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OBJECTIVES The aims of the present study were to investigate the applicability of quantitative flow ratio (QFR) in patients with 3-vessel disease and to demonstrate the impact of functional SYNTAX (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) score derived from QFR (fSS(QFR)) on clinical outcomes.BACKGROUND The applicability of QFR in patients with 3-vessel disease and the feasibility of fSS(QFR) have not yet been investigated.METHODS All lesions interrogated using instantaneous wave-free ratio and/or fractional flow reserve in the SYNTAX II trial were retrospectively screened and analyzed for QFR. The diagnostic performance of QFR was investigated using hybrid wire-derived pressure assessment (instantaneous wave-free ratio and fractional flow reserve), used in the trial as a reference. Patients with analyzable QFR in 3 vessels were stratified according to fSS(QFR) to evaluate its clinical prognostic value on the basis of 2-year patient-oriented composite endpoint.RESULTS QFRs were analyzable in 71.0% of lesions (836 lesions). The diagnostic performance of QFR to predict binary wire-based ischemia was substantial (area under the curve 0.81, accuracy 73.8%), with a positive predictive value of 85.9%. Independent predictors of diagnostic discordance were lesions in side branches, involvement of bifurcation or trifurcation, and small vessel. According to the 2-year patient-oriented composite endpoint, fSS(QFR) reclassified 26.1% of the patients (36 of 138) in the high-to intermediate-risk group into the low-risk group appropriately (net reclassification improvement 0.32; p < 0.001). The area under the curve for fSS(QFR) to predict the 2-year patient-oriented composite endpoint was higher than that of the classic anatomic SYNTAX score (0.68 vs. 0.56; p = 0.002).CONCLUSIONS QFR demonstrated substantial applicability in patients with 3-vessel disease. The fSS(QFR) has the potential to further refine prognostic risk estimation compared with the classic anatomic SYNTAX score. (c) 2019 by the American College of Cardiology Foundation.

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.

Treatment of coronary bifurcation lesions remains an ongoing challenge for interventional cardiologists. Stenting of coronary bifurcations carries higher risk for in-stent restenosis, stent thrombosis, and recurrent clinical events. This review summarizes the current evidence regarding application and use of biomechanical modeling in the study of stent properties, local flow dynamics, and outcomes after percutaneous coronary interventions in bifurcation lesions. Biomechanical modeling of bifurcation stenting involves computational simulations and in vitro bench testing using subject-specific arterial geometries obtained from in vivo imaging. Biomechanical modeling has the potential to optimize stenting strategies and stent design, thereby reducing adverse outcomes. Large-scale clinical studies are needed to establish the translation of pre-clinical findings to the clinical arena. (C) 2015 by the American College of Cardiology Foundation.

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

OBJECTIVES This study sought to demonstrate the feasibility of a dedicated algorithm for automated quantification of stenosis severity on multislice computed tomography in comparison with quantitative coronary angiography (QCA). BACKGROUND Limited information is available on quantification of coronary stenosis, and previous attempts using semiautomated approaches have been suboptimal. METHODS In patients who had undergone 64-slice computed tomography and invasive coronary angiography, the most severe lesion on QCA was quantified per coronary artery using quantitative coronary computed tomography (QCCTA) software. Additionally, visual grading of stenosis severity using a binary approach (50% stenosis as a cutoff) was performed. Diameter stenosis (percentage) was obtained from detected lumen contours at the minimal lumen area, and corresponding reference diameter values were obtained from an automatic trend analysis of the vessel areas within the artery. RESULTS One hundred patients (53 men; 59.8 +/- 8.0 years) were evaluated, and 282 (94%) vessels were analyzed. Good correlations for diameter stenosis were observed for vessel-based (n = 282; r = 0.83; p < 0.01) and patient-based (n = 93; r = 0.86; p < 0.01) analyses. Mean differences between QCCTA and QCA were -3.0% +/- 12.3% and -6.2% +/- 12.4%. Furthermore, good agreement was observed between QCCTA and QCA for semiquantitative assessment of diameter stenosis (accuracy of 95%). Diagnostic accuracy for assessment of >= 50% diameter stenosis was higher using QCCTA compared with visual analysis (95% vs. 87%; p = 0.08). Moreover, a significantly higher positive predictive value was observed with QCCTA when compared with visual analysis (100% vs. 78%; p < 0.05). Although the visual approach showed a reduced diagnostic accuracy for data sets with moderate image quality, QCCTA performed equally well in patients with moderate or good image quality. However, in data sets with good image quality, QCCTA tended to have a reduced sensitivity compared with visual analysis. CONCLUSIONS Good correlations were found for quantification of stenosis severity between QCCTA and QCA. QCCTA showed an improved positive predictive value when compared with visual analysis. (J Am Coll Cardiol Img 2010;3:699-709) (c) 2010 by the American College of Cardiology Foundation

Highly focused Micro-SPECT scanners enable the acquisition of functional small animal data with very high-resolution. To acquire a maximum of emitted photons from a specific structure of interest and at the same time minimize the required acquisition time, typically only a small subvolume of the animal is scanned that contains the organs of interest. This Volume of Interest (VOI) can be defined manually based on photographs of the animal taken prior to SPECT scanning, for example two lateral views and a top view. In these photographs however, only the surface of the animal is visible and therefore visual estimation of the location of these organs may be difficult. In this paper, we propose a novel atlas-based technique for estimating the organ VOI for the major organs by mapping a small animal atlas to optical scout images. The user is required to outline the animal contour in one lateral view, and to mark two lateral landmarks in the top view photograph. These landmarks subsequently serve as fiducial landmarks to define a 3D Thin-Plate-Spline mapping of an anatomical mouse atlas to the photographic coordinate space. Planar projections of the mapped atlas organs on the photographs greatly facilitate the estimation of the size and position of the target organ. To validate the proposed approach, the estimated organ VOIs were compared to manually drawn organ outlines in a Micro-CT scan, which was co-registered to the scout photographs using physical landmarks. The results demonstrate a highly promising volume correspondence between the real and the estimated organ VOIs.

We argue that a more systematic treatment of the many existing options for medical volume visualisation is desirable. We show that combining the most common medical visualisation and navigation techniques in a systematic way leads to a meaningful set of interesting and sometimes novel UI techniques. We also propose a technique for using generated data and tasks suited to non-medical users for conducting user experiments. We evaluate the UI techniques qualitatively to arrive at a set of promising techniques for future research.

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.

For optimal diagnosis and treatment of lesions at coronary artery bifurcations using x-ray angiography, it is of utmost importance to determine proper angiographic viewing angles. Due to the increasing use of CTA as a first line diagnostic tool, 3D CTA data is more frequently available before x-ray angiographic procedures take place. Motivated by this, we propose to use available CTA data for the determination of patient specific optimal x-ray viewing angles. A semi-automatic iterative region growing scheme is developed for the segmentation of the coronary arterial tree. From the segmented arterial tree, a complete hierarchical surface and centerline representation, including bifurcation points, is automatically obtained. The optimal viewing angle for a selected bifurcation is determined as the view rendering the least amount of foreshortening and vessel overlap. For 83 bifurcation areas, viewing angles were automatically determined. The sensitivity of the method to patient positioning in the x-ray system was also studied. Next, the automatically determined angels were both quantitatively and qualitatively compared with angles determined by two experts. The method was found not to be sensitive to the positioning of the patient in the angiographic x-ray system. In 95% of the cases our method produced a clinically usable view (mean score of 8.4 out of 10) as compared to 98% for the experts (mean score of 8.7). Our method produced angiographic views with significantly less foreshortening (mean difference of 10 percentage points) than the angiographic views set by the experts.

This work investigates knowledge driven segmentation of cardiac MR perfusion sequences. We build upon previous work on multi-band AAMs to integrate into the segmentation both spatial priors about myocardial shape as well as temporal priors about characteristic perfusion patterns. Different temporal and spatial features are developed without a strict need for temporal correspondence across the image sequences. We also investigate which combination of spatial and temporal features yields the best segmentation performance. Our evaluation criteria were boundary errors wrt manual segmentations, area overlap, and convergence envelope. From a quantitative evaluation on 19 perfusion studies, we conclude that a combination of the maximum intensity projection feature and gradient orientation map yields the best segmentation performance, with an average point-to-curve error of 0.9-1 pixel wrt manual contours. We also conclude that addition of different temporal features does not necessarily increase performance.

Contrast-enhanced CT Angiography has become an accepted diagnostic tool for detecting Pulmonary Embolism (PE). The CT obstruction index proposed by Qanadli, which is based on the number of obstructed arterial segments, enables the quantification of PE severity. Because the required manual identification of twenty arterial segments is time consuming, we propose a method for automated labeling of the pulmonary arterial tree to identify the arterial segments. Assuming that the peripheral parts of the arterial tree contain most relevant information for labeling, we propose a bottom-up labeling algorithm exploiting the spatial information of the peripheral arteries. A model of reference positions of the arterial segments was trained using manually labeled trees of 9 patients. To improve accuracy, the arterial tree was partitioned into sub-trees enabling an iterative labeling technique that labels each sub-tree separately. The accuracy of the labeling technique was evaluated using manually labeled trees of 10 patients. Initially an accuracy of 74% was obtained, whereas the iterative approach improved accuracy to 85%. The labeling errors had minor effects on the calculated Qanadli index. Therefore, the presented labeling approach is applicable in automated PE quantification.

This paper explores new methods to visualize and fuse multi-2D bioluminescence imaging (BLI) data with structural imaging modalities such as micro CT and MR. A geometric, back-projection-based 3D reconstruction for superficial lesions from multi-2D BLI data is presented, enabling a coarse estimate of the 3D source envelopes from the multi-2D BLI data. Also, an intuitive 3D landmark selection is developed to enable fast BLI / CT registration. Three modes of fused BLI / CT visualization were developed: slice visualization, carousel visualization and 3D surface visualization. The added value of the fused visualization is demonstrated in three small-animal experiments, where the sensitivity of BLI to detect cell clusters is combined with anatomical detail from micro-CT imaging.

OBJECTIVES The purpose of this study was to investigate scaling of the duration of late diastolic left ventricular (LV) filling in relation to AV conduction time (delay) (PR interval on the ECG) in mammals. BACKGROUND From mouse to whale, AV delay increases 10-fold, whereas body mass increases one million-fold. The apparent "mismatch" results from scaling of AV delay versus body and heart mass. METHODS We measured (1) mitral orifice diameter in 138 postmortem hearts of 48 mammalian species weighing between 17 g and 250 kg and (2) transmitral diastolic flow using magnetic resonance imaging (MRI) recordings of 10 healthy human individuals. (3) We visually inspected early and late diastolic LV filling. (4) We developed two physical models to explain scaling of late diastolic LV filling time. RESULTS (1) Diameter of the mitral orifice proportionally relates to heart length (third root of heart mass). (2) Atrial contraction starts at a fixed instant (± 80%) of the (normalized) cardiac cycle and contributes 31% ± 5% to LV filling. (3) MRI shows that during diastole, the left atrium (LA) and LV form a single space. (4) The physical models relate the duration of late diastolic LV filling directly to heart length, the third root of heart mass. CONCLUSIONS (1) Late diastolic (LV) filling time scales with heart length (third root of heart mass). (2) No "mismateh" exists between AV delay and heart size. (3) Knowledge of the actual starting time of atrial contraction may contribute to better treatment of patients with heart failure. (4) The findings suggest that in evolution of mammalian species, hemodynamics commands electrical behavior of the heart.

The quantification of transvalvular blood flow through the mitral valve (MV) and regurgitant flow in particular is difficult with echocardiography, which is the method of choice to diagnose patients selected for valve repair or replacement. With magnetic resonance imaging, information on the intraventricular blood flow can be obtained. Several scanning techniques have attempted to assess the regurgitant flow. These techniques either do not directly assess the complete flow through the MV, or they do not measure the flow at the location of the valve.To investigate the accuracy of a novel method using three-directional velocity-encoded MRI to acquire the transvalvular blood flow directly from the intraventricular blood flow field, also representing the regurgitant flow during systole.Ten volunteers without cardiac valvular disease were recruited. The transvalvular MV flow volume was measured with three-directional velocity-encoded MRI (3-dir MV flow).The transvalvular flow measurements correlate very well with the flow measured in the aorta (rThree-directional velocity-encoded MRI is a patient-friendly and easy-to-use method suitable for quantifying the regurgitant MV flow in clinical practice.

Selection of the best set of scales is problematic when developing signaldriven approaches for pixel-based image segmentation. Often, different possibly conflicting criteria need to be fulfilled in order to obtain the best tradeoff between uncertainty (variance) and location accuracy. The optimal set of scales depends on several factors: the noise level present in the image material, the prior distribution of the different types of segments, the class-conditional distributions associated with each type of segment as well as the actual size of the (connected) segments. We analyse, theoretically and through experiments, the possibility of using the overall and class-conditional error rates as criteria for selecting the optimal sampling of the linear and morphological scale spaces. It is shown that the overall error rate is optimised by taking the prior class distribution in the image material into account. However, a uniform (ignorant) prior distribution ensures constant class-conditional error rates. Consequently, we advocate for a uniform prior class distribution when an uncommitted, scaleinvariant segmentation approach is desired. Experiments with a neural net classifier developed for segmentation of dynamic MR images, acquired with a paramagnetic tracer, support the theoretical results. Furthermore, the experiments show that the addition of spatial features to the classifier, extracted from the linear or morphological scale spaces, improves the segmentation result compared to a signal-driven approach based solely on the dynamic MR signal. The segmentation results obtained from the two types of features are compared using two novel quality measures that characterise spatial properties of labelled images.