Anna Landsmann
YOU?
Author Swipe
View article: Assessment of interstitial lung disease in a systemic sclerosis patient cohort using photon-counting detector CT with ultra-high resolution and a 1024-pixel image matrix
Assessment of interstitial lung disease in a systemic sclerosis patient cohort using photon-counting detector CT with ultra-high resolution and a 1024-pixel image matrix Open
Objective This study assessed the potential of ultra-high resolution (UHR) and a 1024-matrix in photon-counting-detector CT (PCD-CT) for evaluating interstitial lung disease (ILD) in systemic sclerosis (SSc) patients. Methods Sixty-six SSc…
View article: Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network
Automatic Detection of Post-Operative Clips in Mammography Using a U-Net Convolutional Neural Network Open
Background: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, the most probable area for tumor relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural netwo…
View article: Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake
Explainable Precision Medicine in Breast MRI: A Combined Radiomics and Deep Learning Approach for the Classification of Contrast Agent Uptake Open
In DCE-MRI, the degree of contrast uptake in normal fibroglandular tissue, i.e., background parenchymal enhancement (BPE), is a crucial biomarker linked to breast cancer risk and treatment outcome. In accordance with the Breast Imaging Rep…
View article: Multi-Energy Low–Kiloelectron Volt versus Single-Energy Low-Kilovolt Images for Endoleak Detection at CT Angiography of the Aorta
Multi-Energy Low–Kiloelectron Volt versus Single-Energy Low-Kilovolt Images for Endoleak Detection at CT Angiography of the Aorta Open
Purpose To compare image quality, diagnostic performance, and conspicuity between single-energy and multi-energy images for endoleak detection at CT angiography (CTA) after endovascular aortic repair (EVAR). Materials and Methods In this s…
View article: Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient’s Age and Breast Quadrant
Quantitative Study on the Breast Density and the Volume of the Mammary Gland According to the Patient’s Age and Breast Quadrant Open
Objectives: Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patie…
View article: Transurethral injection of autologous muscle precursor cells for treatment of female stress urinary incontinence: a prospective phase I clinical trial
Transurethral injection of autologous muscle precursor cells for treatment of female stress urinary incontinence: a prospective phase I clinical trial Open
Introduction and hypothesis The purpose was to investigate the safety and feasibility of transurethral injections of autologous muscle precursor cells (MPCs) into the external urinary sphincter (EUS) to treat stress urinary incontinence (S…
View article: Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle
Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle Open
Deep convolutional networks can be used to detect and classify benign and suspicious MC in breast-CT images.
View article: BI-RADS-Based Classification of Mammographic Soft Tissue Opacities Using a Deep Convolutional Neural Network
BI-RADS-Based Classification of Mammographic Soft Tissue Opacities Using a Deep Convolutional Neural Network Open
The aim of this study was to investigate the potential of a machine learning algorithm to classify breast cancer solely by the presence of soft tissue opacities in mammograms, independent of other morphological features, using a deep convo…
View article: Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network
Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network Open
The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral…
View article: Potential of Photon-Counting Detector CT for Radiation Dose Reduction for the Assessment of Interstitial Lung Disease in Patients With Systemic Sclerosis
Potential of Photon-Counting Detector CT for Radiation Dose Reduction for the Assessment of Interstitial Lung Disease in Patients With Systemic Sclerosis Open
Objective The aim of this study was to determine the potential of photon-counting detector computed tomography (PCD-CT) for radiation dose reduction compared with conventional energy-integrated detector CT (EID-CT) in the assessment of int…
View article: Quantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image Quality
Quantum Iterative Reconstruction for Abdominal Photon-counting Detector CT Improves Image Quality Open
Background An iterative reconstruction (IR) algorithm was introduced for clinical photon-counting detector (PCD) CT. Purpose To investigate the image quality and the optimal strength level of a quantum IR algorithm (QIR; Siemens Healthcare…
View article: Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density?
Applied Machine Learning in Spiral Breast-CT: Can We Train a Deep Convolutional Neural Network for Automatic, Standardized and Observer Independent Classification of Breast Density? Open
The aim of this study was to investigate the potential of a machine learning algorithm to accurately classify parenchymal density in spiral breast-CT (BCT), using a deep convolutional neural network (dCNN). In this retrospectively designed…