Estimation of multiple components and parameters for quantitative MRI

Magnetic Resonance Imaging (MRI) is a flexible medical imaging technique that facilitates measurement of a wide range of contrasts particularly in soft tissue (e.g. brain and heart). Conventionally, qualitative images are acquired in which certain physical tissue properties are emphasized such as the transverse and longitudinal relaxation times....

Accuracy and repeatability of joint sparsity multi-component estimation in MR Fingerprinting

MR fingerprinting (MRF) is a promising method for quantitative characterization of tissues. Often, voxel-wise measurements are made, assuming a single tissue-type per voxel. Alternatively, the Sparsity Promoting Iterative Joint Non-negative least squares Multi-Component MRF method (SPIJN-MRF) facilitates tissue parameter estimation for...

Multicomponent MR fingerprinting reconstruction using joint-sparsity and low-rank constraints

Purpose: To develop an efficient algorithm for multicomponent MR fingerprinting (MC-MRF) reconstructions directly from highly undersampled data without making prior assumptions about tissue relaxation times and expected number of tissues. Methods: The proposed method reconstructs MC-MRF maps from highly undersampled data by iteratively...

Mitigating undersampling errors in MR fingerprinting by sequence optimization

Purpose: To develop a method for MR Fingerprinting (MRF) sequence optimization that takes both the applied undersampling pattern and a realistic reference map into account. Methods: A predictive model for the undersampling error leveraging on perturbation theory was exploited to optimize the MRF flip angle sequence for improved robustness...

Validation of Quantitative MRI: Fat Quantification and ADC Mapping in the Head-and-Neck Area

Fat fraction (FF) and apparent diffusion coefficient (ADC) values estimated by Dixon MRI and diffusion weighted MRI (DWI) techniques respectively, are relatively new quantitative imaging parameters and increasingly accepted as imaging biomakers for all sorts of purposes. The aim of the BOCASEcA study is to research whether these techniques can...

Recovering Quantitative Maps from Conventional Weighted MR Images using Deep Learning

Long acquisition times impede the routine clinical use of quantitative magnetic resonance imaging (qMRI). qMRI quantifies meaningful tissue parameters in T1-, T2-, and PD-maps, as opposed to conventional (qualitative) weighted MRI (wMRI), which only visualises contrast between tissues. Although methods exist that generate synthetic wMRI from...

Recurrent inference machines as inverse problem solvers for MR relaxometry

In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T₁ and T₂ mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood...

Quantitative MRI of the carotid artery: Simultaneous T1 and T2 mapping on complex image data using maximum likelihood parameter estimation

Carotid atherosclerotic plaque rupture is a major cause in ischemic strokes. The luminal narrowing caused by the growing of the plaque has been commonly used for identification of plaque rupture risk. However, there have been signs that plaque composition is also important in the identification of plaque rupture risk. Quantitative Magnetic...

Multi-voxel algorithm for quantitative bi-exponential MRI T₁ estimation

Quantification of the spin-lattice relaxation time, T₁, of tissues is important for characterization of tissues in clinical magnetic resonance imaging (MRI). In T₁ mapping, T₁ values are estimated from a set of T₁-weighted MRI images. Due to the limited spatial resolution of the T₁...