Whole-body magnetic resonance imaging and positron emission tomography-computed tomography in oncology

The advent of positron emission tomography-computed tomography (PET-CT) and whole-body magnetic resonance imaging (WB-MRI) has introduced tumor imaging with a systemic and functional approach compared with established sequential, multimodal diagnostic algorithms.Whole-body PET with [18F]-fluoro-2-desoxy-glucose is a useful imaging procedure for tumor staging and monitoring that can visualize active tumor tissue by detecting pathological glucose metabolism. The combination of PET with the detailed anatomical information of multislice computed tomography as dual-modality scanners has markedly increased lesion localization and diagnostic accuracy compared with both modalities as standalone applications.Hardware innovations, such as the introduction of multi-receiver channel whole-body MRI scanners at 1.5 and, recently, 3 T, combined with acquisition acceleration techniques, have made high-resolution WB-MRI clinically feasible. Now, a dedicated assessment of individual organs with various soft tissue contrast, spatial resolution, and contrast media dynamics can be combined with whole-body anatomical coverage in a multiplanar imaging approach. More flexible protocols (eg, T1-weighted turbo spin-echo and short inversion recovery imaging, dedicated lung imaging or dynamic contrast-enhanced studies of the abdomen) can be performed within 45 minutes.Whole-body magnetic resonance imaging has recently been proposed for tumor screening of asymptomatic individuals, and potentially life-changing diagnoses, such as formerly unknown malignancy, have been reported. However, larger patient cohort studies will have to show the cost efficiency and the clinical effectiveness of such an approach.For initial tumor staging, PET-CT has proved more accurate for the definition of T-stage and lymph node assessment, mainly because of the missing metabolic information in WB-MRI. However, new applications, such as magnetic resonance whole-body diffusion-weighted imaging or lymphotropic contrast agents, may significantly increase sensitivity in near future. Whole-body magnetic resonance imaging has shown advantages for the detection of distant metastatic disease, especially from tumors frequently spreading to the liver or brain and as a whole-body bone marrow screening application. Within this context, WB-MRI is highly accurate for the detection of skeletal metastases and staging of multiple myeloma. This article summarizes recent developments of CT/PET-CT and WB-MRI and highlights their performance within the scope of systemic oncological imaging.     

Investigating the Impact of Positron Emission Tomography–Computed Tomography Versus Computed Tomography Alone for High-risk Volume Selection in Head and Neck and Lung Patients Undergoing Radiotherapy: Interim Findings

IntroductionThe aim of this study was to quantify the impact of positron emission tomography–computed tomography (PET-CT) on clinical target volume (CTV) selection in non–small cell lung cancer (NSCLC) and head and neck squamous cell cancer (HNSCC) cancer patients.MethodsEight radiation oncologists with expertise in either NSCLC or HNSCC prospectively contoured target volumes with and without PET-CT findings. All volumes were contoured manually, and computed tomography (CT)-alone contours were identified as gross tumour volume CT and clinical target volume (CTV) CT, whereas those contoured with the aid of PET-CT were GTV PET and CTV PET. PET-CT contours were used for actual treatment delivery. Test treatment plans were generated based on the CT-alone volumes and applied to the final PET-CT contours. PET-CT had an impact if the test plans failed department quality assurance guidelines. For each patient, the dose to critical structures and any changes in the treatment plan were recorded.ResultsEighty patients (49 HNSCC and 31 NSCLC) were analyzed. PET-CT impacted 42.9% of HNSCC cases and 45.2% of NSCLC cases. On average, PET-CT volumes were significantly larger than CT-alone volumes for HNSCC cases (P < .01) but not for NSCLC cases (P = .29). For organs at risk, no statistically significant differences were noted, with the exception of mean parotid dose for the right and left parotids (P = .0137and P = .0330, respectively).ConclusionsInterim analysis of data found that the use of PET-CT in the radiation therapy planning process impacted CTV selection, resulting in a major change in radiation therapy plans in 43.7% (HNSCC 42.9% and NSCLC 45.2%) of patients.     

Multi-modality cardiac image computing: A survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities.This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.     

Staging of colon cancer: whole-body MRI vs. whole-body PET-CT—initial clinical experience

Background  To assess the accuracy of whole-body MR imaging (WB-MRI) in comparison with whole-body [18^F]-2-fluoro-2-deoxy-d-glucose (FDG) PET-CT in staging patients with diagnosed colorectal carcinoma (CRC). Methods  Twenty consecutive patients with previously diagnosed CRC underwent WB-MRI (3T) and PET-CT for staging of lymph node (N) and distant metastases (M). Evaluation was done according to the American Joint Committee on Cancer Staging Criteria. MR images were evaluated by two radiologists while PET-CT images by one radiologist and one nuclear medicine physician. Histology and/or a clinical follow-up of 3–6 months served as standard of reference. Results  Lymph node involvement was determined in 10/20 cases as N-positive in WB-MRI and in 15/20 in PET-CT. M-stage was evaluated for liver metastases (27 lesions in 15 patients with WB-MRI, 23/15 patients with PET-CT), lung (19/5 patients with WB-MRI, 25/7 patients with PET-CT), and bone (9/3 patients with WB-MRI, 9/3 patients with PET-CT). Two patients showed peritoneal implants and three patients demonstrated local recurrence at the surgery site on both modalities. No brain metastases were found. Conclusions  WB-MRI is a feasible method for examining colon cancer patients but cannot displace the present role of PET-CT.     

Designing user interfaces to enhance human interpretation of medical content-based image retrieval: application to PET-CT images

Purpose Content-based image retrieval (CBIR) in medicine has been demonstrated to improve evidence-based diagnosis, education, and teaching. However, the low clinical adoption of CBIR is partially because the focus of most studies has been the development of feature extraction and similarity measurement algorithms with limited work on facilitating better understanding of the similarity between complex volumetric and multi-modality medical images. In this paper, we present a method for defining user interfaces (UIs) that enable effective human user interpretation of retrieved images. Methods We derived a set of visualisation and interaction requirements based on the characteristics of modern volumetric medical images. We implemented a UI that visualised multiple views of a single image, displayed abstractions of image data, and provided access to supplementary non-image data. We also defined interactions for refining the search and visually indicating the similarities between images. We applied the UI for the retrieval of multi-modality positron emission tomography and computed tomography (PET-CT) images. We conducted a user survey to evaluate the capabilities of our UI. Results Our proposed method obtained a high rating ( $\ge $ 4 out of 5) in the majority of survey questions. In particular, the survey responses indicated the UI presented all the information necessary to understand the retrieved images, and did so in an intuitive manner. Conclusion Our proposed UI design improved the ability of users to interpret and understand the similarity between retrieved PET-CT images. The implementation of CBIR UIs designed to assist human interpretation could facilitate wider adoption of medical CBIR systems.     

Imaging of peritoneal carcinomatosis with FDG PET-CT: diagnostic patterns,case examples and pitfalls

Early diagnosis of peritoneal spread in malignant disease is essential to prevent unnecessary laparotomies and to select the patients in whom complete cytoreduction is feasible. Although anatomic imaging is the mainstay for evaluating peritoneal seeding, small neoplastic implants can be difficult to detect with CT and MR imaging. FDG PET-CT has the potential to improve detection of peritoneal metastases as lesion conspicuity is high at PET due to low background activity and fused PET-CT offers the combined benefits of anatomic and functional imaging. Correlation of uptake modalities with the pathogenesis of intraperitoneal spread of malignancies, provides a rational system of analysis and is essential to define disease. Distinct patterns appear to predict the presence of either nodular or diffuse peritoneal pathology. Main pitfalls are related to normal physiologic activity in bowel loops and blood vessels or focal retained activity in ureters and urinary bladder. PET-CT is most suitable in patients with high tumor markers and negative or uncertain conventional imaging data and in selecting patients for complete cytoreduction. FDG PET-CT adds to conventional imaging in the detection and staging of peritoneal carcinomatosis and is a useful diagnostic tool in monitoring response to therapy and in long term follow-up.     

Accurate Preoperative Staging of Gastric Cancer with Combined Endoscopic Ultrasonography and PET-CT

Accurate staging of gastric cancer is helpful to determine the most appropriate therapy, but no staging modality has been accepted as the standard. Objective is to evaluate the usefulness of endoscopic ultrasonograph (EUS) combined with position emission tomography and computed tomograph (PET-CT) in gastric cancer staging. A total of 124 patients confirmed with gastric cancer were subjected to staging with EUS and PET-CT scanning. The detection rate of primary tumor was 99.2% by combination use of two modalities and 97.6% by EUS alone (p = 0.6219), but the detection rate was 90.3% (112 of 124) by PET-CT alone (compared with the combination, p = 0.0027; compared with EUS alone, p = 0.0299). The locoregional lymph node invasion was identified in 84/124 (67.7%) by combined PET-CT and EUS, which was obviously higher than that with EUS (52.4%) or PET-CT (43.5%) alone (p = 0.0194 and p = 0.0002, respectively). There was no statistical difference in identification of celiac axis lymph node metastasis among three methods, but the combined examination or PET-CT alone was more effective than EUS alone in the detection of distant metastases (all p < 0.01). Furthermore, the combined EUS and PET-CT was more optimal than using EUS or PET-CT alone in the accurate T and N staging and the effect on change of treatment. The present study indicates that the combination of EUS and PET-CT is an ideal modality in the preoperative staging of gastric cancer and it provides beneficial guidance for the treatment of gastric cancer.     

PET组合融合成像在分化型甲状腺癌复发和转移中的应用进展

甲状腺癌是最常见的内分泌系统恶性肿瘤之一，其中分化型甲状腺癌约占甲状腺癌发病率的90%以上，预后良好。但中国甲状腺癌患者5年相对生存率与一些发达国家差距较大。早期、准确地发现复发性疾病并应用适当的治疗策略，可改善复发性疾病患者的预后。因此，早期发现这些病人的复发和转移是至关重要的。随着诊断技术从系统向分子水平的过渡，多模态分子成像的作用越来越重要。PET能提供肿瘤细胞的功能学信息，而CT、MRI则能提供肿瘤的解剖学信息。功能成像技术和解剖学成像技术相结合能够实现优势互补，对于疾病复发和转移的诊断意义重大。随着近年来PET-CT在分化型甲状腺癌诊断、分期、疗效及预后评估上都较常规影像学检查更具优势，而PET-MRI是继PET-CT之后又一项优秀的多模态成像技术，因其软组织高分辨率和多序列多参数成像特性，所发挥的作用也愈发重要。因此，本文就PET-CT和PET-MRI在分化型甲状腺癌术后复发/转移中临床应用及未来前景进行综述。      

MGN-Net: A multi-view graph normalizer for integrating heterogeneous biological network populations

With the recent technological advances, biological datasets, often represented by networks (i.e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity. Although modern network science opens new frontiers of analyzing connectivity patterns in such datasets, we still lack data-driven methods for extracting an integral connectional fingerprint of a multi-view graph population, let alone disentangling the typical from the atypical variations across the population samples. We present the multi-view graph normalizer network (MGN-Net²), a graph neural network based method to normalize and integrate a set of multi-view biological networks into a single connectional template that is centered, representative, and topologically sound. We demonstrate the use of MGN-Net by discovering the connectional fingerprints of healthy and neurologically disordered brain network populations including Alzheimer’s disease and Autism spectrum disorder patients. Additionally, by comparing the learned templates of healthy and disordered populations, we show that MGN-Net significantly outperforms conventional network integration methods across extensive experiments in terms of producing the most centered templates, recapitulating unique traits of populations, and preserving the complex topology of biological networks. Our evaluations showed that MGN-Net is powerfully generic and easily adaptable in design to different graph-based problems such as identification of relevant connections, normalization and integration.     

Breast lesion co-localisation between X-ray and MR images using finite element modelling

This paper presents a novel X-ray and MR image registration technique based on individual-specific biomechanical finite element (FE) models of the breasts. Information from 3D magnetic resonance (MR) images was registered to X-ray mammographic images using non-linear FE models subject to contact mechanics constraints to simulate the large compressive deformations between the two imaging modalities. A physics-based perspective ray-casting algorithm was used to generate 2D pseudo-X-ray projections of the FE-warped 3D MR images. Unknown input parameters to the FE models, such as the location and orientation of the compression plates, were optimised to provide the best match between the pseudo and clinical X-ray images. The methods were validated using images taken before and during compression of a breast-shaped phantom, for which 12 inclusions were tracked between imaging modalities. These methods were then applied to X-ray and MR images from six breast cancer patients. Error measures (such as centroid and surface distances) of segmented tumours in simulated and actual X-ray mammograms were used to assess the accuracy of the methods. Sensitivity analysis of the lesion co-localisation accuracy to rotation about the anterior–posterior axis was then performed. For 10 of the 12 X-ray mammograms, lesion localisation accuracies of 14 mm and less were achieved. This analysis on the rotation about the anterior–posterior axis indicated that, in cases where the lesion lies in the plane parallel to the mammographic compression plates, that cuts through the nipple, such rotations have relatively minor effects. This has important implications for clinical applicability of this multi-modality lesion registration technique, which will aid in the diagnosis and treatment of breast cancer.     

Semi-Supervised hyperspectral image classification using local low-rank representation

In the area of hyperspectral image (HSI) classification, graph-based semi-supervised learning (SSL) has been proved to be highly effective. Constructing a proper graph is critical for graph-based SSL tasks. In HSI, spectral distance is widely used to calculate the weight of graph edge, though it can be influenced by noise and outliers. Meanwhile, links among all the data points are incorporated in the graph, including those from different subspaces. Thus the constructed graph might contain incorrect information. In this letter, a novel semi-supervised HSI classification method using local low-rank representation (SL²R) is proposed. Edge weight calculation will not be affected by noise or outliers thanks to the robustness of low-rank representation (LRR). Since each graph is constructed at local level, where pixels are basically embedded in the same subspace, links among uncorrelated pixels can be removed. Moreover, spatial context is naturally characterized by low-rank constraint on adjacent pixels. Experimental results on two data sets (Indian Pines and Botswana) confirm the effectiveness of the proposed method.     

Brain multigraph prediction using topology-aware adversarial graph neural network

Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches.     

Hybrid SPECT-CT and PET-CT: Current Concepts and Developments

Cardiac hybrid imaging combines different modalities in order to obtain complementary anatomical and functional information in a single imaging study. Coronary CT angiography (CTA) and myocardial perfusion imaging with single photon emission computed tomography (SPECT) or positron emission tomography (PET) are established noninvasive modalities for the diagnosis of coronary artery disease (CAD). Hybrid SPECT-CT or PET-CT is a promising tool for evaluation of CAD since it allows visualization of coronary atherosclerotic lesions and their consequences on myocardial blood flow in a single study. This appears to offer superior diagnostic accuracy for the detection of flow-limiting stenosis in patients with intermediate risk for CAD as compared with stand-alone imaging, especially by improving the positive predictive value. This article will review the concepts and currently available clinical experiences from cardiac hybrid imaging as well as discuss potential future applications.     

Joint segmentation of anatomical and functional images: Applications in quantification of lesions from PET,PET-CT,MRI-PET,and MRI-PET-CT images

We present a novel method for the joint segmentation of anatomical and functional images. Our proposed methodology unifies the domains of anatomical and functional images, represents them in a product lattice, and performs simultaneous delineation of regions based on random walk image segmentation. Furthermore, we also propose a simple yet effective object/background seed localization method to make the proposed segmentation process fully automatic. Our study uses PET, PET-CT, MRI-PET, and fused MRI-PET-CT scans (77 studies in all) from 56 patients who had various lesions in different body regions. We validated the effectiveness of the proposed method on different PET phantoms as well as on clinical images with respect to the ground truth segmentation provided by clinicians. Experimental results indicate that the presented method is superior to threshold and Bayesian methods commonly used in PET image segmentation, is more accurate and robust compared to the other PET-CT segmentation methods recently published in the literature, and also it is general in the sense of simultaneously segmenting multiple scans in real-time with high accuracy needed in routine clinical use.     

NG2 expression regulates vascular morphology and function in human brain tumours

Tumour angiogenesis is a tightly regulated process involving cross-talk between tumour cells and the host tissue. The underlying mechanisms that regulate such interactions remain largely unknown. NG2 is a transmembrane proteoglycan whose presence on transformed cells has been demonstrated to increase proliferation in vitro and angiogenesis in vivo. To study the effects of NG2 during tumour growth and progression, we engineered an NG2 positive human glioma cell line (U251-NG2) from parental NG2 negative cells (U251-WT) and implanted both cell types stereotactically into immunodeficient nude rat brains. The tumours were longitudinally monitored in vivo using multispectral MRI employing two differently sized contrast agents (Gd-DTPA-BMA and Gadomer) to assess vascular leakiness, vasogenic oedema, tumour volumes and necrosis. Comparisons of Gd-DTPA-BMA and Gadomer revealed differences in their spatial distribution in the U251-NG2 and U251-WT tumours. The U251-NG2 tumours exhibited a higher leakiness of the larger molecular weight Gadomer and displayed a stronger vasogenic oedema (69.9 +/- 15.2, P = 0.018, compared to the controls (10.7 +/- 7.7). Moreover, immunohistochemistry and electron microscopy revealed that the U251-NG2 tumours had a higher microvascular density (11.81 +/- 0.54; P = 0.0010) compared to controls (5.76 +/- 0.87), with vessels that displayed larger gaps between the endothelial cells. Thus, tumour cells can regulate both the function and structure of the host-derived tumour vasculature through NG2 expression, suggesting a role for NG2 in the cross-talk between tumour-host compartments.     

A stereotaxic MRI template set for the rat brain with tissue class distribution maps and co-registered anatomical atlas: application to pharmacological MRI

We describe a stereotaxic rat brain MRI template set with a co-registered digital anatomical atlas and illustrate its application to the analysis of a pharmacological MRI (phMRI) study of apomorphine. The template set includes anatomical images and tissue class probability maps for brain parenchyma and cerebrospinal fluid (CSF). These facilitate the use of standard fMRI software for spatial normalisation and tissue segmentation of rat brain data. A volumetric reconstruction of the Paxinos and Watson rat brain atlas is also co-localised with the template, enabling the atlas structure and stereotaxic coordinates corresponding to a feature within a statistical map to be interactively reported, facilitating the localisation of functional effects. Moreover, voxels falling within selected brain structures can be combined to define anatomically based 3D volumes of interest (VOIs), free of operator bias. As many atlas structures are small relative to the typical resolution of phMRI studies, a mechanism for defining composite structures as agglomerations of individual atlas structures is also described. This provides a simple and robust means of interrogating structures that are otherwise difficult to delineate and an objective framework for comparing and classifying compounds based on an anatomical profile of their activity. These developments allow a closer alignment of pre-clinical and clinical analysis techniques.     

18F-FDG PET-CT的主要局限性和相应处理措施

PET-CT融合了解剖图像和代谢图像而体现出巨大的优势,同时作为一种新型显像技术也有相应的局限性。本文分析了PET-CT的主要局限性以及相应的处理措施。     

Spatially Constrained Context-Aware Hierarchical Deep Correlation Filters for Nucleus Detection in Histology Images

Nucleus detection in histology images is a fundamental step for cellular-level analysis in computational pathology. In clinical practice, quantitative nuclear morphology can be used for diagnostic decision making, prognostic stratification, and treatment outcome prediction. Nucleus detection is a challenging task because of large variations in the shape of different types of nucleus such as nuclear clutter, heterogeneous chromatin distribution, and irregular and fuzzy boundaries. To address these challenges, we aim to accurately detect nuclei using spatially constrained context-aware correlation filters using hierarchical deep features extracted from multiple layers of a pre-trained network. During training, we extract contextual patches around each nucleus which are used as negative examples while the actual nucleus patch is used as a positive example. In order to spatially constrain the correlation filters, we propose to construct a spatial structural graph across different nucleus components encoding pairwise similarities. The correlation filters are constrained to act as eigenvectors of the Laplacian of the spatial graphs enforcing these to capture the nucleus structure. A novel objective function is proposed by embedding graph-based structural information as well as the contextual information within the discriminative correlation filter framework. The learned filters are constrained to be orthogonal to both the contextual patches and the spatial graph-Laplacian basis to improve the localization and discriminative performance. The proposed objective function trains a hierarchy of correlation filters on different deep feature layers to capture the heterogeneity in nuclear shape and texture. The proposed algorithm is evaluated on three publicly available datasets and compared with 15 current state-of-the-art methods demonstrating competitive performance in terms of accuracy, speed, and generalization.     

结直肠癌术前~(18)F-FDGPET-CT显像与术后病理分期比较分析

目的探讨18F-FDP PET-CT对结直肠癌术前TNM分期的价值。方法回顾总结术前行PET-CT的30例(33例次)结直肠癌患者,将PET-CT结果与术后手术病理分期结果对比分析。结果检测直肠癌原发灶灵敏度为96.97%;SUVmax为12.26±5.85(5.31²8.32)。N分期的灵敏度为88.23%;特异度为69.23%,共提示转移性淋巴结69枚,SUVmax为2.526±2.074(0.2728.32)。N分期的灵敏度为88.23%;特异度为69.23%,共提示转移性淋巴结69枚,SUVmax为2.526±2.074(0.27¹1.70),淋巴结总检出率为75%,一致率为80%。PET-CT诊断远处转移灶的灵敏度100%、特异度100%、准确性为100%,SUVmax值6.15±3.63(1.8311.70),淋巴结总检出率为75%,一致率为80%。PET-CT诊断远处转移灶的灵敏度100%、特异度100%、准确性为100%,SUVmax值6.15±3.63(1.83¹5.84)。结论 18F-FDP PET-CT与术后病理比较对结直肠癌进行T分期较为困难,但对于N、M分期有较好的一致性。     

Sonographie in der manuellen Medizin im Kontext osteopathischer viszerofaszialer Techniken

Ultrasonographic imaging of abdominal organs and structures of connective tissue as well as the surrounding muscles enables spatial real-time imaging of organ mobility and the mobility of the anatomical environment by justifiable technical expenditure and without X-rays, especially in relation to breathing and the structure and contractile state of abdominal muscles. Furthermore it allows observation of peristalsis and the filling state of hollow organs. The visualization of topographic relationships and the variation during breathing, as well as before, after and somewhat limited during therapy, has become practically possible and is markedly simplified in comparison to X-ray, computed tomography and magnetic resonance examinations. The methodology of examination is principally the same as conventional ultrasonography although the interpretation focuses on some criteria not usually used in conventional ultrasonographic diagnostics. Because of the permanent mobility of organs, muscles and connective tissue during breathing, peristalsis, body movements and changes of body position, the online version of this paper also contains some essential video clips. These provide a more precise impression of the variability of anatomical structures.