Benjamin Böttcher

Reproducibility of cardiac volumetric parameters derived from fully automatically prescribed image planes: a direct comparison to manual planning at 1.5-T and 3-T MRI

Karolin K. Deyerberg, Felix G. Meinel, Lena-Maria Watzke, Ann‐Christin Klemenz et autres

OBJECTIVES: The prescription of cardiac MRI (CMR) image planes is essential for comparable volumetric assessment, but manual planning is time-consuming and error-prone. This prospective single-center study evaluated automated planning and its impact on the reproducibility of volumetric parameters derived from CMR. MATERIALS …

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Fully Automated Plane Prescription in Cardiac MRI : A Prospective Cohort Study

Benjamin Böttcher, Felix G. Meinel, Karolin K. Deyerberg, Lena‐Maria Watzke et autres

BACKGROUND: Accurate plane positioning is important for high-quality cardiac MRI images but requires specialized training, limiting accessibility. PURPOSE: To evaluate an automated plane positioning tool and compare it with manual planning. STUDY TYPE: Prospective. POPULATION: Fifty-seven healthy volunteers (28 males; median age …

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Intra- and inter-field strength reproducibility of deep-learning based real-time cardiac MRI cine sequences with breath hold and in free breathing

Lena‐Maria Watzke, Ann‐Christin Klemenz, Karolin K. Deyerberg, Benjamin Böttcher et autres

To assess intra- and inter-field strength reproducibility of volumetric parameters using deep-learning-based real-time cardiac cine MRI during breath-hold (BH) and free-breathing (FB). In this prospective single-center study, 56 healthy adults underwent cardiac MRI at 1.5 T. Of these, 33 had a follow-up …

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Impact of Deep Learning–based Artificial Intelligence Assistance on Reader Agreement in Coronary CT Angiography Interpretation

Roberto Farì, Marly van Assen, Raymundo A. Quintana-Quezada, Philipp von Knebel Doeberitz et autres

A fully automated deep learning algorithm significantly enhanced interreader agreement for coronary artery disease detection and stenosis classification, potentially improving diagnostic consistency at coronary CT angiography.

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Transforming CT imaging with deep learning: Noise reduction, artifact management, and clinical applications – A comprehensive review

Asutosh Sahu, Shobhit Mathur, Hiroyuki Takaoka, Joji Ota et autres

Deep Learning Reconstruction (DLR) is emerging as a significant advancement in Computed Tomography (CT) imaging by addressing crucial issues such as noise reduction, artifact suppression, and dose optimization. Traditional CT reconstruction methods, namely Filtered Back Projection (FBP) and the relatively newer Iterative …

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A precious ring: Vieussens’ ring and left main coronary artery atresia – a case report

Danagul Zhexenova, Benjamin Böttcher, Felix G. Meinel

Introduction Atresia of the left main coronary artery (LMCA) refers to a congenital defect in which both the LMCA and its coronary ostium fail to develop, despite the presence of anatomically normal left anterior descending and circumflex branches. This condition is exceedingly …

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Pushing the limits of cardiac MRI: deep-learning based real-time cine imaging in free breathing vs breath hold

Ann‐Christin Klemenz, Lena‐Maria Watzke, Karolin K. Deyerberg, Benjamin Böttcher et autres

OBJECTIVE: To evaluate deep-learning (DL) based real-time cardiac cine sequences acquired in free breathing (FB) vs breath hold (BH). MATERIALS AND METHODS: In this prospective single-centre cohort study, 56 healthy adult volunteers were investigated on a 1.5-T MRI scanner. A set of …

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3D cinematic reconstructions of cardiovascular CT presented in augmented reality: subjective assessment of clinical feasibility and potential use cases

Benjamin Böttcher, Marly van Assen, Roberto Farì, Philipp L. von Knebel Doeberitz et autres

Augmented reality (AR) is a new technique enabling interaction with three-dimensional (3D) holograms of cinematic rendering (CR) reconstructions. Research in this field is in its very early steps, and data is scarce. We evaluated image quality, usability, and potential applications of AR …

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Repeatability of radiomic features in myocardial T1 and T2 mapping

Mathias Manzke, Fabian Christopher Laqua, Benjamin Böttcher, Ann‐Christin Klemenz et autres

PURPOSE: To investigate the test-retest repeatability of radiomic features in myocardial native T1 and T2 mapping. METHODS: In this prospective study, 50 healthy volunteers (29 women and 21 men, mean age 39.4 ± 13.7 years) underwent two identical cardiac magnetic resonance imaging …

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Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases

Benjamin Böttcher, Marly van Assen, Roberto Farì, Philipp L. von Knebel Doeberitz et autres

BACKGROUND: This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT). METHODS: Twenty-eight patients with verified pattern-based ILD diagnoses were …

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Evaluation of a AI-based plane prescription tool in cardiac MRI

Benjamin Böttcher, Ann‐Christin Klemenz, Karolin K. Deyerberg, Lena‐Maria Watzke et autres

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Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data

Mathias Manzke, Simon Iseke, Benjamin Böttcher, Ann‐Christin Klemenz et autres

To develop a deep learning-based model capable of segmenting the left ventricular (LV) myocardium on native T1 maps from cardiac MRI in both long-axis and short-axis orientations. Models were trained on native myocardial T1 maps from 50 healthy volunteers and 75 patients …

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