Geometric Changes in Mitral Valve Apparatus during Long-term Cardiac Resynchronization Therapy as Assessed with Cardiac CT.

Purpose To assess long-term geometric changes of the mitral valve apparatus using cardiac CT in individuals who underwent cardiac resynchronization therapy (CRT). Materials and Methods Participants from a randomized controlled trial with cardiac CT examinations before CRT implantation and at 6 months follow-up (Clinicaltrials.gov identifier NCT01323686) were invited to undergo an additional long-term follow-up cardiac CT examination.

Quantitative analysis of small coronary artery calcium detectability with an accurate simulation and validation on a clinical CT scanner.

Background: The absence of coronary artery calcium (CAC) measured via CT is associated with very favorable prognosis, and current guidelines recommend low-density lipoprotein cholesterol (LDL-c) lowering therapy for individuals with any CAC. This motivates early detection of small granules of CAC; however, calcium scan sensitivity for detecting very low levels of calcium has not been quantified.

Purpose: In this work, the size limit of detectability of small calcium hydroxyapatite (CaHA) granules with clinical CAC scanning was assessed using validated simulations.

Myocardial Regional Shortening from 4D Cardiac CT Angiography for the Detection of Left Ventricular Segmental Wall Motion Abnormality.

Purpose: To investigate whether endocardial regional shortening computed from four-dimensional (4D) CT angiography (RS) can be used as a decision classifier to detect the presence of left ventricular (LV) wall motion abnormalities (WMAs).

Materials And Methods: One hundred electrocardiographically gated cardiac 4D CT studies (mean age, 59 years ± 14 [SD]; 61 male patients) conducted between April 2018 and December 2020 were retrospectively evaluated. Three experts labeled LV wall motion in each of the 16 American Heart Association (AHA) segments as normal or abnormal; they also measured peak RS across one heartbeat in each segment.

Detection of left ventricular wall motion abnormalities from volume rendering of 4DCT cardiac angiograms using deep learning.

Background: The presence of left ventricular (LV) wall motion abnormalities (WMA) is an independent indicator of adverse cardiovascular events in patients with cardiovascular diseases. We develop and evaluate the ability to detect cardiac wall motion abnormalities (WMA) from dynamic volume renderings (VR) of clinical 4D computed tomography (CT) angiograms using a deep learning (DL) framework.

Methods: Three hundred forty-three ECG-gated cardiac 4DCT studies (age: 61 ± 15, 60.

Four-dimensional computed tomography of the left ventricle, Part I: Motion artifact reduction.

Purpose: Standard four-dimensional computed tomography (4DCT) cardiac reconstructions typically include spiraling artifacts that depend not only on the motion of the heart but also on the gantry angle range over which the data was acquired. We seek to reduce these motion artifacts and, thereby, improve the accuracy of left ventricular wall positions in 4DCT image series.

Methods: We use a motion artifact reduction approach (ResyncCT) that is based largely on conjugate pairs of partial angle reconstruction (PAR) images.

Four-dimensional computed tomography of the left ventricle, Part II: Estimation of mechanical activation times.

Purpose: We demonstrate the viability of a four-dimensional X-ray computed tomography (4DCT) imaging system to accurately and precisely estimate mechanical activation times of left ventricular (LV) wall motion. Accurate and reproducible timing estimates of LV wall motion may be beneficial in the successful planning and management of cardiac resynchronization therapy (CRT).

Methods: We developed an anthropomorphically accurate in silico LV phantom based on human CT images with programmed septal-lateral wall dyssynchrony.

Automated cardiac volume assessment and cardiac long- and short-axis imaging plane prediction from electrocardiogram-gated computed tomography volumes enabled by deep learning.

Aims: To develop an automated method for bloodpool segmentation and imaging plane re-slicing of cardiac computed tomography (CT) via deep learning (DL) for clinical use in coronary artery disease (CAD) wall motion assessment and reproducible longitudinal imaging.

Methods And Results: One hundred patients who underwent clinically indicated cardiac CT scans with manually segmented left ventricle (LV) and left atrial (LA) chambers were used for training. For each patient, long-axis (LAX) and short-axis planes were manually defined by an imaging expert.

Constructing a Human Atrial Fibre Atlas.

Atrial anisotropy affects electrical propagation patterns, anchor locations of atrial reentrant drivers, and atrial mechanics. However, patient-specific atrial fibre fields and anisotropy measurements are not currently available, and consequently assigning fibre fields to atrial models is challenging. We aimed to construct an atrial fibre atlas from a high-resolution DTMRI dataset that optimally reproduces electrophysiology simulation predictions corresponding to patient-specific fibre fields, and to develop a methodology for automatically assigning fibres to patient-specific anatomies.

Precise measurement of coronary stenosis diameter with CCTA using CT number calibration.

Purpose: Coronary x-ray computed tomography angiography (CCTA) continues to develop as a noninvasive method for the assessment of coronary vessel geometry and the identification of physiologically significant lesions. The uncertainty of quantitative lesion diameter measurement due to limited spatial resolution and vessel motion reduces the accuracy of CCTA diagnoses. In this paper, we introduce a new technique called computed tomography (CT)-number-Calibrated Diameter to improve the accuracy of the vessel and stenosis diameter measurements with CCTA.

The impact of small motion on the visualization of coronary vessels and lesions in cardiac CT: A simulation study.

Purpose: Coronary x-ray computed tomography angiography (CCTA) is used to non-invasively assess coronary artery geometry and has, combined with computational modeling, demonstrated the potential to identify physiologically significant lesions. These measurements require robust and accurate coronary imaging and delineation of vessels despite the presence of small motion. This simulation study characterizes the impact of small, uncorrected vessel drifts during data acquisition on the assessment of vessel intensity, diameter, and shape.