A5035413133- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- Dental Radiography and Imaging
- Anatomy and Medical Technology
- Radiation Dose and Imaging
- Advanced Radiotherapy Techniques
- Medical Imaging and Analysis
- Cleft Lip and Palate Research
- Orthodontics and Dentofacial Orthopedics
- Lung Cancer Diagnosis and Treatment
- Injury Epidemiology and Prevention
- Trauma and Emergency Care Studies
- 3D Printing in Biomedical Research
- Autopsy Techniques and Outcomes
- Facial Trauma and Fracture Management
- Additive Manufacturing and 3D Printing Technologies
- Dental Implant Techniques and Outcomes
- Nasal Surgery and Airway Studies
- Digital Radiography and Breast Imaging
- Reconstructive Facial Surgery Techniques
- Craniofacial Disorders and Treatments
- Esophageal Cancer Research and Treatment
- Reconstructive Surgery and Microvascular Techniques
- COVID-19 diagnosis using AI
Eindhoven University of Technology
2021-2024
Amsterdam University Medical Centers
2018-2022
Centrum Wiskunde & Informatica
2018-2022
Amsterdam UMC Location VUmc
2014-2021
College of Western Idaho
2019
University of Amsterdam
2017-2019
Vrije Universiteit Amsterdam
2017-2019
Academic Center for Dentistry Amsterdam
2017-2019
University of Sfax
2018
National Medical Research Center of Dentistry and Maxillofacial Surgery
2017
Purpose Imaging phantoms are widely used for testing and optimization of imaging devices without the need to expose humans irradiation. However, commercially available commonly manufactured in simple, generic forms sizes therefore do not resemble clinical situation many patients. Methods Using 3D printing techniques, we created a life‐size phantom based on CT scan thorax from patient with lung cancer. It was assembled bony structures printed gypsum, consisting airways, blood vessels >1...
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density data accurate dose calculations. However, accurately representing patient anatomy challenging, especially adaptive radiotherapy, where CT not acquired daily. Magnetic resonance imaging (MRI) provides superior soft-tissue contrast. Still, it...
Medical additive manufacturing requires standard tessellation language (STL) models. Such models are commonly derived from computed tomography (CT) images using thresholding. Threshold selection can be performed manually or automatically. The aim of this study was to assess the impact manual and default threshold on reliability accuracy skull STL different CT technologies. One female one male human cadaver head were imaged multi-detector row CT, dual-energy two cone-beam scanners. Four...
In order to attain anatomical models, surgical guides and implants for computer-assisted surgery, accurate segmentation of bony structures in cone-beam computed tomography (CBCT) scans is required. However, this image step often impeded by metal artifacts. Therefore, study aimed develop a mixed-scale dense convolutional neural network (MS-D network) bone CBCT affected artifacts.Training data were acquired from 20 dental An experienced medical engineer segmented the all using global...
To develop a deep learning algorithm for anatomy recognition in thoracoscopic video frames from robot-assisted minimally invasive esophagectomy (RAMIE) procedures using learning.
The objective of the present study was to assess and compare effective doses in wrist region resulting from conventional radiography device, multislice computed tomography (MSCT) device two cone beam (CBCT) devices using MOSFET dosemeters a custom made anthropomorphic RANDO phantom according ICRP 103 recommendation. dose for 1.0 μSv. NewTom 5 G CBCT ranged between 0.7 μSv 1.6 μSv, Planmed Verity 2.4 MSCT 8.6 When compared with AP- LAT projections radiographic this showed an 8.6-fold standard...
The reconstruction of computed tomography (CT) images is an active area research. Following the rise deep learning methods, many data-driven models have been proposed in recent years. In this work, we present results a data challenge that organized, bringing together algorithm experts from different institutes to jointly work on quantitative evaluation several methods two large, public datasets during ten day sprint. We focus applications CT, namely, low-dose CT and sparse-angle CT. This...
To improve cone-beam computed tomography (CBCT), deep-learning (DL)-models are being explored to generate synthetic CTs (sCT). The sCT evaluation is mainly focused on image quality and CT number accuracy. However, correct representation of daily anatomy the CBCT also important for sCTs in adaptive radiotherapy. aim this study was emphasize importance anatomical correctness by quantitatively assessing scans generated from using different paired unpaired dl-models.
Purpose Additive manufactured (AM) skull models are increasingly used to plan complex surgical cases and design custom implants. The accuracy of such constructs depends on the standard tessellation language (STL) model, which is commonly obtained from computed tomography (CT) data. aims this study were assess image quality STL acquired using different CT scanners acquisition parameters. Design/methodology/approach Images three dry human skulls two multi-detector row (MDCT) scanners, a dual...
To assess the accuracy of five different computed tomography (CT) scanners for evaluation oropharynx morphology.An existing cone-beam (CBCT) data set was used to fabricate an anthropomorphic phantom upper airway volume that extended from uvula epiglottis (oropharynx) with known dimensions (gold standard). This scanned using two multi-detector row (MDCT) (GE Discovery CT750 HD, Siemens Somatom Sensation) and three CBCT (NewTom 5G, 3D Accuitomo 170, Vatech PaX Zenith 3D). All CT images were...
Over the past decade, convolutional neural networks (CNNs) have revolutionized field of medical image segmentation. Prompted by developments in computational resources and availability large datasets, a wide variety different two-dimensional (2D) three-dimensional (3D) CNN training strategies been proposed. However, systematic comparison impact these on segmentation performance is still lacking. Therefore, this study aimed to compare eight strategies, namely 2D (axial, sagittal coronal...
Abstract The non-invasively measured initial systolic time interval (ISTI) reflects a difference between the electrical and pumping activity of heart depends on cardiac preload, afterload, autonomic nervous control training level. However, duration ISTI has not yet been compared to other markers cycle. present study gauges by comparing end point this interval, C-point, with cycle obtained echocardiography. rate 16 healthy subjects was varied means an exercise stimulus. It found that...
The aim of this study was to assess the reliability and accuracy three different imaging software packages for three-dimensional analysis upper airway using CBCT images.To packages, 15 NewTom 5G® (QR Systems, Verona, Italy) data sets were randomly retrospectively selected. Two observers measured volume, minimum cross-sectional area length Amira® (Visage Imaging Inc., Carlsbad, CA), 3Diagnosys® (3diemme, Cantu, OnDemand3D® (CyberMed, Seoul, Republic Korea) packages. intra- inter-observer...
Additively manufactured bone models, implants and drill guides are becoming increasingly popular amongst maxillofacial surgeons dentists. To date, such constructs commonly using CT technology that induces ionizing radiation. Recently, ultrashort echo time (UTE) MRI sequences have been developed allow radiation-free imaging of facial bones. The aim the present study was to assess feasibility UTE for medical additive manufacturing (AM).Three morphologically different dry human mandibles were...
Surgical reconstruction of cartilaginous defects remains a major challenge. In the current study, we aimed to identify an imaging strategy for development patient-specific constructs that aid in nasal deformities. Magnetic Resonance Imaging (MRI) was performed on human cadaver head find optimal MRI sequence cartilage. This subsequently used volunteer. Images both were assessed by three independent researchers determine measurement error and total segmentation time. Three dimensionally (3D)...
Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory set-up to provide not only from single object but class objects natural variability. For each walnut, CB projections on three different source orbits acquired cone angles as well being able compute artefact-free, high-quality ground...
Background and Objectives: Deep learning is being increasingly used for deformable image registration unsupervised approaches, in particular, have shown great potential. However, the of abdominopelvic Computed Tomography (CT) images remains challenging due to larger displacements compared those brain or prostate Magnetic Resonance Imaging datasets that are typically considered as benchmarks. In this study, we investigate use commonly deep framework VoxelMorph a longitudinal CT dataset...