Perfusion imaging using contrast-agent free MRI measurement
Effects of radiotherapy on normal tissue
Glioblastoma multiforme is the most common primary malignant brain tumor in adults. The standard therapy is maximal surgical resection followed by radiotherapy (RT) with concurrent and adjuvant chemotherapy using temozolomide. Both RT and chemotherapy are, however, associated with risks of cognitive deficits and structural and hemodynamic changes in the normal brain tissue. A detailed understanding of the relation between the received dose during RT and the changes in brain tissue are of obvious relevance for treatment planning as well as for diagnostic evaluation of the disease progression and for determining tissue specific dose tolerance levels.
We have shown the decrease of perfusion in the healthy brain tissue following radiochemotherapy using a non-invasive ASL perfusion imaging. We are currently working on analyzing the structural changes and on comparing these effects between 3D-CRT and IMRT photon therapy and proton-therapy.
References:Partial Volume Effects (PVE)
Despite the recent developments, the spatial resolution of ASL MRI perfusion measurement has remained relatively low with the majority of voxels containing a mixture of perfusion signals from gray matter (GM), white matter (WM), and CSF, a phenomenon referred to as partial volume effects. Since GM perfusion is reported to be 2 — 4.5 times higher than WM perfusion [9, 10] and CSF is not perfused, the measured ASL signal in a given voxel is dependent on the fractional contributions of GM and WM to the voxel, i.e., its tissue composition. Consequently, differences in measured perfusion across regions within a subject or for the same region across subjects could, to a varying degree, be attributable to differences in tissue composition variation rather than actual changes in perfusion. This is an important topic for perfusion imaging in, both, normal tissue and tumor.
We are continuously working on the improvement of methods for partial volume effects correction in ASL. We have several publications on automatic detection of hypo-perfusion and on improved method for obtaining of partial volume maps — a key component to the partial volume correction algorithm. We are currently working on identifying the issues related to geometric deformations and effective resolution and their correction for improved accuracy of partial volume correction.
References:- Petr et al., ISMRM 2016(5)
- Petr et al., Human brain mapping 35.(4), 1179–1189, 2014(6)
- Petr et al., Magnetic Resonance in Medicine 70.(6), 1535–1543, 2013(7)
Translation to clinical research and application
We are a key contributor to the development of the software platform ExploreASL. Initiated through the EU-funded COST-action "ASL In Dementia", ExploreASL is a collaborative framework of researchers and clinical investigators. It was already used to process ~4000 ASL images from all major MRI vendors and ASL sequences, and a large variety of patient populations. The ultimate goal is to combine data from a large number of studies and identify common and different perfusion patterns to enhance knowledge of ASL image analysis and of the role of perfusion and structural changes in neurodegenerative pathophysiology.
Our recent work focused on improving the ASL image processing across different vendors and ASL implementations in order to increase repeatability of ASL across different centers. We have also developped a new biomarker that is extracted from ASL data that is more sensitive to changes in cerebrovascular reactivity than the perfusion itself and has thus promising applications in the tumor research. Our current acitvity focuses on adjusting the processing pipeline to work efficiently work with tumor patients and to overcome several issues in co-registration and perfusion quantification that are specific for this type of patients.
References:- Mutsaerts, Petr et al., Journal of cerebral blood flow and metabolism, in press, 2017(9)
- Mutsaerts, Petr et al., Journal of magnetic resonance imaging, in press, 2017(10)
- Mutsaerts, Petr et al., ESMRMB, 2017(11)
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