Standard Planes Research Articles

A computed tomography–based morphometric analysis of thoracic pedicles in a European population

PurposeThe goal of this retrospective study was to perform a CT imaging assessment of thoracic pedicles to provide a representative understanding of pedicle morphology for pedicle-based fixation systems commonly used in orthopedics, trauma and neurosurgery. This study aimed to better understand the morphology of the spine and give spine surgeons a better understanding of thoracic spine anatomy.MethodsIn this study, we retrospectively measured the thoracic spine pedicles of a total of 16 males and 16 females, totaling in 768 individual pedicles. For the measurements, we used standardized planes in computed tomography imaging with a maximum slice thickness of 1 mm.ResultsIn brief, we identified significant differences in various measurements of male and female pedicle morphology. The medial cortical wall of the pedicles was significantly thicker than the lateral wall, and, in both sexes, the thoracic vertebral body number four was the vertebra with the least amount of cortical bone in the pedicle.ConclusionsSurgeons performing operations involving pedicle screw placement should be aware of the sex-specific differences in thoracic spine pedicle morphology noted in this research.

Safety-Centric Precision Control of a Modified Duodenoscope Designed for Surgical Robotics

There is limited research on robotic systems designed for Endoscopic Retrograde Cholangiopancreatography (ERCP) procedures using a side-view duodenoscope. The unique structure of the duodenoscope presents challenges to safely and precisely control the distal end pose. Control methods applied can reduce potential medical risks. We have redesigned the control section of the duodenoscope to facilitate its manipulation by a robotic system. An orthogonal compensator is employed to rectify the motion planes to standard planes. A hysteresis compensator based on the Prandtl-Ishlinskii model enables precise control of the distal pose of the duodenoscope. Furthermore, we utilize a contact force prediction model to prevent excessive contact force at the distal end. The performance of the modified duodenoscope is comparable to that of the standard duodenoscope. Following orthogonal compensation, the deviation angles of the motion planes is reduced by 32% to 98%. Post-hysteresis compensation, the root mean square error (RMSE) of the output angle of the distal end is decreased from 8.347° to 4.826°. The accuracy of distal end contact force prediction was approximately ±25% under conditions of high contact force. In conclusion, the modification and control strategy we proposed can achieve relatively safe and precise control of bending section, laying the foundation for the subsequent roboticization of duodenoscope systems for ERCP procedures.

Audio-visual modelling in a clinical setting

Auditory and visual signals are two primary perception modalities that are usually present together and correlate with each other, not only in natural environments but also in clinical settings. However, audio-visual modelling in the latter case can be more challenging, due to the different sources of audio/video signals and the noise (both signal-level and semantic-level) in auditory signals—usually speech audio. In this study, we consider audio-visual modelling in a clinical setting, providing a solution to learn medical representations that benefit various clinical tasks, without relying on dense supervisory annotations from human experts for the model training. A simple yet effective multi-modal self-supervised learning framework is presented for this purpose. The proposed approach is able to help find standard anatomical planes, predict the focusing position of sonographer’s eyes, and localise anatomical regions of interest during ultrasound imaging. Experimental analysis on a large-scale clinical multi-modal ultrasound video dataset show that the proposed novel representation learning method provides good transferable anatomical representations that boost the performance of automated downstream clinical tasks, even outperforming fully-supervised solutions. Being able to learn such medical representations in a self-supervised manner will contribute to several aspects including a better understanding of obstetric imaging, training new sonographers, more effective assistive tools for human experts, and enhancement of the clinical workflow.

Automatic Detection of Standard Planes in Fetal Ultrasound Images based on Convolutional Neural Networks and Ensemble Learning

aims: This study aims to leverage artificial intelligence for enhancing medical diagnosis, focusing on ultrasound evaluation of fetal development and detection of fetal diseases. background: Traditional diagnostic methods in ultrasound are known for being time-consuming and laborious, prompting the need for more efficient approaches. objective: The objective of this research is to develop an end-to-end automatic diagnosis system using convolutional neural networks with ensemble learning to enhance robustness and accuracy in classifying ultrasound images. method: The study involves constructing and implementing the automatic diagnosis system, training it on a diverse dataset encompassing six categories: abdomen, brain, femur, thorax, maternal cervix, and other planes. result: Experimental results demonstrate that the proposed end-to-end system significantly improves the detection accuracy of the standard plane in ultrasound images. conclusion: The application of artificial intelligence through an ensemble learning-based automatic diagnosis system shows promise in advancing ultrasound-based medical diagnosis, particularly in fetal development assessment. other: This research contributes to the ongoing efforts in leveraging technology for more efficient and accurate medical diagnostic processes.

Automatic standard plane and diagnostic usability classification in obstetric ultrasounds

ObjectiveThis study introduces an innovative end-to-end deep learning pipeline designed to automatically classify and order fetal ultrasound standard planes in alignment with the guidelines of the Canadian Association of Radiologists, while also assessing the diagnostic usability of each view. The primary objective is to address the manual and cumbersome challenges that interpreting radiologists encounter in the existing obstetric ultrasound workflow. MethodsWe compiled a diverse dataset, comprising 33,561 de-identified two-dimensional obstetrical ultrasound images acquired from January 1, 2010, to June 1, 2020. This dataset was categorized into 19 distinct classes associated with standard planes and further partitioned into training, validation, and testing subsets via a 60:20:20 stratified split. The standard plane and diagnostic usability networks are founded on a convolutional neural network framework and employ the benefits of transfer learning. ResultsThe standard plane classification network demonstrated promising results by achieving 99.4 % and 98.7 % for accuracy and F1 score, respectively. Subsequently, the diagnostic usability network demonstrated strong performance, registering 80 % accuracy and an 82 % F1 score. Notably, this study is the first to investigate whether deep learning methods can surpass sonographers in the standard plane labeling task, with some instances revealing the algorithm's capacity to rectify sonographer mislabeled planes. ConclusionThe results highlight the algorithm's potential to be integrated into a clinical setting by serving as a reliable assistive tool, alleviating the cognitive workload faced by radiologists and enhancing efficiency and diagnostic outcomes in the current obstetric ultrasound process.

Role of artificial-intelligence-assisted automated cardiac biometrics in prenatal screening for coarctation of aorta.

Although remarkable strides have been made in fetal medicine and the prenatal diagnosis of congenital heart disease, around 60% of newborns with isolated coarctation of the aorta (CoA) are not identified prior to birth. The prenatal detection of CoA has been shown to have a notable impact on survival rates of affected infants. To this end, implementation of artificial intelligence (AI) in fetal ultrasound may represent a groundbreaking advance. We aimed to investigate whether the use of automated cardiac biometric measurements with AI during the 18-22-week anomaly scan would enhance the identification of fetuses that are at risk of developing CoA. We developed an AI model capable of identifying standard cardiac planes and conducting automated cardiac biometric measurements. Our data consisted of pregnancy ultrasound image and outcome data spanning from 2008 to 2018 and collected from four distinct regions in Denmark. Cases with a postnatal diagnosis of CoA were paired with healthy controls in a ratio of 1:100 and matched for gestational age within 2 days. Cardiac biometrics obtained from the four-chamber and three-vessel views were included in a logistic regression-based prediction model. To assess its predictive capabilities, we assessed sensitivity and specificity on receiver-operating-characteristics (ROC) curves. At the 18-22-week scan, the right ventricle (RV) area and length, left ventricle (LV) diameter and the ratios of RV/LV areas and main pulmonary artery/ascending aorta diameters showed significant differences, with Z-scores above 0.7, when comparing subjects with a postnatal diagnosis of CoA (n = 73) and healthy controls (n = 7300). Using logistic regression and backward feature selection, our prediction model had an area under the ROC curve of 0.96 and a specificity of 88.9% at a sensitivity of 90.4%. The integration of AI technology with automated cardiac biometric measurements obtained during the 18-22-week anomaly scan has the potential to enhance substantially the performance of screening for fetal CoA and subsequently the detection rate of CoA. Future research should clarify how AI technology can be used to aid in the screening and detection of congenital heart anomalies to improve neonatal outcomes. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Validation of a deep-learning modular prototype to guide novices to acquire diagnostic ultrasound images from urinary system

ImportanceArtificial intelligence (AI) application in guiding the acquisition of ultrasonography images represents a pioneering field of research. A new developed hybrid deep-learning (DL) algorithm, trained on more than high quality 60.000 curated and labelled reference images (distilled from a set of more than 600.000 abdominal ultrasound images) from La Paz Hospital, can provide real-time prescriptive guidance for novice operators to obtain standard planes images of the target organs. ObjectiveThis study aims to evaluate the capability of novice users to acquire diagnostic-quality abdominal ultrasound images of the urinary system using the deep-learning (DL)-based guiding research prototype provided by GMV. DesignSetting, and Participants: This prospective diagnostic study was conducted within the facilities of an academic hospital. A cohort of 24 technically-oriented volunteers, lacking prior knowledge in anatomy or medicine and without experience in conducting ultrasound examinations, was recruited. After a brief training session focused on various organs, each pair of volunteers performed scans of each other's urinary system, exclusively guided by AI support. These scans were subsequently repeated by experienced sonographers using identical ultrasound equipment but without AI assistance. Four radiologists, each with decades of experience, independently and blindly assessed the quality of each acquisition. ResultsOver 90.6 % of the images scanned by volunteers were identified as valuable clinical picture, using only an AI-based guidance system. This is nearly comparable to the results achieved by experienced radiologists, who attained a 98.6 % success rate. ConclusionsThis deep-learning (DL) prototype enables novices lacking experience in ultrasonography to acquire diagnostic ultrasound images suitable for subsequent expert evaluation. The modular prototype works on a large range of ultrasound device models and vendors. This advancement has the potential to extend the application of ultrasound beyond traditional clinical environments, particularly in situations requiring immediate anatomical and functional interrogation, as well as in resource-limited settings.

Fast high-precision 3D shape measurement using linear phase encoding with geometry constraints

A three-dimensional (3D) profile measurement method based on geometric constraints combined with linear phase encoding (GCPE) is proposed. This method encodes the wrapped phase and the linear signal in the same phase domain, achieves the elimination of the phase ambiguity within the local fringe period, and obtains an unambiguous absolute phase within the entire period through geometric constraints. Compared with the traditional phase encoding method, this method solves the problem of period sequence errors caused by a large number of codewords by encoding linear signals in the phase domain and using a period order correction method to deal with the period jump phenomenon caused by noise. At the same time, the measurement range of the fringe projection system under geometric constraints is significantly improved. Experimentally, 3D profiles of standard planes, complex statues, and separated objects were measured by the use of the GCPE. The results show that the GCPE has the advantage of fast speed and high accuracy in measuring the 3D profile of objects.

FetalBrainAwareNet: Bridging GANs with anatomical insight for fetal ultrasound brain plane synthesis

Over the past decade, deep-learning (DL) algorithms have become a promising tool to aid clinicians in identifying fetal head standard planes (FHSPs) during ultrasound (US) examination. However, the adoption of these algorithms in clinical settings is still hindered by the lack of large annotated datasets. To overcome this barrier, we introduce FetalBrainAwareNet, an innovative framework designed to synthesize anatomically accurate images of FHSPs. FetalBrainAwareNet introduces a cutting-edge approach that utilizes class activation maps as a prior in its conditional adversarial training process. This approach fosters the presence of the specific anatomical landmarks in the synthesized images. Additionally, we investigate specialized regularization terms within the adversarial training loss function to control the morphology of the fetal skull and foster the differentiation between the standard planes, ensuring that the synthetic images faithfully represent real US scans in both structure and overall appearance. The versatility of our FetalBrainAwareNet framework is highlighted by its ability to generate high-quality images of three predominant FHSPs using a singular, integrated framework. Quantitative (Fréchet inception distance of 88.52) and qualitative (t-SNE) results suggest that our framework generates US images with greater variability compared to state-of-the-art methods. By using the synthetic images generated with our framework, we increase the accuracy of FHSP classifiers by 3.2% compared to training the same classifiers solely with real acquisitions. These achievements suggest that using our synthetic images to increase the training set could provide benefits to enhance the performance of DL algorithms for FHSPs classification that could be integrated in real clinical scenarios.

Subclinical myocardial involvement in a cohort of patients with antisynthetase syndrome.

There is an increasing interest in knowing whether patients with antisynthetase syndrome (ASSD) may have silent myocardial interstitial involvement. Mapping techniques in cardiac magnetic resonance (CMR) can detect subclinical myocardial involvement. The purpose of this study was to identify alterations in multiparametric CMR in ASSD patients without overt cardiac involvement. Patients diagnosed with ASSD underwent a CMR along with the standard clinical workup, investigation of specific and associated myositis antibodies, and high-resolution chest CT. The CMR protocol includes routine morphologic, functional, and late gadolinium enhancement sequences in standard cardiac planes, as well as native T1 and T2 mapping sequences and extracellular volume (ECV) calculation. Twenty-five patients were included in this study (56% women; median age 56.3 years). Three patients were considered in the acute phase at the time of inclusion. Eight patients (32%) showed pathological findings in CMR (6 stable disease, 2 acute phase). Elevated T1, T2 and ECV mapping values were found in 20% (5/25), 17% (4/25) and 24% (6/25) of the group, respectively. Two patients in the acute phase had increased values of both T2 and ECV. Subclinical myocardial involvement in ASSD is not rare (32%) although its clinical significance is uncertain. Myocardial oedema (T2) was the most frequent finding, followed by increased T1 and/or ECV values likely signalling interstitial fibrosis. Of note, patients in the acute phase showed elevated T2 values.

Automating the Human Action of First-Trimester Biometry Measurement from Real-World Freehand Ultrasound

Objective: Automated medical image analysis solutions should closely mimic complete human actions to be useful in clinical practice. However, more often an automated image analysis solution only represents part of a human task which restricts its practical utility. In the case of ultrasound-based fetal biometry, an automated solution should ideally recognize key fetal structures in free-hand video guidance, select a standard plane from a video stream and perform biometry. A complete automated solution should automate all three sub-actions.Methods: This paper considers how to automate the complete human action of first-trimester biometry measurement from real-world freehand ultrasound. In the proposed hybrid Convolutional Neural Network (CNN) architecture design, a classification-regression-based guidance model detects and tracks fetal anatomical structures (using visual cues) in the ultrasound video. Several high-quality standard planes which contain the mid-sagittal view of the fetus are sampled at multiple timestamps (using a custom-designed confident-frame detector) based on the estimated probability values associated with predicted anatomical structures that define the biometry plane. Automated semantic segmentation is performed on the selected frames to extract fetal anatomical landmarks. A crown-rump length (CRL) estimate is calculated as the mean CRL from these multiple frames.Results: Our fully automated method shows a high correlation with clinical expert CRL measurement (Pearson ρ=0.92, R-Squared (R2)=0.84) and a low mean absolute error of 0.834 (weeks) for fetal age estimation on a test data set of 42 videos.Conclusion: A novel algorithm for standard plane detection employs a quality detection mechanism defined by clinical standards and ensuring precise biometric measurements.

Fully automated kidney image biomarker prediction in ultrasound scans using Fast-Unet++

Any kidney dimension and volume variation can be a remarkable indicator of kidney disorders. Precise kidney segmentation in standard planes plays an undeniable role in predicting kidney size and volume. On the other hand, ultrasound is the modality of choice in diagnostic procedures. This paper proposes a convolutional neural network with nested layers, namely Fast-Unet++, promoting the Fast and accurate Unet model. First, the model was trained and evaluated for segmenting sagittal and axial images of the kidney. Then, the predicted masks were used to estimate the kidney image biomarkers, including its volume and dimensions (length, width, thickness, and parenchymal thickness). Finally, the proposed model was tested on a publicly available dataset with various shapes and compared with the related networks. Moreover, the network was evaluated using a set of patients who had undergone ultrasound and computed tomography. The dice metric, Jaccard coefficient, and mean absolute distance were used to evaluate the segmentation step. 0.97, 0.94, and 3.23 mm for the sagittal frame, and 0.95, 0.9, and 3.87 mm for the axial frame were achieved. The kidney dimensions and volume were evaluated using accuracy, the area under the curve, sensitivity, specificity, precision, and F1.

Artificial intelligence assistance for fetal development: evaluation of an automated software for biometry measurements in the mid-trimester

BackgroundThis study presents CUPID, an advanced automated measurement software based on Artificial Intelligence (AI), designed to evaluate nine fetal biometric parameters in the mid-trimester. Our primary objective was to assess and compare the CUPID performance of experienced senior and junior radiologists.Materials and methodsThis prospective cross-sectional study was conducted at Shenzhen University General Hospital between September 2022 and June 2023, and focused on mid-trimester fetuses. All ultrasound images of the six standard planes, that enabled the evaluation of nine biometric measurements, were included to compare the performance of CUPID through subjective and objective assessments.ResultsThere were 642 fetuses with a mean (±SD) age of 22 ± 2.82 weeks at enrollment. In the subjective quality assessment, out of 642 images representing nine biometric measurements, 617-635 images (90.65-96.11%) of CUPID caliper placements were determined to be accurately placed and did not require any adjustments. Whereas, for the junior category, 447-691 images (69.63-92.06%) were determined to be accurately placed and did not require any adjustments. In the objective measurement indicators, across all nine biometric parameters and estimated fetal weight (EFW), the intra-class correlation coefficients (ICC) (0.843-0.990) and Pearson correlation coefficients (PCC) (0.765-0.978) between the senior radiologist and CUPID reflected good reliability compared with the ICC (0.306-0.937) and PCC (0.566-0.947) between the senior and junior radiologists. Additionally, the mean absolute error (MAE), percentage error (PE), and average error in days of gestation were lower between the senior and CUPID compared to the difference between the senior and junior radiologists. The specific differences are as follows: MAE (0.36-2.53 mm, 14.67 g) compared to (0.64- 8.13 mm, 38.05 g), PE (0.94-9.38%) compared to (1.58-16.04%), and average error in days (3.99-7.92 days) compared to (4.35-11.06 days). In the time-consuming task, CUPID only takes 0.05-0.07 s to measure nine biometric parameters, while senior and junior radiologists require 4.79-11.68 s and 4.95-13.44 s, respectively.ConclusionsCUPID has proven to be highly accurate and efficient software for automatically measuring fetal biometry, gestational age, and fetal weight, providing a precise and fast tool for assessing fetal growth and development.

Radiographic Study of the Nasal Valve in Different CT Evaluation Method in Asian Patients with Unilateral Cleft Lip Nose.

It was the first study to apply and compare two CT methods to assess the validity and clinical significance of structural alterations of the nasal valve in patients with cleft lip nose for assessing nasal ventilation disturbance. The study collected data from the NOSE score, as well as internal nasal valve area, internal nasal valve angle, external nasal valve area, and septal deviation angle, to evaluate the differences and correlations between those factors in patients with cleft lip and nose. There were significant differences among INV transverse and coronal area and INV angle on different axial standardized planes between clefted side and non-clefted side. There were statistically significant negative correlations between NOSE scores and those indicators of standard plane and acoustic-axis standardized coronal plane. NOSE score and NSD angle were the indicators of significant differences in the measured data of different complications groups (p = 0.002, p = 0.017). The correlation comparison showed that two standardized CT imaging transverse planes have similar correlations in NOSE score, NSD angle, and complications. The results of the two CT evaluation methods showed that there was a significant difference in nasal valve area on the cleft and non-cleft sides, which was significantly associated with nasal ventilation disturbance. The CT evaluation method based on standard axial 3D reconstruction is more convenient to use in the clinic, can be used for pre-surgical evaluation of nasal repair in patients with secondary nasal deformities of unilateral cleft lip, and is valuable for treatment. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Artificial Intelligence in Obstetric Anomaly Scan: Heart and Brain.

The ultrasound scan represents the first tool that obstetricians use in fetal evaluation, but sometimes, it can be limited by mobility or fetal position, excessive thickness of the maternal abdominal wall, or the presence of post-surgical scars on the maternal abdominal wall. Artificial intelligence (AI) has already been effectively used to measure biometric parameters, automatically recognize standard planes of fetal ultrasound evaluation, and for disease diagnosis, which helps conventional imaging methods. The usage of information, ultrasound scan images, and a machine learning program create an algorithm capable of assisting healthcare providers by reducing the workload, reducing the duration of the examination, and increasing the correct diagnosis capability. The recent remarkable expansion in the use of electronic medical records and diagnostic imaging coincides with the enormous success of machine learning algorithms in image identification tasks. We aim to review the most relevant studies based on deep learning in ultrasound anomaly scan evaluation of the most complex fetal systems (heart and brain), which enclose the most frequent anomalies.

Intelligent robotic sonographer: Mutual information-based disentangled reward learning from few demonstrations

Ultrasound (US) imaging is widely used for biometric measurement and diagnosis of internal organs due to the advantages of being real-time and radiation-free. However, due to inter-operator variations, resulting images highly depend on the experience of sonographers. This work proposes an intelligent robotic sonographer to autonomously “explore” target anatomies and navigate a US probe to standard planes by learning from the expert. The underlying high-level physiological knowledge from experts is inferred by a neural reward function, using a ranked pairwise image comparison approach in a self-supervised fashion. This process can be referred to as understanding the “language of sonography.” Considering the generalization capability to overcome inter-patient variations, mutual information is estimated by a network to explicitly disentangle the task-related and domain features in latent space. The robotic localization is carried out in coarse-to-fine mode based on the predicted reward associated with B-mode images. To validate the effectiveness of the proposed reward inference network, representative experiments were performed on vascular phantoms (“line” target), two types of ex vivo animal organ phantoms (chicken heart and lamb kidney representing “point” target), and in vivo human carotids. To further validate the performance of the autonomous acquisition framework, physical robotic acquisitions were performed on three phantoms (vascular, chicken heart, and lamb kidney). The results demonstrated that the proposed advanced framework can robustly work on a variety of seen and unseen phantoms as well as in vivo human carotid data. Code: https://github.com/yuan-12138/MI-GPSR. Video: https://youtu.be/u4ThAA9onE0.

Analysis of neural networks for routine classification of sixteen ultrasound upper abdominal cross sections.

Abdominal ultrasound screening requires the capture of multiple standardized plane views as per clinical guidelines. Currently, the extent of adherence to such guidelines is dependent entirely on the skills of the sonographer. The use of neural network classification has the potential to better standardize captured plane views and streamline plane capture reducing the time burden on operators by combatting operator variability. A dataset consisting of 16 routine upper abdominal ultrasound scans from 64 patients was used to test the classification accuracy of 9 neural networks. These networks were tested on both a small, idealised subset of 800 samples as well as full video sweeps of the region of interest using stratified sampling and transfer learning. The highest validation accuracy attained by both GoogLeNet and InceptionV3 is 83.9% using transfer learning and the large sample set of 26,294 images. A top-2 accuracy of 95.1% was achieved using InceptionV3. Alexnet attained the highest accuracy of 79.5% (top-2 of 91.5%) for the smaller sample set of 800 images. The neural networks evaluated during this study were also successfully able to identify problematic individual cross sections such as between kidneys, with right and left kidney being accurately identified 78.6% and 89.7%, respectively. Dataset size proved a more important factor in determining accuracy than network selection with more complex neural networks providing higher accuracy as dataset size increases and simpler linear neural networks providing better results where the dataset is small.

Developing an Educational Program for Ultrasound Hip Screening during Newborn and Infant Home Visits: A Protocol Paper.

Ultrasound hip screening is suitable for the early identification of developmental dysplasia of the hip (DDH). Newborn and infant home visits are good opportunities for hip screening in the community, but studies focusing on nurse-led screenings are lacking. Based on a pre-post design, this study aims to develop and evaluate an ultrasound training program to improve nurses' assessment skills in detecting DDH cases during newborn and infant home visits. Said educational program will include e-learning, hands-on seminars, and clinical training. The primary outcome will be the success rate of imaging standard planes (standardized images for hip assessment) in clinical training. The secondary outcomes will include knowledge test results, objective structured clinical examination scores, time required for imaging, and inter-rater reliability between nurses and physicians. The educational program will address the issue of missed and late detection of DDH cases in resource-limited communities. This study will demonstrate the feasibility of procedures and the effectiveness of the educational program in 2024. The protocol was registered in the University Hospital Medical Information Network Clinical Trial Registry before starting the study (no. UMIN000051929, 16 August 2023).

Integration of a Deep Convolutional Neural Network With Adaptive Channel Weight Technique for Automated Identification of Standard Fetal Biometry Planes

Integration of a Deep Convolutional Neural Network With Adaptive Channel Weight Technique for Automated Identification of Standard Fetal Biometry Planes

High-resolution ultrasound of the supra- and infraclavicular levels of the brachial plexus including the axillary nerve: imaging anatomy based on multiplanar reconstructions and technical guide.

The diagnosis of peripheral nerve injuries remains challenging. Electromyography and nerve conduction studies do not allow precise localization of the lesion and differentiation between lesions in continuity and non-continuity in cases with complete axonotmesis. Improved ultrasound technology allows the examination of almost the entire peripheral nervous system. The complex sono-anatomy of the brachial plexus outside of the standard scanning planes makes it difficult to access this region. On the basis of the Visible Human Project of the National Institutes of Health (NIH), multiplanar reconstructions were created with the 3D Slicer open-source software in the various planes of the ultrasound cross-sections. The ultrasound examination itself and the guidance of the ultrasound probe in relation to the patient were recorded as video files and were synchronized through the audio channel. Subsequently, image matching was performed. Multiplanar reconstructions facilitate visualization of anatomical regions which are challenging to access thereby enabling physicians to evaluate the course of the peripheral nerve of interest in dynamic conditions. Sonographically visible structures could be reproducibly identified in single-frame analysis. With precise knowledge of the ultrasound anatomy, the nerve structures of the brachial plexus can also be dynamically assessed almost in their entire course. An instructional video on ultrasound of the brachial plexus supplements this manuscript and has been published on Vimeo.com.