From Diagnostic CT to DTI Tractography labels: Using Deep Learning for Corticospinal Tract Injury Assessment and Outcome Prediction in Intracerebral Haemorrhage
<|reference_start|>From Diagnostic CT to DTI Tractography labels: Using Deep Learning for Corticospinal Tract Injury Assessment and Outcome Prediction in Intracerebral Haemorrhage: The preservation of the corticospinal tract (CST) is key to good motor recovery after stroke. The gold standard method of assessing the CST with imaging is diffusion tensor tractography. However, this is not available for most intracerebral haemorrhage (ICH) patients. Non-contrast CT scans are routinely available in most ICH diagnostic pipelines, but delineating white matter from a CT scan is challenging. We utilise nnU-Net, trained on paired diagnostic CT scans and high-directional diffusion tractography maps, to segment the CST from diagnostic CT scans alone, and we show our model reproduces diffusion based tractography maps of the CST with a Dice similarity coefficient of 57%.   Surgical haematoma evacuation is sometimes performed after ICH, but published clinical trials to date show that whilst surgery reduces mortality, there is no evidence of improved functional recovery. Restricting surgery to patients with an intact CST may reveal a subset of patients for whom haematoma evacuation improves functional outcome. We investigated the clinical utility of our model in the MISTIE III clinical trial dataset. We found that our model's CST integrity measure significantly predicted outcome after ICH in the acute and chronic time frames, therefore providing a prognostic marker for patients to whom advanced diffusion tensor imaging is unavailable. This will allow for future probing of subgroups who may benefit from surgery.<|reference_end|>
@article{murray2024from,
    title={From Diagnostic CT to DTI Tractography labels: Using Deep Learning for
  Corticospinal Tract Injury Assessment and Outcome Prediction in Intracerebral
  Haemorrhage},
    author={Olivia N Murray, Hamied Haroon, Paul Ryu, Hiren Patel, George Harston,
  Marieke Wermer, Wilmar Jolink, Daniel Hanley, Catharina Klijn, Ulrike
  Hammerbeck, Adrian Parry-Jones, Timothy Cootes},
    journal={arXiv preprint arXiv:2408.06403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06403},
    primaryClass={eess.IV cs.CV physics.med-ph}
}