Search

Aims The purpose of the Multivessel TALENT trial is to compare clinical outcomes of the novel Supraflex Cruz stent with those of the SYNERGY stent in patients with three-vessel disease (3VD) undergoing state-of-the-art percutaneous coronary intervention (PCI). Methods and results In this prospective, randomised, 1:1 balanced, multicentre, open-label trial, 1,550 patients with de novo 3VD without left main disease will be assigned to the Supraflex Cruz or SYNERGY arm. The following treatment principles of "best practice" PCI will be applied: Heart Team consensus based on SYNTAX score II treatment recommendation, functional lesion evaluation by quantitative flow ratio (QFR), stent optimisation by intravascular imaging, optimal pharmacological treatment and prasugrel monotherapy. The primary endpoint is a non-inferiority comparison of the patient-oriented composite endpoint (POCE) of all-cause death, any stroke, any myocardial infarction, or any revascularisation, at 12 months post procedure. The powered secondary endpoint is a superiority comparison of the vessel-oriented composite endpoint (VOCE), defined as vessel-related cardiovascular death, vessel-related myocardial infarction, or clinically and physiologically indicated target vessel revascularisation, at 24 months. Conclusions The Multivessel TALENT trial will be evaluating a novel treatment strategy for complex coronary artery disease with state-of-the-art PCI based on angiography-derived QFR with novel ultra-thin Supraflex Cruz stents, compared with SYNERGY stents. Clinical Trial Registration URL: https://www.clinicaltrials.gov/ct2/show/NCT04390672. Unique Identifier: NCT04390672 Visual summary. Flow chart of the Multivessel TALENT trial. 3VD: three-vessel disease; QFR: quantitative flow ratio; IVUS: intravascular ultrasound; OCT: optical coherence tomography; CTO: chronic total occlusion; OMT: optimal medical therapy; POCE: patient-oriented composite endpoint; VOCE: vessel-oriented composite endpoint.

Bifurcation lesions represent one of the most challenging lesion subsets in interventional cardiology. The European Bifurcation Club (EBC) is an academic consortium whose goal has been to assess and recommend the appropriate strategies to manage bifurcation lesions. The quantitative coronary angiography (QCA) methods for the evaluation of bifurcation lesions have been subject to extensive research. Single-vessel QCA has been shown to be inaccurate for the assessment of bifurcation lesion dimensions. For this reason, dedicated bifurcation software has been developed and validated. These software packages apply the principles of fractal geometry to address the "step-down" in the bifurcation and to estimate vessel diameter accurately. This consensus update provides recommendations on the QCA analysis and reporting of bifurcation lesions based on the most recent scientific evidence from in vitro and in vivo studies and delineates future advances in the field of QCA dedicated bifurcation analysis.

Background and Purpose— Low wall shear stress (WSS) is an early marker in the development of vascular lesions. The present study aims to assess the relationship between diastolic and systolic WSS in the internal carotid artery and periventricular (PWML), deep white matter lesions, and cerebral infarcts (CI). Methods— Early, mid, and late diastolic and peak systolic WSS were derived from shear rate obtained by gradient echo phase contrast magnetic resonance sequences multiplied by individually modeled viscosity. PWML, deep white matter lesions, and CI were derived from proton density (PD), T2, and fluid attenuated inversion recovery (FLAIR) MRI in 329 participants (70–82 years; PROSPER baseline). Analyses were adjusted, if appropriate, for age, gender, intracranial volume, and multiple cardiovascular risk factors. Results— Mid-diastolic WSS was significantly correlated with the presence of PWML (B=−10.15; P =0.006) and CI (B=−2.06; P =0.044), but not with deep white matter lesions (B=−1.30; P =0.050; adjusted for age, gender, WML, and intracranial volume). After adjustment for cardiovascular risk factors, these correlations weakened but remained significant. Systolic WSS was not correlated with any of the cerebrovascular parameters. Conclusions— This study is the first to our knowledge to present a cross-sectional correlation between carotid artery WSS and cerebrovascular pathology such as PWML and CI in a large population. Furthermore, it shows that diastolic hemodynamics may be more important than systolic or mean hemodynamics. Future studies exploring vascular hemodynamic damage should focus on diastolic WSS.

We propose a semi-automatic endocardial border detection method for LV volume estimation in 3D time series of cardiac ultrasound data. It is based on pattern matching and dynamic programming techniques and operates on 2D slices of the 4D data requiring minimal user-interaction. We evaluated on data acquired with the Fast Rotating Ultrasound (FRU) transducer: a linear phased array transducer rotated at high speed around its image axis, generating high quality 2D images of the heart. We automatically select a subset of 2D images at typically 10 rotation angles and 16 cardiac phases. From four manually drawn contours a 4D shape model and a 4D edge pattern model is derived. For the selected images, contour shape and edge patterns are estimated using the models. Pattern matching and dynamic programming is applied to detect the contours automatically. The method allows easy corrections in the detected 2D contours, to iteratively achieve more accurate models and improved detections. An evaluation of this method on FRU data against MRI was done for full cycle LV volumes on 10 patients. Good correlations were found against MRI volumes [r=0.94, y=0.72x + 30.3, difference of 9.6 +/- 17.4 ml (Av +/- SD)] and a low interobserver variability for US (r=0.94, y=1.11x - 16.8, difference of 1.4 +/- 14.2 ml). On average only 2.8 corrections per patient were needed (in a total of 160 images). Although the method shows good correlations with MRI without corrections, applying these corrections can make significant improvements.

This paper presents a new approach for lung registration and cardiac motion prediction, based on a 3D geometric model of the left lung. Feature points, describing a shape of this anatomical object, are automatically extracted from acquired tomographic images. The "goodness-of-fit" measure is assessed at each step in the iterative scheme until spatial alignment between the model and subject's specific data is achieved. We applied the proposed methods to register the 3D lung surfaces of 5 healthy volunteers of thoracic MRI acquired in different respiratory phases. We also utilized this approach to predict the spatial displacement of the human heart due to respiration. The obtained results demonstrate a promising registration performance.

Manual quantitative analysis of cardiac left ventricular function using multi-slice CT is labor intensive because of the large datasets. In previous work, an intrinsically three-dimensional segmentation method for cardiac CT images was presented based on a 3D Active Shape Model (3D-ASM). This model systematically overestimated left ventricular volume and underestimated blood pool volume, due to inaccurate estimation of candidate points during the model update steps. In this paper, we propose a novel ASM candidate point generation method based on a Fuzzy Inference System (FIS), which uses image patches as an input. Visual and quantitative evaluation of the results for 7 out of 9 patients shows substantial improvement for endocardial contours, while the resulting volume errors decrease considerably (blood pool: -39±29 cubic voxels in the previous model, -0.66±6.2 cubic voxels in the current). Standard deviation of the epicardial volume decreases by approximately 50%.

with the image data widely available throughout the hospital by means of cardiovascular PACS systems. Major differences were of course that on cinefilm the coronary arteries were displayed as bright arteries on a darker background, and there was always an associated pincushion distortion caused by the concave input screen of the image intensifier. With the digital systems the arteries are now displayed as dark vessels on a bright background and the modern flat panel X-ray detectors are free from geometric distortions. Although there have been many years of debate about the resolution of cinefilm versus digital, the higher contrast resolution of the digital approach has compensated much of the higher spatial resolution of the 35 mm cinefilm, and thus digital has been completly accepted. Also, extensive validation studies have not proven major differences in accuracy and precision between cinefilm and digital: the variability in the analysis is for both in the order of about pixel, or 0.11 mm [2, 3]. For many years, quantitative coronary arteriography (QCA) has been used in clinical research in the hospitals and in core laboratories to assess regression and progression of coronary obstructions in pharmacological interventions, to assess the efficacy of coronary interventions after the introduction of PTCA and stenting by bare-metal (BMS), drug-eluting (DES) and presently biodegradable stents, and of course for vessel sizing. In all these cases, the analyses were done on straight vessels. New developments have been directed at bifurcation stenting and the associated QCA, and on 3D QCA and registration with IVUS/ OCT. These new issues are elegantly described in the section guest edited by Ricardo A. Costa. It has been well recognized for many years that despite the wide availability of the angiogram and the QCA, an angiogram is only a lumenogram, and that the disease is in the vessel wall. For proper decision making purposes, the interventionalist must know what the composition is of the plaque. This was made possible with intravascular ultrasound, but with the recent advent of Virtual Histology and the iMAP, even more information has become available, which has revived the field of intravascular ultrasound. In this issue, all the possibilities, limitations and applications of IVUS in interventional cardiology are clearly described in the papers selected by guest

We describe a series of experiments that compared the 2D and 3D input methods for selection and positioning tasks related to medical image analysis. For this study, we chose a switchable P5 glove controller, which can be used to provide both 2DOF and 6DOF input control. Our results suggest that for both tasks the overall completion time and accuracy can be improved when the input device with more degrees of freedom is used for manipulation of the visualized medical data. However, 3D input appeared to be more beneficial for the positioning task than for the selection task.

Automated image processing techniques may prove invaluable in the examination of real-time three-dimensional echocardiograms, by providing quantitative and objective measurements of functional parameters such as left ventricular (LV) volume and ejection fraction. In this study, we investigate the use of active appearance models (AAMs) for automatic detection of left ventricular endocardial contours. AAMs are especially useful in segmenting ultrasound images, due to their ability to model the typical LV appearance. However, since only a limited number of images is available for training, the model may be incapable of capturing the large variability in ultrasound image appearance. This may cause standard AAM matching procedures to fail if the model and image are significantly different. Recently, a Jacobian-tuning method for AAM matching was proposed, which allowed the model's training matrix to adapt to the new, unseen image. This may potentially result in a more robust matching. To compare both matching methods, AAMs were built with end-diastolic images from 54 patients. Larger capture ranges and higher accuracy were obtained when the new method was used. In conclusion, this method has great potential for segmentation in echocardiograms and will improve the assessment of LV functional parameters.

This chapter introduces statistical shape- and appearance models and their biomedical applications. Three- and four-dimensional extension of these models are the main focus. Approaches leading to automated landmark definition are introduced and discussed. The applicability is underlined by presenting practical examples of 3D medical image segmentation.