A fast protocol for multicenter and multiparametric quantitative MRI studies in brain tumor patients using vendor sequences.

Background: Multiparametric quantitative MRI (mp-qMRI) provides noninvasive, quantitative measurements sensitive to a variety of tissue properties. In brain tumors (BTs), longitudinal relaxation time (T1), effective transverse relaxation time (T2*), transverse relaxation time (T2), water content (HO), and quantitative susceptibility (χ) give valuable insights into the microenvironment. To generate large multicenter datasets, protocols need to be short and implementable on any scanner.

Biophysical contrast sources for magnetic susceptibility and R2* mapping: A combined 7 Tesla, mass spectrometry and electron paramagnetic resonance study.

Iron is the most abundant trace metal in the human brain and consistently shown elevated in prevalent neurological disorders. Because of its paramagnetism, brain iron can be assessed in vivo by quantitative MRI techniques such as R* mapping and Quantitative Susceptibility Mapping (QSM). While Inductively Coupled Plasma Mass Spectrometry (ICP-MS) has demonstrated good correlations of the total iron content to MRI parameters in gray matter, the relationship to ferritin levels as assessed by Electron Paramagnetic Resonance (EPR) has not been systematically analyzed.

Neuroimaging of Parkinson's disease by quantitative susceptibility mapping.

Parkinson's disease (PD) is a common neurodegenerative disease, and apart from a few rare genetic causes, its pathogenesis remains largely unclear. Recent scientific interest has been captured by the involvement of iron biochemistry and the disruption of iron homeostasis, particularly within the brain regions specifically affected in PD. The advent of Quantitative Susceptibility Mapping (QSM) has enabled non-invasive quantification of brain iron in vivo by MRI, which has contributed to the understanding of iron-associated pathogenesis and has the potential for the development of iron-based biomarkers in PD.

Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group.

This article provides recommendations for implementing QSM for clinical brain research. It is a consensus of the International Society of Magnetic Resonance in Medicine, Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain.

Multimodal analytical tools for the molecular and elemental characterisation of lesions in brain tissue of multiple sclerosis patients.

Multiple sclerosis (MS) is a prevalent immune-mediated inflammatory disease of the central nervous system inducing a widespread degradation of myelin and resulting in neurological deficits. Recent advances in molecular and atomic imaging provide the means to probe the microenvironment in affected brain tissues at an unprecedented level of detail and may provide new insights. This study showcases state-of-the-art spectroscopic and mass spectrometric techniques to compare distributions of molecular and atomic entities in MS lesions and surrounding brain tissues.

Recommended Implementation of Quantitative Susceptibility Mapping for Clinical Research in The Brain: A Consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group.

This article provides recommendations for implementing quantitative susceptibility mapping (QSM) for clinical brain research. It is a consensus of the ISMRM Electro-Magnetic Tissue Properties Study Group. While QSM technical development continues to advance rapidly, the current QSM methods have been demonstrated to be repeatable and reproducible for generating quantitative tissue magnetic susceptibility maps in the brain.

Interpretable brain disease classification and relevance-guided deep learning.

Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial for the classifier's decision. We propose a regularization technique to train convolutional neural network (CNN) classifiers utilizing relevance-guided heat maps calculated online during training.

Hybrid data fidelity term approach for quantitative susceptibility mapping.

Purpose: Susceptibility maps are usually derived from local magnetic field estimations by minimizing a functional composed of a data consistency term and a regularization term. The data-consistency term measures the difference between the desired solution and the measured data using typically the L2-norm. It has been proposed to replace this L2-norm with the L1-norm, due to its robustness to outliers and reduction of streaking artifacts arising from highly noisy or strongly perturbed regions.

Streaking artifact suppression of quantitative susceptibility mapping reconstructions via L1-norm data fidelity optimization (L1-QSM).

Purpose: The presence of dipole-inconsistent data due to substantial noise or artifacts causes streaking artifacts in quantitative susceptibility mapping (QSM) reconstructions. Often used Bayesian approaches rely on regularizers, which in turn yield reduced sharpness. To overcome this problem, we present a novel L1-norm data fidelity approach that is robust with respect to outliers, and therefore prevents streaking artifacts.

QSM reconstruction challenge 2.0: Design and report of results.

Purpose: The aim of the second quantitative susceptibility mapping (QSM) reconstruction challenge (Oct 2019, Seoul, Korea) was to test the accuracy of QSM dipole inversion algorithms in simulated brain data.

Methods: A two-stage design was chosen for this challenge. The participants were provided with datasets of multi-echo gradient echo images synthesized from two realistic in silico head phantoms using an MR simulator.

QSM reconstruction challenge 2.0: A realistic in silico head phantom for MRI data simulation and evaluation of susceptibility mapping procedures.

Purpose: To create a realistic in silico head phantom for the second QSM reconstruction challenge and for future evaluations of processing algorithms for QSM.

Methods: We created a digital whole-head tissue property phantom by segmenting and postprocessing high-resolution (0.64 mm isotropic), multiparametric MRI data acquired at 7 T from a healthy volunteer.

Adaptive slice-specific z-shimming for 2D spoiled gradient-echo sequences.

Purpose: To reduce the misbalance between compensation gradients and macroscopic field gradients, we introduce an adaptive slice-specific z-shimming approach for 2D spoiled multi-echo gradient-echoe sequences in combination with modeling of the signal decay.

Methods: Macroscopic field gradients were estimated for each slice from a fast prescan (15 seconds) and then used to calculate slice-specific compensation moments along the echo train. The coverage of the compensated field gradients was increased by applying three positive and three negative moments.

Cross-sectional and Longitudinal Assessment of Brain Iron Level in Alzheimer Disease Using 3-T MRI.

Background Deep gray matter structures in patients with Alzheimer disease (AD) contain higher brain iron concentrations. However, few studies have included neocortical areas, which are challenging to assess with MRI. Purpose To investigate baseline and change in brain iron levels using MRI at 3 T with R2* relaxation rate mapping in individuals with AD compared with healthy control (HC) participants.

The 2016 QSM Challenge: Lessons learned and considerations for a future challenge design.

Purpose: The 4th International Workshop on MRI Phase Contrast and QSM (2016, Graz, Austria) hosted the first QSM Challenge. A single-orientation gradient recalled echo acquisition was provided, along with COSMOS and the χ STI component as ground truths. The submitted solutions differed more than expected depending on the error metric used for optimization and were generally over-regularized.

Assessment and correction of macroscopic field variations in 2D spoiled gradient-echo sequences.

Purpose: To model and correct the dephasing effects in the gradient-echo signal for arbitrary RF excitation pulses with large flip angles in the presence of macroscopic field variations.

Methods: The dephasing of the spoiled 2D gradient-echo signal was modeled using a numerical solution of the Bloch equations to calculate the magnitude and phase of the transverse magnetization across the slice profile. Additionally, regional variations of the transmit RF field and slice profile scaling due to macroscopic field gradients were included.

DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in vivo. Thereby, QSM can also reveal pathological changes of these key components in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. While the ill-posed field-to-source-inversion problem underlying QSM is conventionally assessed by the means of regularization techniques, we trained a fully convolutional deep neural network - DeepQSM - to directly invert the magnetic dipole kernel convolution.

White Matter Hyperintensities in Alzheimer's Disease: A Lesion Probability Mapping Study.

Background/objective: Higher white matter hyperintensity (WMH) load has been reported in Alzheimer's disease (AD) patients in different brain regions when compared to controls. We aimed to assess possible differences of WMH spatial distribution between AD patients and age-matched controls by means of lesion probability maps.

Methods: The present study included MRI scans of 130 probable AD patients with a mean age of 73.

SHARQnet - Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network.

Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson's disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging.

Quantitative Susceptibility Mapping to Assess Cerebral Vascular Compliance.

This study explored whether autoregulatory shifts in cerebral blood volume induce susceptibility changes large enough to be depicted by quantitative susceptibility mapping. Eight healthy subjects underwent fast quantitative susceptibility mapping at 3T while lying down to slowly decrease mean arterial pressure. A linear relationship between mean arterial pressure and susceptibility was observed in cortical and subcortical structures, likely representing vessels involved in autoregulation.

Nigral iron deposition in common tremor disorders.

Objective: We investigated R2* relaxation rates as a marker of iron content in the substantia nigra in patients with common tremor disorders and explored their diagnostic properties.

Methods: Mean nigral R2* rates were measured in 40 patients with tremor-dominant Parkinson's disease (PD), 15 with tremor in dystonia, 25 with essential tremor, and 25 healthy controls.

Results: Tremor-dominant PD patients had significantly higher nigral R2* values (34.

Weak-harmonic regularization for quantitative susceptibility mapping.

Purpose: Background-field removal is a crucial preprocessing step for quantitative susceptibility mapping (QSM). Remnants from this step often contaminate the estimated local field, which in turn leads to erroneous tissue-susceptibility reconstructions. The present work aimed to mitigate this undesirable behavior with the development of a new approach that simultaneously decouples background contributions and local susceptibility sources on QSM inversion.

The impact of vascular risk factors on brain volume and lesion load in patients with early multiple sclerosis.

Background: Vascular risk factors (VRF) in multiple sclerosis (MS) patients have been associated with lower brain volumes. It is currently unknown if this association already exists in early MS and how it develops over time.

Methods: We identified 82 patients with clinically isolated syndrome (CIS) ( n = 61) or with early relapsing-remitting MS ( n = 21) and assessed their VRF including arterial hypertension, hyperlipidaemia, diabetes mellitus and smoking.

Quantitative susceptibility mapping: Report from the 2016 reconstruction challenge.

Purpose: The aim of the 2016 quantitative susceptibility mapping (QSM) reconstruction challenge was to test the ability of various QSM algorithms to recover the underlying susceptibility from phase data faithfully.

Methods: Gradient-echo images of a healthy volunteer acquired at 3T in a single orientation with 1.06 mm isotropic resolution.