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....

White matter changes measured by multi-component MR Fingerprinting in multiple sclerosis

T₂-hyperintense lesions are the key imaging marker of multiple sclerosis (MS). Previous studies have shown that the white matter surrounding such lesions is often also affected by MS. Our aim was to develop a new method to visualize and quantify the extent of white matter tissue changes in MS based on relaxometry properties. We...

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...

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...

Accelerated white matter lesion analysis based on simultaneous T₁ and T2∗ quantification using magnetic resonance fingerprinting and deep learning

Purpose: To develop an accelerated postprocessing pipeline for reproducible and efficient assessment of white matter lesions using quantitative magnetic resonance fingerprinting (MRF) and deep learning. Methods: MRF using echo-planar imaging (EPI) scans with varying repetition and echo times were acquired for whole brain quantification of ...

Myelin water imaging from multi-echo T₂ MR relaxometry data using a joint sparsity constraint

Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce...

Fast multi-component analysis using a joint sparsity constraint for MR fingerprinting

<br/>Purpose<br/><br/>To develop an efficient algorithm for multi‐component analysis of magnetic resonance fingerprinting (MRF) data without making a priori assumptions about the exact number of tissues or their relaxation properties.<br/>Methods<br/><br/>Different tissues or components within a voxel are potentially separable in MRF because of...

Implementation of a quantum private query using single-photon transistors

We propose a quantum-mechanical device consisting of transmon qubits connected to single-photon transmission lines that can be used to perform a boolean quantum private query on a database. Using numerical simulations verified by analytical calculations we analyse the dynamics of the query process in the proposed system by calculating the...