Magnetic Resonance in Medicine
Papers 10,000
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#1Gregory Simchick (UW: University of Wisconsin–Madison)H-Index: 1
#2Ruiyang Zhao (UW: University of Wisconsin–Madison)H-Index: 2
Last. Diego Hernando (UW: University of Wisconsin–Madison)H-Index: 36
view all 5 authors...
PURPOSE To evaluate the precision profile (repeatability and reproducibility) of quantitative STEAM-MRS and to determine the relationships between multiple MR biomarkers of chronic liver disease in subjects with iron overload at both 1.5 Tesla (T) and 3T. METHODS MRS data were acquired in patients with known or suspected liver iron overload. Two STEAM-MRS sequences (multi-TE and multi-TE-TR) were acquired at both 1.5T and 3T (same day), including test-retest acquisition. Each acquisition enabled...
#1Noemi G. Gyori (UCL: University College London)H-Index: 3
#2Marco Palombo (UCL: University College London)H-Index: 17
Last. Daniel C. Alexander (UCL: University College London)H-Index: 65
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PURPOSE Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distributions on the accuracy and precision of parameter estimates when supervised ML is used for fitting. METHODS We fit a two- and three-compartment biophysical model to diffusion measurements from in-vivo human brain, as well as simulated diffusion data, using...
#1James G. Pipe (Mayo Clinic)H-Index: 5
#2Daniel D Borup (UR: University of Rochester)
Purpose null To generate efficient gradient waveforms for spiral MRI which mitigate the high-frequency attenuation inherent in gradient systems. null Theory and methods null Spiral MRI has many clinical advantages, including high temporal and SNR efficiency. One of the challenges for robust spiral MRI is a high sensitivity to imperfections in the gradient system, which requires some form of correction in order to map data correctly in k-space. A previous numerical algorithm for generating spiral...
#1Alessandro Sciarra (OvGU: Otto-von-Guericke University Magdeburg)H-Index: 6
#2Hendrik Mattern (OvGU: Otto-von-Guericke University Magdeburg)H-Index: 7
Last. Oliver SpeckH-Index: 45
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PURPOSE Quantitative assessment of prospective motion correction (PMC) capability at 7T MRI for compliant healthy subjects to improve high-resolution images in the absence of intentional motion. METHODS Twenty-one healthy subjects were imaged at 7 T. They were asked not to move, to consider only unintentional motion. An in-bore optical tracking system was used to monitor head motion and consequently update the imaging volume. For all subjects, high-resolution T1 (3D-MPRAGE), T2 (2D turbo spin ec...
#1Misung Han (UCSF: University of California, San Francisco)H-Index: 13
#2Radhika Tibrewala (UCSF: University of California, San Francisco)H-Index: 4
Last. Sharmila Majumdar (UCSF: University of California, San Francisco)H-Index: 78
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Purpose null To validate the potential of quantifying R2 -R1ρ using one pair of signals with T1ρ preparation and T2 preparation incorporated to magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS) acquisition and to find an optimal preparation time (Tprep ) for in vivo knee MRI. null Methods null Bloch equation simulations were first performed to assess the accuracy of quantifying R2 -R1ρ using T1ρ - and T2 -prepared signals with an equivalent Tprep ...
#1Antonio Tristán-Vega (University of Valladolid)H-Index: 18
#2Guillem París (University of Valladolid)
Last. Santiago Aja-Fernández (University of Valladolid)H-Index: 26
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Purpose null To accurately estimate the partial volume fraction of free water in the white matter from diffusion MRI acquisitions not demanding strong sensitizing gradients and/or large collections of different b-values. Data sets considered comprise null null ∼ null null 32-64 gradients near null null null b null null = null null 1000 null null null null s null null / null null null mm null null 2 null null null null null null plus null null ∼ null null 6 gradients near null null null b null nu...
#1Gastao Cruz (KCL: King's College London)H-Index: 17
#2Haikun Qi (KCL: King's College London)H-Index: 10
Last. Claudia Prieto (KCL: King's College London)H-Index: 28
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PURPOSE Develop a novel low-rank motion-corrected (LRMC) reconstruction for nonrigid motion-corrected MR fingerprinting (MRF). METHODS Generalized motion-corrected (MC) reconstructions have been developed for steady-state imaging. Here we extend this framework to enable nonrigid MC for transient imaging applications with varying contrast, such as MRF. This is achieved by integrating low-rank dictionary-based compression into the generalized MC model to reconstruct MC singular images, reducing mo...
#1William T. Clarke (University of Oxford)H-Index: 21
#2Mark Chiew (University of Oxford)H-Index: 12
Purpose: Low-rank denoising of MRSI data results in an apparent increase in spectral SNR. However, it is not clear if this translates to a lower uncertainty in metabolite concentrations after spectroscopic fitting. Estimation of the true uncertainty after denoising is desirable for downstream analysis in spectroscopy. In this work the uncertainty reduction from low-rank denoising methods based on spatio-temporal separability and linear predictability in MRSI are assessed. A new method for estima...
#1Christian Kames (UBC: University of British Columbia)H-Index: 6
#2Jonathan Doucette (UBC: University of British Columbia)H-Index: 5
Last. Alexander Rauscher (UBC: University of British Columbia)H-Index: 31
view all 4 authors...
Purpose null To develop a deep neural network to recover filtered phase from clinical MR phase images to enable the computation of QSMs. null Methods null Eighteen deep learning networks were trained to recover combinations of 13 SWI phase-filtering pipelines. SWI-filtered data were computed offline from five multiorientation, multiecho MRI scans yielding 132 3D volumes (118/7/7 training/validation/testing). Two experiments were conducted to show the efficacy of the networks. First, using QSM pr...
#1Hanwen Liu (UBC: University of British Columbia)H-Index: 6
#2Tigris S. Joseph (UBC: University of British Columbia)
Last. Cornelia LauleH-Index: 27
view all 9 authors...
PURPOSE The decomposition of multi-exponential decay data into a T2 spectrum poses substantial challenges for conventional fitting algorithms, including non-negative least squares (NNLS). Based on a combination of the resolution limit constraint and machine learning neural network algorithm, a data-driven and highly tailorable analysis method named spectrum analysis for multiple exponentials via experimental condition oriented simulation (SAME-ECOS) was proposed. THEORY AND METHODS The theory of...
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