Changes of in vivo electrical conductivity in the brain and torso related to age, fat fraction and sex using MRI.

This work was inspired by the observation that a majority of MR-electrical properties tomography studies are based on direct comparisons with ex vivo measurements carried out on post-mortem samples in the 90's. As a result, the in vivo conductivity values obtained from MRI in the megahertz range in different types of tissues (brain, liver, tumors, muscles, etc.) found in the literature may not correspond to their ex vivo equivalent, which still serves as a reference for electromagnetic modelling. This study aims to pave the way for improving current databases since the definition of personalized electromagnetic models (e.g. for Specific Absorption Rate estimation) would benefit from better estimation. Seventeen healthy volunteers underwent MRI of both brain and thorax/abdomen using a three-dimensional ultrashort echo-time (UTE) sequence. We estimated conductivity (S/m) in several classes of macroscopic tissue using a customized reconstruction method from complex UTE images, and give general statistics for each of these regions (mean-median-standard deviation). These values are used to find possible correlations with biological parameters such as age, sex, body mass index and/or fat volume fraction, using linear regression analysis. In short, the collected in vivo values show significant deviations from the ex vivo values in conventional databases, and we show significant relationships with the latter parameters in certain organs for the first time, e.g. a decrease in brain conductivity with age.

A Generalisable Heartbeat Classifier Leveraging Self-Supervised Learning for ECG Analysis During Magnetic Resonance Imaging.

Electrocardiogram (ECG) is acquired during Magnetic Resonance Imaging (MRI) to monitor patients and synchronize image acquisition with the heart motion. ECG signals are highly distorted during MRI due to the complex electromagnetic environment. Automated ECG analysis is therefore complicated in this context and there is no reference technique in MRI to classify pathological heartbeats. Imaging arrhythmic patients is hence difficult in MRI. Deep Learning based heartbeat classifier have been suggested but require large databases whereas existing annotated sets of ECG in MRI are very small. We proposed a Siamese network to leverage a large database of unannotated ECGs outside MRI. This was used to develop an efficient representation of ECG signals, further used to develop a heartbeat classifier. We extensively tested several data augmentations and self-supervised learning (SSL) techniques and assessed the generalization of the obtained classifier to ECG signals acquired in MRI. These augmentations included random noises and a model simulating MRI specific artefacts. SSL pretraining improved the generalizability of heartbeat classifiers in MRI (F1=0.75) compared to Deep Learning not relying on SSL (F1=0.46) and another classical machine learning approach (F1=0.40). These promising results seem to indicate that the use of SSL techniques can learn efficient ECG signal representation, and are useful for the development of Deep Learning models even when only scarce annotated medical data are available.

Neural mechanisms of odour imagery induced by non-figurative visual cues.

Odour imagery, the ability to experience smell when an appropriate stimulus is absent, has widely been documented as being particularly difficult. However, previous studies have shown the beneficial effect of visual cues (e.g., pictures or words) to facilitate performance in numerous tasks of olfactory nature. Therefore, the use of visual cues to evoke odours seems relevant. In this study, our interest is directed towards non-figurative coloured arrangements, which result from a patented technology and aim at chromatically representing any smell from its chemical composition and sensory description. The aim of this study was to characterise the neural mechanisms of odour imagery facilitated by these non-figurative coloured arrangements. Using functional magnetic resonance imaging, we recorded and compared hemodynamic responses during odour imagery facilitated by non-figurative coloured arrangements and pictures. Our findings reveal that the use of non-figurative coloured arrangements during odour imagery solicits olfactory and non-olfactory brain regions (orbitofrontal cortex, insula, hippocampus, thalamus, dorsolateral prefrontal cortex and supplementary motor area), which are mainly involved in olfactory processing and multimodal integration. Moreover, very similar cortical activity was found between the use of non-figurative coloured arrangements and pictures during odour imagery, with increased activity in the supplementary motor area during the use of coloured arrangements only. Overall, non-figurative coloured arrangements could become a robust tool to visually evoke odours without requiring prior familiarity with the depicted odour. Future studies should use psychometric measures to determine the relationships between brain activation, odour imagery ability and vividness of the generated odour images.

A Human-Centered AI Framework for Efficient Labelling of ECGs From Drug Safety Trials.

Drug safety trials require substantial ECG labelling like, in thorough QT studies, measurements of the QT interval, whose prolongation is a biomarker of proarrhythmic risk. The traditional method of manually measuring the QT interval is time-consuming and error-prone. Studies have demonstrated the potential of deep learning (DL)-based methods to automate this task but expert validation of these computerized measurements remains of paramount importance, particularly for abnormal ECG recordings. In this paper, we propose a highly automated framework that combines such a DL-based QT estimator with human expertise. The framework consists of 3 key components: (1) automated QT measurement with uncertainty quantification (2) expert review of a few DL-based measurements, mostly those with high model uncertainty and (3) recalibration of the unreviewed measurements based on the expert-validated data. We assess its effectiveness on 3 drug safety trials and show that it can significantly reduce effort required for ECG labelling-in our experiments only 10% of the data were reviewed per trial-while maintaining high levels of QT accuracy. Our study thus demonstrates the possibility of productive human-machine collaboration in ECG analysis without any compromise on the reliability of subsequent clinical interpretations.

Super-Resolved Dynamic 3D Reconstruction of the Vocal Tract during Natural Speech.

MRI is the gold standard modality for speech imaging. However, it remains relatively slow, which complicates imaging of fast movements. Thus, an MRI of the vocal tract is often performed in 2D. While 3D MRI provides more information, the quality of such images is often insufficient. The goal of this study was to test the applicability of super-resolution algorithms for dynamic vocal tract MRI. In total, 25 sagittal slices of 8 mm with an in-plane resolution of 1.6 × 1.6 mm2 were acquired consecutively using a highly-undersampled radial 2D FLASH sequence. The volunteers were reading a text in French with two different protocols. The slices were aligned using the simultaneously recorded sound. The super-resolution strategy was used to reconstruct 1.6 × 1.6 × 1.6 mm3 isotropic volumes. The resulting images were less sharp than the native 2D images but demonstrated a higher signal-to-noise ratio. It was also shown that the super-resolution allows for eliminating inconsistencies leading to regular transitions between the slices. Additionally, it was demonstrated that using visual stimuli and shorter text fragments improves the inter-slice consistency and the super-resolved image sharpness. Therefore, with a correct speech task choice, the proposed method allows for the reconstruction of high-quality dynamic 3D volumes of the vocal tract during natural speech.

Feasibility of online non-rigid motion correction for high-resolution supine breast MRI.

PURPOSE: Conventional breast MRI is performed in the prone position with a dedicated coil. This allows high-resolution images without breast motion, but the patient position is inconsistent with that of other breast imaging modalities or interventions. Supine breast MRI may be an interesting alternative, but respiratory motion becomes an issue. Motion correction methods have typically been performed offline, for instance, the corrected images were not directly accessible from the scanner console. In this work, we seek to show the feasibility of a fast, online, motion-corrected reconstruction integrated into the clinical workflow. METHODS: Fully sampled T2 -weighted (T2 w) and accelerated T1 -weighted (T1 w) breast supine MR images were acquired during free-breathing and were reconstructed using a non-rigid motion correction technique (generalized reconstruction by inversion of coupled systems). Online reconstruction was implemented using a dedicated system combining the MR raw data and respiratory signals from an external motion sensor. Reconstruction parameters were optimized on a parallel computing platform, and image quality was assessed by objective metrics and by radiologist scoring. RESULTS: Online reconstruction time was 2 to 2.5 min. The metrics and the scores related to the motion artifacts significantly improved for both T2 w and T1 w sequences. The overall quality of T2 w images was approaching that of the prone images, whereas the quality of T1 w images remained significantly lower. CONCLUSION: The proposed online algorithm allows a noticeable reduction of motion artifacts and an improvement of the diagnostic quality for supine breast imaging with a clinically acceptable reconstruction time. These findings serve as a starting point for further development aimed at improving the quality of T1 w images.

Panoramic Magnetic Resonance Imaging of the Breast With a Wearable Coil Vest.

BACKGROUND: Breast cancer, the most common malignant cancer in women worldwide, is typically diagnosed by x-ray mammography, which is an unpleasant procedure, has low sensitivity in women with dense breasts, and involves ionizing radiation. Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality and works without ionizing radiation, but is currently constrained to the prone imaging position due to suboptimal hardware, therefore hampering the clinical workflow. OBJECTIVES: The aim of this work is to improve image quality in breast MRI, to simplify the clinical workflow, shorten measurement time, and achieve consistency in breast shape with other procedures such as ultrasound, surgery, and radiation therapy. MATERIALS AND METHODS: To this end, we propose "panoramic breast MRI"-an approach combining a wearable radiofrequency coil for 3 T breast MRI (the "BraCoil"), acquisition in the supine position, and a panoramic visualization of the images. We demonstrate the potential of panoramic breast MRI in a pilot study on 12 healthy volunteers and 1 patient, and compare it to the state of the art. RESULTS: With the BraCoil, we demonstrate up to 3-fold signal-to-noise ratio compared with clinical standard coils and acceleration factors up to 6 × 4. Panoramic visualization of supine breast images reduces the number of slices to be viewed by a factor of 2-4. CONCLUSIONS: Panoramic breast MRI allows for high-quality diagnostic imaging and facilitated correlation to other diagnostic and interventional procedures. The developed wearable radiofrequency coil in combination with dedicated image processing has the potential to improve patient comfort while enabling more time-efficient breast MRI compared with clinical coils.

Methodology for quantitative evaluation of mandibular condyles motion symmetricity from real-time MRI in the axial plane.

Diagnosis of temporomandibular disorders is currently based on clinical examination and static MRI. Real-time MRI enables tracking of condylar motion and, thus, evaluation of their motion symmetricity (which could be associated with temporomandibular joint disorders). The purpose of this work is to propose an acquisition protocol, an image processing approach, and a set of parameters enabling objective assessment of motion asymmetry; to check the reliability and find the limitations of the approach, and to verify if the automatically calculated parameters are associated with the motion symmetricity. A rapid radial FLASH sequence was used to acquire a dynamic set of axial images for 10 subjects. One more subject was involved to estimate the dependence of the motion parameters on the slice placement. The images were segmented with a semi-automatic approach based on U-Net convolutional neural network, and the condyles' mass centers were projected on the mid-sagittal axis. Resulting projection curves were used for the extraction of various motion parameters including latency, velocity peak delay, and maximal displacement between the right and the left condyle. These automatically calculated parameters were compared with the physicians' scores. The proposed segmentation approach allowed a reliable center of mass tracking. Latency and velocity peak delay were found to be invariant to the slice position, and maximal displacement difference considerably varied. The automatically calculated parameters demonstrated a significant correlation with the experts' scores. The proposed acquisition and data processing protocol enables the automatizable extraction of quantitative parameters that characterize the symmetricity of condylar motion.

Evaluation of Image Quality of Motion-Corrected Supine Breast MRI.

Breast MRI is the most performant modality for breast cancer diagnosis and could be widespread in the future. The gold standard breast MRI is performed in the prone position, but comfort and correlation with surgery or biopsy positioning can be problematic, while supine MRI could be an interesting alternative. In this work, we evaluated the image quality of T2-weighted supine breast MRI in healthy volunteers after online correction of respiratory motion artifacts compared to standard vendor's reconstruction and to standard prone MRI. T2-weighted images were acquired in the prone and free-breathing supine position in 10 volunteers. Two types of reconstructions were evaluated for supine acquisitions: the standard vendor's reconstruction and an online version of a nonrigid motion correction technique (generalized reconstruction by inversion of coupled system). Image quality criteria, including overall quality, sharpness, uniformity, and different types of artifacts, were assessed and scored by 2 radiologists in a randomized fashion. Interobserver agreement was verified by Weighted Cohen's Kappa calculation and a comparison between the different acquisitions was made by Wilcoxon signed-rank test. Generalized Reconstruction by Inversion of Coupled Systems (GRICS) reconstruction method significantly increased image quality in comparison to the standard reconstruction of supine acquisition. It allows a comparable quality, slightly lower than the gold standard prone MRI in T2-weighted images but it needs to be assessed with more patients and with target lesions before it can be used in clinical practice.

Angiographic collateral venous phase: a novel landmark for leptomeningeal collaterals evaluation in acute ischemic stroke.

BACKGROUND: Although recanalization rates constantly increase (>80%), a favorable clinical outcome is achieved in only 45-55% of patients undergoing mechanical thrombectomy (MT) for anterior circulation stroke. Collateral circulation seems to play a major role in determining this discrepancy. The aim of the study was to investigate a novel angiographic landmark assessing the collateral venous phase (CVP) compared with the American Society of Interventional and Therapeutic Neuroradiology/Society of Interventional Radiology (ASITN/SIR) score, based on the arterial collateral assessment. METHODS: Two hundred patients with anterior circulation stroke treated by MT between 2016 and 2021 were included. The ASITN/SIR score and the presence of CVP were blindly evaluated by expert neuroradiologists. Three subanalyses were performed comparing patients with good versus poor collaterals, CVP presence versus absence, and a composite analysis including both ASITN/SIR and CVP grading results. RESULTS: Good collateral circulation (ASITN >2) was observed in 113 patients (56.5%) whereas CVP was present in 90 patients (45%) and mostly in patients with good collaterals. Favorable clinical and neuroradiological outcomes were more likely observed in patients with both good collaterals and the presence of CVP than in those with good collaterals and absence of CVP (modified Rankin Scale score 0-2: 77.3% vs 7.9%, p<0.0001; mortality: 9.3% vs 26.3%, p=0.02; 24-hour Alberta Stroke Program Early CT Score: 8 vs 6, p<0.0001), while ASITN/SIR score alone was not significantly associated with clinical outcomes. CONCLUSIONS: The presence of CVP improves the angiographic assessment of collateral circulation. CVP could be proposed as a new imaging landmark to better understand the functionality of collaterals.

AI-Assisted QT Measurements for Highly Automated Drug Safety Studies.

Rate-corrected QT interval (QTc) prolongation has been suggested as a biomarker for the risk of drug-induced torsades de pointes, and is therefore monitored during clinical trials for the assessment of drug safety. Manual QT measurements by expert ECG analysts are expensive, laborious and prone to errors. Wavelet-based delineators and other automatic methods do not generalize well to different T wave morphologies and may require laborious tuning. Our study investigates the robustness of convolutional neural networks (CNNs) for QT measurement. We trained 3 CNN-based deep learning models on a private ECG database with human expert-annotated QT intervals. Among these models, we propose a U-Net model, which is widely used for segmentation tasks, to build a novel clinically useful QT estimator that includes QT delineation for better interpretability. We tested the 3 models on four external databases, amongst which a clinical trial investigating four drugs. Our results show that the deep learning models are in stronger agreement with the experts than the state-of-the-art wavelet-based algorithm. Indeed, the deep learning models yielded up to 71% of accurate QT measurements (absolute difference between manual and automatic QT below 15 ms) whereas the wavelet-based algorithm only allowed 52% of QT accuracy. For the 2 studies of drugs with small to no QT prolonging effect, a mean absolute difference of 6 ms (std = 5 ms) was obtained between the manual and deep learning methods. For the other 2 drugs with more significant effect on the volunteers, a mean difference of up to 17 ms (std = 17 ms) was obtained. The proposed models are therefore promising for automated QT measurements during clinical trials. They can analyze various ECG morphologies from a diversity of individuals although some QT-prolonged ECGs can be challenging. The U-Net model is particularly interesting for our application as it facilitates expert review of automatic QT intervals, which is still required by regulatory bodies, by providing QRS onset and T offset positions that are consistent with the estimated QT intervals.

3D Dynamic Spatiotemporal Atlas of the Vocal Tract during Consonant-Vowel Production from 2D Real Time MRI.

In this work, we address the problem of creating a 3D dynamic atlas of the vocal tract that captures the dynamics of the articulators in all three dimensions in order to create a global speaker model independent of speaker-specific characteristics. The core steps of the proposed method are the temporal alignment of the real-time MR images acquired in several sagittal planes and their combination with adaptive kernel regression. As a preprocessing step, a reference space was created to be used in order to remove anatomical information of the speakers and keep only the variability in speech production for the construction of the atlas. The adaptive kernel regression makes the choice of atlas time points independently of the time points of the frames that are used as an input for the construction. The evaluation of this atlas construction method was made by mapping two new speakers to the atlas and by checking how similar the resulting mapped images are. The use of the atlas helps in reducing subject variability. The results show that the use of the proposed atlas can capture the dynamic behavior of the articulators and is able to generalize the speech production process by creating a universal-speaker reference space.

A hardware and software system for MRI applications requiring external device data.

PURPOSE: Numerous MRI applications require data from external devices. Such devices are often independent of the MRI system, so synchronizing these data with the MRI data is often tedious and limited to offline use. In this work, a hardware and software system is proposed for acquiring data from external devices during MR imaging, for use online (in real-time) or offline. METHODS: The hardware includes a set of external devices - electrocardiography (ECG) devices, respiration sensors, microphone, electronics of the MR system etc. - using various channels for data transmission (analog, digital, optical fibers), all connected to a server through a universal serial bus (USB) hub. The software is based on a flexible client-server architecture, allowing real-time processing pipelines to be configured and executed. Communication protocols and data formats are proposed, in particular for transferring the external device data to an open-source reconstruction software (Gadgetron), for online image reconstruction using external physiological data. The system performance is evaluated in terms of accuracy of the recorded signals and delays involved in the real-time processing tasks. Its flexibility is shown with various applications. RESULTS: The real-time system had low delays and jitters (on the order of 1 ms). Example MRI applications using external devices included: prospectively gated cardiac cine imaging, multi-modal acquisition of the vocal tract (image, sound, and respiration) and online image reconstruction with nonrigid motion correction. CONCLUSION: The performance of the system and its versatile architecture make it suitable for a wide range of MRI applications requiring online or offline use of external device data.

In Vivo Super-Resolution Cardiac Diffusion Tensor MRI: A Feasibility Study.

A super-resolution (SR) technique is proposed for imaging myocardial fiber architecture with cardiac magnetic resonance. Images were acquired with a motion-compensated cardiac diffusion tensor imaging (cDTI) sequence. The heart left ventricle was covered with three stacks of thick slices, in short axis, horizontal and vertical long axes orientations, respectively. The three low-resolution stacks (2 × 2 × 8 mm3) were combined into an isotropic volume (2 × 2 × 2 mm3) by a super-resolution reconstruction. For in vivo measurements, each slice was acquired during a breath-hold period. Bulk motion was corrected by optimizing a similarity metric between intensity profiles from all intersecting slices in the dataset. The benefit of the proposed approach was evaluated using a numerical heart phantom, a physical helicoidal phantom with artificial fibers, and six healthy subjects. The SR technique showed improved results compared to the native scans, in terms of image quality and cDTI metrics. In particular, the myocardial helix angle (HA) was more accurately estimated in the physical phantom (HA = 41.5° ± 1.1°, with the ground truth being 42.0°). In vivo, it resulted in a sharper rate of change of HA across the myocardial wall (-0.993°/% ± 0.007°/% against -0.873°/% ± 0.010°/%).

Surgical anatomy of hepatic arteries from its origin to segmental branching: A 500 cases CT study.

BACKGROUND: The aim of this study was to present an overview of variations of the hepatic artery from the origin to the segmental branching. METHODS: Abdominal Computed Tomography performed on consecutive patients in our tertiary center between 2019 and 2020 were analyzed. Hepatic arterial branching and its relationship to the portal veins were reported. RESULTS: Out of 500 imaging, 16 anatomic patterns were found for the origin of hepatic artery, with 65.6% conventional origin at celiac axis (n = 328); 10 patterns for the left hepatic artery, 23 for segment IV artery, and more than 21 for the right hepatic artery (RHA), with conventional branching in respectively 66.8%, 39.6% and in 46.4% of patients. Conventional anatomy from celiac axis to segmental branching was found in 10.4% of patients. CONCLUSION: Dedicated thin-section imaging appears to be essential for preoperative planning in liver surgery, given the high variability of arterial distribution and their surgical implications.

Magnetic Resonance Elastography for Assessing Fibrosis in Patients with Crohn's Disease: A Pilot Study.

BACKGROUND: Patients with Crohn's disease can develop intestinal strictures, containing various degrees of inflammation and fibrosis. Differentiation of the main component of a stricturing lesion is the key for defining the therapeutic management. AIMS: We assessed for the first time the accuracy of magnetic resonance elastography in detecting intestinal fibrosis and predicting clinical course in patients with Crohn's disease. METHODS: This was a prospective study of adult patients with Crohn's disease and magnetic resonance imaging examination, including magnetic resonance elastography, between April 2019 and February 2020. The association between the bowel stiffness value and the degree of fibrosis was evaluated. The relationship between the stiffness value and the occurrence of clinical events was also investigated. RESULTS: A total of 69 patients were included. The stiffness value measured by magnetic resonance elastography was correlated with the degree of fibrosis (p < 0.001). A bowel stiffness ≥ 3.57 kPa predicted the occurrence of clinical events with an area under the curve of 0.82 (95% CI 0.71-0.93). Bowel stiffness ≥ 3.57 kPa was associated with an increased risk of clinical events (p < 0.0001). CONCLUSION: In Crohn's disease, magnetic resonance elastography is a reliable tool for detecting intestinal fibrosis and predicting a worse disease outcome.

Impact of Pretreatment Ischemic Location on Functional Outcome after Thrombectomy.

Pretreatment ischemic location may be an important determinant for functional outcome prediction in acute ischemic stroke. In total, 143 anterior circulation ischemic stroke patients in the THRACE study were included. Ischemic lesions were semi-automatically segmented on pretreatment diffusion-weighted imaging and registered on brain atlases. The percentage of ischemic tissue in each atlas-segmented region was calculated. Statistical models with logistic regression and support vector machine were built to analyze the predictors of functional outcome. The investigated parameters included: age, baseline National Institutes of Health Stroke Scale score, and lesional volume (three-parameter model), together with the ischemic percentage in each atlas-segmented region (four-parameter model). The support vector machine with radial basis functions outperformed logistic regression in prediction accuracy. The support vector machine three-parameter model demonstrated an area under the curve of 0.77, while the four-parameter model achieved a higher area under the curve (0.82). Regions with marked impacts on outcome prediction were the uncinate fasciculus, postcentral gyrus, putamen, middle occipital gyrus, supramarginal gyrus, and posterior corona radiata in the left hemisphere; and the uncinate fasciculus, paracentral lobule, temporal pole, hippocampus, inferior occipital gyrus, middle temporal gyrus, pallidum, and anterior limb of the internal capsule in the right hemisphere. In conclusion, pretreatment ischemic location provided significant prognostic information for functional outcome in ischemic stroke.

Multimodal dataset of real-time 2D and static 3D MRI of healthy French speakers.

The study of articulatory gestures has a wide spectrum of applications, notably in speech production and recognition. Sets of phonemes, as well as their articulation, are language-specific; however, existing MRI databases mostly include English speakers. In our present work, we introduce a dataset acquired with MRI from 10 healthy native French speakers. A corpus consisting of synthetic sentences was used to ensure a good coverage of the French phonetic context. A real-time MRI technology with temporal resolution of 20 ms was used to acquire vocal tract images of the participants speaking. The sound was recorded simultaneously with MRI, denoised and temporally aligned with the images. The speech was transcribed to obtain phoneme-wise segmentation of sound. We also acquired static 3D MR images for a wide list of French phonemes. In addition, we include annotations of spontaneous swallowing.

Flexible Multi-Turn Multi-Gap Coaxial RF Coils: Design Concept and Implementation for Magnetic Resonance Imaging at 3 and 7 Tesla.

Magnetic resonance has become a backbone of medical imaging but suffers from inherently low sensitivity. This can be alleviated by improved radio frequency (RF) coils. Multi-turn multi-gap coaxial coils (MTMG-CCs) introduced in this work are flexible, form-fitting RF coils extending the concept of the single-turn single-gap CC by introducing multiple cable turns and/or gaps. It is demonstrated that this enables free choice of the coil diameter, and thus, optimizing it for the application to a certain anatomical site, while operating at the self-resonance frequency. An equivalent circuit for MTMG-CCs is modeled to predict their resonance frequency. Possible configurations regarding size, number of turns and gaps, and cable types for different B 0 field strengths are calculated. Standard copper wire loop coils (SCs) and flexible CCs made from commercial coaxial cable were fabricated as receive-only coils for 3 T and transmit/receive coils at 7 T with diameters between 4 and 15 cm. Electromagnetic simulations are used to investigate the currents on MTMG-CCs, and demonstrate comparable specific absorption rate of 7 T CCs and SCs. Signal-to-noise ratio (SNR), transmit efficiency, and active detuning performance of CCs were compared in bench tests and MR experiments. For the form-fitted receive-only CCs at 3 T no significant SNR degradation was found as compared to flat SCs on a balloon phantom. Form-fitted transmit/receive CCs at 7 T showed higher transmit efficiency and SNR. MTMG-CCs can be sized to optimize sensitivity, are flexible and lightweight, and could therefore enable the fabrication of wearable coils with improved patient comfort.