Treatment for middle cerebral artery bifurcation aneurysms: in silico comparison of the novel Contour device and conventional flow-diverters.

Endovascular treatment has become the standard therapy for cerebral aneurysms, while the effective treatment for middle cerebral artery (MCA) bifurcation aneurysms remains a challenge. Current flow-diverting techniques with endovascular coils cover the aneurysm orifice as well as adjacent vessel branches, which may lead to branch occlusion. Novel endovascular flow disruptors, such as the Contour device (Cerus Endovascular), are of great potential to eliminate the risk of branch occlusion. However, there is a lack of valid comparison between novel flow disruptors and conventional (intraluminal) flow-diverters. In this study, two in silico MCA bifurcation aneurysm models were treated by specific Contour devices and flow-diverters using fast-deployment algorithms. Computational fluid dynamic simulations were used to examine the performance and efficiency of deployed devices. Hemodynamic parameters, including aneurysm inflow and wall shear stress, were compared among each Contour device, conventional flow-diverter, and untreated condition. Our results show that the placement of devices can effectively reduce the risk of aneurysm rupture, while the deployment of a Contour device causes more flow reduction than using flow-diverters (e.g. Silk Vista Baby). Besides, the Contour device presents the flow diversion capability of targeting the aneurysm neck without occluding the daughter vessel. In summary, the in silico aneurysm models presented in this study can serve as a powerful pre-planning tool for testing new treatment techniques, optimising device deployment, and predicting the performance in patient-specific aneurysm cases. Contour device is proved to be an effective treatment of MCA bifurcation aneurysms with less daughter vessel occlusion.

Understanding neck collar preferences and user experiences in motor neuron disease: A survey-based study.

BACKGROUND: Motor Neurone Disease (MND), is a debilitating neurodegenerative condition, which significantly impacts the quality of life of those affected. Neck weakness is one challenge faced by those living with MND and as such may require a neck collar to assist. However, the user experience and requirements related to these neck collars have not been comprehensively explored. Understanding these priorities is crucial for enhancing the well-being of MND patients. OBJECTIVE: To understand the priorities of people living with Motor Neurone Disease (MND) including user experience, requirements and the importance of neck collars used to aid neck weakness. METHODS: An online survey was used to investigate the perspectives and experiences of off the shelf neck collars used by people living with MND. The MND Association was selected as a strategic partner by their affiliations and access to large data base of MND patients. RESULTS: Survey highlighted a disparity between the actual duration MND patients wear their current neck collars and their desired duration, emphasising the need to integrate collars into daily activities. Key areas for improvement with existing neck collars centred on comfort and reduced restriction, with respondents expressing a preference for collars that offer support without impeding movement. Additionally, addressing pressure on the anterior neck region during collar use emerged as a critical requirement. CONCLUSION: Current collars do not cause any clinical complications; however, they do fall short of meeting the expected needs of people living with MND, including discomfort, restricted movement, and pressure to the anterior region of the neck. This study highlights need to improve current collar designs to provide better quality of life for MND patients.

Comparison of endothelial shear stress between ultrathin strut bioresorbable polymer drug-eluting stent vs durable-polymer drug-eluting stent post-stent implantation: An optical coherence tomography substudy from BIOFLOW II.

BACKGROUND: Recent clinical data indicate a different performance of biodegradable polymer (BP)-drug eluting stent (DES) compared to durable polymer (DP)-DES. Whether this can be explained by a beneficial impact of BP-DES stent design on the local hemodynamic forces distribution remains unclear. OBJECTIVES: To compare endothelial shear stress (ESS) distribution after implantation of ultrathin (us) BP-DES and DP-DES and examine the association between ESS and neointimal thickness (NIT) distribution in the two devices at 9 months follow up. METHODS AND RESULTS: We retrospectively identified patients from the BIOFLOW II trial that had undergone OCT imaging. OCT data were utilized to reconstruct the surface of the stented segment at baseline and 9 months follow-up, simulate blood flow, and measure ESS and NIT in the stented segment. The patients were divided into 3 groups depending on whether DP-DES (N = 8, n = 56,160 sectors), BP-DES with a stent diameter of >3 mm (strut thickness of 80 µm, N = 6, n = 36,504 sectors), or BP-DES with a stent diameter of ≤3 mm (strut thickness of 60 µm, N = 8, n = 50,040 sectors) were used for treatment. The ESS, and NIT distribution and the association of these two variables were estimated and compared among the 3 groups. RESULTS: In the DP-DES group mean NIT was 0.18 ± 0.17 mm and ESS 1.68 ± 1.66 Pa; for the BP-DES ≤3 mm group the NIT was 0.17 ± 0.11 mm and ESS 1.49 ± 1.24 Pa and for the BP-DES >3 mm group 0.20 ± 0.23 mm and 1.42 ± 1.24 Pa respectively (p < 0.001 for both NIT and ESS comparisons across groups). A negative correlation between NIT and baseline ESS was found, the correlation coefficient for all the stented segments was -0.33, p < 0.001. CONCLUSION: In this OCT sub-study of the BIOFLOW II trial, the NIT was statistically different between groups of patients treated with BP-DES and DP-DES. In addition, regions of low ESS were associated with increased NIT in all studied devices.

Efficacy of human experts and an automated segmentation algorithm in quantifying disease pathology in coronary computed tomography angiography: A head-to-head comparison with intravascular ultrasound imaging.

BACKGROUND: Coronary computed tomography angiography (CCTA) analysis is currently performed by experts and is a laborious process. Fully automated edge-detection methods have been developed to expedite CCTA segmentation however their use is limited as there are concerns about their accuracy. This study aims to compare the performance of an automated CCTA analysis software and the experts using near-infrared spectroscopy-intravascular ultrasound imaging (NIRS-IVUS) as a reference standard. METHODS: Fifty-one participants (150 vessels) with chronic coronary syndrome who underwent CCTA and 3-vessel NIRS-IVUS were included. CCTA analysis was performed by an expert and an automated edge detection method and their estimations were compared to NIRS-IVUS at a segment-, lesion-, and frame-level. RESULTS: Segment-level analysis demonstrated a similar performance of the two CCTA analyses (conventional and automatic) with large biases and limits of agreement compared to NIRS-IVUS estimations for the total atheroma (ICC: 0.55 vs 0.25, mean difference:192 (-102-487) vs 243 (-132-617) and percent atheroma volume (ICC: 0.30 vs 0.12, mean difference: 12.8 (-5.91-31.6) vs 20.0 (0.79-39.2). Lesion-level analysis showed that the experts were able to detect more accurately lesions than the automated method (68.2 â€‹% and 60.7 â€‹%) however both analyses had poor reliability in assessing the minimal lumen area (ICC 0.44 vs 0.36) and the maximum plaque burden (ICC 0.33 vs 0.33) when NIRS-IVUS was used as the reference standard. CONCLUSIONS: Conventional and automated CCTA analyses had similar performance in assessing coronary artery pathology using NIRS-IVUS as a reference standard. Therefore, automated segmentation can be used to expedite CCTA analysis and enhance its applications in clinical practice.

Survival and acute exacerbation for patients with idiopathic pulmonary fibrosis (IPF) or non-IPF idiopathic interstitial pneumonias: 5-year follow-up analysis of a prospective multi-institutional patient registry.

OBJECTIVE: Few prospective cohort studies with relatively large numbers of patients with non-idiopathic pulmonary fibrosis (non-IPF) of idiopathic interstitial pneumonia (IIP) have been described. We aimed to assess disease progression and cause of death for patients with non-IPF IIPs or IPF under real-life conditions. METHODS: Data were analysed for a prospective multi-institutional cohort of 528 IIP patients enrolled in Japan between September 2013 and April 2016. Diagnosis of IPF versus non-IPF IIPs was based on central multidisciplinary discussion, and follow-up surveillance was performed for up to 5 years after patient registration. Survival and acute exacerbation (AE) were assessed. RESULTS: IPF was the most common diagnosis (58.0%), followed by unclassifiable IIPs (35.8%) and others (6.2%). The 5-year survival rate for non-IPF IIP and IPF groups was 72.8% and 53.7%, respectively, with chronic respiratory failure being the primary cause of death in both groups. AE was the second most common cause of death for both non-IPF IIP (24.1%) and IPF (23.5%) patients. The cumulative incidence of AE did not differ significantly between the two groups (p=0.36), with a 1-year incidence rate of 7.4% and 9.0% in non-IPF IIP and IPF patients, respectively. We found that 30.2% and 39.4% of non-IPF IIP and IPF patients, respectively, who experienced AE died within 3 months after an AE event, whereas 55.8% and 66.7% of such patients, respectively, died within 5 years after registration. CONCLUSION: Closer monitoring of disease progression and palliative care interventions after AE are important for non-IPF IIP patients as well as for IPF patients.

Novel near-infrared spectroscopy-intravascular ultrasound-based deep-learning methodology for accurate coronary computed tomography plaque quantification and characterization.

Aims: Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS). Methods and results: Seventy patients were prospectively recruited who underwent CCTA and NIRS-IVUS imaging. Corresponding cross sections were matched using an in-house developed software, and the estimations of NIRS-IVUS for the lumen, vessel wall borders, and plaque composition were used to train a convolutional neural network in 138 vessels. The performance was evaluated in 48 vessels and compared against the estimations of NIRS-IVUS and the conventional CCTA expert analysis. Sixty-four patients (186 vessels, 22 012 matched cross sections) were included. Deep-learning methodology provided estimations that were closer to NIRS-IVUS compared with the conventional approach for the total atheroma volume (ΔDL-NIRS-IVUS: -37.8 ± 89.0 vs. ΔConv-NIRS-IVUS: 243.3 ± 183.7 mm3, variance ratio: 4.262, P < 0.001) and percentage atheroma volume (-3.34 ± 5.77 vs. 17.20 ± 7.20%, variance ratio: 1.578, P < 0.001). The DL methodology detected lesions more accurately than the conventional approach (Area under the curve (AUC): 0.77 vs. 0.67, P < 0.001) and quantified minimum lumen area (ΔDL-NIRS-IVUS: -0.35 ± 1.81 vs. ΔConv-NIRS-IVUS: 1.37 ± 2.32 mm2, variance ratio: 1.634, P < 0.001), maximum plaque burden (4.33 ± 11.83% vs. 5.77 ± 16.58%, variance ratio: 2.071, P = 0.004), and calcific burden (-51.2 ± 115.1 vs. -54.3 ± 144.4, variance ratio: 2.308, P < 0.001) more accurately than conventional approach. The DL methodology was able to segment a vessel on CCTA in 0.3 s. Conclusions: The DL methodology developed for CCTA analysis from co-registered NIRS-IVUS and CCTA data enables rapid and accurate assessment of lesion morphology and is superior to expert analysts (Clinicaltrials.gov: NCT03556644).

Rapid Lewis Acid Screening and Reaction Optimization Using 3D-Printed Catalyst-Impregnated Stirrer Devices in the Synthesis of Heterocycles.

We describe the development of Lewis acid (LA) catalyst-impregnated 3D-printed stirrer devices and demonstrate their ability to facilitate the rapid screening of reaction conditions to synthesize heterocycles. The stereolithography 3D-printed stirrer devices were designed to fit round-bottomed flasks and Radleys carousel tubes using our recently reported solvent-resistant resin, and using CFD modeling studies and experimental data, we demonstrated that the device design leads to rapid mixing and rapid throughput over the device surface. Using a range of LA 3D-printed stirrers, the reaction between a diamine and an aldehyde was optimized for the catalyst and solvent, and we demonstrated that use of the 3D-printed catalyst-embedded devices led to higher yields and reduced reaction times. A library of benzimidazole and benzothiazole compounds were synthesized, and the use of devices led to efficient formation of the product as well as low levels of the catalyst in the resultant crude mixture. The use of these devices makes the process of setting up multiple reactions simpler by avoiding weighing out of catalysts, and the devices, once used, can be simply removed from the reaction, making the process of compound library synthesis more facile.

A physics-based machine learning technique rapidly reconstructs the wall-shear stress and pressure fields in coronary arteries.

With the global rise of cardiovascular disease including atherosclerosis, there is a high demand for accurate diagnostic tools that can be used during a short consultation. In view of pathology, abnormal blood flow patterns have been demonstrated to be strong predictors of atherosclerotic lesion incidence, location, progression, and rupture. Prediction of patient-specific blood flow patterns can hence enable fast clinical diagnosis. However, the current state of art for the technique is by employing 3D-imaging-based Computational Fluid Dynamics (CFD). The high computational cost renders these methods impractical. In this work, we present a novel method to expedite the reconstruction of 3D pressure and shear stress fields using a combination of a reduced-order CFD modelling technique together with non-linear regression tools from the Machine Learning (ML) paradigm. Specifically, we develop a proof-of-concept automated pipeline that uses randomised perturbations of an atherosclerotic pig coronary artery to produce a large dataset of unique mesh geometries with variable blood flow. A total of 1,407 geometries were generated from seven reference arteries and were used to simulate blood flow using the CFD solver Abaqus. This CFD dataset was then post-processed using the mesh-domain common-base Proper Orthogonal Decomposition (cPOD) method to obtain Eigen functions and principal coefficients, the latter of which is a product of the individual mesh flow solutions with the POD Eigenvectors. Being a data-reduction method, the POD enables the data to be represented using only the ten most significant modes, which captures cumulatively greater than 95% of variance of flow features due to mesh variations. Next, the node coordinate data of the meshes were embedded in a two-dimensional coordinate system using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. The reduced dataset for t-SNE coordinates and corresponding vector of POD coefficients were then used to train a Random Forest Regressor (RFR) model. The same methodology was applied to both the volumetric pressure solution and the wall shear stress. The predicted pattern of blood pressure, and shear stress in unseen arterial geometries were compared with the ground truth CFD solutions on "unseen" meshes. The new method was able to reliably reproduce the 3D coronary artery haemodynamics in less than 10 s.

CARDIAN: a novel computational approach for real-time end-diastolic frame detection in intravascular ultrasound using bidirectional attention networks.

Introduction: Changes in coronary artery luminal dimensions during the cardiac cycle can impact the accurate quantification of volumetric analyses in intravascular ultrasound (IVUS) image studies. Accurate ED-frame detection is pivotal for guiding interventional decisions, optimizing therapeutic interventions, and ensuring standardized volumetric analysis in research studies. Images acquired at different phases of the cardiac cycle may also lead to inaccurate quantification of atheroma volume due to the longitudinal motion of the catheter in relation to the vessel. As IVUS images are acquired throughout the cardiac cycle, end-diastolic frames are typically identified retrospectively by human analysts to minimize motion artefacts and enable more accurate and reproducible volumetric analysis. Methods: In this paper, a novel neural network-based approach for accurate end-diastolic frame detection in IVUS sequences is proposed, trained using electrocardiogram (ECG) signals acquired synchronously during IVUS acquisition. The framework integrates dedicated motion encoders and a bidirectional attention recurrent network (BARNet) with a temporal difference encoder to extract frame-by-frame motion features corresponding to the phases of the cardiac cycle. In addition, a spatiotemporal rotation encoder is included to capture the IVUS catheter's rotational movement with respect to the coronary artery. Results: With a prediction tolerance range of 66.7 ms, the proposed approach was able to find 71.9%, 67.8%, and 69.9% of end-diastolic frames in the left anterior descending, left circumflex and right coronary arteries, respectively, when tested against ECG estimations. When the result was compared with two expert analysts' estimation, the approach achieved a superior performance. Discussion: These findings indicate that the developed methodology is accurate and fully reproducible and therefore it should be preferred over experts for end-diastolic frame detection in IVUS sequences.

Angiography and optical coherence tomography derived shear stress: are they equivalent in my opinion?

Advances in image reconstruction using either single or multimodality imaging data provide increasingly accurate three-dimensional (3D) patient's arterial models for shear stress evaluation using computational fluid dynamics (CFD). We aim to evaluate the impacts on endothelial shear stress (ESS) derived from a simple image reconstruction using 3D-quantitative coronary angiography (3D-QCA) versus a multimodality reconstruction method using optical coherence tomography (OCT) in patients' vessels treated with bioresorbable scaffolds. Seven vessels at baseline and five-year follow-up of seven patients from a previous CFD investigation were retrospectively selected for a head-to-head comparison of angiography-derived versus OCT-derived ESS. 3D-QCA significantly underestimated the minimum stent area [MSA] (-2.38mm2) and the stent length (-1.46 mm) compared to OCT-fusion method reconstructions. After carefully co-registering the region of interest for all cases with a sophisticated statistical method, the difference in MSA measurements as well as the inability of angiography to visualise the strut footprint in the lumen surface have translated to higher angiography-derived ESS than OCT-derived ESS (1.76 Pa or 1.52 times for the overlapping segment). The difference in ESS widened with a more restricted region of interest (1.97 Pa or 1.63 times within the scaffold segment). Angiography and OCT offer two distinctive methods of ESS calculation. Angiography-derived ESS tends to overestimate the ESS compared to OCT-derived ESS. Further investigations into ESS analysis resolution play a vital role in adopting OCT-derived ESS.

Quantitative aortography for assessment of aortic regurgitation in the era of percutaneous aortic valve replacement.

Paravalvular leak (PVL) is a shortcoming that can erode the clinical benefits of transcatheter valve replacement (TAVR) and therefore a readily applicable method (aortography) to quantitate PVL objectively and accurately in the interventional suite is appealing to all operators. The ratio between the areas of the time-density curves in the aorta and left ventricular outflow tract (LVOT-AR) defines the regurgitation fraction (RF). This technique has been validated in a mock circulation; a single injection in diastole was further tested in porcine and ovine models. In the clinical setting, LVOT-AR was compared with trans-thoracic and trans-oesophageal echocardiography and cardiac magnetic resonance imaging. LVOT-AR > 17% discriminates mild from moderate aortic regurgitation on echocardiography and confers a poor prognosis in multiple registries, and justifies balloon post-dilatation. The LVOT-AR differentiates the individual performances of many old and novel devices and is being used in ongoing randomized trials and registries.

POST-IVUS: A perceptual organisation-aware selective transformer framework for intravascular ultrasound segmentation.

Intravascular ultrasound (IVUS) is recommended in guiding coronary intervention. The segmentation of coronary lumen and external elastic membrane (EEM) borders in IVUS images is a key step, but the manual process is time-consuming and error-prone, and suffers from inter-observer variability. In this paper, we propose a novel perceptual organisation-aware selective transformer framework that can achieve accurate and robust segmentation of the vessel walls in IVUS images. In this framework, temporal context-based feature encoders extract efficient motion features of vessels. Then, a perceptual organisation-aware selective transformer module is proposed to extract accurate boundary information, supervised by a dedicated boundary loss. The obtained EEM and lumen segmentation results will be fused in a temporal constraining and fusion module, to determine the most likely correct boundaries with robustness to morphology. Our proposed methods are extensively evaluated in non-selected IVUS sequences, including normal, bifurcated, and calcified vessels with shadow artifacts. The results show that the proposed methods outperform the state-of-the-art, with a Jaccard measure of 0.92 for lumen and 0.94 for EEM on the IVUS 2011 open challenge dataset. This work has been integrated into a software QCU-CMS2 to automatically segment IVUS images in a user-friendly environment.

Agreement of wall shear stress distribution between two core laboratories using three-dimensional quantitative coronary angiography.

Wall shear stress (WSS) estimated in models reconstructed from intravascular imaging and 3-dimensional-quantitative coronary angiography (3D-QCA) data provides important prognostic information and enables identification of high-risk lesions. However, these analyses are time-consuming and require expertise, limiting WSS adoption in clinical practice. Recently, a novel software has been developed for real-time computation of time-averaged WSS (TAWSS) and multidirectional WSS distribution. This study aims to examine its inter-corelab reproducibility. Sixty lesions (20 coronary bifurcations) with a borderline negative fractional flow reserve were processed using the CAAS Workstation WSS prototype to estimate WSS and multi-directional WSS values. Analysis was performed by two corelabs and their estimations for the WSS in 3 mm segments across each reconstructed vessel was extracted and compared. In total 700 segments (256 located in bifurcated vessels) were included in the analysis. A high intra-class correlation was noted for all the 3D-QCA and TAWSS metrics between the estimations of the two corelabs irrespective of the presence (range: 0.90-0.92) or absence (range: 0.89-0.90) of a coronary bifurcation, while the ICC was good-moderate for the multidirectional WSS (range: 0.72-0.86). Lesion level analysis demonstrated a high agreement of the two corelabls for detecting lesions exposed to an unfavourable haemodynamic environment (WSS > 8.24 Pa, κ = 0.77) that had a high-risk morphology (area stenosis > 61.3%, κ = 0.71) and were prone to progress and cause events. The CAAS Workstation WSS enables reproducible 3D-QCA reconstruction and computation of WSS metrics. Further research is needed to explore its value in detecting high-risk lesions.

Neck Collar Assessment for People Living With Motor Neuron Disease: Are Current Outcome Measures Suitable?

A majority of people living with motor neuron disease (MND) experience weakness of the neck and as a result, experience head drop. This exacerbates problems with everyday activities (eating, talking, breathing, etc). Neck collars are often used to support head drop; however, these are typically designed for prehospitalization settings to manage and brace the cervical region of the spine. As a result, it has been recorded that people living with MND often reject these collars for a variety of reasons but most notably because they are too restricting. The current standardized outcome measures (most notably restricting cervical range of motion) used for neck collars are summarized herein along with whether they are suitable for a bespoke neck collar specifically designed for people living with MND.

An optical coherence tomography and endothelial shear stress study of a novel bioresorbable bypass graft.

Endothelial shear stress (ESS) plays a key role in the clinical outcomes in native and stented segments; however, their implications in bypass grafts and especially in a synthetic biorestorative coronary artery bypass graft are yet unclear. This report aims to examine the interplay between ESS and the morphological alterations of a biorestorative coronary bypass graft in an animal model. Computational fluid dynamics (CFD) simulation derived from the fusion of angiography and optical coherence tomography (OCT) imaging was used to reconstruct data on the luminal anatomy of a bioresorbable coronary bypass graft with an endoluminal "flap" identified during OCT acquisition. The "flap" compromised the smooth lumen surface and considerably disturbed the local flow, leading to abnormally low ESS and high oscillatory shear stress (OSI) in the vicinity of the "flap". In the presence of the catheter, the flow is more stable (median OSI 0.02384 versus 0.02635, p < 0.0001; maximum OSI 0.4612 versus 0.4837). Conversely, OSI increased as the catheter was withdrawn which can potentially cause back-and-forth motions of the "flap", triggering tissue fatigue failure. CFD analysis in this report provided sophisticated physiological information that complements the anatomic assessment from imaging enabling a complete understanding of biorestorative graft pathophysiology.

Use of portable air purifiers to reduce aerosols in hospital settings and cut down the clinical backlog.

SARS-CoV-2 has severely affected capacity in the National Health Service (NHS), and waiting lists are markedly increasing due to downtime of up to 50 min between patient consultations/procedures, to reduce the risk of infection. Ventilation accelerates this air cleaning, but retroactively installing built-in mechanical ventilation is often cost-prohibitive. We investigated the effect of using portable air cleaners (PAC), a low-energy and low-cost alternative, to reduce the concentration of aerosols in typical patient consultation/procedure environments. The experimental setup consisted of an aerosol generator, which mimicked the subject affected by SARS-CoV-19, and an aerosol detector, representing a subject who could potentially contract SARS-CoV-19. Experiments of aerosol dispersion and clearing were undertaken in situ in a variety of rooms with two different types of PAC in various combinations and positions. Correct use of PAC can reduce the clearance half-life of aerosols by 82% compared to the same indoor-environment without any ventilation, and at a broadly equivalent rate to built-in mechanical ventilation. In addition, the highest level of aerosol concentration measured when using PAC remains at least 46% lower than that when no mitigation is used, even if the PAC's operation is impeded due to placement under a table. The use of PAC leads to significant reductions in the level of aerosol concentration, associated with transmission of droplet-based airborne diseases. This could enable NHS departments to reduce the downtime between consultations/procedures.