Differentiable Deconvolution for Improved Stroke Perfusion Analysis

TL;DR

This paper introduces a neural network-based method that optimizes arterial input function selection through differentiable deconvolution, enhancing stroke perfusion analysis accuracy without manual annotations.

Contribution

It presents the first differentiable deconvolution model integrated with neural networks to optimize arterial input function selection for stroke imaging.

Findings

Achieves expert-level core lesion segmentation performance.

Automatically selects arterial input functions without manual annotations.

Improves perfusion imaging quantification accuracy.

Abstract

Perfusion imaging is the current gold standard for acute ischemic stroke analysis. It allows quantification of the salvageable and non-salvageable tissue regions (penumbra and core areas respectively). In clinical settings, the singular value decomposition (SVD) deconvolution is one of the most accepted and used approaches for generating interpretable and physically meaningful maps. Though this method has been widely validated in experimental and clinical settings, it might produce suboptimal results because the chosen inputs to the model cannot guarantee optimal performance. For the most critical input, the arterial input function (AIF), it is still controversial how and where it should be chosen even though the method is very sensitive to this input. In this work we propose an AIF selection approach that is optimized for maximal core lesion segmentation performance. The AIF is regressed by a neural network optimized through a differentiable SVD deconvolution, aiming to maximize core lesion segmentation agreement with ground truth data. To our knowledge, this is the first work exploiting a differentiable deconvolution model with neural networks. We show that our approach is able to generate AIFs without any manual annotation, and hence avoiding manual rater's influences. The method achieves manual expert performance in the ISLES18 dataset. We conclude that the methodology opens new possibilities for improving perfusion imaging quantification with deep neural networks.