Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

International audienceThe January 2013 Special Issue of IEEE transactions on medical imaging discusses papers on medical imaging and image computing in computational physiology. Aslanid and co-researchers present an experimental technique based on stained micro computed tomography (CT) images to construct very detailed atrial models of the canine heart. The paper by Sebastian proposes a model of the cardiac conduction system (CCS) based on structural information derived from stained calf tissue. Ho, Mithraratne and Hunter present a numerical simulation of detailed cerebral venous flow. The third category of papers deals with computational methods for simulating medical imagery and incorporate knowledge of imaging physics and physiology/biophysics. The work by Morales showed how the combination of device modeling and virtual deployment, in addition to patient-specific image-based anatomical modeling, can help to carry out patient-specific treatment plans and assess alternative therapeutic strategies

Simulations of time harmonic blood flow in the Mesenteric artery: comparing finite element and lattice Boltzmann methods

<p>Abstract</p> <p>Background</p> <p>Systolic blood flow has been simulated in the abdominal aorta and the superior mesenteric artery. The simulations were carried out using two different computational hemodynamic methods: the finite element method to solve the Navier Stokes equations and the lattice Boltzmann method.</p> <p>Results</p> <p>We have validated the lattice Boltzmann method for systolic flows by comparing the velocity and pressure profiles of simulated blood flow between methods. We have also analyzed flow-specific characteristics such as the formation of a vortex at curvatures and traces of flow.</p> <p>Conclusion</p> <p>The lattice Boltzmann Method is as accurate as a Navier Stokes solver for computing complex blood flows. As such it is a good alternative for computational hemodynamics, certainly in situation where coupling to other models is required.</p

Wearable devices can predict the outcome of standardized 6-minute walk tests in heart disease

Wrist-worn devices with heart rate monitoring have become increasingly popular. Although current guidelines advise to consider clinical symptoms and exercise tolerance during decision-making in heart disease, it remains unknown to which extent wearables can help to determine such functional capacity measures. In clinical settings, the 6-minute walk test has become a standardized diagnostic and prognostic marker. We aimed to explore, whether 6-minute walk distances can be predicted by wrist-worn devices in patients with different stages of mitral and aortic valve disease. A total of n = 107 sensor datasets with 1,019,748 min of recordings were analysed. Based on heart rate recordings and literature information, activity levels were determined and compared to results from a 6-minute walk test. The percentage of time spent in moderate activity was a predictor for the achievement of gender, age and body mass index-specific 6-minute walk distances (p < 0.001; R2 = 0.48). The uncertainty of these predictions is demonstrated

Operator-dependent variability of angiography-derived fractional flow reserve and the implications for treatment

Aims To extend the benefits of physiologically-guided percutaneous coronary intervention to many patients, angiography-derived or ‘virtual’ fractional flow reserve (vFFR) has been developed, in which FFR is computed, based upon the images, instead of being measured invasively. The effect of operator experience with these methods upon vFFR accuracy remains unknown. We investigated variability in vFFR results based upon operator experience with image-based computational modelling techniques. Methods vFFR was computed using a proprietary method (VIRTUheart) from the invasive angiograms of patients with coronary artery disease. Each case was processed by an expert (>100 vFFR cases) and a non-expert (<20 vFFR cases) operator and results were compared. The primary outcome was the variability in vFFR between experts and non-experts and the impact this had upon treatment strategy (PCI vs conservative management). Results 231 vessels (199 patients) were processed. Mean non-expert and expert vFFRs were similar overall (0.76 (0.13) and 0.77 (0.16)) but there was significant variability between individual results (variability coefficient 12%, intra-class correlation coefficient 0.58), with only moderate agreement (κ = 0.46), and this led to a statistically significant change in management strategy in 27% of cases. Variability was significantly lower, and agreement higher, for expert operators; a change in their recommended management occurred in 10% of repeated expert measurements and 14% of inter-expert measurements. Conclusions vFFR results are influenced by operator experience of vFFR processing. This had implications for treatment allocation. These results highlight the importance of training and quality assurance to ensure reliable, repeatable vFFR results

Mitral regurgitation quantification by cardiac magnetic resonance imaging (MRI) remains reproducible between software solutions [version 3; peer review: 1 approved, 1 approved with reservations]

BACKGROUND: The reproducibility of mitral regurgitation (MR) quantification by cardiovascular magnetic resonance (CMR) imaging using different software solutions remains unclear. This research aimed to investigate the reproducibility of MR quantification between two software solutions: MASS (version 2019 EXP, LUMC, Netherlands) and CAAS (version 5.2, Pie Medical Imaging). METHODS: CMR data of 35 patients with MR (12 primary MR, 13 mitral valve repair/replacement, and ten secondary MR) was used. Four methods of MR volume quantification were studied, including two 4D-flow CMR methods (MRMVAV and MRJet) and two non-4D-flow techniques (MRStandard and MRLVRV). We conducted within-software and inter-software correlation and agreement analyses. RESULTS: All methods demonstrated significant correlation between the two software solutions: MRStandard (r=0.92, p<0.001), MRLVRV (r=0.95, p<0.001), MRJet (r=0.86, p<0.001), and MRMVAV (r=0.91, p<0.001). Between CAAS and MASS, MRJet and MRMVAV, compared to each of the four methods, were the only methods not to be associated with significant bias. CONCLUSIONS: We conclude that 4D-flow CMR methods demonstrate equivalent reproducibility to non-4D-flow methods but greater levels of agreement between software solutions

Share and enjoy: anatomical models database-generating and sharing cardiovascular model data using web services

Sharing data between scientists and with clinicians in cardiac research has been facilitated significantly by the use of web technologies. The potential of this technology has meant that information sharing has been routinely promoted through databases that have encouraged stakeholder participation in communities around these services. In this paper we discuss the Anatomical Model Database (AMDB) (Gianni et al. Functional imaging and modeling of the heart. Springer, Heidelberg, 2009; Gianni et al. Phil Trans Ser A Math Phys Eng Sci 368:3039–3056, 2010) which both facilitate a database-centric approach to collaboration, and also extends this framework with new capabilities for creating new mesh data. AMDB currently stores cardiac geometric models described in Gianni et al. (Functional imaging and modelling of the heart. Springer, Heidelberg, 2009), a number of additional cardiac models describing geometry and functional properties, and most recently models generated using a web service. The functional models represent data from simulations in geometric form, such as electrophysiology or mechanics, many of which are present in AMDB as part of a benchmark study. Finally, the heartgen service has been added for producing left or bi-ventricle models derived from binary image data using the methods described in Lamata et al. (Med Image Anal 15:801–813, 2011). The results can optionally be hosted on AMDB alongside other community-provided anatomical models. AMDB is, therefore, a unique database storing geometric data (rather than abstract models or image data) combined with a powerful web service for generating new geometric models

Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans

Expert recommendations on the assessment of wall shear stress in human coronary arteries : existing methodologies, technical considerations, and clinical applications

The aim of this manuscript is to provide guidelines for appropriate use of CFD to obtain reproducible and reliable wall shear stress maps in native and instrumented human coronary arteries. The outcome of CFD heavily depends on the quality of the input data, which include vessel geometrical data, proper boundary conditions, and material models. Available methodologies to reconstruct coronary artery anatomy are discussed in ‘Imaging coronary arteries: a brief review’ section. Computational procedures implemented to simulate blood flow in native coronary arteries are presented in ‘Wall shear stress in native arteries’ section. The effect of including different geometrical scales due to the presence of stent struts in instrumented arteries is highlighted in ‘Wall shear stress in stents’ section. The clinical implications are discussed in ‘Clinical applications’ section, and concluding remarks are presented in ‘Concluding remarks’ section