# Deep generative computed perfusion-deficit mapping of ischaemic stroke

## TL;DR

This paper uses deep learning to map perfusion deficits in stroke patients from CT scans, revealing how brain regions relate to clinical symptoms before treatment.

## Contribution

A novel deep generative model infers neural substrates of stroke deficits from perfusion maps without needing lesion data.

## Key findings

- Computed perfusion maps from CT/CTA accurately localize NIHSS sub-scores' neural correlates.
- The model replicates known lesion-deficit relationships without direct lesion information.
- The approach identifies novel neural dependencies disrupted by perfusion deficits in acute stroke.

## Abstract

Focal deficits in ischaemic stroke arise primarily from impaired perfusion downstream of a critical vascular occlusion. Though the consequent parenchymal lesion is traditionally used to predict clinical deficits, the underlying pattern of disrupted perfusion provides information upstream of the lesion, potentially yielding earlier predictive and localising signals. We previously developed a technique to compute perfusion maps from routine CT and CT angiography (CTA), an imaging modality widely deployed in clinical practice and available at large data scales. Analysing computed perfusion maps (derived from CT and CTA) from 1393 CTA-imaged patients with confirmed acute ischaemic stroke, here we use deep generative perfusion-deficit inference to localise the neural substrates of NIHSS sub-scores, explicitly disentangling the distinct topologies of disrupted perfusion and neural dependence. We show that our approach replicates known lesion-deficit relations without knowledge of the lesion itself and reveals novel neural dependents. The high achieved anatomical fidelity suggests acute CTA-derived computed perfusion maps may be of substantial clinical and scientific value in rich phenotyping of acute stroke. By relying only on an imaging modality well-established in the hyperacute setting, deep generative perfusion-deficit inference could power highly expressive models of functional anatomical relations in ischaemic stroke within the critical pre-interventional window.

Deep generative perfusion-deficit mapping of CTA-derived computed perfusion maps in 1,393 patients reveals the neural substrates of clinical deficits in the hyperacute phase of ischaemic stroke.