Rolf A. Heckemann MD PhD
Professor
Department of Medical Radiation Sciences
Blå stråket 2B
Sahlgrenska University Hospital
413 45 Göteborg
Sweden
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Rolf A. Heckemann MD PhD
Professor of Medical Imaging and Image Analysis Department of Medical Radiation Sciences Blå stråket 2B Sahlgrenska University Hospital 413 45 Göteborg Sweden Email: rolf.heckemann@medtechwest.se |
Research Mission
To discover novel ways of detecting disease and predicting outcomes using medical imaging.
Medical doctors today have at their disposal a marvellous range of technologies for taking pictures from inside the living body. The venerable projection radiograph using X-rays is still a mainstay of diagnostic imaging over a hundred years after its invention. More recently, other modalities have been developed that employ a variety of probes to show the structure as well as the function of the human body (CT, MRI, ultrasonography, PET, SPECT, and others). The amount of image data obtained each time a patient is scanned is vast, and it has to be boiled down to the salient information. This is image interpretation: understanding images so as to know whether and how to treat the patient. Image interpretation is a highly developed skill: diagnostic radiology training takes years in addition to basic medical training. It also takes time: a single patient's CT of the abdomen, for example, can have many hundreds of slices that need to be reviewed. Human expertise is thus the bottleneck in the system. In fact, more generally, our capability to generate images exceeds our capability to understand them.
In my research, I look for ways to automatically extract information from images. For this, I employ cutting-edge computer algorithms, for example for image registration. One approach is to identify anatomical structures (image segmentation) and to measure their volume, extent, and shape. Such properties, termed morphometric descriptors, can be used for statistical comparisons between subjects. If a morphometric descriptor is systematically different in patients with a disease, or if it is systematically associated with a certain outcome, it has potential as a marker for diagnosing that disease or for predicting the outcome. Such markers can help radiologists make better diagnostic decisions.
Part of my calling is to build bridges between engineering and clinical medicine. Theoretical advances in mathematics and informatics need to be translated into practical tools for doctors and ultimately benefit people who are ill.
Achievements
- Developed a method (multi-atlas propagation with enhanced registration, MAPER) for automatic anatomical segmentations of MR images of the human brain.
- Developed pincram, a highly accurate method for brain extraction/skull stripping.
- Contributed anatomical segmentations to ADNI, the Alzheimer's Disease Neuroimaging Initiative, available to download from the project's website.
- Applied MAPER to various neurological conditions (temporal lobe epilepsy, Alzheimer's disease, traumatic brain injury, schizophrenia, multiple sclerosis, spatial hemineglect, EAST syndrome, Graves' Disease, rheumatoid arthritis, and others) and other tasks (thalamic subsegmentation, DTI tractography, marker integration in visual image interpretation, attenuation correction for PET image analysis)
- Coordinated multiple national (UK, France, Sweden) and international collaborative research projects
- Authored and coauthored multiple papers on MAPER and related methods, as well as other imaging-related subjects
- Won research funding for various projects worth in excess of 2 M€ (equivalent to 20 MSEK)
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