Improving B1 homogeneity in abdominal imaging at 3 T with light and compact metasurface

Radiofrequency field inhomogeneity is a significant issue in imaging large fields of view in high- and ultrahigh-field MRI. Passive shimming with coupled coils or dielectric pads is the most common approach at 3 T. We introduce and test light and compact metasurface, providing the same homogeneity improvement in clinical abdominal imaging at 3 T as a conventional dielectric pad. The metasurface comprising a periodic structure of copper strips and parallel-plate capacitive elements printed on a flexible polyimide substrate supports propagation of slow electromagnetic waves similar to a high-permittivity slab. We compare the metasurface operating inside a transmit body birdcage coil to the state-of-the-art pad by numerical simulations and in vivo study on healthy volunteers. Numerical simulations with different body models show that the local minimum of B1+ causing a dark void in the abdominal domain is removed by the metasurface with comparable resulting homogeneity as for the pad without noticeable SAR change. In vivo results confirm similar homogeneity improvement and demonstrate the stability to body mass index. The light, flexible, and cheap metasurface can replace a relatively heavy and expensive pad based on the aqueous suspension of barium titanate in abdominal imaging at 3 T.Comment: 18 pages, 6 figures, 4 supplementary figure

Deep learning-based fully automatic segmentation of wrist cartilage in MR images

The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch-based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in twenty multi-slice MRI datasets acquired with two different coils in eleven subjects (healthy volunteers and patients). The validation included a comparison with the alternative state-of-the-art CNN methods for the segmentation of joints from MR images and the ground-truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image-based and patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter- and intra-observer variability of the manual wrist cartilage segmentation (DSC=0.78-0.88 and 0.9, respectively). The proposed deep-learning-based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy

A Quantitative Study of a New RF-coil for 7 Tesla Small-Animal Imaging

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Tunable hybrid metasurfaces for MRI applications

One of many exciting application of metasurfaces is in the magnetic resonance imaging (MRI). Here we demonstrate theoretically and experimentally how to improve substantially the MRI sensitivity by employing the concept of hybrid metasurfaces. We design a novel hybrid metasurface as an array of nonmagnetic metallic wires with high-permittivity dielectric blocks at the edges. We demonstrate that tunability of this metasurface can be achieved via a change of the effective permittivity of blocks near the edges. Moreover, altering the coupling strength between the dielectric and metallic elements allows to obtain nearly homogeneous shapes of the near-field magnetic modes.This work was supported by Ministry of Education and Science of the Russian Federation (Zadanie No. 3.2465.2017/4.6) and by the Grant of the Government of the Russian Federation (No. 074-U01). AGW acknowledges support by European Research Council Advanced Grant 670629 NOMA MRI and NWO Topsubside

Tunable hybrid metasurfaces for image quality enhancement

Metasurfaces created a new paradigm in the research of artificial electromagnetic structures owing to their potential to overcome many challenges typically associated with bulk metamaterials. However, a majority of demonstrated metasurface structures possess fixed properties, e.g. fixed operational bandwidth or functionality. An active control of metasurface functionalities and bandwidth is highly desirable for engineering an advanced electromagnetic and photonic devices. Here, we suggest and demonstrate experimentally a novel type of metasurface capable of dramatic enhancing the image quality.This work was supported by Russian Science Foundation (Project No. 15-19-20054)

A mechanically tunable and efficient ceramic probe for MR-microscopy at 17 Tesla

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