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QMRITools is written in Mathematica using Wolfram Workbench and Eclipse and contains a collection of tools and functions for processing quantitative MRI data. The toolbox does not provide a GUI and its primary goal is to allow for fast and batch data processing, and facilitate development and prototyping of new functions. The core of the toolbox contains various functions for data manipulation and restructuring.doi:10.5281/zenodo.3239261 fatcat:hxs5oqdrtbbabjj5tte23hspde
Froeling, (2019). QMRTools: a Mathematica toolbox for quantitative MRI analysis.. Journal of Open Source Software, 4(38), 1204. https: //doi.org/10.21105/joss.01204 Froeling , (2019). ... Froeling , (2019). QMRTools: a Mathematica toolbox for quantitative MRI analysis.. ...doi:10.21105/joss.01204 fatcat:bqe6nyonsfcdbncm73cjplb3bi
Radiofrequency (RF) coils are an essential MRI hardware component. They directly impact the spatial and temporal resolution, sensitivity, and uniformity in MRI. Advances in RF hardware have resulted in a variety of designs optimized for specific clinical applications. RF coils are the "antennas" of the MRI system and have two functions: first, to excite the magnetization by broadcasting the RF power (Tx-Coil) and second to receive the signal from the excited spins (Rx-Coil). Transmit RF Coilsdoi:10.1002/jmri.26187 pmid:29897651 fatcat:by2c223zpzforku6sminjzgswi
more »... it magnetic field pulses (B 1 1 ) to rotate the net magnetization away from its alignment with the main magnetic field (B 0 ), resulting in a transverse precessing magnetization. Due to the precession around the static main magnetic field, the magnetic flux in the receive RF Coil (B 2 1 ) changes, which generates a current I. This signal is "picked-up" by an antenna and preamplified, usually mixed down to a lower frequency, digitized, and processed by a computer to finally reconstruct an image or a spectrum. Transmit and receive functionality can be combined in one RF Coil (Tx/Rx Coils). This review looks at the fundamental principles of an MRI RF coil from the perspective of clinicians and MR technicians and summarizes the current advances and developments in technology. Level of Evidence: 1 Technical Efficacy: Stage 6 J. MAGN. RESON. IMAGING 2018;00:000-000. S tarting with the initial studies by Lauterbur 1 and Mansfield and Grannell, 2 magnetic resonance imaging (MRI) has seen a tremendous growth as a diagnostic and research imaging modality. MRI offers excellent soft-tissue contrast at high spatial and temporal resolutions, with the ability to have a 3D tomographic representation of the subject of interest. MRI is unique in its ability to move beyond anatomical imaging, as it allows visualizing metabolic functions and chemical processes via spectroscopic imaging, and offers advanced methods to measure physiologic properties such as tissue oxygenation, flow, diffusion, and perfusion. While advances in the MRI hardware such as increased field strength and improved gradient performance have been substantial, advances in the radiofrequency (RF) technology have also proved to be valuable to improve the resolution and shorten the duration of MRI examinations. MRI RF coils are essential components for every MRI examination, as they are responsible for the excitation and the reception of the MR signal. This review on MRI RF Coils and their basic principles is written from the perspective of clinicians and MR technicians, and is divided into three sections. Basic Concepts and Terminology of MRI RF Coils are explained and a broad overview of the application fields is given. Then an in-depth explanation of current state-of-the-art transmit and receive RF coils is given, and novel technologies, approaches, and current advances in the field of MRI RF Coils are discussed in the last part. Every part includes topic-specific, selected references and further literature recommendations. Part A: Basic Concepts and Overview Transmit and Receive RF Coils and Basic Terminology Every MRI system (Fig. 1) is a mix of several subsystems that each provide necessary functionality to generate images of an object. View this article online at wileyonlinelibrary.com.
b-values was: 0, 10, 20, 30, 40, 60, 100, 150, 200, 250, 300, 400, 600, 800, 1000, 1250, 1500, 1750, 2000, 2250 and 2500 s/mm 2 van RIJSSEL ET AL. 9 of 19 How to cite this article: van Rijssel MJ, Froeling ...doi:10.1002/nbm.4372 pmid:32701224 fatcat:nrn7kezqubhqbcqaktjpahkxui
Quantitative MRI and MRS of muscle are increasingly being used to measure individual pathophysiological processes in Becker muscular dystrophy (BMD). In particular, muscle fat fraction was shown to be highly associated with functional tests in BMD. However, the muscle strength per unit of contractile cross-sectional area is lower in patients with BMD compared with healthy controls. This suggests that the quality of the non-fat-replaced (NFR) muscle tissue is lower than in healthy controls.doi:10.1002/nbm.4385 pmid:32754921 fatcat:bpmgca5ivzbqxiv3z5l6iq6di4
more »... quently, a measure that reflects changes in muscle tissue itself is needed. Here, we explore the potential of water T2 relaxation times, diffusion parameters and phosphorus metabolic indices as early disease markers in patients with BMD. For this purpose, we examined these measures in fat-replaced (FR) and NFR lower leg muscles in patients with BMD and compared these values with those in healthy controls. Quantitative proton MRI (three-point Dixon, multi-spin-echo and diffusion-weighted spin-echo echo planar imaging) and 2D chemical shift imaging 31 P MRS data were acquired in 24 patients with BMD (age 18.8-66.2 years) and 13 healthy controls (age 21.3-63.6 years). Muscle fat fractions, phosphorus metabolic indices, and averages and standard deviations (SDs) of the water T2 relaxation times and diffusion tensor imaging (DTI) parameters were assessed in six individual leg muscles. Phosphodiester levels were increased in the NFR and FR tibialis anterior, FR peroneus and FR gastrocnemius lateralis muscles. No clear pattern was visible for the other metabolic indices. Increased T2 SD was found in the majority of FR muscles compared with NFR and healthy control muscles. No differences in average water T2 relaxation times or DTI indices were found between groups. Overall, our results indicate that primarily muscles that are further along in the disease process showed increases in T2 heterogeneity and changes in some metabolic indices. No clear differences were found for the DTI indices between groups.
NMR in Biomedicine
The diffusion-weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo-diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcomingdoi:10.1002/nbm.3965 pmid:30052293 fatcat:zjp7ypsxdrcvzonftjc72g3hva
more »... make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal-to-noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data-driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro-compartments, which were consistent with hindered diffusion, free water and pseudo-diffusion. Taking free water and pseudo-diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co-existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels. --- This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
ORCID Johannes Forsting https://orcid.org/0000-0001-6647-3167 Martijn Froeling https://orcid.org/0000-0003-3841-0497 Lara Schlaffke https://orcid.org/0000-0002-0716-3780 ...doi:10.1002/nbm.4430 pmid:33217106 fatcat:cqd3fesmuffjzlaf7qnln6jyrq
NMR in Biomedicine
ORCID Mark Gosselink https://orcid.org/0000-0003-1578-3696 Martijn Froeling https://orcid.org/0000-0003-3841-0497 , parallel transmission, RF transmit coils Abbreviations used: AP, anterior posterior ...doi:10.1002/nbm.4491 pmid:33567471 pmcid:PMC8244117 fatcat:n5nfvf3v35cwppdg2fzpmoag3a
In diffusion MRI, spherical deconvolution approaches can resolve local white matter (WM) fiber orientation distributions (FOD) to produce state-of-the-art fiber tractography reconstructions. In contrast, spherical deconvolution typically produces suboptimal results in grey matter (GM). The advent of multi-shell diffusion MRI data has seen a growing interest toward GM, but its modelling remains confined to that of an isotropic diffusing process. However, evidence from both histology anddoi:10.1101/739136 fatcat:4x4mijdwcjbbpktnqvmhqyn3ym
more »... lution diffusion MRI studies suggests a marked anisotropic character of the diffusion process in GM, which might be exploited by appropriate modelling to improve the description of the cortical organization. Indeed, improving the performance of spherical deconvolution in GM might allow to track WM bundles to their start/endpoint in the cortex, a crucial step to improve the accuracy of fiber tractography reconstructions. In this study, we investigated whether performing spherical deconvolution with tissue specific models of both WM and GM improves the FOD characterization in GM while also retaining state-of-the-art performances in WM. To this end, we devised a framework able to simultaneously accommodate multiple tissue response functions, to estimate multiple, tissue-specific, fiber orientation distributions (mFOD). As proof of principle, we used the diffusion kurtosis imaging model to represent the WM signal, and the neurite orientation dispersion and density imaging (NODDI) model to represent the GM signal. The feasibility of mFOD is shown with numerical simulations, then the performances of the method were compared to gold-standard multi-shell constrained spherical deconvolution (MSCSD) with data of a subject from the Human Connectome Project (HCP). Results of simulations show mFOD can accurately estimate mixture of two FODs given sufficient SNR (≥ 50 at b = 0s/mm2) is achieved. With HCP data, mFOD provided improved FOD estimation in GM as compared to MSCSD, whereas the performances of the two methods were comparable in WM. When performing fiber tractography, tracts with mFOD entered the cortex with more spatial contiguity and for a longer distance as compared to MSCSD. In conclusion, mFOD allows to perform spherical deconvolution of multiple anisotropic response functions, improving the performances of spherical deconvolution in grey matter.
OPEN ACCESS Citation: Oudeman J, Verhamme C, Engbersen MP, Caan MWA, Maas M, Froeling M, et al. (2018) Diffusion tensor MRI of the healthy brachial plexus. ...doi:10.1371/journal.pone.0196975 pmid:29742154 pmcid:PMC5942843 fatcat:thtxybkdqnfpbllogsyuy4jlne
© 2015 Froeling et al; licensee BioMed Central Ltd. ...doi:10.1186/1532-429x-17-s1-p15 pmcid:PMC4328866 fatcat:6zyvweoepzf5tic7n32radkov4
Purpose: To investigate previously unreported effects of signal drift as a result of temporal scanner instability on diffusion MRI data analysis and to propose a method to correct this signal drift. Methods: We investigated the signal magnitude of nondiffusion-weighted EPI volumes in a series of diffusion-weighted imaging experiments to determine whether signal magnitude changes over time. Different scan protocols and scanners from multiple vendors were used to verify this on phantom data, anddoi:10.1002/mrm.26124 pmid:26822700 fatcat:e5jbekg4ojf5bbb5gqamvfwpq4
more »... he effects on diffusion kurtosis tensor estimation in phantom and in vivo data were quantified. Scalar metrics (eigenvalues, fractional anisotropy, mean diffusivity, mean kurtosis) and directional information (first eigenvectors and tractography) were investigated. Results: Signal drift, a global signal decrease with subsequently acquired images in the scan, was observed in phantom data on all three scanners, with varying magnitudes up to 5% in a 15-min scan. The signal drift has a noticeable effect on the estimation of diffusion parameters. All investigated quantitative parameters as well as tractography were affected by this artifactual signal decrease during the scan. Conclusion: By interspersing the non-diffusion-weighted images throughout the session, the signal decrease can be estimated and compensated for before data analysis; minimizing the detrimental effects on subsequent MRI analyses. Magn Reson Med 77:285-299, 2017. V C 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.
Purpose: In this work we present the MASSIVE (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation) brain dataset of a single healthy subject, which is intended to facilitate diffusion MRI (dMRI) modeling and methodology development. Methods: MRI data of one healthy subject (female, 25 y) were acquired on a clinical 3 T system (Philips Achieva) with an 8-channel head coil. In total, the subject was scanned on 18 different occasions with a total acquisitiondoi:10.1002/mrm.26259 pmid:27173617 fatcat:v77w3ozubfgxfaocbs7wzht7s4
more »... me of 22.5 hours. The dMRI data were acquired with an isotropi c resolution of 2.5 mm 3 and distributed over five shells with b-values up to 4000 s/mm 2 and two Cartesian grids with b-values up to 9000 s/mm 2 . Results: The final dataset consists of 8000 dMRI volumes, corresponding B 0 field maps and noise maps for subsets of the dMRI scans, and ten 3D FLAIR, T 1 -, and T 2 -weighted scans. The average signal-to-noise-ratio (SNR) of the non-diffusion-weighted images was roughly 35. Conclusion: This unique set of in vivo MRI data will provide a robust framework to evaluate novel diffusion processing techniques and to reliably compare different approaches for diffusion modeling. The MASSIVE dataset is made publically available (both unprocessed and processed) on www.massive-data.org.
Alberto De Luca: Conceptualization, Methodology, Investigation, Formal analysis, Software, Visualization, Writing Original Draft, Reviewing and Editing Fenghua Guo: Methodology, Editing Martijn Froeling ...doi:10.1016/j.neuroimage.2020.117206 pmid:32745681 fatcat:enpc2pvrlndt3ev2ttasq7jxy4
Data preprocessing comprised three steps: (1) Rician noise suppression (Froeling et al. 2009 ); (2) affine registration of the diffusion-weighted images to the nonweighted image to correct for motion ... tractography have been applied to visualize muscle architecture in various regions in the human body, including leg, forearm, heart, spine, pelvis, and tongue (Gilbert and Napadow 2005; Heemskerk et al. 2009; Froeling ...doi:10.14814/phy2.13012 pmid:28003562 pmcid:PMC5210383 fatcat:hc6a3dcecnedxcljyjwf4odu7m
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