Deep learning to predict DTI tractography labels from diagnostic CT scans in acute ICH

Motivation: Assessing white matter integrity may enhance intracerebral haemorrhage (ICH) surgical trial selection. However, patients often only receive CT imaging, on which delineating white matter is challenging. Goal(s): This study aimed to train a model to delineate white matter in CT scans, using paired DTI tractography maps, and test if the model predicted outcome in an external dataset. Approach: Tractography maps were generated from the DTI images. A nnU-Net model was trained on paired CT and registered tractography data, and then run on an external dataset of ICH diagnostic CT scans. Results: The model performed at 58% Dice, and significantly predicted outcome after ICH. Impact: Our model can predict tractography labels of the corticospinal tract on diagnostic CT scans without the need for DTI, allowing an enhanced prediction of outcome after intracerebral haemorrhage, and potentially leading to more informed selection of candidates for surgical trials.