Perfusion Linearity and Its Applications

TL;DR

This paper introduces a new validation approach for perfusion algorithms based on the Perfusion Linearity Property, enhancing the reliability and robustness of blood flow parameter computations in clinical settings.

Contribution

It proposes a novel validation method leveraging PLP, revealing issues in current deconvolution techniques and guiding the development of more dependable perfusion analysis methods.

Findings

PLP is present in various perfusion techniques.

PLP-based validation uncovers hidden problems in existing algorithms.

The approach suggests pathways for more reliable perfusion computations.

Abstract

Perfusion analysis computes blood flow parameters (blood volume, blood flow, mean transit time) from the observed flow of contrast agent, passing through the patient's vascular system. Perfusion deconvolution has been widely accepted as the principal numerical tool for perfusion analysis, and is used routinely in clinical applications. This extensive use of perfusion in clinical decision-making makes numerical stability and robustness of perfusion computations vital for accurate diagnostics and patient safety. The main goal of this paper is to propose a novel approach for validating numerical properties of perfusion algorithms. The approach is based on Perfusion Linearity Property (PLP), which we find in perfusion deconvolution, as well as in many other perfusion techniques. PLP allows one to study perfusion values as weighted averages of the original imaging data. This, in turn, uncovers hidden problems with the existing deconvolution techniques, and may be used to suggest more reliable computational approaches and methodology.