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.

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.

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.

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.

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

BACKGROUND: Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. PURPOSE: To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. METHODS: Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. RESULTS: The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45 mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48 mm or degree). The resulting motion-corrected image, I D L , P A R ${I}_{DL,\ PAR}$ , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 ± $ \pm $ 33HU to 37 ± $ \pm $ 9HU, structural similarity index (SSIM) increases from 0.60 ± $ \pm $ 0.06 to 0.98 ± $ \pm $ 0.01) and the phantom study (image MAE drops from 117 ± $ \pm $ 17HU to 42 ± $ \pm $ 19HU, SSIM increases from 0.83 ± $ \pm $ 0.04 to 0.98 ± $ \pm $ 0.02). CONCLUSIONS: We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

Size specific dose estimation in pediatric CT: preliminary study and conversion factors.

The objective of this paper is to compare the differences between volumetric CT dose index (CTDIVOL) and size-specific dose estimate (SSDEWED) based on water equivalent diameter (WED) in radiation dose measurement, and explore a new method for fast calculation of SSDEWED. The imaging data of 1238 cases of head, 1152 cases of chest and 976 cases of abdominopelvic were analyzed retrospectively, and they were divided into five age groups: ≤ 0.5, 0.5 ~ ≤ 1, 1 ~ ≤ 5, 5 ~ ≤ 10 and 10 ~ ≤ 15 years according to age. The area of interest (AR), CT value (CTR), lateral diameter (LAT) and anteroposterior diameter (AP) of the median cross-sectional image of the standard scanning range and the SSDEWED were manually calculated, and a t-test was used to compare the differences between CTDIVOL and SSDEWED in different age groups. Pearson analyzed the correlations between DE and age, DE and WED, f and age, and counted the means of conversion factors in each age group, and analyze the error ratios between SSDE calculated based on the mean age group conversion factors and actual measured SSDE. The CTDIVOL in head was (9.41 ± 1.42) mGy and the SSDEWED was (8.25 ± 0.70) mGy: the difference was statistically significant (t = 55.04, P < 0.001); the CTDIVOL of chest was (2.68 ± 0.91) mGy and the SSDEWED was (5.16 ± 1.16) mGy, with a statistically significant difference (t = -218.78, P < 0.001); the CTDIVOL of abdominopelvic was (3.09 ± 1.58) mGy and the SSDEWED was (5.89 ± 2.19) mGy: the difference was also statistically significant (t = -112.28, P < 0.001). The CTDIVOL was larger than the SSDEWED in the head except for the ≤ 0.5 year subgroup, and CTDIVOL was smaller than SSDEWED within each subgroup in chest and abdominopelvic. There were strong negative correlations between f and age (head: r = -0.81; chest: r = -0.89; abdominopelvic: r = -0.86; P < 0.001). The mean values of f at each examination region were 0.81 ~ 1.01 for head, 1.65 ~ 2.34 for chest and 1.71 ~ 2.35 for abdominopelvic region. The SSDEWED could be accurately estimated using the mean f of each age subgroup. SSDEWED can more accurately measure the radiation dose of children. For children of different ages and examination regions, the SSDEWED conversion factors based on age subgroup can be quickly adjusted and improve the accuracy of radiation dose estimation.

Sparse-view cone-beam computed tomography iterative reconstruction based on new multi-gradient direction total variation.

AIM: The accurate reconstruction of cone-beam computed tomography (CBCT) from sparse projections is one of the most important areas for study. The compressed sensing theory has been widely employed in the sparse reconstruction of CBCT. However, the total variation (TV) approach solely uses information from the i-coordinate, j-coordinate, and k-coordinate gradients to reconstruct the CBCT image. MATERIALS AND METHODS: It is well recognized that the CBCT image can be reconstructed more accurately with more gradient information from different directions. Thus, this study introduces a novel approach, named the new multi-gradient direction total variation minimization method. The method uses gradient information from the ij-coordinate, ik-coordinate, and jk-coordinate directions to reconstruct CBCT images, which incorporates nine different types of gradient information from nine directions. RESULTS: This study assessed the efficacy of the proposed methodology using under-sampled projections from four different experiments, including two digital phantoms, one patient's head dataset, and one physical phantom dataset. The results indicated that the proposed method achieved the lowest RMSE index and the highest SSIM index. Meanwhile, we compared the voxel intensity curves of the reconstructed images to assess the edge structure preservation. Among the various methods compared, the curves generated by the proposed method exhibited the highest level of consistency with the gold standard image curves. CONCLUSION: In summary, the proposed method showed significant potential in enhancing the quality and accuracy of CBCT image reconstruction.

Establishment and utilisation of national diagnostic reference level for adult computed tomography examinations in Ghana.

The International Atomic Energy Agency, as part of the new regional project (RAF/9/059), recommend the establishment of diagnostic reference levels (DRLs) in Africa. In response to this recommendation, this project was designed to establish and utilise national DRLs of routine computed tomography (CT) examinations. These were done by estimating CT dose index and dose length product (DLP) from a minimum of 20 patient dose report of the most frequently used procedures using 75th percentile distribution of the median values. In all, 22 centres that formed 54% of all CT equipment in the country took part in this study. Additionally, a total of 2156 adult patients dose report were randomly selected, with a percentage distribution of 60, 12, 21 and 7% for head, chest, abdomen-pelvis and lumber spine, respectively. The established DRL for volume CT dose index were 60.0, 15.7, 20.5 and 23.8 mGy for head, chest, abdomen-pelvis and lumber spine, respectively. While the established DRL for DLP were 962.9, 1102.8, 1393.5 and 824.6 mGy-cm for head, chest, abdomen-pelvis, and lumber spine, respectively. These preliminary results were comparable with data from 16 other African countries, European Commission and the International Commission on Radiological Protection. Hence, this study would serve as a baseline for the establishment of a more generalised regional and national adult DRLs for Africa and other developing countries.

State-of-the-art mobile head CT scanner delivers nearly the same image quality as a conventional stationary CT scanner.

The use of mobile head CT scanners in the neurointensive care unit (NICU) saves time for patients and NICU staff and can reduce transport-related mishaps, but the reduced image quality of previous mobile scanners has prevented their widespread clinical use. This study compares the image quality of SOMATOM On.Site (Siemens Healthineers, Erlangen, Germany), a state-of-the-art mobile head CT scanner, and a conventional 64-slice stationary CT scanner. The study included 40 patients who underwent head scans with both mobile and stationary scanners. Gray and white matter signal and noise were measured at predefined locations on axial slices, and signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) were calculated. Artifacts below the cranial calvaria and in the posterior fossa were also measured. In addition, image quality was subjectively assessed by two radiologists in terms of corticomedullary differentiation, subcalvarial space, skull artifacts, and image noise. Quantitative measurements showed significantly higher image quality of the stationary CT scanner in terms of noise, SNR and CNR of gray and white matter. Artifacts measured in the posterior fossa were higher with the mobile CT scanner, but subcalvarial artifacts were comparable. Subjective image quality was rated similarly by two radiologists for both scanners in all domains except image noise, which was better for stationary CT scans. The image quality of the SOMATOM On.Site for brain scans is inferior to that of the conventional stationary scanner, but appears to be adequate for daily use in a clinical setting based on subjective ratings.

Imaging manifestations of head and neck lymphatic malformations: A single-center experience of 170 surgical cases.

OBJECTIVES: To discuss the imaging manifestations and the utility of preoperative ultrasonography (US), contrast-enhanced computed tomography (CE-CT) and contrast enhanced magnetic resonance imaging (CE-MRI) in diagnosing the pediatric head and neck lymphatic malformations (HNLMs). METHODS: We performed a retrospective review of 170 children who were referred to our hospital in the past 9 years for the treatment of HNLMs. RESULTS: The diagnostic rates of US, CE-CT and CE-MRI were 93.0% (146/157), 94.7% (143/151) and 100% (45/45), respectively. As in multilocular cases, intracystic septa detection rate was 91.5% (130/142), 50.4% (68/135) and 88.1% (37/42), and which had a statistical difference (χ2 = 25.8131, p < 0.05). US showed capsule contents anechoic in 51.0% (80/157) cases, hypoechoic or mixed echoic in 49.0% (77/157) cases, and flocculent or dotted echo floating in 36.9% (58/157) cases. CT showed low density of the capsule contents without enhancement in 69.5% (105/151) cases and mixed density with enhancement in 30.4% (46/151) cases. Liquid-liquid levers were seen in 8.6% (13/151) cases. MRI showed T1WI high signal and T2WI low signal of the capsule contents without enhancement in 28.9% (13/45) cases and mixed density in 71.1% (32/45) cases. Liquid-liquid levers were seen in 46.7% (21/45) cases. There were statistically significant differences between pure HNLMs and intracystic hemorrhage in capsule content (echo, density, signal), enhancement, and liquid-liquid lever (all p < 0.05). Among US, CE-CT and CE-MRI, intracystic hemorrhage diagnostic accuracy had a statistical difference (χ2 = 25.4152, p < 0.05). CONCLUSIONS: For clinical diagnosis and evaluation of HNLMs, we suggest that US combined with CE-CT for acute cases, and for stable cases, US combined with CE-MRI.

Development of a Helmet-Shape Dual-Channel RF coil for brain imaging at 54 mT using inverse boundary element method.

Very-low field (VLF) magnetic resonance imaging (MRI) offers advantages in term of size, weight, cost, and the absence of robust shielding requirements. However, it encounters challenges in maintaining a high signal-to-noise ratio (SNR) due to low magnetic fields (below 100 mT). Developing a close-fitting radio frequency (RF) receive coil is crucial to improve the SNR. In this study, we devised and optimized a helmet-shaped dual-channel RF receive coil tailored for brain imaging at a magnetic field strength of 54 mT (2.32 MHz). The methodology integrates the inverse boundary element method (IBEM) to formulate initial coil structures and wiring patterns, followed by optimization through introducing regularization terms. This approach frames the design process as an inverse problem, ensuring a close fit to the head contour. Combining theoretical optimization with physical measurements of the coil's AC resistance, we identified the optimal loop count for both axial and radial coils as nine and eight loops, respectively. The effectiveness of the designed dual-channel coil was verified through the imaging of a CuSO4 phantom and a healthy volunteer's brain. Notably, the in-vivo images exhibited an approximate 16-25 % increase in SNR with poorer B1 homogeneity compared to those obtained using single-channel coils. The high-quality images achieved by T1, T2-weighted, and fluid-attenuated inversion-recovery (FLAIR) protocols enhance the diagnostic potential of VLF MRI, particularly in cases of cerebral stroke and trauma patients. This study underscores the adaptability of the design methodology for the customization of RF coil structures in alignment with individual imaging requirements.

Image quality of photon-counting detector CT virtual monoenergetic and polyenergetic reconstructions for head and neck CT angiography.

We compared image quality of head and neck CT angiography (CTA) obtained with a photon-counting detector CT (PCD-CT), including virtual monoenergetic images and polyenergetic reconstructions, and conventional energy-integrating detectors CT (EID-CT) in three patients. PCD-CT monoenergetic reconstructions at 70 keV and lower provided excellent image quality, with improved signal-to-noise and contrast-to-noise compared to EID-CT and PCD-CT polyenergetic reconstructions. PCD-CT may enable radiation dose and iodinated contrast dose reduction for cerebrovascular imaging.

Soft Tissue Calcifications in the Head and Neck Region.

This article provides an overview of the soft tissue calcifications in the head and neck region as noted on dental imaging, with particular focus on the radiographic appearance of these entities..

A Combined Magnetoelectric Sensor Array and MRI-Based Human Head Model for Biomagnetic FEM Simulation and Sensor Crosstalk Analysis.

Magnetoelectric (ME) magnetic field sensors are novel sensing devices of great interest in the field of biomagnetic measurements. We investigate the influence of magnetic crosstalk and the linearity of the response of ME sensors in different array and excitation configurations. To achieve this aim, we introduce a combined multiscale 3D finite-element method (FEM) model consisting of an array of 15 ME sensors and an MRI-based human head model with three approximated compartments of biological tissues for skin, skull, and white matter. A linearized material model at the small-signal working point is assumed. We apply homogeneous magnetic fields and perform inhomogeneous magnetic field excitation for the ME sensors by placing an electric point dipole source inside the head. Our findings indicate significant magnetic crosstalk between adjacent sensors leading down to a 15.6% lower magnetic response at a close distance of 5 mm and an increasing sensor response with diminishing crosstalk effects at increasing distances up to 5 cm. The outermost sensors in the array exhibit significantly less crosstalk than the sensors located in the center of the array, and the vertically adjacent sensors exhibit a stronger crosstalk effect than the horizontally adjacent ones. Furthermore, we calculate the ratio between the electric and magnetic sensor responses as the sensitivity value and find near-constant sensitivities for each sensor, confirming a linear relationship despite magnetic crosstalk and the potential to simulate excitation sources and sensor responses independently.

High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting.

Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution.Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N= 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated.Main results.For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA.Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.

[Virtual DEGUM-certified course in the head and neck region-a useful complement to conventional course formats?] / Virtuell abgehaltene DEGUM-zertifizierte Kurse im Kopf-Hals-Bereich ­ eine sinnvolle Ergänzung zum konventionellen Kursformat?

BACKGROUND: Training in clinical ultrasound has become highly relevant for working as an otorhinolaryngologist. While there is a high demand for standardized and certified training courses, until recently, there was no possibility to attend web-based and exclusively virtual head and neck ultrasound courses certified by the Deutsche Gesellschaft für Ultraschall in der Medizin (DEGUM; German Society for Ultrasound in Medicine). OBJECTIVE: The aim of this study was to provide a qualitative and semi-quantitative analysis of the first purely virtual DEGUM-certified head and neck ultrasound courses. MATERIALS AND METHODS: In 2021, three purely web-based DEGUM-certified head and neck ultrasound courses were carried out and then qualitatively analyzed using questionnaires including an examination. RESULTS: The purely virtual implementation of head and neck ultrasound courses proved to be a viable alternative to the conventional course format, with a high level of acceptance among the participants. The lack of practice among the participants remains a relevant criticism. CONCLUSION: A more dominant role of web-based and remote ultrasound training is likely and should be considered as an alternative depending on existing conditions. Nevertheless, acquisition of practical sonographic skills remains a major hurdle if courses are purely digital.

Estimating organ dose with optimized peak dose index in cone-beam CT scans.

PURPOSE: Organ dose evaluation is important for optimizing cone beam computed tomography (CBCT) scan protocols. However, an evaluation method for various CBCT scanners is yet to be established. In this study, we developed scanner-independent conversion coefficients to estimate organ doses using appropriate peak dose (f(0)) indices. METHODS: This study included various scanners (angiography scanners and linear accelerators) and protocols for the head and body (thorax, abdomen, and pelvis) scan regions. f(0) was measured at five conventional positions (center position (f(0)c) and four peripheral positions (f(0)p) at 90° intervals) in the CT dose index (CTDI) phantom. To identify appropriate measurement positions for organ dose estimation, various f(0) indices were considered. Organ doses were measured by using optically stimulated luminescence dosimeters positioned in an anthropomorphic phantom. Thereafter, the conversion coefficients were calculated from each obtained f(0) value and organ or tissue dose using a linear fit for all scanners, and the coefficient of variation (CV) of the conversion coefficients was calculated for each organ or tissue. The f(0) index with the minimum CV value was proposed as the appropriate index. RESULTS: The appropriate f(0) index was determined as f(0)c for the body region and a maximum of four f(0)p values for the head region. Using the proposed conversion coefficients based on the appropriate f(0) index, the organ/tissue doses were well estimated with a mean error of 14.2% across all scanners and scan regions. CONCLUSIONS: The proposed scanner-independent coefficients are useful for organ dose evaluation using CBCT scanners.

Evaluation of coaxial dipole antennas as transceiver elements of human head array for ultra-high field MRI at 9.4T.

PURPOSE: The aim of this work is to evaluate a new eight-channel transceiver (TxRx) coaxial dipole array for imaging of the human head at 9.4T developed to improve specific absorption rate (SAR) performance, and provide for a more compact and robust alternative to the state-of-the art dipole arrays. METHODS: First, the geometry of a single coaxial element was optimized to minimize peak SAR and sensitivity to the load variation. Next, a multi-tissue voxel model was used to numerically simulate a TxRx array coil that consisted of eight coaxial dipoles with the optimal configuration. Finally, we compared the developed array to other human head dipole arrays. Results of numerical simulations were verified on a bench and in the scanner including in vivo measurements on a healthy volunteer. RESULTS: The developed eight-element coaxial dipole TxRx array coil showed up to 1.1times higher SAR-efficiency than a similar in geometry folded-end and fractionated dipole array while maintaining whole brain coverage and low sensitivity of the resonance frequency to variation in the head size. CONCLUSION: As a proof of concept, we developed and constructed a prototype of a 9.4T (400 MHz) human head array consisting of eight TxRx coaxial dipoles. The developed array improved SAR-efficiency and provided for a more compact and robust alternative to the folded-end dipole design. To the best of our knowledge, this is the first example of using coaxial dipoles for human head MRI at ultra-high field.

Level of agreement between midwives and obstetricians performing ultrasound examination during labor.

OBJECTIVE: To evaluate the level of agreement between ultrasound measurements to evaluate fetal head position and progress of labor by attending midwives and obstetricians after appropriate training. METHODS: In this prospective study, women in the first stage of labor giving birth to a single baby in cephalic presentation at our Obstetric Unit between March 2018 and December 2019 were invited to participate; 109 women agreed. Transperineal and transabdominal ultrasound was independently performed by a trained midwife and an obstetrician. Two paired measurements were available for comparisons in 107 cases for the angle of progression (AoP), in 106 cases for the head-to-perineum distance (HPD), in 97 cases for the cervical dilatation (CD), and in 79 cases for the fetal head position. RESULTS: We found a good correlation between the AoP measured by obstetricians and midwives (intra-class correlation coefficient [ICC] = 0.85; 95% confidence interval [CI] 0.80-0.89). There was a moderate correlation between the HPD (ICC = 0.75; 95% CI 0.68-0.82). There was a very good correlation between the CD measured (ICC = 0.94; 95% CI 0.91-0.96). There was a very good level of agreement in the classification of the fetal head position (Cohen's κ = 0.89; 95% CI 0.80-0.98). CONCLUSIONS: Ultrasound assessment of fetal head position and progress of labor can effectively be performed by attending midwives without previous experience in ultrasound.