Mikkel Bo Hansen1, Anna Tietze1,2,3, Jayashree Kalpathy-Cramer4, Elizabeth R Gerstner5, Tracy T Batchelor5, Leif Østergaard1,2, Kim Mouridsen1. 1. Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark. 2. Department of Neuroradiology, Aarhus University Hospital, Aarhus, Denmark. 3. Institute of Neuroradiology, Charité - Universitätsmedizin Berlin, Charitéplatz 1, D-10117, Berlin. 4. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA. 5. Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA.
Abstract
PURPOSE: To present and quantify the performance of a method to compute tissue hemodynamic parameters from dynamic susceptibility contrast (DSC) MRI data in brain tissue with possible nonintact blood-brain barrier. THEORY AND MATERIALS AND METHODS: We propose a Bayesian scheme to obtain perfusion metrics, including capillary transit-time heterogeneity (CTH), from DSC-MRI data in the presence of contrast agent extravasation. Initial performance assessment is performed through simulations. Next, we assessed possible over- or under correction for tracer extravasation in two patients receiving contrast agent preloading and two patients not receiving preloading. Perfusion metrics for N = 60 patients diagnosed with either grade III (N = 14) or grade IV gliomas (N = 46) were analyzed across tissue types to evaluate the ability to distinguish regions with different hemodynamic patterns. Finally, N = 4 patient cases undergoing anti-angiogenic treatment are evaluated qualitatively for treatment effects. All patient data were acquired at 3.0 Tesla. RESULTS: The simulation studies showed good robustness against low signal-to-noise ratios, exemplified with Pearson correlations of R = 0.833 (mean transit time) and R = 0.738 (CTH) at signal-to-noise ratio = 20. Region-of-interest analysis of the N = 60 glioma patients showed that cerebral blood volume (CBV) significantly separated enhancing core from edema (grade IV: P < 10-8 , grade III: P < 0.05) and enhancing core from normal appearing ipsilateral white matter (NAWM) (grade IV: P < 10-8 , grade III: P < 0.05). The microvascular parameters were particularly good in separating edematous tissue from NAWM tissue in grade IV gliomas (P < 0.001). Finally, CTH separated grade III and grade IV core tissue (P < 0.05). CONCLUSION: We have demonstrated robustness of the proposed Bayesian algorithm against experimental noise and demonstrated complementary value in microvascular parameters to the CBV parameter in separating tissue types in gliomas. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:537-549.
PURPOSE: To present and quantify the performance of a method to compute tissue hemodynamic parameters from dynamic susceptibility contrast (DSC) MRI data in brain tissue with possible nonintact blood-brain barrier. THEORY AND MATERIALS AND METHODS: We propose a Bayesian scheme to obtain perfusion metrics, including capillary transit-time heterogeneity (CTH), from DSC-MRI data in the presence of contrast agent extravasation. Initial performance assessment is performed through simulations. Next, we assessed possible over- or under correction for tracer extravasation in two patients receiving contrast agent preloading and two patients not receiving preloading. Perfusion metrics for N = 60 patients diagnosed with either grade III (N = 14) or grade IV gliomas (N = 46) were analyzed across tissue types to evaluate the ability to distinguish regions with different hemodynamic patterns. Finally, N = 4 patient cases undergoing anti-angiogenic treatment are evaluated qualitatively for treatment effects. All patient data were acquired at 3.0 Tesla. RESULTS: The simulation studies showed good robustness against low signal-to-noise ratios, exemplified with Pearson correlations of R = 0.833 (mean transit time) and R = 0.738 (CTH) at signal-to-noise ratio = 20. Region-of-interest analysis of the N = 60 gliomapatients showed that cerebral blood volume (CBV) significantly separated enhancing core from edema (grade IV: P < 10-8 , grade III: P < 0.05) and enhancing core from normal appearing ipsilateral white matter (NAWM) (grade IV: P < 10-8 , grade III: P < 0.05). The microvascular parameters were particularly good in separating edematous tissue from NAWM tissue in grade IV gliomas (P < 0.001). Finally, CTH separated grade III and grade IV core tissue (P < 0.05). CONCLUSION: We have demonstrated robustness of the proposed Bayesian algorithm against experimental noise and demonstrated complementary value in microvascular parameters to the CBV parameter in separating tissue types in gliomas. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:537-549.
Authors: Andreas Stadlbauer; Kim Mouridsen; Arnd Doerfler; Mikkel Bo Hansen; Stefan Oberndorfer; Max Zimmermann; Michael Buchfelder; Gertraud Heinz; Karl Roessler Journal: J Cereb Blood Flow Metab Date: 2017-02-24 Impact factor: 6.200
Authors: S Hara; Y Tanaka; S Hayashi; M Inaji; T Maehara; M Hori; S Aoki; K Ishii; T Nariai Journal: AJNR Am J Neuroradiol Date: 2019-10-10 Impact factor: 3.825
Authors: Arne Lauer; Xiao Da; Mikkel Bo Hansen; Gregoire Boulouis; Yangming Ou; Xuezhu Cai; Afonso Liberato Celso Pedrotti; Jayashree Kalpathy-Cramer; Paul Caruso; Douglas L Hayden; Natalia Rost; Kim Mouridsen; Florian S Eichler; Bruce Rosen; Patricia L Musolino Journal: Brain Date: 2017-12-01 Impact factor: 13.501
Authors: Mikkel B Hansen; Anna Tietze; Søren Haack; Jesper Kallehauge; Irene K Mikkelsen; Leif Østergaard; Kim Mouridsen Journal: PLoS One Date: 2019-01-03 Impact factor: 3.240