Early detection of infarcts by improving brain perfusion imaging analysis

Outcome after severe ischemic stroke may improve with thrombolysis. Some studies have shown that parametric perfusion maps and other information might be useful in selecting patients for this potentially hazardous treatment. Traditionally, perfusion source images are deconvolved in order to create these parametric maps; such as cerebral blood flow and volume [1]. The time-domain information latent in the perfusion source images has great potential to discriminate abnormal tissues and we have recently made advances in applying denoising and signal processing methods [2] [3], to detecting stroke lesions using parallelised algorithms [4].

The research would investigate machine learning combined with image and signal processing to identify lesion extent as will as predict response to treatment in stroke patients [5]. The aim would be to produce new methods and validated models that are also suitable for reuse in other brain image interpretation tasks. The goal would be to develop reliable methods that could be used by technical staff in daily clinical practice. (This would require validation and commercial collaboration that is not part of the PhD.)

The input data come from clinical measurements (NIH Stroke Score etc) in combination with perfusion weighted and other imaging types (angiography, structural, etc). The research would use machine-learning techniques, such as pattern recognition, clustering (tissue type segmentation, lesion classification) and association rule learning, and develop methods of improving evidential power. The research would apply to both CT and MRI images with the objective of developing a clinical tool and exploring whether the additional contrast mechanisms available in MR (diffusion, FLAIR etc.) have a significant impact upon treatment decisions.

This project may be funded by CIVIS.