Quantification of anatomical aortic valve area by multi-detector computed tomography: A pilot 3D-morphological modeling of the stenotic aortic valve.

BACKGROUND: Aortic-valve-stenosis (AS) is a frequent degenerative valvular-disease and carries dismal outcome under-medical-treatment. Transvalvular pressure gradient reflects severity of the valve-disease but is highly dependent on flow-conditions and on other valvular/aortic characteristics. Alternatively, aortic-valve-area (AVA) represents a measure of aortic-valve lesion severity conceptually essential and practically widely-recognized but exhibits multiple-limitations. METHODS: We analyzed the 4D multi-detector computed tomography(MDCT) of 20 randomly selected patients with severe AS. For each-patient, we generated the 3D-model of the valve and of its calcifications, and we computed the anatomical AVA accounting for the 3D-morphology of the leaflets in three-different-ways. Finally, we compared our results vs. Doppler-based AVAE measurements and vs. 2D-planimetric AVA-measurements. RESULTS: 3D-reconstruction and identification of the cusps were successful in 90% of the cases. The calcification patterns where highly-variable over patients, ranging from multiple small deposits to wide and c-shaped deposits running from commissure-to-commissure. AVAE was 82 ± 15 mm2. When segmenting 18 image planes, AVATight, AVAProj-Ann, AVAProj-Tip and their average AVAAve were equal to 80 ± 16, 88 ± 20, 93 ± 21 and 87 ± 19 mm2, respectively, while AVAPlan was equal to 143 ± 50 mm2. Linear-regression of the three measurements vs. AVAE yielded regression slopes equal to 1.26, 1.13 and 0.93 for AVAProj-Ann, AVAProj-Tip and AVATight, respectively. The respective Pearson-coefficients were 0.85,0.86 and 0.90. Conversely, when comparing AVAPlan vs. AVAE, linear regression yielded a slope of 1.73 and a Pearson coefficient of 0.53. CONCLUSIONS: We described a new-method to obtain a set of flow-independent quantifications that complement pressure gradient measurements and combine the advantages of previously proposed methods, while bypassing the corresponding-limitations.

Left ventricle diastolic vortex ring characterization in ischemic cardiomyopathy: insight into atrio-ventricular interplay.

Diastolic vortex ring (VR) plays a key role in the blood-pumping function exerted by the left ventricle (LV), with altered VR structures being associated with LV dysfunction. Herein, we sought to characterize the VR diastolic alterations in ischemic cardiomyopathy (ICM) patients with systo-diastolic LV dysfunction, as compared to healthy controls, in order to provide a more comprehensive understanding of LV diastolic function. 4D Flow MRI data were acquired in ICM patients (n = 15) and healthy controls (n = 15). The λ2 method was used to extract VRs during early and late diastolic filling. Geometrical VR features, e.g., circularity index (CI), orientation (α), and inclination with respect to the LV outflow tract (ß), were extracted. Kinetic energy (KE), rate of viscous energy loss ( EL ˙ ), vorticity (W), and volume (V) were computed for each VR; the ratios with the respective quantities computed for the entire LV were derived. At peak E-wave, the VR was less circular (p = 0.032), formed a smaller α with the LV long-axis (p = 0.003) and a greater ß (p = 0.002) in ICM patients as compared to controls. At peak A-wave, CI was significantly increased (p = 0.034), while α was significantly smaller (p = 0.016) and ß was significantly increased (p = 0.036) in ICM as compared to controls. At both peak E-wave and peak A-wave, EL ˙ VR / EL ˙ LV , WVR/WLV, and VVR/VLV significantly decreased in ICM patients vs. healthy controls. KEVR/VVR showed a significant decrease in ICM patients with respect to controls at peak E-wave, while VVR remained comparable between normal and pathologic conditions. In the analyzed ICM patients, the diastolic VRs showed alterations in terms of geometry and energetics. These derangements might be attributed to both structural and functional alterations affecting the infarcted wall region and the remote myocardium.

Influence of Hematocrit Level and Integrin αIIbßIII Function on vWF-Mediated Platelet Adhesion and Shear-Induced Platelet Aggregation in a Sudden Expansion.

Purpose: Shear-mediated thrombosis is a clinically relevant phenomenon that underlies excessive arterial thrombosis and device-induced thrombosis. Red blood cells are known to mechanically contribute to physiological hemostasis through margination of platelets and vWF, facilitating the unfurling of vWF multimers, and increasing the fraction of thrombus-contacting platelets. Shear also plays a role in this phenomenon, increasing both the degree of margination and the near-wall forces experienced by vWF and platelets leading to unfurling and activation. Despite this, the contribution of red blood cells in shear-induced platelet aggregation has not been fully investigated-specifically the effect of elevated hematocrit has not yet been demonstrated. Methods: Here, a microfluidic model of a sudden expansion is presented as a platform for investigating platelet adhesion at hematocrits ranging from 0 to 60% and shear rates ranging from 1000 to 10,000 s-1. The sudden expansion geometry models nonphysiological flow separation characteristic to mechanical circulatory support devices, and the validatory framework of the FDA benchmark nozzle. PDMS microchannels were fabricated and coated with human collagen. Platelets were fluorescently tagged, and blood was reconstituted at variable hematocrit prior to perfusion experiments. Integrin function of selected blood samples was inhibited by a blocking antibody, and platelet adhesion and aggregation over the course of perfusion was monitored. Results: Increasing shear rates at physiological and elevated hematocrit levels facilitate robust platelet adhesion and formation of large aggregates. Shear-induced platelet aggregation is demonstrated to be dependent on both αIIbßIII function and the presence of red blood cells. Inhibition of αIIbßIII results in an 86.4% reduction in overall platelet adhesion and an 85.7% reduction in thrombus size at 20-60% hematocrit. Hematocrit levels of 20% are inadequate for effective platelet margination and subsequent vWF tethering, resulting in notable decreases in platelet adhesion at 5000 and 10,000 s-1 compared to 40% and 60%. Inhibition of αIIbßIII triggered dramatic reductions in overall thrombus coverage and large aggregate formation. Stability of platelets tethered by vWF are demonstrated to be αIIbßIII-dependent, as adhesion of single platelets treated with A2A9, an anti-αIIbßIII blocking antibody, is transient and did not lead to sustained thrombus formation. Conclusions: This study highlights driving factors in vWF-mediated platelet adhesion that are relevant to clinical suppression of shear-induced thrombosis and in vitro assays of platelet adhesion. Primarily, increasing hematocrit promotes platelet margination, permitting shear-induced platelet aggregation through αIIbßIII-mediated adhesion at supraphysiological shear rates. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-024-00796-0.

Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI.

BACKGROUND AND OBJECTIVE: 4D flow magnetic resonance imaging provides time-resolved blood flow velocity measurements, but suffers from limitations in spatio-temporal resolution and noise. In this study, we investigated the use of sinusoidal representation networks (SIRENs) to improve denoising and super-resolution of velocity fields measured by 4D flow MRI in the thoracic aorta. METHODS: Efficient training of SIRENs in 4D was achieved by sampling voxel coordinates and enforcing the no-slip condition at the vessel wall. A set of synthetic measurements were generated from computational fluid dynamics simulations, reproducing different noise levels. The influence of SIREN architecture was systematically investigated, and the performance of our method was compared to existing approaches for 4D flow denoising and super-resolution. RESULTS: Compared to existing techniques, a SIREN with 300 neurons per layer and 20 layers achieved lower errors (up to 50% lower vector normalized root mean square error, 42% lower magnitude normalized root mean square error, and 15% lower direction error) in velocity and wall shear stress fields. Applied to real 4D flow velocity measurements in a patient-specific aortic aneurysm, our method produced denoised and super-resolved velocity fields while maintaining accurate macroscopic flow measurements. CONCLUSIONS: This study demonstrates the feasibility of using SIRENs for complex blood flow velocity representation from clinical 4D flow, with quick execution and straightforward implementation.

An evaluation of physical and augmented patient-specific intracranial aneurysm simulators on microsurgical clipping performance and skills: a randomized controlled study.

OBJECTIVE: In the era of flow diversion, there is an increasing demand to train neurosurgeons outside the operating room in safely performing clipping of unruptured intracranial aneurysms. This study introduces a clip training simulation platform for residents and aspiring cerebrovascular neurosurgeons, with the aim to visualize peri-aneurysm anatomy and train virtual clipping applications on the matching physical aneurysm cases. METHODS: Novel, cost-efficient techniques allow the fabrication of realistic aneurysm phantom models and the additional integration of holographic augmented reality (AR) simulations. Specialists preselected suitable and unsuitable clips for each of the 5 patient-specific models, which were then used in a standardized protocol involving 9 resident participants. Participants underwent four sessions of clip applications on the models, receiving no interim training (control), a video review session (video), or a video review session and holographic clip simulation training (video + AR) between sessions 2 and 3. The study evaluated objective microsurgical skills, which included clip selection, number of clip applications, active simulation time, wrist tremor analysis during simulations, and occlusion efficacy. Aneurysm occlusions of the reference sessions were assessed by indocyanine green videoangiography, as well as conventional and photon-counting CT scans. RESULTS: A total of 180 clipping procedures were performed without technical complications. The measurements of the active simulation times showed a 39% improvement for all participants. A median of 2 clip application attempts per case was required during the final session, with significant improvement observed in experienced residents (postgraduate year 5 or 6). Wrist tremor improved by 29% overall. The objectively assessed aneurysm occlusion rate (Raymond-Roy class 1) improved from 76% to 80% overall, even reaching 93% in the extensively trained cohort (video + AR) (p = 0.046). CONCLUSIONS: The authors introduce a newly developed simulator training platform combining physical and holographic aneurysm clipping simulators. The development of exchangeable, aneurysm-comprising housings allows objective radio-anatomical evaluation through conventional and photon-counting CT scans. Measurable performance metrics serve to objectively document improvements in microsurgical skills and surgical confidence. Moreover, the different training levels enable a training program tailored to the cerebrovascular trainees' levels of experience and needs.

A fully automated deep learning approach for coronary artery segmentation and comprehensive characterization.

Coronary computed tomography angiography (CCTA) allows detailed assessment of early markers associated with coronary artery disease (CAD), such as coronary artery calcium (CAC) and tortuosity (CorT). However, their analysis can be time-demanding and biased. We present a fully automated pipeline that performs (i) coronary artery segmentation and (ii) CAC and CorT objective analysis. Our method exploits supervised learning for the segmentation of the lumen, and then, CAC and CorT are automatically quantified. 281 manually annotated CCTA images were used to train a two-stage U-Net-based architecture. The first stage employed a 2.5D U-Net trained on axial, coronal, and sagittal slices for preliminary segmentation, while the second stage utilized a multichannel 3D U-Net for refinement. Then, a geometric post-processing was implemented: vessel centerlines were extracted, and tortuosity score was quantified as the count of branches with three or more bends with change in direction forming an angle >45°. CAC scoring relied on image attenuation. CAC was detected by setting a patient specific threshold, then a region growing algorithm was applied for refinement. The application of the complete pipeline required <5 min per patient. The model trained for coronary segmentation yielded a Dice score of 0.896 and a mean surface distance of 1.027 mm compared to the reference ground truth. Tracts that presented stenosis were correctly segmented. The vessel tortuosity significantly increased locally, moving from proximal, to distal regions (p < 0.001). Calcium volume score exhibited an opposite trend (p < 0.001), with larger plaques in the proximal regions. Volume score was lower in patients with a higher tortuosity score (p < 0.001). Our results suggest a linked negative correlation between tortuosity and calcific plaque formation. We implemented a fast and objective tool, suitable for population studies, that can help clinician in the quantification of CAC and various coronary morphological parameters, which is helpful for CAD risk assessment.