Impact of multiple ballooning on coronary lesions as assessed by optical coherence tomography and intravascular ultrasound.

Background: Optimal lesion preparation for coronary lesions has been reappraised in the interventional community, given the increasing use of drug-coated balloons for de novo lesions; however, whether multiple ballooning could achieve more favorable angiographic results compared with single ballooning remains unknown. We aimed to investigate the incremental effect of multiple ballooning on de novo coronary lesions over single ballooning as assessed by optical coherence tomography (OCT) and intravascular ultrasound (IVUS) among patients undergoing percutaneous coronary intervention (PCI).

Methods: Patients with chronic coronary syndrome (CCS) undergoing PCI were enrolled.

Comparison of left ventricular mass and wall thickness between cardiac computed tomography angiography and cardiac magnetic resonance imaging using machine learning algorithms.

Aims: Cardiac magnetic resonance imaging (MRI) is the gold standard in the assessment of left ventricle (LV) mass and wall thickness. In recent years, cardiac computed tomography angiography (CCTA) has gained widespread usage as an imaging modality. Despite this, limited previous investigations have specifically addressed the potential of CCTA as an alternative modality for quantitative LV assessment.

Efficacy of Coronary Calcium Score in Predicting Coronary Artery Morphology in Patients With Obstructive Coronary Artery Disease.

Background: Coronary artery calcium score (CACS) is an established marker of coronary artery disease (CAD) and has been extensively used to stratify risk in asymptomatic individuals. However, the value of CACS in predicting plaque morphology in patients with advanced CAD is less established. The present analysis aims to assess the association between CACS and plaque characteristics detected by near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS) imaging in patients with obstructive CAD.

First use of a new extended reality tool for preoperative planning in coronary artery bypass surgery: a case-report.

A 73-year-old male presented with angina symptoms and was diagnosed with three-vessel coronary artery disease by use of computed tomography angiography and coronary angiography. This diagnosis necessitated coronary artery bypass grafting (CABG) surgery. A custom made AI-driven algorithm was used to generate a patient-specific three-dimensional coronary artery model from computed tomography angiography imaging data.

Prediction of recurrence risk in endometrial cancer with multimodal deep learning.

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials.

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images.

Purpose: The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.

A novel deep learning model for a computed tomography diagnosis of coronary plaque erosion.

Patients with acute coronary syndromes caused by plaque erosion might be managed conservatively without stenting. Currently, the diagnosis of plaque erosion requires an invasive imaging procedure. We sought to develop a deep learning (DL) model that enables an accurate diagnosis of plaque erosion using coronary computed tomography angiography (CTA).

Computational Modeling of Thermal Ablation Zones in the Liver: A Systematic Review.

Purpose: This systematic review aims to identify, evaluate, and summarize the findings of the literature on existing computational models for radiofrequency and microwave thermal liver ablation planning and compare their accuracy.

Methods: A systematic literature search was performed in the MEDLINE and Web of Science databases. Characteristics of the computational model and validation method of the included articles were retrieved.

Automatic Quantification of Local Plaque Thickness Differences as Assessed by Serial Coronary Computed Tomography Angiography Using Scan-Quality-Based Vessel-Specific Thresholds.

Introduction: The use of serial coronary computed tomography angiography (CCTA) allows for the early assessment of coronary plaque progression, a crucial factor in averting major adverse cardiac events (MACEs). Traditionally, serial CCTA is assessed using anatomical landmarks to match baseline and follow-up scans. Recently, a tool has been developed that allows for the automatic quantification of local plaque thickness differences in serial CCTA utilizing plaque contour delineation.

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.

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.

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.

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.

Intraprocedural assessment of ablation margins using computed tomography co-registration in hepatocellular carcinoma treatment with percutaneous ablation: IAMCOMPLETE study.

Purpose: The primary objective of this study was to determine the feasibility of ablation margin quantification using a standardized scanning protocol during thermal ablation (TA) of hepatocellular carcinoma (HCC), and a rigid registration algorithm. Secondary objectives were to determine the inter- and intra-observer variability of tumor segmentation and quantification of the minimal ablation margin (MAM).

Materials And Methods: Twenty patients who underwent thermal ablation for HCC were included.