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Associate professor in radiation physics combined with employment as MR-physicist at the University Hospital of Umeå. Research in quantitative imaging, mainly magnetic resonance imaging (MRI) and its applications to radiation therapy.

Medical 3D imaging techniques such as computed tomography (CT), MRI and positron emission tomography (PET) are indispensable tools in today’s cancer care and central to modern radiotherapy. From diagnosis, to planning of radiotherapy, to follow up, imaging is used to get detailed high-resolution information about tumors and surrounding tissue. The more detailed the information is, the better one can adapt the radiation treatment to the target at hand and by that maximizing the chance for cure with reduced side-effects. There is a great potential in the use of MRI and PET to quantitatively measure tumor properties to yield three dimensional images of for instance blood flow, cell distributions and metabolism and subsequently use this to adapt the radiation treatment based on the tumor biology.

MRI is a versatile technology that can be made sensitive to many different tissue properties, for instance; cell density, cell orientation and cell size, blood flow, blood oxygenation, temperature and pH-value just to mention a few. However, there are also several challenges measuring these properties. There is always a trade-off between how fast a measurement can be performed and its precision due to noise. In addition, several undesired factors may also influence the accuracy of a measured property. For these reasons, I am interested in how one can efficiently estimate quantitative tissue properties from limited noisy data. Central to this research is also that the uncertainty is equally important as the in the estimated properties, since that determine the extent one can trust a measurement in a practical situation.

To get closer to the goal of enabling accurate measurements of tumor properties with known uncertainty as high-resolution images we are using a resource that in contrast to most other increases every year; namely computing power. The unprecedented development of computers in the last decades enables the use advanced statistical models and artificial intelligence to push the limit of what is possible with quantitative MRI. Our hope is that this research will lead to more individualized radiotherapy that improves the prognosis for patient treated for cancer.