- Oral and Maxillofacial Surgery
- Plastic, Reconstructive and Handsurgery
- Medical Psychology and Psychotherapy (MPP)
- Health Decision Sciences
- Public Health
- Genetic Epidemiology
- Clinical Epidemiology
- Medical Informatics
- Musculoskeletal Science
- Rehabilitation Medicine
- Surgical Research
- Gastroenterology & Hepatology
- Transplantation Medicine
- Periconception and Prenatal Medicine, Obstetrics and Reproduction
- Pediatric Research
- Gynaecology and Gynaecologic Oncology
- Medical Oncology
- Cardiovascular research
- Endocrinology, Neuro-Endocrine Immunology, and Clinical and Medical Immunology
Theme 1: Musculoskeletal Imaging
Principal investigator: Dr. E.H.G. Oei (firstname.lastname@example.org)
The Advanced Musculoskeletal Imaging Research Erasmus MC (ADMIRE; www.admire-group.com group of the Department of Radiology & Nuclear Medicine focuses on advanced imaging of musculoskeletal diseases, particularly those with a large burden for patients and society, such as osteoarthritis, osteoporosis, and sports injuries. There is a large need for sensitive and accurate imaging biomarkers in order to detect these diseases earlier, providing better understanding of their etiology and pathophysiology, and discovery of new therapies. Therefore, we develop, improve, and validate innovative CT, MRI, ultrasound and nuclear imaging techniques, and apply these in clinical studies on early osteoarthritis, sports injuries and other musculoskeletal conditions. Many of these techniques are aimed at visualizing and quantitatively measuring changes in the structure and composition of musculoskeletal tissues such as cartilage, synovium and tendon. We also have a growing body of research on artificial intelligence applied to musculoskeletal imaging.In this work, we collaborate closely with MRI physicists and image analysis experts from the Biomedical Imaging Group Rotterdam (BIGR). Potential student projects in this area focus on the validation of novel imaging techniques, and assessing their additional value in patient care.
Another important research focus is on musculoskeletal population imaging. We utilize information from MRI and other imaging techniques in the large-scale population based Rotterdam Study to address the epidemiology and genetics of musculoskeletal diseases such as osteoarthritis and osteoporosis. In the Generation R cohort we apply MRI to study the development of muscuskeletal tissues and changes in body composition. Potential student projects in the field of musculoskeletal population imaging relate to describing the various imaging biomarkers derived in these large-scale studies and to assess their relationship with other measures of health and clinical endpoints.
Theme 2: Population Imaging
Prof. dr. Meike Vernooij, Dr. Daniel Bos, Dr. Tonya White
Imaging is playing an increasingly important role in studying associations between determinants and disease, by allowing us to non-invasively directly study the tissue at risk. Population Imaging, the large-scale acquisition of medical images in controlled population-based cohorts, allows to investigate structural and functional changes in the human body that may indicate abnormal development, early disease, can be used to identify persons at risk of developing disease, or may aid in disease prediction.
Our Population Imaging studies at Erasmus MC primarily take place within two large cohorts. The Rotterdam Study is a prospective, population-based study aimed at investigating determinants of chronic and disabling diseases among nearly 15,000 persons aged 45 years and over. The Generation R Study is a prospective cohort study among 10,000 children who are followed from fetal life until young adulthood in a multi-ethnic urban population. Whereas the Rotterdam Study focuses at disease at old age, Generation R mainly aims to study child development, both physically and mentally.
Population imaging within the Rotterdam Study currently comprises brain MR imaging (in over 8,000 individuals), CT-assessed arterial calcification (2,500 persons), carotid MR imaging (over 1,500 persons) and musculoskeletal imaging (knee MRI in over 800 subjects). Major collaborations are set up with epidemiology, neurology, medical informatics, biomedical engineering. Within Generation R, brain imaging is completed in over 4250 nine to eleven year old children and we are currently scanning over 1,500 early adolescents.
Subtheme 1: Neuroimaging of the aging brain (Prof. dr. Meike Vernooij)
The main research questions in this theme are: How does the brain change with ageing, and what factors influence structural and functional brain changes? How does vascular and degenerative brain pathology affect the development of dementia or ischemic and hemorrhagic stroke? How can we predict an individual’s risk to develop dementia or stroke, using brain imaging? Can we unravel the genetic basis of dementia and stroke by using imaging markers as endophenotypes? Over 6,000 participants have already been scanned and follow-up brain MRI is available in over 4,000 individuals. The imaging protocol within the Rotterdam Study includes structural brain imaging for volumetric and shape analysis of various brain structures. This provides for assessing focal structural abnormalities—including brain infarcts and lacunes, white matter lesions, and microbleeds. In addition, diffusion tensor imaging yields quantitative information on the integrity of normal appearing white matter. Furthermore, we are using freely available software, such as Freesurfer, to obtain quantitative information on structural volumes, e.g. cortical thickness. Since 2012, resting-state functional MRI has been added to the imaging protocol, in order to assess measures of functional brain connectivity. All scans are also read for incidental abnormalities, which has yielded unprecedented information on occurrence and natural course of brain abnormalities in community-dwelling persons.
Subtheme 2: Cardiovascular imaging in aging (Dr. Daniel Bos)
Cardiovascular imaging research contributes to the understanding of the natural history of cardiovascular disease and the processes leading to the progression and/or stabilization of the disease, as well as the assessment of disease burden and therapeutic efficacy. Atherosclerosis, the key cause of cardiovascular events, may occur throughout the whole arterial system but has a predilection for the coronary arteries, the aorta and the carotid arteries. Measurement of atherosclerosis at these sites may improve risk prediction of coronary heart disease and stroke. The new generation of fast multidetector computed tomography (MDCT) scanners enables accurate assessment of arterial calcification, the hallmark of atherosclerosis, in the coronary and extra-coronary vessel beds.
From 2003- 2006, in total 2,500 Rotterdam Study participants have undergone MDCT of the coronary arteries, aortic arch, and the extra- and intracranial part of the internal carotid arteries . Using these data, we investigate genetic and non-genetic determinants of calcification at these sites, and examine the consequences in terms of clinical outcomes, including coronary heart disease, stroke, and dementia. A specific focus has been on the location-specific differences of atherosclerotic calcification in the different vessel beds. In addition to CT-imaging, we also perform multi-sequence magnetic resonance imaging (MRI) of carotid atherosclerosis. Since 2007, over 2000 persons have undergone this examination. MRI has the advantage over CT that it is able to characterize the whole atherosclerotic plaque, instead of only assessing calcification as marker of atherosclerosis. It is increasingly recognized that so-called ‘vulnerable’ plaque characteristics (e.g. lipid core or hemorrhage) rather than plaque size or plaque load are related to the risk of cerebrovascular events. We examine the structure of atherosclerotic plaques in the carotid arteries, and their development to overt cardiovascular disease. The ultimate aim is to develop a novel risk assessment strategy that includes the assessment of vulnerable plaque.
Subtheme 3: Neuroimaging of brain development (Dr. Tonya White)
The prenatal period of life is a time of considerable brain growth and development, as the brain differentiates from a single cell to a brain that very much resembles an adult brain by the time of birth. This period of extensive growth is also a critical period where environmental factors, such as nutrition, cannabis, cigarette smoking, medication, and other factors can influence optimal brain development. Following birth, the brain undergoes continued brain development and postnatal factors can also have an influence on optimal brain development. While brain development is largely driven by genes, environmental factors during the prenatal and early postnatal period can influence neurodevelopment and potentially affect cognitive functioning and behavior. In the Generation R Study, we have multiple measures of brain growth and development in children that begins in utero with prenatal ultrasounds and includes three waves of neuroimaging using MRI scans. In addition to studying determinants of optimal brain development, large population-based studies can evaluate measures of psychopathology across the continuum. Finally, in order to understand brain development gone awry, it is also crucial to understand typical development.
Thus, one major goal of the neurodevelopmental group is to develop and apply novel techniques to measure the trajectories of typical brain development. This will allow for us to assess the temporal relationship between typically developing children and those children who have cognitive, behavioral, or emotional disorders.
Theme 3: Cardiac Imaging (MRI and CT)
Principal Investigators: Dr. Ricardo Budde; Dr. Alexander Hirsch
The cardiac CT and MRI group is a joint initiative by the departments of cardiology and radiology and collaborates with several (pre)-clinical departments within the Erasmus MC.
Research activities involving CT include assessment of several technological innovations and new clinical applications, i.e. various scan protocols to reduce radiation exposure, dynamic assessment of prosthetic heart valves, CT-FFR and stress myocardial perfusion imaging to assess the hemodynamic significance of obstructive coronary disease.
Ongoing investigation into the implementation of cardiac CT in clinical practice includes the use of cardiac CT in patients with a suspicion of endocarditis, patients with stable angina (fast-track chest pain clinic), to exclude coronary disease in patients with congestive heart failure and after heart transplantation, as well as a tool for triage of acute chest pain in the emergency ward. Also the potential role of (cardiac) CT in planning minimally invasive heart valve implantations, assessment of the thoracic aorta and predicting the effect of coronary stenting is being investigated.
Cardiovascular Magnetic Resonance Imaging (CMR) has become an important imaging technique for patients with a wide variety of heart disease. Beside anatomy, quantification of function, and assessment of cardiac fibrosis, there are major breakthroughs in the last decade including flow quantification with 4D flow and tissue characterization using T1-, T2-mapping and assessment of the extracellular volume. Research activities involving CMR include several studies with non-ischemic cardiomyopathies. For example, the role of 4D flow and strain analysis in hypertrophic cardiomyopathy is studied and also the role of CMR in non-compaction cardiomyopathy is a topic of investigation. Furthermore, CMR is used in a study looking at the late cardiac toxicity of chemotherapy and radiation in patients with Hodgkin lymphoma.
Erasmus MC is an expert center for treatment of congenital heart disease and imaging is an important topic of investigation. CMR including 4D flow is used in a large study with bicuspid valve and Turner patients and also in patients with Tetralogy of Fallot and Fontan. Finally we started with exercise CMR looking at cardiac reserve during physical exercise with CMR.
Theme 4: Neurovascular imaging (MRI and CT)
Principal Investigator: Prof. A. van der Lugt
Ischemic cerebral infarcts are related to the presence of atherosclerotic disease in the carotid artery. Severity of the stenosis is a predictor of clinical symptoms and is used as parameter in the therapeutic decision as to which patients will benefit from carotid intervention. Next to stenosis severity, plaque morphology is thought to be a major determinant of cerebrovascular events.
Within this project, imaging of the atherosclerotic plaque in the carotid bifuction with multidetector CT and MRI is evaluated.
We focus on 1) The validation of imaging parameters by comparison of images with histology, 2) Development of new structural and haemodynamic parameters atherosclerortid disease, 3) Development and validation of automated measurements, 4) Prospective studies in patients and healthy volunteers to prove the additional value of plaque parameters in risk prediction, 5) Serial imaging studies to evaluate the natural course of the atherosclerotric disease, 6) Studies into the relationship between atherosclerotic plaque parameters and brain infarcts and white matter lesions on CT and MRI.
Theme 5: Biomedical Image Analysis & Machine Learning
Principal Investigator: Prof. dr. WJ Niessen
In the management of disease, advances in imaging devices have drastically increased our capabilities to (non-invasively) study both anatomy and function. With these advances, the sheer size, complexity, and heterogeneity of imaging data available for biomedical research and clinical practice have increased enormously. To fully utilize the wealth of information available in imaging data, techniques for automated analysis and interpretation are required. In this subtheme, quantitative image analysis techniques are developed and applied to improve diagnosis, therapy planning and therapy monitoring. Application areas include cardiovascular disease, neurodegenerative diseases and oncology. In the cardiovascular domain, we e.g. quantify atherosclerotic disease from non-invasive imaging techniques, for improved diagnosis and prognosis. In the neurodegenerative domain, we perform large scale analysis of neuro imaging data, both from population imaging studies and clinical studies, to improve early detection, differential diagnosis, and prognosis of neurodegenerative disease. In oncology, we aim to improve treatment planning and predict and monitor treatment success.
Subtheme 1: Cardiovascular Image Analysis
Prof. dr. Wiro Niessen
State-of-the art imaging techniques have the potential to provide detailed information on the vessel wall, such as plaque composition, elastic wall properties, and even biochemical processes that take place in the plaque. In addition, dynamic and perfusion imaging can provide functional information, e.g. for determining the perfusion or motion of the heart, or to study tumor activity. Owing to the growing complexity and sheer size of cardiovascular data, in combination with the large increase in the number of studies in clinical practice and biomedical research, there is a strong and increasing interest in robust, automated processing tools to aid in the analysis of these data. This research line aims to develop and evaluate novel image processing techniques for visualization, quantification and integrated analysis of multimodal anatomical and functional cardiovascular imaging data.
Subtheme 2: AI in Medical Image Analysis
Prof. dr. Marleen de Bruijne
This research group develops novel techniques for quantitative analysis of medical images, with a focus on machine learning – and especially deep learning – in large scale image-based studies. An important theme is the application of so-called supervised machine learning techniques in differential diagnosis and prognosis of disease.
Using models learned from a database of images for which the diagnosis has already been established, or for which the future course of the disease is known from clinical follow-up, such techniques are more widely applicable and often give better results than conventional image analysis methods. Another important theme is the development of robust and fair prediction models based on clinically realistic situations. Machine learning techniques often work well on large, well-curated, fully annotated datasets, but how do we learn reliable models if datasets are small or heterogenous and have few, weak, or noisy annotations? Our main applications are in computer-aided diagnosis of neurodegenerative, (cardio)vascular, and pulmonary disease.
Subtheme 3: Image Guidance in Interventions
Dr. ir. Theo van Walsum
Minimally invasive interventions have distinct advantages for patients. Image guidance is often essential in these interventions, to visualize the target anatomy and the instruments. Current interventional modalities have limitations, which may hamper effective image guidance. E.g. ultrasound imaging often is only 2D, is hard to interpret, and does not always give appropriate contrast between tissues. X-ray imaging is a projection imaging modality, uses harmful ionizing radiation, and requires contrast agents to visualize the vasculature. By incorporating information from pre-operative, diagnostic imaging, is expected to improve image guidance.
This research line aims to develop and evaluate novel image processing techniques for better image guidance, by registering information from e.g. preoperative imaging to the interventional scene. We focus on motion and deformation modeling, and integrating these models in the registration and tracking of target anatomy and instruments during the intervention. In addition, we are developing advanced visualization approaches for intra-operative information, using augmented reality.
Subtheme 4: Image Registration
Dr. ir. Stefan Klein
Registration is an important technique in medical image processing. It refers to the process of spatially aligning datasets from different modalities, time points, and/or subjects, in order to facilitate more accurate and in-depth analysis. In the Image Registration group we develop and evaluate novel image registration methods and use these in several applications. Besides, an increasing amount of our research is focusing on the development of machine learning methods that aid image interpretation for improved diagnosis, fine-grained disease staging, and accurate patient stratification. Key application areas are oncology (various cancer types), ophthalmology (glaucoma, macular degeneration), neurology (dementia), and orthopedics (osteoarthritis). This subtheme has a strong cross-disciplinary focus: we collaborate with numerous clinical experts within and outside Erasmus MC, in order to maximise the clinical relevance of our technical developments. We embrace the open science paradigm, by sharing software and data enabling others to reproduce and reuse our results.
Subtheme 5: Neuroimage analysis & machine learning
Dr. Esther Bron
Brain diseases such as dementia impose an enormous burden to the individual and to society. As a consequence, there is an urgent need to develop effective preventive and therapeutic strategies. Early detection and accurate prediction of the progression of at-risk subjects are key in this development. Early detection is important for successful treatment and accurate prediction will play a major role in clinical trials, e.g. for selecting homogenous patient groups to reduce variability in outcome measures.
Neuroimage analysis and machine learning play a herein a crucial role, i.e. for developing robust quantitative brain imaging biomarkers and methods for early detection and accurate prediction. Our research interest is to optimally combine brain imaging, clinical data and artificial intelligence techniques to promote an accurate and early diagnosis, and eventually the right treatment, for patients with neurodegenerative disease. The group’s research focuses on development of both novel biomarkers and methods for detection and prediction. While AI is showing great experimental results and large high-quality datasets are available, methods are not yet finding their way into clinical practice. Therefore we aim to develop and collect accurate diagnostic and prediction methodology, to validate those methods on large and clinically representative datasets, to identify and overcome challenges for clinical implementation.
Subtheme 6: Quantitative MRI reconstruction
Dr. Dirk Poot
Quantitative MRI is becoming increasingly relevant in the era of precision medicine. Current clinically used MRI protocols are still mostly limited to weighted images, such as T1-weighted or T2-weighted. This delivers images optimized for visual inspection by a radiologist that is looking for structural abnormalities. However, these images do not provide measurements of the actual magnetic resonance properties of the tissue; e.g. the T1 or T2 relaxation time, or the diffusion or perfusion rate. Also, there might be substantial variability in the images between scanners, or even from the same scanner at different moments in time. This lack of standardization hampers the detection of subtle diffuse, disease induced, changes in the tissues. The key objective of quantitative MRI is to complement the qualitative images with quantitative measurements of tissue properties.
We develop new methods for reconstruction of quantitative MRI maps. In this we more and more take into account a-priori knowledge as well as information shared within the acquisitions that are performed. This allows to speed up the acquisition process, which increases patient comfort and reduces costs. Additionally, in collaboration with the MR physics group, we are trying to extract more advanced physiological information from the tissues by improving the MR acquisitions.
Subtheme 7: Computational Population Biology
Dr. Gennady Roshchupkin
Computational Population Biology group develops and applies methods for the integrative analysis of large-scale biological, epidemiological and clinical data. Our goals are to improve the understanding of how various omics affect the complex traits and to make use of such insights to improve the diagnosis, prevention and treatment of diseases whenever possible.
The group uses the latest approaches in genomics, medical imaging, computer science, statistics and machine learning to sort through increasingly rich and massive amount of data. We focused on three directions: methods development, quantitative traits analysis, data visualization and accessibility.
Theme 6: Perfusion MRI (pMRI) in neuro-oncology
The value of perfusion imaging in the surveillance of patients with a brain tumour
Principal investigator: prof. dr. Marion Smits
Perfusion MRI (pMRI) is an imaging technique that is commonly applied in patients with a brain tumour to assess tumour grade and to distinguish tumour progression from treatment induced radiological abnormalities. There is currently no consensus on the use of pMRI for brain tumour surveillance in the Netherlands, resulting a wide variety of implementation practices.
This project aims to obtain the necessary evidence to provide clear guidance on the cost-effective implementation of pMRI for brain tumour surveillance throughout the Netherlands. We hypothesise that it is cost-effective to include pMRI in the routine brain tumour surveillance imaging protocol. Data are currently being collected in both a prospective and a historical (2008-2018) cohort of patients with glioma and brain metastasis in 15 centres in the Netherlands. Data include all demographic and clinical characteristics, MRI scans and imaging findings, costs, and health related quality of life measurements.
Research questions that are addressed in this project are:
• What is patient overall survival and health related quality of life with and without pMRI?
• What is the patient experience and time investment of undergoing tumour surveillance with (p)MRI?
• What is the cost of pMRI and total cost?
• What is the diagnostic accuracy of pMRI for a) predicting malignant transformation of low grade glioma; and b) distinguishing tumour progression from treatment induced radiological abnormalities?
• What is the clinical impact of pMRI in terms of clinician’s confidence in the diagnosis as well as the use of additional diagnostic procedures (surgery/biopsy, metabolic imaging, etc)?
• In what proportion of cases is pMRI not interpretable/does not contain diagnostic information?
• What is the agreement between local and central interpretation of pMRI in a) predicting malignant transformation of low grade glioma; and b) distinguishing tumour progression from treatment induced radiological abnormalities?
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