Can fully automated detection of corticospinal tract damage be used in stroke patients?

Objective: We compared manual infarct definition, which is time-consuming and open to bias, with an automated abnormal tissue detection method in measuring corticospinal tract-infarct overlap volumes in chronic stroke patients to help predict motor outcome. Methods: Using diffusion tensor imaging and probabilistic tractography, 4 corticospinal tracts from the primary motor cortex, dorsal and ventral premotor cortices, and supplementary motor area to the ipsilateral lower pons were reconstructed in 23 healthy controls. Tract-infarct overlap volume of each of the 4 corticospinal tracts was determined by overlapping the patients' lesions onto the control tract templates, using both manually and automatically defined infarcts in 51 patients. Correlations with upper limb motor impairment were assessed and both methods were directly compared using intraclass correlations (ICC). Results: Greater impairment was seen in patients with greater corticospinal tract-infarct overlap with either method (rmanualrange = 0.32-0.46; rautomated range = 0.42-0.57). Consistency between manual and automated methods was good to excellent for all 4 corticospinal tracts (ICC range = 0.71-0.80). Conclusions: Our results demonstrate that automated infarct identification performs equally as well as a manual method in quantifying corticospinal tract-infarct overlap following stroke.