Magnetic Resonance in Medicine
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#1Junjie Ma (UTSW: University of Texas Southwestern Medical Center)H-Index: 1
#2Craig R. Malloy (UTSW: University of Texas Southwestern Medical Center)H-Index: 76
Last. Jae Mo Park (UTSW: University of Texas Southwestern Medical Center)H-Index: 12
view all 7 authors...
PURPOSE Previous cardiac imaging studies using hyperpolarized (HP) [1-13 C]pyruvate were acquired at end-diastole (ED). Little is known about the interaction between cardiac cycle and metabolite content in the myocardium. In this study, we compared images of HP pyruvate and products at end-systole (ES) and ED. METHODS A dual-phase 13 C MRI sequence was implemented to acquire two sequential HP images within a single cardiac cycle at ES and ED during successive R-R intervals in an interleaved mann...
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#1Seok-Jin Yeo (SKKU: Sungkyunkwan University)H-Index: 1
#2So-Hee Lee (SKKU: Sungkyunkwan University)H-Index: 2
Last. Seung-Kyun LeeH-Index: 21
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PURPOSE Most previous work on the calculation of susceptibility-induced static magnetic field (B0 ) inhomogeneity has considered strictly unidirectional magnetic fields. Here, we present the theory and implementation of a computational method to rapidly calculate static magnetic field vectors produced by an arbitrary distribution of voxelated magnetization vectors. THEORY AND METHODS Two existing B0 calculation methods were systematically extended to include arbitrary orientations of the magneti...
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#1Tobias WechH-Index: 10
#2Markus J. Ankenbrand (University of Würzburg)H-Index: 2
Last. Julius F. HeidenreichH-Index: 3
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PURPOSE Image acquisition and subsequent manual analysis of cardiac cine MRI is time-consuming. The purpose of this study was to train and evaluate a 3D artificial neural network for semantic segmentation of radially undersampled cardiac MRI to accelerate both scan time and postprocessing. METHODS A database of Cartesian short-axis MR images of the heart (148,500 images, 484 examinations) was assembled from an openly accessible database and radial undersampling was simulated. A 3D U-Net architec...
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#1Christian Kames (UBC: University of British Columbia)H-Index: 5
#2Jonathan Doucette (UBC: University of British Columbia)H-Index: 4
Last. Alexander Rauscher (UBC: University of British Columbia)H-Index: 32
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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...
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#1Mariana B L Falcão (UNIL: University of Lausanne)
#2Lorenzo Di Sopra (UNIL: University of Lausanne)H-Index: 5
Last. Matthias Stuber (UNIL: University of Lausanne)H-Index: 68
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PURPOSE: In this work, we integrated the pilot tone (PT) navigation system into a reconstruction framework for respiratory and cardiac motion-resolved 5D flow. We tested the hypotheses that PT would provide equivalent respiratory curves, cardiac triggers, and corresponding flow measurements to a previously established self-gating (SG) technique while being independent from changes to the acquisition parameters. METHODS: Fifteen volunteers and 9 patients were scanned with a free-running 5D flow s...
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#1Vahid Ghodrati (UCLA: University of California, Los Angeles)H-Index: 4
#2Yair Rivenson (UCLA: University of California, Los Angeles)H-Index: 34
Last. Peng Hu (UCLA: University of California, Los Angeles)H-Index: 23
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PURPOSE: To automate the segmentation of the peripheral arteries and veins in the lower extremities based on ferumoxytol-enhanced MR angiography (FE-MRA). METHODS: Our automated pipeline has 2 sequential stages. In the first stage, we used a 3D U-Net with local attention gates, which was trained based on a combination of the Focal Tversky loss with region mutual loss under a deep supervision mechanism to segment the vasculature from the high-resolution FE-MRA datasets. In the second stage, we us...
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#1Linfang Xiao (HKU: University of Hong Kong)H-Index: 4
#2Yilong Liu (HKU: University of Hong Kong)H-Index: 5
Last. Ed X. Wu (HKU: University of Hong Kong)H-Index: 55
view all 8 authors...
PURPOSE: To provide a complex-valued deep learning approach for partial Fourier (PF) reconstruction of complex MR images. METHODS: Conventional PF reconstruction methods, such as projection onto convex sets (POCS), uses low-resolution image phase information from the central symmetrically sampled k-space for image reconstruction. However, this smooth phase constraint undermines the phase estimation accuracy in presence of rapid local phase variations, causing image artifacts and limiting the ext...
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#1Christian T. McHugh (UNC: University of North Carolina at Chapel Hill)H-Index: 1
#2Michele Kelley (UNC: University of North Carolina at Chapel Hill)H-Index: 2
Last. Rosa T. Branca (UNC: University of North Carolina at Chapel Hill)H-Index: 17
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PURPOSE HyperCEST contrast relies on the reduction of the solvent signal after selective saturation of the solute magnetization. The scope of this work is to outline the experimental conditions needed to obtain a reliable hyperCEST contrast in vivo, where the "solvent" signal (ie, the dissolved-phase signal) may change over time due to the increase in xenon (Xe) accumulation into tissue. METHODS Hyperpolarized 129 Xe was delivered to mice at a constant volume and rate using a mechanical ventilat...
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#1Gastao Cruz ('KCL': King's College London)H-Index: 14
#2Haikun Qi ('KCL': King's College London)H-Index: 7
Last. Claudia Prieto ('KCL': King's College London)H-Index: 26
view all 7 authors...
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...
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#1Misung Han (UCSF: University of California, San Francisco)H-Index: 13
#2Radhika Tibrewala (UCSF: University of California, San Francisco)H-Index: 3
Last. Sharmila Majumdar (UCSF: University of California, San Francisco)H-Index: 103
view all 5 authors...
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 ...
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