Early silent coronary bypass graft occlusion following coronary bypass surgery, implication of routine coronary computed tomography angiography.

Objective: To evaluate incidence and predictors of early silent bypass occlusion following coronary bypass surgery using cardiac computed tomography angiography.

Methods: A total of 439 consecutive patients with mean age of 66 ± 10 years comprising 17% ( = 75) females underwent isolated coronary bypass surgery followed by CT scan before discharge. Graft patency was evaluated in 1,319 anastomoses where 44% ( = 580) arterial and 56% ( = 739) vein graft anastomosis were performed.

Associations between 3D-based Left Atrial Volumetric and Blood Flow Parameters in a Single-Site Cohort of the Multi-Ethnic Study of Atherosclerosis.

Purpose To investigate associations between left atrial volume (LAV) and function with impaired three-dimensional hemodynamics from four-dimensional flow MRI. Materials and Methods A subcohort of participants from the Multi-Ethnic Study of Atherosclerosis from Northwestern University underwent prospective 1.5-T cardiac MRI including whole-heart four-dimensional flow and short-axis cine imaging between 2019 and 2020.

Robust and fast stochastic 4D flow vector-field signature technique for quantifying composite flow dynamics from 4D flow MRI: Application to left atrial flow in atrial fibrillation.

4D flow MRI is an emerging imaging modality that maps voxel-wise blood flow information as velocity vector fields that is acquired in 7-dimensional image volumes (3 spatial dimensions + 3 velocity directions + time). Blood flow in the cardiovascular system is often complex and composite involving multiple flow dynamics and patterns (e.g.

Magnetic Resonance Imaging Findings Corresponding to Vasculitis as Defined via [F]FDG Positron Emission Tomography or Ultrasound.

We sought to investigate magnetic resonance imaging (MRI) parameters that correspond to vasculitis observed via [F]FDG positron emission tomography/computed tomography (PET/CT) and ultrasound in patients with large-vessel giant cell arteritis (LV-GCA). We performed a cross-sectional analysis of patients diagnosed with LV-GCA. Patients were selected if MRI, PET/CT, and vascular ultrasound were performed at the time of LV-GCA diagnosis.

MRI Quantification of Left Atrial Circumferential Strain in Mitral Regurgitation: A Feasibility and Reproducibility Study.

Background: MRI-derived left atrial (LA) longitudinal strain has been shown to be a marker for mitral regurgitation, but the utility of LA circumferential strain remains unclear.

Purpose: To assess feasibility and reproducibility of LA circumferential strain, identify changes in mitral regurgitation patients compared to healthy volunteers, and determine strain's association with mitral regurgitation severity and cardiac function.

Study Type: Retrospective.

TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images.

Purpose: To present a deep learning segmentation model that can automatically and robustly segment all major anatomic structures on body CT images.

Materials And Methods: In this retrospective study, 1204 CT examinations (from 2012, 2016, and 2020) were used to segment 104 anatomic structures (27 organs, 59 bones, 10 muscles, and eight vessels) relevant for use cases such as organ volumetry, disease characterization, and surgical or radiation therapy planning. The CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, abnormalities, scanners, body parts, sequences, and sites).

Aortic arch and frozen elephant trunk repair of a right-sided aortic arch with pseudoaneurysm rupture.

A 53-year-old man with a history of vascular ring repair secondary to a right-sided aortic arch with a retroesophageal subclavian artery and ligamentum arteriosum to the descending thoracic aorta presented to our institution with a large aortic pseudoaneurysm of the distal aortic arch. Computed tomography demonstrated a right-sided aortic arch with a 5.8-cm pseudoaneurysm arising from the distal arch with concern for rupture.

Comparison of Biplane Area-Length Method and 3D Volume Quantification by Using Cardiac MRI for Assessment of Left Atrial Volume in Atrial Fibrillation.

Purpose: To compare maximum left atrial (LA) volume (LAV) from the routinely used biplane area-length (BAL) method with three-dimensional (3D)-based volumetry from late gadolinium-enhanced MRI (3D LGE MRI) and contrast-enhanced MR angiography (3D CE-MRA) in patients with atrial fibrillation (AF).

Materials And Methods: Sixty-four patients with AF (mean age, 63 years ± 9 [SD]; 40 male patients) were retrospectively included from a prospective cohort acquired between October 2018 and February 2021. All patients underwent a research MRI examination that included standard two- and four-chamber cine acquisitions, 3D CE-MRA, and 3D LGE MRI performed prior to the atrial kick.

Quantification of left atrial function by the area-length method overestimates left atrial emptying fraction.

Purpose: The biplane area-length method is commonly used in cardiac magnetic resonance (CMR) to assess left atrial (LA) volume (LAV) and function. Associations between left atrial emptying fraction (LAEF) and clinical outcomes have been reported. However, only limited data are available on the calculation of LAEF using the biplane method compared to 3D assessment.

Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.

Objectives: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to the performance of a deep learning (DL) application for fully-automated VCD and flow quantification and corrected semi-automated analysis (corSAA).

Utilization of a cloud-based radiology analytics platform to monitor imaging volumes at a large tertiary center.

Rationale And Objective: In this study, we evaluate the ability of a novel cloud-based radiology analytics platform to continuously monitor imaging volumes at a large tertiary center following institutional protocol and policy changes.

Materials And Methods: We evaluated response to environmental factors through the lens of the COVID-19 pandemic. Analysis involved 11 CT/18 MR imaging systems at a large tertiary center.

Performance of a deep learning tool to detect missed aortic dilatation in a large chest CT cohort.

Purpose: Thoracic aortic (TA) dilatation (TAD) is a risk factor for acute aortic syndrome and must therefore be reported in every CT report. However, the complex anatomy of the thoracic aorta impedes TAD detection. We investigated the performance of a deep learning (DL) prototype as a secondary reading tool built to measure TA diameters in a large-scale cohort.

Baseline 4D Flow-Derived Hemodynamic Parameters Stratify Descending Aortic Dissection Patients With Enlarging Aortas.

Purpose: The purpose of our study was to assess the value of true lumen and false lumen hemodynamics compared to aortic morphological measurements for predicting adverse-aorta related outcomes (AARO) and aortic growth in patients with type B aortic dissection (TBAD).

Materials And Methods: Using an IRB approved protocol, we retrospectively identified patients with descending aorta (DAo) dissection at a large tertiary center. Inclusion criteria includes known TBAD with ≥ 6 months of clinical follow-up after initial presentation for TBAD or after ascending aorta intervention for patients with repaired type A dissection with residual type B aortic dissection (rTAAD).

Blood Flow Dynamics in a Giant Left Atrial Appendage Aneurysm Visualized by 4D-Flow CMR.

Aneurysms of the left atrial appendage (LAA) are rare entities that often require surgical intervention. We demonstrate multimodality imaging features of a giant LAA aneurysm, with a focus on 3-dimensional blood flow dynamics by using 4-dimensional-flow cardiac magnetic resonance. ().

Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium.

Background: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process.

Fully automated guideline-compliant diameter measurements of the thoracic aorta on ECG-gated CT angiography using deep learning.

Background: Manually performed diameter measurements on ECG-gated CT-angiography (CTA) represent the gold standard for diagnosis of thoracic aortic dilatation. However, they are time-consuming and show high inter-reader variability. Therefore, we aimed to evaluate the accuracy of measurements of a deep learning-(DL)-algorithm in comparison to those of radiologists and evaluated measurement times (MT).