The referenceless correction enables robust single-scan imaging under changing conditions-such as patient motion and changes in shimming over time-without the need of ancillary navigators. This opens new options for real-time MRI and interactive scanning.
Mammalian models, and mouse studies in particular, play a central role in our understanding of placental development. Magnetic resonance imaging (MRI) could be a valuable tool to further these studies, providing both structural and functional information. As fluid dynamics throughout the placenta are driven by a variety of flow and diffusion processes, diffusion-weighted MRI could enhance our understanding of the exchange properties of maternal and fetal blood pools-and thereby of placental function. These studies, however, have so far been hindered by the small sizes, the unavoidable motions, and the challenging air/water/fat heterogeneities, associated with mouse placental environments. The present study demonstrates that emerging methods based on the spatiotemporal encoding (SPEN) of the MRI information can robustly overcome these obstacles. Using SPEN MRI in combination with albumin-based contrast agents, we analyzed the diffusion behavior of developing placentas in a cohort of mice. These studies successfully discriminated the maternal from the fetal blood flows; the two orders of magnitude differences measured in these fluids' apparent diffusion coefficients suggest a nearly free diffusion behavior for the former and a strong flow-based component for the latter. An intermediate behavior was observed by these methods for a third compartment that, based on maternal albumin endocytosis, was associated with trophoblastic cells in the interphase labyrinth. Structural features associated with these dynamic measurements were consistent with independent intravital and ex vivo fluorescence microscopy studies and are discussed within the context of the anatomy of developing mouse placentas.robust diffusion MRI | high-field placental MRI | placental ADC maps | multimodal imaging
Diffusion-Weighted (DW) MRI is a powerful modality for studying microstructure in normal and pathological tissues. DW MRI, however, is of limited use in regions suffering from large magnetic field or chemical shift heterogeneities. Spatio-temporal encoding (SPEN) is a single-scan imaging technique that can deliver its information with a remarkable insensitivity to field inhomogeneities; this study explores the use of diffusion-weighted SPEN (dSPEN) MRI as an alternative for acquiring this kind of information. Owing to SPEN's combined use of gradients and radiofrequency-swept pulses, spatially-dependent diffusion weightings arise in these sequences that are not present in conventional k-space DW MRI. In order to account for these phenomena an analytical formalism is presented that extends Stejskal & Tanner's and Karlicek & Lowe's work, to derive the b-values arising upon taking into account the effects of adiabatic pulses, of imaging as well as diffusion gradients, and of cross-terms between them. Excellent agreement is found between the new features predicted by these analytical and numerical derivations, and SPEN diffusion experiments in phantoms and in anisotropic ex vivo systems. Examinations of apparent diffusion coefficients in human breast volunteers also verify the advantages of the new methods in vivo, which exhibit substantial robustness vis-à-vis comparable DW echo planar imaging.
To develop and evaluate a neural network-based method for Gibbs artifact and noise removal. Methods: A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Results: Both machine learning methods were able to mitigate artifacts in diffusionweighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. Conclusions: The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
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