Medical Image Visualization Research Articles

Enhancing Text-to-Image Generation: Integrating CLIP and Diffusion Models for Improved Visual Accuracy and Semantic Consistency

Abstract. Text-to-Image (T2I) generation focuses on producing images that precisely match given textual descriptions by combining techniques from computer vision and natural language processing (NLP). Existing studies have shown an innovative approach to enhance T2I generation by integrating Contrastive Language-Image Pretraining (CLIP) embeddings with a Diffusion Model (DM). The method involves initially extracting rich and meaningful text embeddings using CLIP, which are then transformed into corresponding images. These images are progressively refined through an iterative denoising process enabled by diffusion models. Comprehensive experiments conducted on the MS-COCO dataset validate the proposed method, demonstrating significant improvements in image fidelity and the alignment between text and images. When compared to traditional models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which often struggle with maintaining both visual quality and semantic accuracy, this hybrid model shows superior performance. Future research could explore optimizing hybrid models further and applying T2I technology to specialized fields, such as medical imaging and scientific visualization, expanding its potential use cases.

Evaluation of monocular and binocular contrast perception on virtual reality head-mounted displays.

Visualization of medical images on a virtual reality (VR) head-mounted display (HMD) requires binocular fusion of a stereoscopic pair of graphical views. However, current image quality assessment on VR HMDs for medical applications has been primarily limited to time-consuming monocular optical bench measurement on a single eyepiece. As an alternative to optical bench measurement to quantify the image quality on VR HMDs, we developed a WebXR test platform to perform contrast perceptual experiments that can be used for binocular image quality assessment. We obtained monocular and binocular contrast sensitivity responses (CSRs) from participants on a Meta Quest 2 VR HMD using varied interpupillary distance (IPD) configurations. The perceptual result shows that contrast perception on VR HMDs is primarily affected by optical aberration of the VR HMD. As a result, monocular CSR degrades at a high spatial frequency greater than 4 cycles per degree when gazing at the periphery of the display field of view, especially for mismatched IPD settings consistent with optical bench measurements. On the contrary, binocular contrast perception is dominated by the monocular view with superior image quality measured by the contrast. We developed a test platform to investigate monocular and binocular contrast perception by performing perceptual experiments. The test method can be used to evaluate monocular and/or binocular image quality on VR HMDs for potential medical applications without extensive optical bench measurements.

A Generative Adversarial Network for Upsampling of Direct Volume Rendering Images

AbstractDirect volume rendering (DVR) is an important tool for scientific and medical imaging visualization. Modern GPU acceleration has made DVR more accessible; however, the production of high‐quality rendered images with high frame rates is computationally expensive. We propose a deep learning method with a reduced computational demand. We leveraged a conditional generative adversarial network (cGAN) to upsample DVR images (a rendered scene), with a reduced sampling rate to obtain similar visual quality to that of a fully sampled method. Our dvrGAN is combined with a colour‐based loss function that is optimized for DVR images where different structures such as skin, bone, etc. are distinguished by assigning them distinct colours. The loss function highlights the structural differences between images, by examining pixel‐level colour, and thus helps identify, for instance, small bones in the limbs that may not be evident with reduced sampling rates. We evaluated our method in DVR of human computed tomography (CT) and CT angiography (CTA) volumes. Our method retained image quality and reduced computation time when compared to fully sampled methods and outperformed existing state‐of‐the‐art upsampling methods.

Attention-driven visual emphasis for medical volumetric image visualization

AbstractDirect volume rendering (DVR) is a commonly utilized technique for three-dimensional visualization of volumetric medical images. A key goal of DVR is to enable users to visually emphasize regions of interest (ROIs) which may be occluded by other structures. Conventional methods for ROIs visual emphasis require extensive user involvement for the adjustment of rendering parameters to reduce the occlusion, dependent on the user’s viewing direction. Several works have been proposed to automatically preserve the view of the ROIs by eliminating the occluding structures of lower importance in a view-dependent manner. However, they require pre-segmentation labeling and manual importance assignment on the images. An alternative to ROIs segmentation is to use ‘saliency’ to identify important regions. This however lacks semantic information and thus leads to the inclusion of false positive regions. In this study, we propose an attention-driven visual emphasis method for volumetric medical image visualization. We developed a deep learning attention model, termed as focused-class attention map (F-CAM), trained with only image-wise labels for automated ROIs localization and importance estimation. Our F-CAM transfers the semantic information from the classification task for use in the localization of ROIs, with a focus on small ROIs that characterize medical images. Additionally, we propose an attention compositing module that integrates the generated attention map with transfer function within the DVR pipeline to automate the view-dependent visual emphasis of the ROIs. We demonstrate the superiority of our method compared to existing methods on a multi-modality PET-CT dataset and an MRI dataset.

Methodology for integrating MCNP6 Monte Carlo simulation into PLUNC for prostate brachytherapy planning

Brachytherapy is a well-established therapeutic technique for treating tumours involving the placement of radioactive encapsulated sources close to or in direct contact with a tumour. Considering this type of treatments, the purpose of this work is to incorporate Monte Carlo (MC) simulation into brachytherapy treatment planning. This approach allows for precise estimation of dose distribution in target organs as well as in Organs At Risk (OAR), due to the accuracy of the MC methods in describing dose deposition.To carry out this work, the medical image visualization and dose analysis tools provided by the open-source planner PlanUNC (PLUNC) were utilized. Additionally, a set of programs was developed which automatize the main steps of a treatment planning segmentation, from DICOM images, exporting geometries determining materials and seeds positions, creating an input file for the simulation code (MCNP6.2) and, finally, reading and visualization of the simulation results in the format of the used planning software. With this methodology, results of 3D dose distribution, isodoses curves, Dose-Volume Histograms (DVH), and absorbed dose in OAR were obtained. Thus, the integration of the MC simulation into PLUNC for brachytherapy treatments, has demonstrate its utility for this purpose, showing adaptability to different anatomical geometries and extensibility to various types of treatments.

Fast Semi-Supervised t-SNE for Transfer Function Enhancement in Direct Volume Rendering-Based Medical Image Visualization

Magnetic resonance imaging and computed tomography produce three-dimensional volumetric medical images. While a scalar value represents each individual volume element, or voxel, volumetric data are characterized by features derived from groups of neighboring voxels and their inherent relationships, which may vary depending on the specific clinical application. Labeled samples are also required in most applications, which can be problematic for large datasets such as medical images. We propose a direct volume rendering (DVR) framework based on multi-scale dimensionality reduction neighbor embedding that generates two-dimensional transfer function (TF) domains. In this way, we present FSS.t-SNE, a fast semi-supervised version of the t-distributed stochastic neighbor embedding (t-SNE) method that works over hundreds of thousands of voxels without the problem of crowding and with better separation in a 2D histogram compared to traditional TF domains. Our FSS.t-SNE scatters voxels of the same sub-volume in a wider region through multi-scale neighbor embedding, better preserving both local and global data structures and allowing for its internal exploration based on the original features of the multi-dimensional space, taking advantage of the partially provided labels. Furthermore, FSS.t-SNE untangles sample paths among sub-volumes, allowing us to explore edges and transitions. In addition, our approach employs a Barnes–Hut approximation to reduce computational complexity from O(N2) (t-SNE) to O(NlogN). Although we require the additional step of generating the 2D TF domain from multiple features, our experiments show promising performance in volume segmentation and visual inspection.

Evaluation of the correlation between trigeminal nerve atrophy and trigeminal neuralgia using multimodal image fusion: A single-center retrospective study

Background3D-Slicer is an open-source medical image processing and visualization software. In the surgical treatment of trigeminal neuralgia, it is commonly used to predict the responsible vessels. However, there are few reports on the use of 3D-Slicer software to quantitatively measure the bilateral trigeminal nerve volume in patients with primary trigeminal neuralgia (PTN) based on the three-dimensional images. Therefore, this study aims to explore the role of three-dimensional fused images processed by 3D-Slicer in the evaluation of trigeminal nerve atrophy, providing an objective basis for the diagnosis of PTN. Methods57 PTN patients who underwent microvascular decompression (MVD) or percutaneous balloon compression (PBC) surgery in Hebei general hospital between January 2020 and April 2023 were included. Additionally, 30 patients with facial spasms(HFS) were included as a control group. All patients underwent 3D-TOF-MRA and 3D-FIESTA sequence examinations. Comparisons of bilateral trigeminal nerve volumes within and between groups were conducted by performing image fusion using 3D-slicer. ResultsThe volume of the affected trigeminal nerve in the MVD group (33.96 mm³±12.61 mm³) and PBC group (23.05 mm³±7.71 mm³) was smaller than that of the unaffected trigeminal nerve in the MVD group (39.61 mm³±12.83 mm³) and PBC group (26.14 mm³±6.42 mm³), as well as the average volume of the trigeminal nerve in the control group (40.27 mm³±10.25 mm³) (P<0.05). The differences in bilateral trigeminal ganglion volume (∆V) was significant between the MVD group (∆V=23.59 %±14.32 %) and the control group (∆V=14.64 %±10.00 %) (P<0.05). There was no statistical difference in the trigeminal nerve volume difference between the MVD group (∆V=23.59 %±14.32 %) and the PBC group (∆V=26.52 %±15.00 %) (P>0.05). ConclusionTrigeminal nerve atrophy is correlated with primary trigeminal neuralgia. 3D-slicer software can quantitatively measure trigeminal nerve volume and assist in the diagnosis of primary trigeminal neuralgia based on the difference in bilateral trigeminal nerve volumes. However, trigeminal nerve atrophy is not associated with postoperative pain recurrence in patients.

System for medical image visualization: SYFANMI

System for medical image visualization: SYFANMI

Enabling reliable usability assessment and comparative analysis of medical software: a comprehensive framework for multimodal biomedical imaging platforms

PurposeA literature review reveals that, at the moment, all usability tests for Software as a Medical Device (SaMD) are designed in compliance with international standards but it also reveals a lack of formalization in the implementation and administration of such usability tests, which prevents the comparison of results from different tests for the same class of SaMD. This study aims to provide a reproducible usability testing framework for SaMD to establish a standardized protocol which can ensure repeatability and comparisons of similar SaMD for the visualization of medical images and data.MethodsThe devised protocol aligns with international standards and literature recommendations for usability and human factors engineering. It encompasses participant selection, testing environments, equipment setup for various testing methods (HDMI vs. wireless), and hardware interfaces (keyboard/mouse vs. touchscreen), as well as the roles of the required testers. The protocol consists of two distinct sections: exploratory tasks and specific scenarios, to assess software functions and real-life tasks, respectively. Effectiveness and efficiency are evaluated using video analysis and a custom Stopwatch software, while user satisfaction is measured through post-test questionnaires.ResultsThe usability testing protocol was applied to a Multimodal Biomedical Imaging Platform All-in-One software developed by Imaginalis S.r.l. (Sesto Fiorentino, Italy) for validation. The results of the usability testing protocol applied to the case-study software demonstrate good values of software’s effectiveness and efficiency, along with user satisfaction supporting the prior heuristic evaluation. The outcomes confirm the robustness, applicability, and reproducibility of the usability testing protocol, aligning with best practices.ConclusionsThe proposed usability testing framework enables reliable usability assessment and comparative analysis of medical software. Furthermore, the obtained results can serve as a reference for assessing other biomedical imaging platforms under development or ready for release.

Associations Between Visceral Fat, Abdominal Muscle, and Coronary Artery Calcification: A Cross-Sectional Analysis of the Multi-Ethnic Study of Atherosclerosis

The associations of body composition components, including muscle and adipose tissue, and markers of subclinical coronary artery disease are unclear. We examined the relation between abdominal computed tomography (CT)-derived measures of the area and density of fat and muscle with coronary artery calcification (CAC), using data from the Multi-Ethnic Study of Atherosclerosis (MESA). A total of 1,974 randomly selected MESA participants free of coronary heart disease underwent abdominal CT scans at examinations 2 or 3, with the resulting images interrogated for abdominal body composition. Using 6 cross-sectional slices spanning L2 to L5, the Medical Imaging Processing Analysis and Visualization software was used to determine abdominal muscle and fat composition using appropriate Hounsfield units ranges. CT chest scans were used to obtain CAC scores, calculated using the Agatston method and spatially weighted calcium score. Multivariable linear and logistic regression analyses were performed to assess the relation between abdominal visceral fat and muscle area and density to prevalent CAC. A total of 1,089 participants had a CAC >0, with an average CAC score of 310. In the fully adjusted model, for every 10-cm2 increase in visceral fat area, the likelihood of having a CAC greater than 0 increased by 0.60% (p <0.001). In the minimally adjusted model, abdominal muscle area was significantly associated with CAC >0, which became nonsignificant in the fully adjusted model. For the density of visceral fat, every 1-Hounsfield unit increase (less lipid-dense fat tissue), the likelihood of having a CAC score >0 decreased by 0.29% (p <0.05). No significant relation was observed between density of abdominal muscle and CAC >0. A greater area and higher lipid density of abdominal visceral fat were associated with an increased likelihood of having CAC, whereas there was no significant relation between abdominal muscle area or density and CAC. The quantity and the quality of fat have associations, with an important marker of subclinical atherosclerosis, CAC, and their significance with respect to cardiovascular outcomes, require further evaluation.

Ultrafast Response Organic Photoswitch Materials and Their Application in Volumetric 3D Display.

Volumetric three-dimensional (3D) display technology based on static screens is a crucial branch of 3D displays. The essential component in volumetric 3D displays is selectively excitable display media that can generate voxels at any position. Here, we synthesized a series of organic photoswitch materials to meet the specific requirements of 3D display mediums. In these photoswitch solutions, voxels are activated ultrafast within tens of picoseconds at the intersection of two control lasers and faded rapidly within tens of milliseconds when switching light is turned off. An experimental volumetric 3D display system utilizing an organic photoswitch solution as a screen is demonstrated. The system not only achieves a dynamic 3D display but also enables 360° viewing. The volumetric 3D display system can display true 3D images without auxiliary glasses and is expected to be applicable in fields as diverse as virtual reality, medical imaging, architectural design, and military visualization.

Modern Image-Guided Surgery: A Narrative Review of Medical Image Processing and Visualization.

Medical image analysis forms the basis of image-guided surgery (IGS) and many of its fundamental tasks. Driven by the growing number of medical imaging modalities, the research community of medical imaging has developed methods and achieved functionality breakthroughs. However, with the overwhelming pool of information in the literature, it has become increasingly challenging for researchers to extract context-relevant information for specific applications, especially when many widely used methods exist in a variety of versions optimized for their respective application domains. By being further equipped with sophisticated three-dimensional (3D) medical image visualization and digital reality technology, medical experts could enhance their performance capabilities in IGS by multiple folds. The goal of this narrative review is to organize the key components of IGS in the aspects of medical image processing and visualization with a new perspective and insights. The literature search was conducted using mainstream academic search engines with a combination of keywords relevant to the field up until mid-2022. This survey systemically summarizes the basic, mainstream, and state-of-the-art medical image processing methods as well as how visualization technology like augmented/mixed/virtual reality (AR/MR/VR) are enhancing performance in IGS. Further, we hope that this survey will shed some light on the future of IGS in the face of challenges and opportunities for the research directions of medical image processing and visualization.

Abstract 12439: Abdominal Visceral Fat Area and Density Are Associated With Coronary Artery Calcification: A Cross-Sectional Analysis of the Multi-Ethnic Study of Atherosclerosis

Introduction: The associations of body composition components, including muscle and adipose tissue, and markers of subclinical coronary artery disease are unclear. We examined the relationship between abdominal CT-derived measures of the area and density of fat and muscle with coronary artery calcification (CAC), using data from the Multi-Ethnic Study of Atherosclerosis (MESA). Methods: A randomly selected total of 1,974 MESA participants free of coronary heart disease underwent abdominal CT scans at exams 2 or 3, with the resulting images interrogated for abdominal body composition. Using six cross-sectional slices spanning L2-L5, Medical Imaging Processing Analysis and Visualization (MIPAV) software was used to determine abdominal muscle and fat composition by utilizing appropriate Hounsfield units (HU) ranges. CT chest scans obtained during the exam 1 were used to obtain CAC scores, calculated using the Agatston method and spatially weighted calcium score (SWCS). Multivariable linear regression analysis was performed to assess the relationship between both abdominal visceral fat and muscle area and density to prevalent CAC. Results: A total of 1,089 participants had a CAC&gt;0, with an average CAC score of 310. In the fully adjusted model, for every 10 cm 2 increase in visceral fat area, the likelihood of having a CAC &gt;0 increased by 0.60% (p&lt;0.001). In the minimally adjusted model, abdominal muscle area was significantly associated with CAC&gt;0, which became non-significant in the fully adjusted model. For density of visceral fat , every 1 HU increase (less lipid dense fat tissue), the likelihood of having a CAC score&gt;0 decreased by 0.29% (p&lt;0.05). No significant relationship was observed between density of abdominal muscle and CAC&gt;0. Conclusion: Greater area and higher lipid density of abdominal visceral fat was associated with an increased likelihood of having CAC, while there was no significant relationship between abdominal muscle area or density and CAC. Both the quantity and the quality of fat have associations with an important marker of subclinical atherosclerosis, CAC, and their significance with respect to cardiovascular outcomes requires further evaluation.

Connect Brain, a Mobile App for Studying Depth Perception in Angiography Visualization: Gamification Study

Background One of the bottlenecks of visualization research is the lack of volunteers for studies that evaluate new methods and paradigms. The increased availability of web-based marketplaces, combined with the possibility of implementing volume rendering, a computationally expensive method, on mobile devices, has opened the door for using gamification in the context of medical image visualization studies. Objective We aimed to describe a gamified study that we conducted with the goal of comparing several cerebrovascular visualization techniques and to evaluate whether gamification is a valid paradigm for conducting user studies in the domain of medical imaging. Methods The study was implemented in the form of a mobile game, Connect Brain, which was developed and distributed on both Android (Google LLC) and iOS (Apple Inc) platforms. Connect Brain features 2 minigames: one asks the player to make decisions about the depth of different vessels, and the other asks the player to determine whether 2 vessels are connected. Results The gamification paradigm, which allowed us to collect many data samples (5267 and 1810 for the depth comparison and vessel connectivity tasks, respectively) from many participants (N=111), yielded similar results regarding the effectiveness of visualization techniques to those of smaller in-laboratory studies. Conclusions The results of our study suggest that the gamification paradigm not only is a viable alternative to traditional in-laboratory user studies but could also present some advantages.

Overview of modern digital diagnostic image markup tools

Background. In modern medicine, artificial intelligence algorithms are being actively introduced, for testing and training of which a large amount of labeled datasets is required. Software for labeling (annotation) of digital diagnostic images is a necessary element when creating datasets.&#x0D; Aim. To review the capabilities and comparative analysis of the functionality of the most common available software for annotating digital diagnostic images.&#x0D; Material and methods. Five free and one commercial software product for annotation of digital diagnostic images participated in the comparative analysis. When testing the marking process on medical images for several target types of pathology, the usability of the graphical user interface and functionality was evaluated. The functionality of the software products has been tested by radiologists with over 5 years of experience. In addition, a review of semi-automatic segmentation methods implemented in the studied software products was carried out. As initial medical images, datasets of computed tomography studies obtained from open sources, were used.&#x0D; Results. Comparison of software functionality for annotation of digital diagnostic images was made: supported formats; loading, presenting and saving original images and annotation data; the possibility of visualization of medical images; annotation tools. The algorithms underlying semi-automatic segmentation methods were studied and systematized. The requirements for the basic functionality of software for labeling digital diagnostic images have been formulated. The results obtained create a systematic basis for developing recommendations for radiologists on the choice and use of digital diagnostic image marking tools.&#x0D; Conclusion. The most complete functionality in the field of segmentation of digital diagnostic images among the considered free software has 3D Slicer; in the case of annotation for detection tasks, it is convenient to use the Supervisely, CVAT platforms; for automatic segmentation of some types of pathology and organs, 3D Slicer extensions and ready-made models in Medseg can be used.

Virtual and Augmented Reality in Interventional Radiology: Current Applications, Challenges, and Future Directions

Virtual reality (VR) and augmented Reality (AR) are emerging technologies with the potential to revolutionize Interventional radiology (IR). These innovations offer advantages in patient care, interventional planning, and educational training by improving the visualization and navigation of medical images. Despite progress, several challenges hinder their widespread adoption, including limitations in navigation systems, cost, clinical acceptance, and technical constraints of AR/VR equipment. However, ongoing research holds promise with recent advancements such as shape-sensing needles and improved organ deformation modeling. The development of deep learning techniques, particularly for medical imaging segmentation, presents a promising avenue to address existing accuracy and precision issues. Future applications of AR/VR in IR include simulation-based training, preprocedural planning, intraprocedural guidance, and increased patient engagement. As these technologies advance, they are expected to facilitate telemedicine, enhance operational efficiency, and improve patient outcomes, marking a new frontier in interventional radiology.

Online view enhancement for exploration inside medical volumetric data using virtual reality

Background and Objective:Medical image visualization is an essential tool for conveying anatomical information. Ray-casting-based volume rendering is commonly used for generating visualizations of raw medical images. However, exposing a target area inside the skin often requires manual tuning of transfer functions or segmentation of original images, as preset parameters in volume rendering may not work well for arbitrary scanned data. This process is tedious and unnatural. To address this issue, we propose a volume visualization system that enhances the view inside the skin, enabling flexible exploration of medical volumetric data using virtual reality. Methods:In our proposed system, we design a virtual reality interface that allows users to walk inside the data. We introduce a view-dependent occlusion weakening method based on geodesic distance transform to support this interaction. By combining these methods, we develop a virtual reality system with intuitive interactions, facilitating online view enhancement for medical data exploration and annotation inside the volume. Results:Our rendering results demonstrate that the proposed occlusion weakening method effectively weakens obstacles while preserving the target area. Furthermore, comparative analysis with other alternative solutions highlights the advantages of our method in virtual reality. We conducted user studies to evaluate our system, including area annotation and line drawing tasks. The results showed that our method with enhanced views achieved 47.73% and 35.29% higher accuracy compared to the group with traditional volume rendering. Additionally, subjective feedback from medical experts further supported the effectiveness of the designed interactions in virtual reality. Conclusions:We successfully address the occlusion problems in the exploration of medical volumetric data within a virtual reality environment. Our system allows for flexible integration of scanned medical volumes without requiring extensive manual preprocessing. The results of our user studies demonstrate the feasibility and effectiveness of walk-in interaction for medical data exploration.

Ensembled EfficientNetB3 architecture for multi-class classification of tumours in MRI images

Healthcare informatics is one of the major concern domains in the processing of medical imaging for the diagnosis and treatment of brain tumours all over the world. Timely diagnosis of abnormal structures in brain tumours helps the clinical applications, medicines, doctors etc. in processing and analysing the medical imaging. The multi-class image classification of brain tumours faces challenges such as the scaling of large dataset, training of image datasets, efficiency, accuracy etc. EfficientNetB3 neural network scales the images in three dimensions resulting in improved accuracy. The novel neural network framework utilizes the optimization of an ensembled architecture of EfficientNetB3 with U-Net for MRI images which applies a semantic segmentation model for pre-trained backbone networks. The proposed neural model operates on a substantial network which will adapt the robustness by capturing the extraction of features in the U-Net encoder. The decoder will be enabling pixel-level localization at the definite precision level by an average ensemble of segmentation models. The ensembled pre-trained models will provide better training and prediction of abnormal structures in MRI images and thresholds for multi-classification of medical image visualization. The proposed model results in mean accuracy of 99.24 on the Kaggle dataset with 3064 images with a mean Dice score coefficient (DSC) of 0.9124 which is being compared with two state-of-art neural models.

A Medical Image Visualization Technique Assisted with AI-Based Haptic Feedback for Robotic Surgery and Healthcare

A lesson learned during the pandemic is that social distancing saves lives. As it was shown recently, the healthcare industry is structured in a way that cannot protect medical staff from possible infectious diseases, such as COVID-19. Today’s healthcare services seem anachronistic and not convenient for both doctors and patients. Although there have been several advances in recent years, especially in developed countries, the need for a holistic change is imperative. Evidently, future technologies should be introduced in the health sector, where Virtual Reality, Augmented Reality, Artificial Intelligence, and Tactile Internet can have vast applications. Thus, the healthcare industry could take advantage of the great evolution of pervasive computing. In this paper, we point out the challenges from the current visualization techniques and present a novel visualization technique assisted with haptics which is enhanced with artificial intelligent algorithms in order to offer remote patient examination and treatment through robotics. Such an approach provides a more detailed method of medical image data visualization and eliminates the possibility of diseases spreading, while reducing the workload of the medical staff.

Applied Sciences—Special Issue on Emerging Techniques in Imaging, Modelling and Visualization for Cardiovascular Diagnosis and Therapy

Ongoing developments in computing and data acquisition, along with continuous advances in medical imaging technology, computational modelling, robotics and visualization have revolutionized many medical specialties and, in particular, diagnostic and interventional cardiology [...]