Perfusion Imaging: A Data Assimilation Approach

TL;DR

This paper introduces PIANO, a data assimilation method for perfusion imaging that estimates tissue velocity and diffusion without needing arterial input functions, improving stroke assessment accuracy.

Contribution

The work presents a novel, contrast-agent free data assimilation approach that models perfusion using an advection-diffusion framework, accounting for spatial dependencies.

Findings

PIANO accurately estimates velocity and diffusion fields.

It outperforms conventional perfusion measures in stroke assessment.

The method does not require arterial input function estimation.

Abstract

Perfusion imaging (PI) is clinically used to assess strokes and brain tumors. Commonly used PI approaches based on magnetic resonance imaging (MRI) or computed tomography (CT) measure the effect of a contrast agent moving through blood vessels and into tissue. Contrast-agent free approaches, for example, based on intravoxel incoherent motion, also exist, but are so far not routinely used clinically. These methods rely on estimating on the arterial input function (AIF) to approximately model tissue perfusion, neglecting spatial dependencies, and reliably estimating the AIF is also non-trivial, leading to difficulties with standardizing perfusion measures. In this work we therefore propose a data-assimilation approach (PIANO) which estimates the velocity and diffusion fields of an advection-diffusion model that best explains the contrast dynamics. PIANO accounts for spatial dependencies and neither requires estimating the AIF nor relies on a particular contrast agent bolus shape. Specifically, we propose a convenient parameterization of the estimation problem, a numerical estimation approach, and extensively evaluate PIANO. We demonstrate that PIANO can successfully resolve velocity and diffusion field ambiguities and results in sensitive measures for the assessment of stroke, comparing favorably to conventional measures of perfusion.