Piotr Sowa1, Atle Bjørnerud2, Gro O Nygaard3, Soheil Damangir4, Gabriela Spulber5, Elisabeth G Celius6, Paulina Due-Tønnessen7, Hanne F Harbo8, Mona K Beyer9. 1. Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway. Electronic address: piotr.sowa@medisin.uio.no. 2. Intervention Center, Oslo University Hospital, Oslo, Norway; Department of Physics, University of Oslo, Oslo, Norway. Electronic address: atle.bjornerud@fys.uio.no. 3. Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Neurology, Oslo University Hospital, Oslo, Norway. Electronic address: g.o.nygaard@medisin.uio.no. 4. Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden. Electronic address: soheil.damangir@ki.se. 5. Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden. Electronic address: gabriela.spulber@ki.se. 6. Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Neurology, Oslo University Hospital, Oslo, Norway. Electronic address: uxelgu@ous-hf.no. 7. Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway. Electronic address: pdue@ous-hf.no. 8. Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Neurology, Oslo University Hospital, Oslo, Norway. Electronic address: h.f.harbo@medisin.uio.no. 9. Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway; Department of Life Sciences and Health, Oslo and Akershus University College of Applied Sciences, Oslo, Norway. Electronic address: mona.beyer@lyse.net.
Abstract
OBJECTIVE: To investigate dynamic susceptibility contrast (DSC) perfusion weighted imaging (PWI) in white matter lesions (WML) in patients with multiple sclerosis (MS), using automatically generated binary masks of brain tissue. BACKGROUND: WML in MS have in some studies demonstrated perfusion abnormalities compared to normal appearing white matter (NAWM), however perfusion changes in WML in MS have in general not been well documented. METHODS: DSC PWI was performed at 1.5 Tesla in 69 newly diagnosed MS patients. Parametric perfusion maps representing cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) were obtained. Binary masks of WML, white matter (WM) and grey matter (GM) were automatically generated and co-registered to the perfusion maps. The WML mask was manually edited and modified to correct for errors in the automatic lesion detection. Perfusion parameters were derived both from WML and NAWM using the manually modified WML mask, and using the original non-modified WML mask (with and without GM exclusion mask). Differences in perfusion measures between WML and NAWM were analyzed. RESULTS: CBF was significantly lower (p<0.001) and MTT significantly higher (p<0.001) in WML compared to NAWM. CBV did not show significant difference between WML and NAWM. The non-modified WML mask gave similar results as manually modified WML mask if the GM exclusion mask was used in the analysis. CONCLUSIONS: DSC PWI revealed lower CBF and higher MTT, consistent with reduced perfusion, in WML compared to NAWM in patients with early MS. Automatically generated binary masks are a promising tool in perfusion analysis of WML.
OBJECTIVE: To investigate dynamic susceptibility contrast (DSC) perfusion weighted imaging (PWI) in white matter lesions (WML) in patients with multiple sclerosis (MS), using automatically generated binary masks of brain tissue. BACKGROUND: WML in MS have in some studies demonstrated perfusion abnormalities compared to normal appearing white matter (NAWM), however perfusion changes in WML in MS have in general not been well documented. METHODS: DSC PWI was performed at 1.5 Tesla in 69 newly diagnosed MS patients. Parametric perfusion maps representing cerebral blood volume (CBV), cerebral blood flow (CBF) and mean transit time (MTT) were obtained. Binary masks of WML, white matter (WM) and grey matter (GM) were automatically generated and co-registered to the perfusion maps. The WML mask was manually edited and modified to correct for errors in the automatic lesion detection. Perfusion parameters were derived both from WML and NAWM using the manually modified WML mask, and using the original non-modified WML mask (with and without GM exclusion mask). Differences in perfusion measures between WML and NAWM were analyzed. RESULTS: CBF was significantly lower (p<0.001) and MTT significantly higher (p<0.001) in WML compared to NAWM. CBV did not show significant difference between WML and NAWM. The non-modified WML mask gave similar results as manually modified WML mask if the GM exclusion mask was used in the analysis. CONCLUSIONS: DSC PWI revealed lower CBF and higher MTT, consistent with reduced perfusion, in WML compared to NAWM in patients with early MS. Automatically generated binary masks are a promising tool in perfusion analysis of WML.