A5021109295- Advanced MRI Techniques and Applications
- Advanced Radiotherapy Techniques
- Medical Imaging Techniques and Applications
- Radiomics and Machine Learning in Medical Imaging
- MRI in cancer diagnosis
- Atomic and Subatomic Physics Research
- Radiation Therapy and Dosimetry
- Advanced X-ray and CT Imaging
- Advanced Neuroimaging Techniques and Applications
- Lung Cancer Diagnosis and Treatment
- Radiation Dose and Imaging
- Colorectal Cancer Surgical Treatments
- Nuclear Physics and Applications
- Head and Neck Cancer Studies
- Advanced NMR Techniques and Applications
- Digital Radiography and Breast Imaging
- Colorectal Cancer Screening and Detection
- Advances in Oncology and Radiotherapy
- Cerebrovascular and Carotid Artery Diseases
- Vascular Malformations Diagnosis and Treatment
- Prostate Cancer Diagnosis and Treatment
- Liver Disease Diagnosis and Treatment
- Ultrasound and Hyperthermia Applications
- Radiopharmaceutical Chemistry and Applications
- Ultrasound Imaging and Elastography
Catharina Ziekenhuis
2020-2025
Radboud University Nijmegen
2020-2025
Eindhoven University of Technology
2025
University Medical Center Utrecht
2013-2022
Heidelberg University
2016-2019
University Hospital Heidelberg
2016-2019
Utrecht University
2016-2019
Wellcome Centre for Integrative Neuroimaging
2011-2016
University of Oxford
2011-2016
Maastricht University Medical Centre
2008
The integration of 1.5 T MRI functionality with a radiotherapy linear accelerator (linac) has been pursued since 1999 by the UMC Utrecht in close collaboration Elekta and Philips. idea behind this integrated device is to offer unrivalled, online real-time, soft-tissue visualization tumour surroundings for more precise radiation delivery. proof concept was given 2009 demonstrating simultaneous irradiation MR imaging on phantoms, then further developed commercialized Elekta. aim work...
Magnetic Resonance linear accelerator (MR-linac) systems represent a new type of technology that allows for online MR-guidance high precision radiotherapy (RT). Currently, the first MR-linac installations are being introduced clinically. Since imaging performance these integrated is critical their application, thorough commissioning MRI essential. However, guidelines on MR-guided RT not yet defined and data MR-linacs available.Here we describe comprehensive protocol, which contains standard...
Respiratory motion introduces substantial uncertainties in abdominal radiotherapy for which traditionally large margins are used. The MR-Linac will open up the opportunity to acquire high resolution MR images just prior radiation and during treatment. However, volumetric MRI time series not able characterize 3D tumor organ-at-risk with sufficient temporal resolution. In this study we propose a method estimate deformation vector fields (DVFs) spatial based on fast 2D imaging subject-specific...
Online adaptive radiotherapy using the 1.5 Tesla MR-linac is feasible for SBRT (5 × 7 Gy) of pelvic lymph node oligometastases. The workflow allows full online planning based on daily anatomy. Session duration less than 60 min. Quality assurance tests, including independent 3D dose calculations and film measurements were passed.
Purpose: MR-guided Radiation Therapy (MRgRT) allows for high-precision radiotherapy under real-time MR visualization. This enables margin reduction and subsequent dose escalation which may lead to higher tumor control less toxicity. The Unity MR-linac (Elekta AB, Stockholm, Sweden) integrates a linear accelerator with 1.5T diagnostic quality MRI an online adaptive workflow. A prospective international registry was established facilitate the evidence-based implementation of into clinical...
Background and purposeDaily online adaptation of the clinical target volume (CTV) using MR-guided radiotherapy enables margin reduction planning (PTV). This study describes implementation initial experience on 1.5T MR-linac evaluates treatment time, patient compliance, coverage, including an assessment reduction.Materials methodsPatients were treated a (7MV, FFF). At each fraction 3D T2 weighted (T2w) MR-sequence was acquired which CTV adapted after deformable registration contours from...
To enable magnetic resonance imaging (MRI)-guided radiotherapy with real-time adaptation, motion must be quickly estimated low latency. The estimate is used to adapt the radiation beam current anatomy, yielding a more conformal dose distribution. As MR acquisition largest component of latency, deep learning (DL) may reduce total latency by enabling much higher undersampling factors compared conventional reconstruction and estimation methods. benefit DL on image was investigated for obtaining...
To quantify intrafractional motion to determine population-based radiotherapy treatment margins for head-and-neck tumors.Cine MR imaging was performed in 100 patients with cancer on a 3T scanner setup. images were analyzed using deformable image registration (optical flow algorithm) and changes tumor contour position used calculate the motion. The together patient setup errors (450 patients) PTV margins.Tumor quantified 84 (12/43/29 nasopharynx/oropharynx/larynx, 16 excluded). mean maximum...
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.
Magnetic resonance guided adaptive radiotherapy (MRgART) enables hypofractionated prostate by daily contour adaptation. The MRgART workflow, however, is labour intensive and in many institutes still requires the presence of radiation oncologist (RO). Transferring online adaptation task to therapist (RTT) will release clinician from attending each treatment fraction making MRgRT more efficient cost effective. In this study we investigate viability RTT-led on a low-field MR-linac, assessing...
Labeling cone-beam computed tomography (CBCT) images is challenging due to poor image quality. Training auto-segmentation models without labelled data often involves deep-learning generate synthetic CBCTs (sCBCT) from planning CTs (pCT), which can result in anatomical mismatches and inaccurate labels. To prevent this issue, study assesses an model for female pelvic CBCT scans exclusively trained on delineated pCTs, were transformed into sCBCT using a physics-driven approach. replicate noise...
Purpose The aim of this study was to develop, implement, and demonstrate a three‐dimensional (3D) extension the readout‐segmented echo‐planar imaging (rs‐EPI) sequence for diffusion imaging. Theory Methods Potential k‐space acquisition schemes were assessed by simulating their associated spatial point spread functions. Motion‐induced phase artifacts also simulated test navigator corrections real‐time reordering relative cardiac cycle. strategy preferentially chooses readout segments closer...
Hybrid MR-linac systems can use fast dynamic MR sequences for tumor tracking and adapt the radiation treatment in real-time. For this imaging latency must be as short possible. This work describes how different acquisition parameters influence latency. First, was measured Cartesian readouts with phase encode orderings linear, reverse-linear, high-low. Second, radial linear golden angle profile orderings. To reduce latency, a spatio-temporal (k-t) filter that suppresses k-space center of...