Development of clinical prediction model to guide the use of CT head scans for non-traumatic Thai patient with seizure: A cross-sectional study.

The aim of this study was to develop clinical predictor tools for guiding the use of computed tomography (CT) head scans in non-traumatic Thai patients presented with seizure. A prediction model using a retrospective cross-sectional design was conducted. We recruited adult patients (aged ≥ 18 years) who had been diagnosed with seizures by their physicians and had undergone CT head scans for further investigation. Positive CT head defined as the presence of any new lesion that related to the patient's presented seizure officially reported by radiologist. A total of 9 candidate predictors were preselected. The prediction model was developed using a full multivariable logistic regression with backward stepwise elimination. We evaluated the model's predictive performance in terms of its discriminative ability and calibration via AuROC and calibration plot. The application was then constructed based on final model. A total of 362 patients were included into the analysis which comprising of 71 patients with positive CT head findings and 291 patients with normal results. Six final predictors were identified including: Glasgow coma scale, the presence of focal neurological deficit, history of malignancy, history of CVA, Epilepsy, and the presence of alcohol withdrawal symptom. In terms of discriminative ability, the final model demonstrated excellent performance (AuROC of 0.82 (95% CI: 0.76-0.87)). The calibration plot illustrated a good agreement between observed and predicted risks. This prediction model offers a reliable tool for effectively reduce unnecessary use and instill confidence in supporting physicians in determining the need for CT head scans in non-traumatic patients with seizures.

Head of Zebu cattle (Bos Taurus indicus): sectional anatomy and 3D computed tomography.

The research was designed to use computed tomography (CT) with 3D-CT reconstruction imaging techniques and the various anatomical sections-plana transversalia, frontalis, and dorsalia-to describe the anatomical architecture of the Zebu cattle head. Our study used nine mature heads. The CT bone window created detailed images of cranial bones, mandibles, teeth, and hyoid bones. All of the head cavities were evaluated, including the cranial, orbital, oral, auricular, and nasal cavities with their paranasal and conchal sinuses. The septum nasi, attached to the vomer and maxillary bones, did not reach the nasal cavity floor caudally at the level of the second premolar teeth, resulting in a single median channel from the choanae to the nasopharynx. The positions, boundaries, and connections of the paranasal sinuses were clearly identified. There were four nasal conchal sinuses (that were named the dorsal, middle, ethmoidal, and ventral) and five paranasal sinuses that were described as the following: sinus frontalis, maxillaris, palatinorum, and lacrimalis, as defined in the different anatomical sections and computed tomographic images. The complicated sinus frontalis caused the pneumatization of all bones that surrounded the cranial cavity, with the exception of the ethmoidal and body of basisphenoid bones. The sinus maxillaris was connected to the sinus lacrimalis and palatinorum through the maxillolacrimal and palatomaxillary openings, and to the middle nasal meatus through the nasomaxillary opening. Our findings provide a detailed anatomical knowledge for disease diagnosis to internal medicine veterinarians and surgeons by offering a comprehensive atlas of the Zebu cattle anatomy.

Assessing the effectiveness of artificial intelligence (AI) in prioritising CT head interpretation: study protocol for a stepped-wedge cluster randomised trial (ACCEPT-AI).

INTRODUCTION: Diagnostic imaging is vital in emergency departments (EDs). Accessibility and reporting impacts ED workflow and patient care. With radiology workforce shortages, reporting capacity is limited, leading to image interpretation delays. Turnaround times for image reporting are an ED bottleneck. Artificial intelligence (AI) algorithms can improve productivity, efficiency and accuracy in diagnostic radiology, contingent on their clinical efficacy. This includes positively impacting patient care and improving clinical workflow. The ACCEPT-AI study will evaluate Qure.ai's qER software in identifying and prioritising patients with critical findings from AI analysis of non-contrast head CT (NCCT) scans. METHODS AND ANALYSIS: This is a multicentre trial, spanning four diverse sites, over 13 months. It will include all individuals above the age of 18 years who present to the ED, referred for an NCCT. The project will be divided into three consecutive phases (pre-implementation, implementation and post-implementation of the qER solution) in a stepped-wedge design to control for adoption bias and adjust for time-based changes in the background patient characteristics. Pre-implementation involves baseline data for standard care to support the primary and secondary outcomes. The implementation phase includes staff training and qER solution threshold adjustments in detecting target abnormalities adjusted, if necessary. The post-implementation phase will introduce a notification (prioritised flag) in the radiology information system. The radiologist can choose to agree with the qER findings or ignore it according to their clinical judgement before writing and signing off the report. Non-qER processed scans will be handled as per standard care. ETHICS AND DISSEMINATION: The study will be conducted in accordance with the principles of Good Clinical Practice. The protocol was approved by the Research Ethics Committee of East Midlands (Leicester Central), in May 2023 (REC (Research Ethics Committee) 23/EM/0108). Results will be published in peer-reviewed journals and disseminated in scientific findings (ClinicalTrials.gov: NCT06027411) TRIAL REGISTRATION NUMBER: NCT06027411.

Fetal Head and Neck Imaging.

Prenatal MRI plays an essential role in the evaluation of the head and neck. This article overviews technical considerations and both isolated and syndromic anomalies of the fetal calvarium, globes and orbits, ears, maxilla, mandible, and neck.

Exploring the Complexities: Understanding the Clinicopathological Spectrum of Tuberculosis in the Head-and-neck Region.

INTRODUCTION: Tuberculosis (TB) affecting the head-and-neck area can often resemble cancer, leading to misdiagnosis and delayed treatment. A better understanding of this condition is necessary for early diagnosis and prompt treatment initiation. This study examines the clinical and pathological characteristics of different types of TB in the head-and-neck region. METHODS: This retrospective study analyzed patients diagnosed with TB in the head-and-neck region at a health center between January 1, 2018, and January 1, 2024. The study population consisted of patients who were diagnosed with TB of the head and neck. RESULTS: The study analyzed data from 30 patients, comprising 14 (47%) males and 16 (53%) females, all of whom tested negative for HIV. Most cases (15, 50%) were observed in the age group of 15-24 years, with 5 (15.6%) subjects falling in the age bracket of 0-14 years. Among the types of lesions detected, cervical tubercular adenitis was the most frequently observed lesion, found in 22 (73%) subjects. Females are more susceptible to cervical tubercular adenitis, while males are more likely to experience laryngeal TB. CONCLUSION: The clinical manifestation of TB affecting the head-and-neck region can exhibit a diverse range of symptoms, which may lead to misinterpretation and diagnostic errors. Therefore, health-care practitioners must understand and include the condition in differential diagnoses.

Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging.

Optical tracking of head pose via fiducial markers has been proven to enable effective correction of motion artifacts in the brain during magnetic resonance imaging but remains difficult to implement in the clinic due to lengthy calibration and set up times. Advances in deep learning for markerless head pose estimation have yet to be applied to this problem because of the sub-millimetre spatial resolution required for motion correction. In the present work, two optical tracking systems are described for the development and training of a neural network: one marker-based system (a testing platform for measuring ground truth head pose) with high tracking fidelity to act as the training labels, and one markerless deep-learning-based system using images of the markerless head as input to the network. The markerless system has the potential to overcome issues of marker occlusion, insufficient rigid attachment of the marker, lengthy calibration times, and unequal performance across degrees of freedom (DOF), all of which hamper the adoption of marker-based solutions in the clinic. Detail is provided on the development of a custom moiré-enhanced fiducial marker for use as ground truth and on the calibration procedure for both optical tracking systems. Additionally, the development of a synthetic head pose dataset is described for the proof of concept and initial pre-training of a simple convolutional neural network. Results indicate that the ground truth system has been sufficiently calibrated and can track head pose with an error of <1 mm and <1°. Tracking data of a healthy, adult participant are shown. Pre-training results show that the average root-mean-squared error across the 6 DOF is 0.13 and 0.36 (mm or degrees) on a head model included and excluded from the training dataset, respectively. Overall, this work indicates excellent feasibility of the deep-learning-based approach and will enable future work in training and testing on a real dataset in the MRI environment.

Standardised and automated assessment of head computed tomography reliably predicts poor functional outcome after cardiac arrest: a prospective multicentre study.

PURPOSE: Application of standardised and automated assessments of head computed tomography (CT) for neuroprognostication after out-of-hospital cardiac arrest. METHODS: Prospective, international, multicentre, observational study within the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Routine CTs from adult unconscious patients obtained > 48 h ≤ 7 days post-arrest were assessed qualitatively and quantitatively by seven international raters blinded to clinical information using a pre-published protocol. Grey-white-matter ratio (GWR) was calculated from four (GWR-4) and eight (GWR-8) regions of interest manually placed at the basal ganglia level. Additionally, GWR was obtained using an automated atlas-based approach. Prognostic accuracies for prediction of poor functional outcome (modified Rankin Scale 4-6) for the qualitative assessment and for the pre-defined GWR cutoff < 1.10 were calculated. RESULTS: 140 unconscious patients were included; median age was 68 years (interquartile range [IQR] 59-76), 76% were male, and 75% had poor outcome. Standardised qualitative assessment and all GWR models predicted poor outcome with 100% specificity (95% confidence interval [CI] 90-100). Sensitivity in median was 37% for the standardised qualitative assessment, 39% for GWR-8, 30% for GWR-4 and 41% for automated GWR. GWR-8 was superior to GWR-4 regarding prognostic accuracies, intra- and interrater agreement. Overall prognostic accuracy for automated GWR (area under the curve [AUC] 0.84, 95% CI 0.77-0.91) did not significantly differ from manually obtained GWR. CONCLUSION: Standardised qualitative and quantitative assessments of CT are reliable and feasible methods to predict poor functional outcome after cardiac arrest. Automated GWR has the potential to make CT quantification for neuroprognostication accessible to all centres treating cardiac arrest patients.

Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.

In this retrospective study, we aimed to assess the objective and subjective image quality of different reconstruction techniques and a deep learning-based software on non-contrast head computed tomography (CT) images. In total, 152 adult head CT scans (77 female, 75 male; mean age 69.4 ± 18.3 years) obtained from three different CT scanners using different protocols between March and April 2021 were included. CT images were reconstructed using filtered-back projection (FBP), iterative reconstruction (IR), and post-processed using a deep learning-based algorithm (PS). Post-processing significantly reduced noise in FBP-reconstructed images (up to 15.4% reduction) depending on the protocol, leading to improvements in signal-to-noise ratio of up to 19.7%. However, when deep learning-based post-processing was applied to FBP images compared to IR alone, the differences were inconsistent and partly non-significant, which appeared to be protocol or site specific. Subjective assessments showed no significant overall improvement in image quality for all reconstructions and post-processing. Inter-rater reliability was low and preferences varied. Deep learning-based denoising software improved objective image quality compared to FBP in routine head CT. A significant difference compared to IR was observed for only one protocol. Subjective assessments did not indicate a significant clinical impact in terms of improved subjective image quality, likely due to the low noise levels in full-dose images.

Polynomial chaos expansion of SAR and temperature increase variability in 3 T MRI due to stochastic input data.

Objective.Numerical simulations are largely adopted to estimate dosimetric quantities, e.g. specific absorption rate (SAR) and temperature increase, in tissues to assess the patient exposure to the radiofrequency (RF) field generated during magnetic resonance imaging (MRI). Simulations rely on reference anatomical human models and tabulated data of electromagnetic and thermal properties of biological tissues. However, concerns may arise about the applicability of the computed results to any phenotype, introducing a significant degree of freedom in the simulation input data. In addition, simulation input data can be affected by uncertainty in relative positioning of the anatomical model with respect to the RF coil. The objective of this work is the to estimate the variability of SAR and temperature increase at 3 T head MRI due to different sources of variability in input data, with the final aim to associate a global uncertainty to the dosimetric outcomes.Approach.A stochastic approach based on arbitrary Polynomial Chaos Expansion is used to evaluate the effects of several input variability's (anatomy, tissue properties, body position) on dosimetric outputs, referring to head imaging with a 3 T MRI scanner.Main results.It is found that head anatomy is the prevailing source of variability for the considered dosimetric quantities, rather than the variability due to tissue properties and head positioning. From knowledge of the variability of the dosimetric quantities, an uncertainty can be attributed to the results obtained using a generic anatomical head model when SAR and temperature increase values are compared with safety exposure limits.Significance.This work associates a global uncertainty to SAR and temperature increase predictions, to be considered when comparing the numerically evaluated dosimetric quantities with reference exposure limits. The adopted methodology can be extended to other exposure scenarios for MRI safety purposes.

A computational modelling tool for prediction of head reshaping following endoscopic strip craniectomy and helmet therapy for the treatment of scaphocephaly.

BACKGROUND: Endoscopic strip craniectomy followed by helmet therapy (ESCH) is a minimally invasive approach for correcting sagittal craniosynostosis. The treatment involves a patient-specific helmet designed to facilitate lateral growth while constraining sagittal expansion. In this study, finite element modelling was used to predict post-treatment head reshaping, improving our comprehension of the necessary helmet therapy duration. METHOD: Six patients (aged 11 weeks to 9 months) who underwent ESCH at Connecticut Children's Hospital were enrolled in this study. Day-1 post-operative 3D scans were used to create skin, skull, and intracranial volume models. Patient-specific helmet models, incorporating areas for growth, were designed based on post-operative imaging. Brain growth was simulated through thermal expansion, and treatments were modelled according to post-operative Imaging available. Mechanical testing and finite element modelling were combined to determine patient-specific mechanical properties from bone samples collected from surgery. Validation compared simulated end-of-treatment skin surfaces with optical scans in terms of shape matching and cranial index estimation. RESULTS: Comparison between the simulated post-treatment head shape and optical scans showed that on average 97.3 ± 2.1 % of surface data points were within a distance range of -3 to 3 mm. The cranial index was also accurately predicted (r = 0.91). CONCLUSIONS: In conclusion, finite element models effectively predicted the ESCH cranial remodeling outcomes up to 8 months postoperatively. This computational tool offers valuable insights to guide and refine helmet treatment duration. This study also incorporated patient-specific material properties, enhancing the accuracy of the modeling approach.

Multicentric characterization of organ-based tube current modulation in head computed tomography: A dosimetric and image quality study.

PURPOSE: To evaluate the efficiency of organ-based tube current modulation (OBTCM) in head Computed Tomography (CT) for different radiology departments and manufacturers. MATERIALS AND METHODS: Five CT scanners from four radiology departments were evaluated in this study. All scans were performed using a standard and a routine head protocol. A scintillating fiber optic detector was placed directly on the gantry to measure the tube exit kerma. Image quality was quantified on a 16-cm HEAD phantom by measuring the signal-to-noise ratio (SNR) and the standard deviation of the Hounsfield units (HU) of circular regions of interest placed in the phantom. The Noise Power Spectrum (NPS) was also studied. Measured values were compared on images with and without OBTCM. RESULTS: The reduction rates in tube exit kerma, on the anterior part, vary between 11 % and 74 % depending on the CT scanner and the protocol used. The tube exit kerma on the posterior part remains unchanged in GE and Canon CT scanners. On the contrary, the tube exit kerma to the posterior part increases by up to 39 % in Siemens CT scanner. Image noise and SNR increase by up to 10 % in the five CT scanners. Nonetheless, the differences in noise and SNR are statistically significant (p-value < 0.05).The analysis of the NPS indicates that the noise texture remains unchanged. CONCLUSION: OBTCM reduces the tube exit kerma to the anterior part of the gantry without reducing substantially image quality for head protocols.

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation.

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications.

Noise-based correction for electrical impedance tomography.

Objective.Noisy measurements frequently cause noisy and inaccurate images in impedance imaging. No post-processing technique exists to calculate the propagation of measurement noise and use this to suppress noise in the image. The objectives of this work were (1) to develop a post-processing method for noise-based correction (NBC) in impedance tomography, (2) to test whether NBC improves image quality in electrical impedance tomography (EIT), (3) to determine whether it is preferable to use correlated or uncorrelated noise for NBC, (4) to test whether NBC works within vivodata and (5) to test whether NBC is stable across model and perturbation geometries.Approach.EIT was performedin silicoin a 2D homogeneous circular domain and an anatomically realistic, heterogeneous 3D human head domain for four perturbations and 25 noise levels in each case. This was validated by performing EIT for four perturbations in a circular, saline tank in 2D as well as a human head-shaped saline tank with a realistic skull-like layer in 3D. Images were assessed on the error in the weighted spatial variance (WSV) with respect to the true, target image. The effect of NBC was also tested forin vivoEIT data of lung ventilation in a human thorax and cortical activity in a rat brain.Main results.On visual inspection, NBC maintained or increased image quality for all perturbations and noise levels in 2D and 3D, both experimentally andin silico. Analysis of the WSV showed that NBC significantly improved the WSV in nearly all cases. When the WSV was inferior with NBC, this was either visually imperceptible or a transformation between noisy reconstructions. Forin vivodata, NBC improved image quality in all cases and preserved the expected shape of the reconstructed perturbation.Significance.In practice, uncorrelated NBC performed better than correlated NBC and is recommended as a general-use post-processing technique in EIT.

RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images.

The segmentation of the fetal head (FH) and pubic symphysis (PS) from intrapartum ultrasound images plays a pivotal role in monitoring labor progression and informing crucial clinical decisions. Achieving real-time segmentation with high accuracy on systems with limited hardware capabilities presents significant challenges. To address these challenges, we propose the real-time segmentation network (RTSeg-Net), a groundbreaking lightweight deep learning model that incorporates innovative distribution shifting convolutional blocks, tokenized multilayer perceptron blocks, and efficient feature fusion blocks. Designed for optimal computational efficiency, RTSeg-Net minimizes resource demand while significantly enhancing segmentation performance. Our comprehensive evaluation on two distinct intrapartum ultrasound image datasets reveals that RTSeg-Net achieves segmentation accuracy on par with more complex state-of-the-art networks, utilizing merely 1.86 M parameters-just 6 % of their hyperparameters-and operating seven times faster, achieving a remarkable rate of 31.13 frames per second on a Jetson Nano, a device known for its limited computing capacity. These achievements underscore RTSeg-Net's potential to provide accurate, real-time segmentation on low-power devices, broadening the scope for its application across various stages of labor. By facilitating real-time, accurate ultrasound image analysis on portable, low-cost devices, RTSeg-Net promises to revolutionize intrapartum monitoring, making sophisticated diagnostic tools accessible to a wider range of healthcare settings.

Magnetic voluntary head-fixation in transgenic rats enables lifespan imaging of hippocampal neurons.

The precise neural mechanisms within the brain that contribute to the remarkable lifetime persistence of memory are not fully understood. Two-photon calcium imaging allows the activity of individual cells to be followed across long periods, but conventional approaches require head-fixation, which limits the type of behavior that can be studied. We present a magnetic voluntary head-fixation system that provides stable optical access to the brain during complex behavior. Compared to previous systems that used mechanical restraint, there are no moving parts and animals can engage and disengage entirely at will. This system is failsafe, easy for animals to use and reliable enough to allow long-term experiments to be routinely performed. Animals completed hundreds of trials per session of an odor discrimination task that required 2-4 s fixations. Together with a reflectance fluorescence collection scheme that increases two-photon signal and a transgenic Thy1-GCaMP6f rat line, we are able to reliably image the cellular activity in the hippocampus during behavior over long periods (median 6 months), allowing us track the same neurons over a large fraction of animals' lives (up to 19 months).

PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head.

During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation of the relative positional relationship between the pubic symphysis and fetal head (PSFH). Accurate assessment of fetal head descent and the prediction of the most suitable mode of delivery heavily rely on this relationship. However, achieving an objective and quantitative interpretation of the ultrasound images necessitates precise PSFH segmentation (PSFHS), a task that is both time-consuming and demanding. Integrating the potential of artificial intelligence (AI) in the field of medical ultrasound image segmentation, the development and evaluation of AI-based models rely significantly on access to comprehensive and meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging this critical gap, we introduce a PSFHS dataset comprising 1358 images, meticulously annotated at the pixel level. The annotation process adhered to standardized protocols and involved collaboration among medical experts. Remarkably, this dataset stands as the most expansive and comprehensive resource for PSFHS to date.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

Background: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Improved hypertensive stroke classification based on multi-scale feature fusion of head axial CT angiogram and multimodal learning.

PURPOSE: Strokes are severe cardiovascular and circulatory diseases with two main types: ischemic and hemorrhagic. Clinically, brain images such as computed tomography (CT) and computed tomography angiography (CTA) are widely used to recognize stroke types. However, few studies have combined imaging and clinical data to classify stroke or consider a factor as an Independent etiology. METHODS: In this work, we propose a classification model that automatically distinguishes stroke types with hypertension as an independent etiology based on brain imaging and clinical data. We first present a preprocessing workflow for head axial CT angiograms, including noise reduction and feature enhancement of the images, followed by an extraction of regions of interest. Next, we develop a multi-scale feature fusion model that combines the location information of position features and the semantic information of deep features. Furthermore, we integrate brain imaging with clinical information through a multimodal learning model to achieve more reliable results. RESULTS: Experimental results show our proposed models outperform state-of-the-art models on real imaging and clinical data, which reveals the potential of multimodal learning in brain disease diagnosis. CONCLUSION: The proposed methodologies can be extended to create AI-driven diagnostic assistance technology for categorizing strokes.

Performance of receive head arrays versus ultimate intrinsic SNR at 7 T and 10.5 T.

PURPOSE: We examined magnetic field dependent SNR gains and ability to capture them with multichannel receive arrays for human head imaging in going from 7 T, the most commonly used ultrahigh magnetic field (UHF) platform at the present, to 10.5 T, which represents the emerging new frontier of >10 T in UHFs. METHODS: Electromagnetic (EM) models of 31-channel and 63-channel multichannel arrays built for 10.5 T were developed for 10.5 T and 7 T simulations. A 7 T version of the 63-channel array with an identical coil layout was also built. Array performance was evaluated in the EM model using a phantom mimicking the size and electrical properties of the human head and a digital human head model. Experimental data was obtained at 7 T and 10.5 T with the 63-channel array. Ultimate intrinsic SNR (uiSNR) was calculated for the two field strengths using a voxelized cloud of dipoles enclosing the phantom or the digital human head model as a reference to assess the performance of the two arrays and field depended SNR gains. RESULTS: uiSNR calculations in both the phantom and the digital human head model demonstrated SNR gains at 10.5 T relative to 7 T of 2.6 centrally, ˜2 at the location corresponding to the edge of the brain, ˜1.4 at the periphery. The EM models demonstrated that, centrally, both arrays captured ˜90% of the uiSNR at 7 T, but only ˜65% at 10.5 T, leading only to ˜2-fold gain in array SNR in going from 7 to 10.5 T. This trend was also observed experimentally with the 63-channel array capturing a larger fraction of the uiSNR at 7 T compared to 10.5 T, although the percentage of uiSNR captured were slightly lower at both field strengths compared to EM simulation results. CONCLUSIONS: Major uiSNR gains are predicted for human head imaging in going from 7 T to 10.5 T, ranging from ˜2-fold at locations corresponding to the edge of the brain to 2.6-fold at the center, corresponding to approximately quadratic increase with the magnetic field. Realistic 31- and 63-channel receive arrays, however, approach the central uiSNR at 7 T, but fail to do so at 10.5 T, suggesting that more coils and/or different type of coils will be needed at 10.5 T and higher magnetic fields.