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

Comparative analysis of SINC-shaped and SLR pulses performance for contiguous multi-slice fast spin-echo imaging using metamaterial-based MRI

International audienc

CNN ‐based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures

Purpose Automatic measurement of wrist cartilage volume in MR images. Methods We assessed the performance of four manually optimized variants of the U‐Net architecture, nnU‐Net and Mask R‐CNN frameworks for the segmentation of wrist cartilage. The results were compared to those from a patch‐based convolutional neural network (CNN) we previously designed. The segmentation quality was assessed on the basis of a comparative analysis with manual segmentation. The best networks were compared using a cross‐validation approach on a dataset of 33 3D VIBE images of mostly healthy volunteers. Influence of some image parameters on the segmentation reproducibility was assessed. Results The U‐Net‐based networks outperformed the patch‐based CNN in terms of segmentation homogeneity and quality, while Mask R‐CNN did not show an acceptable performance. The median 3D DSC value computed with the U‐Net_AL (0.817) was significantly larger than DSC values computed with the other networks. In addition, the U‐Net_AL provided the lowest mean volume error (17%) and the highest Pearson correlation coefficient (0.765) with respect to the ground truth values. Of interest, the reproducibility computed using U‐Net_AL was larger than the reproducibility of the manual segmentation. Moreover, the results indicate that the MRI‐based wrist cartilage volume is strongly affected by the image resolution. Conclusions U‐Net CNN with attention layers provided the best wrist cartilage segmentation performance. In order to be used in clinical conditions, the trained network can be fine‐tuned on a dataset representing a group of specific patients. The error of cartilage volume measurement should be assessed independently using a non‐MRI method

Metasurface-based wireless coils for magnetic resonance imaging

Magnetic resonance imaging (MRI) is the cornerstone diagnostics technique for medicine, biology, and neuroscience. This imaging method is innovative, noninvasive and its impact continues to grow. During the past several decades the quality of MRI scans has been substantially improved through the design of very sensitive multichannel receivers and compatible receive radio-frequency (RF) coil arrays for parallel imaging. However, conventional designs of coils have already reached their “saturation point” in terms of provided signal-to-noise ratio. In this contribution unique properties of metasurfaces are used in designing new surface and volumetric wireless coils improving imaging efficiency of high-field MR systems. We employ metasurfaces organized as arrays of parallel metal wires placed close to a scanned subject inside an MRI bore to produce a wireless coil, which is driven by an external body coil via inductive coupling. The wireless coil in this approach enhances both the transmit power efficiency and the receive sensitivity of the body coil with respect to the region of interest. By full-wave numerical simulations the two metasurface-based wireless coils were compared: the surface coil using a single array of wires and the volumetric coil using two separate arrays of wires.This work was supported by the Ministry of Education and Science of the Russian Federation (project No. 14.587.21.0041 with the unique identifier RFMEFI58717X0041)

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

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Volumetric wireless coil based on periodically coupled split-loop resonators for clinical wrist imaging

PURPOSE: Design and characterization of a new inductively driven wireless coil (WLC) for wrist imaging at 1.5 T with high homogeneity operating due to focusing the B1 field of a birdcage body coil. METHODS: The WLC design has been proposed based on a volumetric self-resonant periodic structure of inductively coupled split-loop resonators with structural capacitance. The WLC was optimized and studied regarding radiofrequency fields and interaction to the birdcage coil (BC) by electromagnetic simulations. The manufactured WLC was characterized by on-bench measurements and in vivo and phantom study in comparison to a standard cable-connected receive-only coil. RESULTS: The WLC placed into BC gave the measured B1+ increase of the latter by 8.6 times for the same accepted power. The phantom and in vivo wrist imaging showed that the BC in receiving with the WLC inside reached equal or higher signal-to-noise ratio than the conventional clinical setup comprising the transmit-only BC and a commercial receive-only flex-coil and created no artifacts. Simulations and on-bench measurements proved safety in terms of specific absorption rate and reflected transmit power. CONCLUSIONS: The results showed that the proposed WLC could be an alternative to standard cable-connected receive coils in clinical magnetic resonance imaging. As an example, with no cable connection, the WLC allowed wrist imaging on a 1.5 T clinical machine using a full-body BC for transmitting and receive with the desired signal-to-noise ratio, image quality, and safety