Academic Center for Quantitative Imaging

Last updated: 365 days ago.

Imaging plays an essential role in tailoring medical care to the individual patient, by providing rich quantitative information on anatomy, function, and pathology. Quantitative extraction of available information from images is becoming increasingly important. It increases sensitivity and specificity for diagnostic and prognostic classification, supports comparative studies, enables studying both subtle deviations and changes over time, and allows comparison with reference data.

The objectives of the QI ACE are to:

  • Optimize image acquisition techniques to deliver quantifiable measures.
  • Develop algorithms to accurately, robustly and automatically extract quantitative information from image data.
  • Discover novel quantitative imaging biomarkers in a wide range of diseases.
  • Assess added value of these new markers over or in combination with existing biomarkers.
  • Educate students, researchers and clinical staff in QI and its application in research & clinical care.
Academic Center of Excellence

Research Activities

- Fundamentals of image acquisition: optimizing image acquisition and reconstruction (CT/MRI) for extraction of quantitative imaging biomarkers in clinical and preclinical studies, and clinical practice. - Epidemiological: application of quantitative imaging principles in a prospective population-based design (The Rotterdam Study and Generation R), with world-class research lines investigating etiologic factors of major cardiovascular, pulmonary, neurodegenerative and neurodevelopmental illnesses and developing early diagnostic and prognostic markers, and targets for preventive action.

- Clinical: clinical studies on use of quantitative imaging as outcome measure in cardiovascular, pulmonary, neurodegenerative and oncological diseases, with direct implications for clinical care, and assessing value of quantitative imaging biomarkers in clinical practice using health technology assessment techniques.

- Across all three areas the Biomedical Imaging Group Rotterdam (BIGR) develops and validates novel computer-based algorithms to extract quantitative information from medical images. Activities range from the development of novel machine learning, image segmentation, and image registration techniques (fundamental) to their application in large-scale studies to derive novel imaging biomarkers of various diseases (epidemiological), the implementation of such algorithms in prototype clinical workstations to enable clinical studies using these quantitative techniques (translational and clinical), and the integration of quantitative imaging data with other data, e.g. genetics data.

Type of

Collaborations

Educational

Contributions

Current education:

- Bachelor education is covered by active participation of various ACE members in the BSc program Clinical Technology within Medical Delta, a multidisciplinary study program on the boundary between medicine and technology that delivers technical-medical professionals who will be part of a medical treatment team. Courses cover both underlying techniques and applications of quantitative imaging in various diseases (dementia, cardiovascular disease, cancer, cystic fibrosis). - Bachelor education in the Medical Curriculum.

- The ACE participates actively in the Research Master of COEUR, NIHES and ASCI research schools. - PhD education: combined number of PhD # in all groups is ±35. - Resident (AIOS) education: direct translation of quantitative imaging in clinical practice is ensured by teaching residents hands-on how to apply the various acquisition techniques and image processing in a clinical work flow. Work stations are provided on the job, with state-of-the-art available tools, updated when new algorithms are available. - ACE members organize and participate in post-doctoral education in the form of symposia, courses, hands-on workshops, both within the Netherlands and internationally.

Future plans: The ACE is actively involved in the design of the new MSc program in Clinical Technology that will start in 2017.

Patient

Care Activities

Current level of this ACE in patient care is:

- This ACE provides quantitative imaging markers for personalized medicine in the areas of prevention (through link with our population-based research group), diagnostics and therapy monitoring. An example of direct translation from research to individual patient care is a workstation that is being developed to automatically quantify brain scans to aid the diagnosis of dementia in the memory clinic.

- The ACE addresses the use of quantitative image information to aid diagnosis in the clinical pathway, in a multidisciplinary approach, with current focus areas of cardiovascular, pulmonary,, neurodevelopmental, neurodegenerative, and oncological disease – all of these at academic level. This covers the major threats to public health for the next decades. - Expertise from the Assessment of Radiological Technology (ART) group ensures that newly developed biomarkers can be assessed with regard to cost-effectiveness and medical decision making. (Future)

activities are:

- Further development and validation of quantitative imaging biomarkers and decisions models to personalize treatments and hence patient outcomes and cost-effectiveness.

- Establish state-of-the-art IT infrastructure for storage, access, sharing and standardized analysis of imaging data. - Increasing activities in emerging areas such as imaging genetics, radiomics, radiogenomics, and integrated diagnostics.

Societal Relevance to Research, Education and Patient Care

The development of (multimodal) quantitative imaging approaches is key to the future of personalized medicine. Both data size and complexity is increasing at a rapid pace, and algorithms to fully exploit these data will be fundamental to advances in both biomedical research and clinical practice. Research in the ACE QI will lead to the development of novel biomarkers for (early) disease detection, prognosis, and prediction and measurement of treatment effect.

These biomarkers can thus be used for both the determination of the underlying pathology, as well as determining the appropriate treatment. The heterogeneity associated with individual differences related to the imaging biomarkers, in the context of big data, can be used to address the individual within the context of their heterogeneity.

The 'one size fits all' approach that is not uncommon in the medical education process, will be replaced by personalized medicine, and tailoring the treatment to the individual for the best outcome. Quantitative methods are required to bring forth these changes. This will alter educational curricula, as the standards of care become more individualized.

Viability of Research, Education and Patient Care

Research in the field of quantitative imaging and image analysis at Erasmus MC is internationally at the forefront. PI's of the ACE have leading positions in international organizations active in in this field, including e.g. the imaging group at EORTC (Marion Smits), the European Institute of Biomedical Imaging Research (Gabriel Krestin, Myriam Hunink, Aad van der Lugt, Wiro Niessen), the European Imaging Biomarker Alliance (Smits, van der Lugt) and the Medical Image Computing and Computer-Assisted Interventions Society (Wiro Niessen).

The ACE brings together experts in image acquisition (hardware), image analysis (algorithms, software), clinical care and epidemiology. This stimulates collaboration between the relevant stake holders in the development and validation of quantitative imaging biomarkers. As such the ACE will set the standards for QIB evaluation. All PhD students are supervised by at least two supervisors with different background, often technological and clinical. This is the basis for early detection and fostering of talents in multidisciplinary research.

The ACE has a large group of PhD students (±34FTE), which is frequently monitored by the Research Committee of the Department of Radiology and Nuclear Medicine. Because of participation in many European projects, regular presentations at international conferences, and internships at collaborating international partners (e.g. University College London), our PhD students are naturally building up their own international networks during the course of their PhD study. To ensure knowledge sharing among junior and senior ACE members, many regular research meetings are in place, in which PhD students present their latest progress. Bibliometric analyses indicate excellent output, but in terms of quantity, and quality.

Key and relevant publications of the last five years

  • Dalm SU, Nonnekens J, Doeswijk GN, de Blois E, van Gent DC, Konijnenberg MW, de Jong M. Comparison of the Therapeutic Response to Treatment with a 177Lu-Labeled Somatostatin Receptor Agonist and Antagonist in Preclinical Models. J Nucl Med. 201657(2):260-5
  • Dedic A, Lubbers MM, Schaap J, Lammers J, Lamfers EJ, Rensing BJ, Braam RL, Nathoe HM, Post JC, Nielen T, Beelen D, le Cocq d'Armandville MC, Rood PP, Schultz CJ, Moelker A, Ouhlous M, Boersma E, Nieman K. Coronary CT Angiography for Suspected ACS in the Era of High-Sensitivity Troponins: Randomized Multicenter Study. J Am Coll Cardiol. 2016;67:16-26.
  • Akoudad S, Portegies ML, Koudstaal PJ, Hofman A, van der Lugt A, Ikram MA, Vernooij MW. Cerebral Microbleeds are Associated With an Increased Risk of Stroke: The Rotterdam Study. Circulation. 2015 Jul 2.
  • Berkhemer OA, Roos YB, van der Lugt A, van Oostenbrugge RJ, Majoie CB, Dippel DW; MR CLEAN Investigators. A randomized trial of intraarterial treatment for acute ischemic stroke. N Engl J Med. 2015 Jan 1;372:11-20.
  • Blanken LME, Mous SE, Ghassabian A, Muetzel RL, Schoemaker NK, El Marroun H, van der Lugt A, Jaddoe VWV, Hofman A, Verhulst FC, Tiemeier H, White T (2015) Cortical morphology in 6-to-10 year old children with autistic traits – A population-based neuroimaging study. Amer J Psychiatr 172: 479-486.
  • Fakhry F, Spronk S, … , Rouwet E, Hunink MGM, Endovascular Revascularization plus Supervised Exercise versus Supervised Exercise only in Patients with Peripheral Artery Disease and Intermittent Claudication. A Randomized Clinical Trial. JAMA. 2015;314:1936-44.
  • de Groot M, Cremers LG, Ikram MA, Hofman A, Krestin GP, van der Lugt A, Niessen WJ, Vernooij MW. White Matter Degeneration with Aging: Longitudinal Diffusion MR Imaging Analysis. Radiology. 2015 Nov 3:150103.
  • Rosenow T, Oudraad MC, Murray CP, Turkovic L, Kuo W, de Bruijne M, Ranganathan SC, Tiddens HA, Stick SM; AREST CF. PRAGMA-CF: a quantitative structural lung disease CT outcome in young children with cystic fibrosis. Am J Respir Crit Care Med 2015; 191: 1158-1165.
  • van Engelen, A., Wannarong, T., Parraga, G., Niessen, W. J., Fenster, A., Spence, J. D., & de Bruijne, M. (2014). Three-dimensional carotid ultrasound plaque texture predicts vascular events. Stroke; a journal of cerebral circulation, 45, 2695.
  • Bis J, De Carli C, Vernon Smith A, (….) Vernooij M, (…) Mosley T, Schmidt R, Tzourio C, Launer L, Ikram MA, Seshadri S, for the CHARGE consortium Group. Common variants at 12q14 and 12q24 are associated with hippocampal volume. Nat Genet. 2012;44:545-51.

PhD theses of the last five years

  • M. Loeve (2012) Chest CT in early and advanced CF lung disease: Optimizing protocol, image analysis and further validation.
  • Daniel Bos (2013) Atherosclerotic Calcification: Determinants and Clinical Neurological Consequences
  • K. Hameeteman (2013) Imaging biomarkers for carotid artery atherosclerosis
  • Lisan Neefjes (2013) CT Coronary Angiography to Detect CAD: Low-dose Protocols in High-risk Individuals
  • Arna van Engelen (2014) Multimodal Image Analysis for Carotid Artery Plaque Characterization
  • Marius de Groot (2014) Cross-Subject Iamge Analysis in Diffusion Brain MRI
  • Mariana Selwaness (2014) Magnetic Resonance Imaging of Carotid Atherosclerosis
  • Saloua Akoudad (2015) Cerebral Microbleeds
  • Sabine Mous (2015) The Distracted Brain: The neurobiology and neuropsychology of attention-deficit/hyperactivity problems in the general population. Rotterdam, the Netherlands
  • E.E. Bron (2016): Advanced MRI Analysis for Computer-Aided Diagnosis of Dementia

Non-scientific publications related to the ACE

Principal coordinator(s)