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Magnetic resonance imaging protocols for the assessment of quantitative information suffer from long acquisition times since multiple measurements in a parametric dimension are required. To facilitate the clinical applicability, accelerating the acquisition is of high importance. To this end, we propose a model‐based optimization framework in conjunction with undersampling 3D radial stack‐of‐stars data.
Theory and Methods
High resolution 3D T 1 maps are generated from subsampled data by employing model‐based reconstruction combined with a regularization functional, coupling information from the spatial and parametric dimension, to exploit redundancies in the acquired parameter encodings and across parameter maps. To cope with the resulting non‐linear, non‐differentiable optimization problem, we propose a solution strategy based on the iteratively regularized Gauss‐Newton method. The importance of 3D‐spectral regularization is demonstrated by a comparison to 2D‐spectral regularized results. The algorithm is validated for the variable flip angle (VFA) and inversion recovery Look‐Locker (IRLL) method on numerical simulated data, MRI phantoms, and in vivo data.
Evaluation of the proposed method using numerical simulations and phantom scans shows excellent quantitative agreement and image quality. T 1 maps from accelerated 3D in vivo measurements, e.g. 1.8 s/slice with the VFA method, are in high accordance with fully sampled reference reconstructions.
The proposed algorithm is able to recover T 1 maps with an isotropic resolution of 1 mm3 from highly undersampled radial data by exploiting structural similarities in the imaging volume and across parameter maps.
constrained reconstruction, inversion‐recovery Look‐Locker, imaging, model‐based reconstruction, MRI, T1 quantification, variable flip angle
Rapid T1 quantification from high resolution 3D data with model‐based reconstruction
Oliver Maier,corresponding author 1 , 2 Jasper Schoormans, 3 Matthias Schloegl, 1 , 2 Gustav J. Strijkers, 3 Andreas Lesch, 1 , 2 Thomas Benkert, 4 , 5 Tobias Block, 4 , 5 Bram F. Coolen, 3 Kristian Bredies, 2 , 6 and Rudolf Stollberger 1 , 2
2018 Oct 22
Both instrumental data analyses and coupled ocean-atmosphere models indicate that Atlantic meridional overturning circulation (AMOC) variability is tightly linked to abrupt tropical North Atlantic (TNA) climate change through both atmospheric and oceanic processes. Although a slowdown of AMOC results in an atmospheric-induced surface cooling in the entire TNA, the subsurface experiences an even larger warming because of rapid reorganizations of ocean circulation patterns at intermediate water depths. Here, we reconstruct high-resolution temperature records using oxygen isotope values and Mg/Ca ratios in both surface- and subthermocline-dwelling planktonic foraminifera from a sediment core located in the TNA over the last 22 ky. Our results show significant changes in the vertical thermal gradient of the upper water column, with the warmest subsurface temperatures of the last deglacial transition corresponding to the onset of the Younger Dryas. Furthermore, we present new analyses of a climate model simulation forced with freshwater discharge into the North Atlantic under Last Glacial Maximum forcings and boundary conditions that reveal a maximum subsurface warming in the vicinity of the core site and a vertical thermal gradient change at the onset of AMOC weakening, consistent with the reconstructed record. Together, our proxy reconstructions and modeling results provide convincing evidence for a subsurface oceanic teleconnection linking high-latitude North Atlantic climate to the tropical Atlantic during periods of reduced AMOC across the last deglacial transition.
Mg/Ca paleothermometry, paleoclimate modeling, Bonaire Basin, Heinrich Event, sea surface temperature
Impact of abrupt deglacial climate change on tropical Atlantic subsurface temperatures
Matthew W. Schmidt,a,1 Ping Chang,a,b Jennifer E. Hertzberg,a Theodore R. Them, II,a Link Ji,a and Bette L. Otto-Bliesnerc
2012 Aug 20.
The aim of this study was to track dental pulp stem cells (DPSCs) labeled with dextran-coated superparamagnetic iron oxide nanoparticles (SPIONs) using magnetic resonance imaging (MRI). Dental pulp was isolated from male Sprague Dawley rats and cultured in Dulbecco’s modified Eagle’s medium F12 (DMEM-F12) and 10% fetal bovine serum. Effects of SPIONs on morphology, viability, apoptosis, stemness, and osteogenic and adipogenic differentiation of DPSCs were assessed. Prussian blue staining and MRI were conducted to determine in vitro efficiency of SPIONs uptake by the cells. Both non-labeled and labeled DPSCs were adherent to culture plates and showed spindle-shape morphologies, respectively. They were positive for osteogenic and adipogenic induction and expression of cluster of differentiation (CD) 73 and CD90 biomarkers, but negative for expression of CD34 and CD45 biomarkers. The SPIONs were non-toxic and did not induce apoptosis in doses less than 25 mg/mL. Internalization of the SPIONs within the DPSCs was confirmed by Prussian blue staining and MRI. Our findings revealed that the MRI-based method could successfully monitor DPSCs labeled with dextran-coated SPIONs without any significant effect on osteogenic and adipogenic differentiation, viability, and stemness of DPSCs. We provided the in vitro evidence supporting the feasibility of an MRI-based method to monitor DPSCs labeled with SPIONs without any significant reduction in viability, proliferation, and differentiation properties of labeled cells, showing that internalization of SPIONs within DPSCs were not toxic at doses less than 25 mg/mL. In general, the SPION labeling does not seem to impair cell survival or differentiation. SPIONs are biocompatible, easily available, and cost effective, opening a new avenue in stem cell labeling in regenerative medicine.
MRI, tracking, labeling, dental pulp stem cells, iron oxide, nanoparticle
MRI-Tracking of Dental Pulp Stem Cells In Vitro and In Vivo Using Dextran-Coated Superparamagnetic Iron Oxide Nanoparticles
Shahrokh Zare,1,2 Davood Mehrabani,1,3,4,5,* Reza Jalli,6 Mahdi Saeedi Moghadam,6 Navid Manafi,7 Golshid Mehrabani,8 Iman Jamhiri,1 and Samad Ahadian9,*
2019 Sep 9