In clinical research on diagnosis and evaluation of brain tumors, longitudinal perfusion MRI studies are acquired for tumor grading as well as to monitor and assess treatment response and patient prognosis. Within this work, we demonstrate how visual analysis techniques can be adapted to multidimensional datasets from such studies within a framework to support the computer-aided diagnosis of brain tumors. Our solution builds on two innovations: First, we introduce a pipeline yielding comparative, co-registered quantitative perfusion parameter maps over all time steps of the longitudinal study. Second, based on these time-dependent parameter maps, visual analysis methods were developed and adapted to reveal valuable insight into tumor progression, especially regarding the clinical research area of low grade glioma transformation into high grade gliomas. Our examination of four longitudinal brain studies demonstrates the suitability of the presented visual analysis methods and comprises new possibilities for the clinical researcher to characterize the development of low grade gliomas.

Date Published: 28 February 2013
PDF: 11 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700Z (28 February 2013); doi: 10.1117/12.2007878

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)