The robustness and accuracy of current MR methods to differentiate brain tumours is limited. In this study we investigate the potential of dynamic susceptibility perfusion-weighted imaging (DSC-PWI) normalized time-intensity-curves (nTIC) to support lymphoma diagnosis by harnessing voxelwise and temporal information to train a convolutional neural network (CNN). This novel approach discriminated patients with lymphoma from glioblastoma and metastasis with an average accuracy of 0.94, using only a limited number of patients for training, outperforming standard DSC-PWI measurements. Furthermore, it provides voxel-by-voxel lymphoma probability maps to further help visual diagnosis of neuroradiologists.
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