A5080119870- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- AI in cancer detection
- Digital Radiography and Breast Imaging
- MRI in cancer diagnosis
- Global Cancer Incidence and Screening
- Radiation Dose and Imaging
- Medical Imaging Techniques and Applications
- Systemic Sclerosis and Related Diseases
- Urological Disorders and Treatments
- Cardiac, Anesthesia and Surgical Outcomes
- Aortic aneurysm repair treatments
- Urinary Bladder and Prostate Research
- Autopsy Techniques and Outcomes
- Cardiac Imaging and Diagnostics
- Pleural and Pulmonary Diseases
- Atomic and Subatomic Physics Research
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Forensic Entomology and Diptera Studies
- Photoacoustic and Ultrasonic Imaging
- Lung Cancer Diagnosis and Treatment
- Pelvic floor disorders treatments
University Hospital of Zurich
2022-2024
University of Zurich
2022-2024
Kantonsspital Baden
2024
Society of Interventional Radiology
2022
Maastricht University
2022
Siemens Healthcare (Germany)
2022
Background An iterative reconstruction (IR) algorithm was introduced for clinical photon-counting detector (PCD) CT. Purpose To investigate the image quality and optimal strength level of a quantum IR (QIR; Siemens Healthcare) virtual monoenergetic images polychromatic (T3D) in phantom patients undergoing portal venous abdominal PCD Materials Methods In this retrospective study, noise power spectrum (NPS) measured water-filled phantom. Consecutive oncologic who underwent CT between March...
The aim of this study was to determine the potential photon-counting detector computed tomography (PCD-CT) for radiation dose reduction compared with conventional energy-integrated CT (EID-CT) in assessment interstitial lung disease (ILD) systemic sclerosis (SSc) patients.In retrospective study, SSc patients receiving a follow-up noncontrast chest examination on PCD-CT were included between May 2021 and December 2021. Baseline scans generated dual-source EID-CT by selecting tube current-time...
Radiation dose should be as low reasonably achievable. With the invention of photon-counting detector computed tomography (PCD-CT), radiation may considerably reduced.
The purpose was to investigate the safety and feasibility of transurethral injections autologous muscle precursor cells (MPCs) into external urinary sphincter (EUS) treat stress incontinence (SUI) in female patients.Prospective randomised phase I clinical trial. Standardised 1-h pad test, International Consultation on Incontinence Questionnaire-Urinary Short Form (ICIQ-UI-SF), urodynamic study, MRI pelvis were performed at baseline 6 months after treatment. MPCs gained through open biopsy...
Purpose To compare image quality, diagnostic performance, and conspicuity between single-energy multi-energy images for endoleak detection at CT angiography (CTA) after endovascular aortic repair (EVAR). Materials Methods In this single-center prospective randomized controlled trial, individuals undergoing CTA EVAR August 2020 May 2022 were allocated to imaging using either low-kilovolt (SEI; 80 kV, group A) or low-kiloelectron volt virtual monoenergetic (VMI) 40 50 keV from (80/Sn150 B)....
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. accordance with Breast Imaging Reporting & Data System (BI-RADS), it should be visually classified into four classes. The susceptibility such an assessment inter-reader variability highlights urgent need for standardized classification algorithm. this retrospective study, first post-contrast...
Abstract Objectives Development of automated segmentation models enabling standardized volumetric quantification fibroglandular tissue (FGT) from native volumes and background parenchymal enhancement (BPE) subtraction dynamic contrast-enhanced breast MRI. Subsequent assessment the developed in context FGT BPE Breast Imaging Reporting Data System (BI-RADS)-compliant classification. Methods For training validation attention U-Net models, data coming a single 3.0-T scanner was used. testing,...
The aim of this study was to investigate the potential a machine learning algorithm classify breast cancer solely by presence soft tissue opacities in mammograms, independent other morphological features, using deep convolutional neural network (dCNN). Soft were classified based on their radiological appearance ACR BI-RADS atlas. We included 1744 mammograms from 438 patients create 7242 icons manual labeling. sorted into three categories: "no opacities" (BI-RADS 1), "probably benign 2/3) and...
Abstract Background We investigated whether features derived from texture analysis (TA) can distinguish breast density (BD) in spiral photon-counting computed tomography (PC-BCT). Methods In this retrospective single-centre study, we analysed 10,000 images 400 PC-BCT examinations of 200 patients. Images were categorised into four-level scale ( a – d ) using Breast Imaging Reporting and Data System (BI-RADS)-like criteria. After manual definition representative regions interest, 19 (TFs)...
Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess percent (PBD) and mammary glands volume (MGV) according patient's age quadrant. We propose a regression model estimate PBD MGV as function age.The composition in 1027 spiral CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The (BTV), MGV, breasts measured entire each four quadrants. three...
The aim of this study was to investigate the potential a machine learning algorithm accurately classify parenchymal density in spiral breast-CT (BCT), using deep convolutional neural network (dCNN). In retrospectively designed study, 634 examinations 317 patients were included. After image selection and preparation, 5589 images from different BCT sorted by four-level scale, ranging A D, ACR BI-RADS-like criteria. Subsequently four dCNN models (differences optimizer spatial resolution)...
The purpose of this study was to determine the feasibility a deep convolutional neural network (dCNN) accurately detect abnormal axillary lymph nodes on mammograms. In retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled three classes: (1) "breast tissue", (2) "benign nodes", and (3) "suspicious nodes". Following data preprocessing, dCNN model trained validated with 5385 images. Subsequently, tested "real-world" dataset performance...
Background: After breast conserving surgery (BCS), surgical clips indicate the tumor bed and, thereby, most probable area for relapse. The aim of this study was to investigate whether a U-Net-based deep convolutional neural network (dCNN) may be used detect in follow-up mammograms after BCS. Methods: 884 and 517 tomosynthetic images depicting calcifications were manually segmented classified. A segmentation trained with 922 validated 394 images. An external test dataset consisting 39...
Abstract 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) systemic sclerosis (SSc) patients. Methods Sixty-six SSc patients who underwent ILD-CT screening on first-generation PCD-CT were retrospectively included. Scans performed UHR mode at 100 kVp with two different matrix sizes (512×512 1024x1024) reconstructed slice thicknesses 1.5 0.2 mm. Image noise,...
Abstract The aims of this study are to retrospectively evaluate the diagnostic value T 1 - and 2 -weighted 3-T magnetic resonance imaging (MRI) for postmortem detection myocardial infarction (MI) in terms sensitivity specificity compare MRI appearance infarct area with age stages. Postmortem examinations ( n = 88) were reviewed presence or absence MI by two raters blinded autopsy results. calculated using results as gold standard. A third rater, who was not findings, all cases which detected...