MRI is the established method for assessment of patients with brain tumors. However, the response assessment in neuro-oncology (RANO) criteria currently rely on conventional imaging, which provides only a rough estimation of enhancing and non-enhancing lesion volumes. These parameters fail to reliably distinguish between different tissue components such as tumor progression versus therapy response, and treatment ramifications are influenced by the correct understanding of the nature of the lesion.

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Our studies aim to improve therapy response assessment in patients with brain tumors by providing quantitative and reliable tools; using advanced methods of MR data acquisition, state of the art image processing and machine learning algorithms for data analysis.

The development of the human brain involves extensive structural, functional and neuro-chemical changes throughout life, with different tissue types, brain structures, and neural circuits exhibiting distinct developmental trajectories. MRI provides information that enables the study of brain development and characterizes age-related changes in brain structure, function and metabolism. Using advanced methods for image acquisition and analysis, we can improve our understanding regarding typical development and better characterize and diagnose abnormal development.  

Quantitative MRI has numerous applications in diagnosis, follow-up and therapy response assessment. Many parameters provide indirect information regarding physical, physiological and microstructural properties of the tissue. However, accurate quantification is often dependent on the subject's physiology, as well as acquisition and analysis methods. In our research we develop acquisition and analysis methods to extract accurate and reproducible MR parameters.