Ultrasound and light: friend or foe? On the role of intravascular ultrasound in the era of optical coherence tomography

More than 20 years after its introduction, intravascular ultrasound (IVUS) has outlived many other intracoronary techniques. IVUS was useful to solve many interventional problems and assisted us in understanding the dynamics of atherosclerosis. It serves as an established imaging endpoint in large progression-regression trial and as an important workhorse in many catheterization laboratories. Nowadays, increasingly complex lesions are treated with drug-eluting stents. The application of IVUS during such interventions can be very useful. Recently, optical coherence tomography (OCT), a light-based imaging technique, has entered the clinical arena. The “omnipresence” of OCT during scientific sessions and live courses with PCI may raise in many the question: Does IVUS have a future in the “era of OCT”? Three review articles, highlighted by this editorial, demonstrate the broad spectrum of current IVUS applications and underline the significant role of IVUS during the last two decades. OCT, the much younger technique, still has to prove its value. Yet OCT is likely to take over some of the current indications of IVUS as a research tool. In addition, OCT is currently gaining clinical significance for stent optimization during complex interventional procedures. Nevertheless, there is little doubt that IVUS still has a major role in studies on coronary atherosclerosis and for guidance of coronary stenting. Thus, ultrasound and light—are they friend or foe? In fact, both methods are good in their own rights. They are complementary rather than competitive. Moreover, in combination, at least for certain indications, they could be even better

Observation of nonlinear bands in near-field scanning optical microscopy of a photonic-crystal waveguide

We have measured the photonic bandstructure of GaAs photonic-crystal waveguides with high energy and momentum resolution using near-field scanning optical microscopy. Intriguingly, we observe additional bands that are not predicted by eigenmode solvers, as was recently demonstrated by Huisman et al. [Phys. Rev. B 86, 155154 (2012)]. We study the presence of these additional bands by performing measurements of these bands while varying the incident light power, revealing a non-linear power dependence. Here, we demonstrate experimentally and theoretically that the observed additional bands are caused by a waveguide-specific near- field tip effect not previously reported, which can significantly phase-modulate the detected field.Comment: 6 pages, 5 figure

Multicenter assessment of the reproducibility of volumetric radiofrequency-based intravascular ultrasound measurements in coronary lesions that were consecutively stented

To assess in a multicenter design the between-center reproducibility of volumetric virtual histology intravascular ultrasound (VH-IVUS) measurements with a semi-automated, computer-assisted contour detection system in coronary lesions that were consecutively stented. To evaluate the reproducibility of volumetric VH-IVUS measurements, experienced analysts of 4 European IVUS centers performed independent analyses (in total 8,052 cross-sectional analyses) to obtain volumetric data of 40 coronary segments (length 20.0 ± 0.3 mm) from target lesions prior to percutaneous intervention that were performed in the setting of stable (65%) or unstable angina pectoris (35%). Geometric and compositional VH-IVUS measurements were highly correlated for the different comparisons. Overall intraclass correlation for vessel, lumen, plaque volume and plaque burden was 0.99, 0.92, 0.96, and 0.83, respectively; for fibrous, fibro-lipidic, necrotic core and calcified volumes overall intraclass correlation was 0.96, 0.94, 0.98, and 0.99, respectively. Nevertheless, significant differences for both geometrical and compositional measurements were seen. Of the plaque components, fibrous tissue and necrotic core showed on average the highest measurement reproducibility. A central analysis for VH-IVUS multicenter studies of lesions prior to PCI should be pursued. Moreover, it may be problematical to pool VH-IVUS data of individual trials analyzed by independent center

Serial intravascular ultrasound assessment of changes in coronary atherosclerotic plaque dimensions and composition: an update

This manuscript reviews the use of serial intravascular ultrasound (IVUS) examination of coronary atherosclerosis in recent observational studies and randomized trials that revealed the effects of cholesterol-lowering and lipid-modifying therapies and offered novel insight into plaque progression and regression. We discuss the value of plaque progression–regression as complementary imaging endpoint and potential surrogate marker of cardiovascular event risk. In addition, the progress in serial assessment of coronary plaque composition and plaque vulnerability by radiofrequency-based analyses is reviewed. Finally, we report on the evaluation of true vessel remodelling in recent serial IVUS trials and discuss the future perspective of serial invasive imaging of coronary atherosclerosis

Impact of analyzing fewer image frames per segment during offline volumetric radiofrequency-based intravascular ultrasound measurements of target lesions prior to percutaneous coronary interventions

In the present study, we evaluated the impact of a 50% reduction in number of image frames (every second frame) on the analysis time and variability of offline volumetric radiofrequency-based intravascular ultrasound (RF-IVUS) measurements in target lesions prior to percutaneous coronary interventions (PCI). Volumetric RF-IVUS data of vessel geometry and plaque composition are generally obtained by a semi-automated analysis process that includes time-consuming manual contour editing. A reduction in the number of frames used for volumetric analysis may speed up the analysis, but could increase measurement variability. We repeatedly performed offline volumetric analyses in RF-IVUS image sets of 20 mm-long coronary segments that contained 30 de novo lesions prior to PCI. A 50% reduction in frames decreased the analysis time significantly (from 57.5 ± 7.3 to 35.7 ± 3.7 min; P < 0.0001) while geometric and compositional RF-IVUS measurements did not differ significantly from measurements obtained from all frames. The variability between measurements on the reduced number of frames versus all frames was comparable to the intra-observer measurement variability. In target lesions prior to PCI, offline volumetric RF-IVUS analyses can be performed using a reduced number of image frames (every second frame). This reduces the time of analysis without substantially increasing measurement variability

Threesomes destabilise certain relationships: multispecies interactions between wood decay fungi in natural resources

Understanding interspecific interactions is key to explaining and modelling community development and associated ecosystem function. Most interactions research has focused on pairwise combinations, overlooking the complexity of multispecies communities. This study investigated three-way interactions between saprotrophic fungi in wood and across soil, and indicated that pairwise combinations are often inaccurate predictors of the outcomes of multispecies competition in wood block interactions. This inconsistency was especially true of intransitive combinations, resulting in increased species coexistence within the resource. Further, the addition of a third competitor frequently destabilised the otherwise consistent outcomes of pairwise combinations in wood blocks, which occasionally resulted in altered resource decomposition rates, depending on the relative decay abilities of the species involved. Conversely, interaction outcomes in soil microcosms were unaffected by the presence of a third combatant. Multispecies interactions promoted species diversity within natural resources, and made community dynamics less consistent than could be predicted from pairwise interaction studies

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

Quality of life of adult retinoblastoma survivors in the Netherlands

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts