magnitude magnetic resonance (MR) images. Special attention has been paid to the estimation of T 1 -and T 2 -relaxation parameters. It is shown that most of the conventional estimation methods, when applied to magnitude MR images, yield biased results. Also, it is shown how the knowledge of the proper probability density function of magnitude MR data (i.e., the Rice distribution) can be exploited so as to avoid (or at least reduce) such systematic errors. The proposed method is based on maximum likelihood (ML) estimation.
2019 27th European Signal Processing Conference (EUSIPCO), 2019
In clinical and biological applications of T2 relaxometry, a multi-exponential decay model proved to be representative of the relaxation signal inside each voxel of the MRI images. However, estimating and exploiting the model parameters for magnitude data is a large-scale ill-posed inverse problem. This paper presents a parameter estimation method that combines a spatial regularization with a Maximum-Likelihood criterion based on the Rician distribution of the noise. In order to properly carry out the estimation on the image level, a Majorization-Minimization approach is implemented alongside an adapted non-linear least-squares algorithm. We propose a method for exploiting the reconstructed maps by clustering the parameters using a K-means classification algorithm applied to the extracted relaxation time and amplitude maps. The method is illustrated on real MRI data of food sample analysis.
Efficiency analysis for quantitative MRI of T1 and T2 relaxometry methods
Physics in Medicine and Biology, 2021
This study presents a comparison of quantitative MRI methods based on an efficiency metric that quantifies their intrinsic ability to extract information about tissue parameters. Under a regime of unbiased parameter estimates, an intrinsic efficiency metric η was derived for fully-sampled experiments which can be used to both optimize and compare sequences. Here we optimize and compare several steady-state and transient gradient-echo based qMRI methods, such as magnetic resonance fingerprinting (MRF), for joint T1 and T2 mapping. The impact of undersampling was also evaluated, assuming incoherent aliasing that is treated as noise by parameter estimation. In vivo validation of the efficiency metric was also performed. Transient methods such as MRF can be up to 3.5 times more efficient than steady-state methods, when spatial undersampling is ignored. If incoherent aliasing is treated as noise during least-squares parameter estimation, the efficiency is reduced in proportion to the SNR...
A modified Rician LMMSE estimator for the restoration of magnitude MR images
In this article two modified versions of a simple and recently proposed method which is known as linear minimum mean square error (LMMSE) estimator for denoising of magnetic resonance (MR) images are presented. In the introduced approaches the self-similarity and natural redundancy of the acquired MR image are considered to achieve the higher performance of unknown signal estimation. Since, the MR data are in a large majority 3D, the proposed methods are developed to deal with 3D volumes. The introduced methods are compared with related state-of-the-art schemes over both clinical and simulated MR data. The quantitative and qualitative results show their superior denoising ability.
2019
The extraction of multi-exponential decay parameters from multi-temporal images corrupted with Rician noise and with limited time samples proves to be a challenging problem frequently encountered in clinical and food MRI studies. This work aims at proposing a method for the estimation of multi-exponential transverse relaxation times from noisy magnitude MRI images. A spatially regularized Maximum-Likelihood estimator accounting for the Rician distribution of the noise is introduced. To deal with the large-scale optimization problem, a Majoration-Minimization approach coupled with an adapted non-linear least squares algorithm is implemented. The proposed algorithm is numerically fast, stable and leads to accurate results. Its effectiveness is illustrated by an application to a simulated phantom and to magnitude multi spin echo MRI images acquired from a tomato sample.
HAL (Le Centre pour la Communication Scientifique Directe), 2019
The extraction of multi-exponential decay parameters from multi-temporal images corrupted with Rician noise and with limited time samples proves to be a challenging problem frequently encountered in clinical and food MRI studies. This work aims at proposing a method for the estimation of multiexponential transverse relaxation times from noisy magnitude MRI images. A spatially regularized Maximum-Likelihood estimator accounting for the Rician distribution of the noise is introduced. To deal with the large-scale optimization problem, a Majoration-Minimization approach coupled with an adapted non-linear least squares algorithm is implemented. The proposed algorithm is numerically fast, stable and leads to accurate results. Its effectiveness is illustrated by an application to a simulated phantom and to magnitude multi spin echo MRI images acquired from a tomato sample.
Quantitative Mapping in Magnetic Resonance Imaging
2016
Magnetic Resonance Imaging (MRI) produces superior soft tissue contrast that is mostly determined by the tissue relaxation times (T1 and T2) and spin density (PD). This dissertation introduces novel methods to quantify T1, T2 and PD, and explored their value for disease classification, and tracking delivery of cell therapies. First, a novel T2 measurement (Dual-τ) method that employs adiabatic pulses is proposed, that exploits the property that the spins undergo T2 decay during excitation by long adiabatic pulses. The new method is relatively immune to MR static and excitation field inhomogeneity, and has a higher efficiency than the conventional methods. The adiabatic excitation pulse can also serve as a preparation pulse that introduces T2 contrast into the MRI, and can be combined with T1 quantification methods to produce T1 and T2 simultaneously. The method is shown to be most accurate at short T2s. The T2 measurements were validated in phantoms and in vivo in human studies. Second, three methods of mapping T1, T2, and PD simultaneously with the least possible number of acquisitions are presented, also utilizing adiabatic pulses. The first, Dual-τ-Dual-FA method, encodes T1 by varying excitation flip-angle (FA). The second, Dual-τ-Dual-TR method, encodes T1 using the variations in the sequence repetition time (TR). The third method incorporates the FA self-correction to eliminate T1 errors caused by field inhomogeneities, and is called the Four-FA method. All three methods were validated in phantom studies, and the Dual-τ-Dual-FA and Four-FA methods were validated in human brain studies as well. The Four-FA method is demonstrated to have the best overall accuracy compared to the existing methods, such as DESPOT1/2, IR TrueFISP, etc. iii Combining the multi-parametric mapping methods with intravascular (IV) MRI potentially offers a means of reducing the scan time and increasing the local SNR. For the first time, multi-parametric high-resolution (<200μm) T1, T2, PD and fat images of human vessels are obtained. These maps were used to train a machine-learning based classifier to automatically distinguish early-and advanced-stage vessel disease from healthy and smooth muscle. This application enables differentiation of vessel wall disease types with high sensitivity and specificity compared with histology as the standard. The contrast of cells delivered as therapeutic agents in MRI can be enhanced using capsules impregnated with MRI-sensitive contrast agents. At the end of the dissertation, we explore quantitative cell tracking using 19 F-labeled capsules that provide dual modality contrast for both computed tomography (CT) and MRI. The method was validated in rabbit diseased models using clinical imaging systems. Compared with CT, 19 F MRI was able to accurately track cells non-invasively in vivo, without the use of ionizing radiation. Two weeks after the cell administration, no significant changes in the volume or concentration of the capsules were observed, and the cells preserved high viability according to histology.
Estimation of the Noise in Magnitude MR Images
Magnetic Resonance Imaging, 1998
Magnitude Magnetic Resonance (MR) data are Rician distributed. In this note a new method is proposed to estimate the image noise variance for this type of data distribution. The method is based on a double image acquisition, thereby exploiting the knowledge of the Rice distribution moments.
Recovery of relaxation rates in MRI T 2− weighted brain images via exponential fitting
Exponential Data Fitting and Its Applications, 2010
Abstract. We consider synthetic magnetic resonance images of a brain slice generated with the BrainWeb resource. They correspond to measurements taken at various times and record the intensity of the response signal of the probed tissue to a magnetic pulse. The specific property measured, which is considered in this chapter, is transverse magnetization. The transverse magnetization decay technique can be used to obtain several images for a given axial slice of tissue. Namely, for each pixel the time uniform sequence of transverse ...
MR Parameter Map Suite: ITK Classes for Calculating Magnetic Resonance T2 and T1 Parameter Maps
Insight Journal, 2008
This document describes a suite of new multi-threaded classes for calculating magnetic resonance (MR) T 2 and T 1 parameter maps implemented using the Insight Toolkit ITK (www.itk.org). Similar to MR diffusion tensor imaging (DTI), MR T 2 and T 1 parameter maps provide a non-invasive means for quantitatively measuring disease or pathology in-vivo. Included in the suite are classes for reading proprietary Bruker 2dseq and Philips PAR/REC images and example programs and data for validating the new classes.