Jens Wetzl

A comparison of vendor artificial intelligence solutions for automated post-processing of short-axis cine images in cardiovascular magnetic resonance imaging

Thomas Hadler, Clemens Ammann, Hadil Saad, Yashraj Bhoyroo et autres

Abstract Automated segmentation of cardiac magnetic resonance (CMR) imaging is integrated into clinical workflows, yet comparative performance across vendor AI solutions remains insufficiently characterized. This study assessed three models (two commercial, one research) for short-axis cine segmentation in a diverse cohort of …

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Deep‐Learning‐Based Image Reconstruction to Improve End‐Diastolic and Systolic Cardiac T1 Mapping

Daniel Amsel, Jens Wetzl, Daniel Giese, Christoph Tillmanns et autres

ABSTRACT Purpose To develop an image reconstruction method that enables increased spatial resolution cardiac T1 mapping in both the end‐diastolic and systolic phase, that shows high T1 agreement with the clinical standard. The resolution gain is achieved by increasing the acceleration rate …

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Equivalence of feature-tracking–derived myocardial strain across spatial resolution and compressed sensing acceleration

Baptiste O. P. Wyssa, Lindsey A. Crowe, Jean-François Deux, Teodora Chițiboi et autres

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Which resolution is needed for a 3D MRA based automated CMR planning

Karen J. Kettless, Jens Wetzl, Daniel Giese, M Schmidt et autres

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Localizer meets diagnostic scan: 3D MRA slice planning for a fast, ECG-free, fully automated cardiac exam

Jens Wetzl, Daniel Giese, Karen J. Kettless, Jakob Møllenbach Møller et autres

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Evaluation of Three Artificial Intelligence Solutions for Automated Short-Axis Cine Segmentation in Cardiovascular Magnetic Resonance Imaging

Thomas Hadler, Clemens Ammann, Hadil Saad, Yashraj Bhoyroo et autres

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Fully automated free-breathing cardiac magnetic resonance imaging at 3T: A prospective randomized study of image quality, efficiency, and workflow burden

Keyi Li, Wenli Zhou, Xinling Yang, Lin Chen et autres

BACKGROUND: Automation in cardiovascular magnetic resonance (CMR) scans holds the potential to improve examination efficiency and workflow consistency. Prospective clinical evidence validating automated scan workflows in routine CMR practice remains limited. METHODS: In this prospective randomized study, consecutive patients referred for non-stress …

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Assessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypes

Hadil Saad, Clemens Ammann, Thomas Hadler, Yashraj Bhoyroo et autres

PURPOSE: To introduce an artificial intelligence-based cardiovascular magnetic resonance segmentation algorithm (Nick) for automated quantification of function and parametric mapping across cardiac phenotypes reflecting clinical routine. METHODS: Nick was compared to manual gold standard (GS) segmentations in 359 multi-centre cases at 1.5T …

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Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection

Andreea Bianca Popescu, Andreas Seitz, M. Becker, Heiko Mahrholdt et autres

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Coronary Artery Blood Flow Imaging Using 3D Flow MRI

Denise Lichthardt, Michaela Schmidt, Jens Wetzl, Peter Speier et autres

PURPOSE: In this work, a 3D phase contrast (PC) technique is presented, which enables flow velocity measurements in three spatial directions, simultaneously covering the proximal left (LCA) and right (RCA) coronary arteries and yielding both morphological and flow information. METHODS: Data is …

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Quantitative Confounder Analysis of Electrocardiogram Signals in Cardiac Magnetic Resonance at 1.5, 3 and 7 T—Assessing Standardized Electrode Positions and Sequence Types—Towards Quality Assurance

Richard Hickstein, Stephanie Wiesemann, Darian Viezzer, Denise Kleindienst et autres

BACKGROUND: The electrocardiogram (ECG) used for gating in cardiac MRI may be compromised by multiple confounders inside the scanner bore. PURPOSE: To quantify the influence of magnetic field strengths (1.5 T/3 T/7 T), standardized electrode positions, and imaging sequences on ECG signals …

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High spatial resolution cardiac T1 mapping on 1.5T and 3T utilizing deep learning-based image reconstruction

Daniel Amsel, Jens Wetzl, Daniel Giese, Rolf Gebker et autres

Motivation: MOLLI T1 mapping of the heart has limited spatial resolution due to the limitation of acquiring inversion recovery images in single-shot acquisitions. Goal(s): To increase the spatial resolution of T1 maps using k-space undersampling and reconstruction with a dedicated neural network. …