Comparison of Different Post-Processing Algorithms for Dynamic Susceptibility Contrast Perfusion Imaging of Multiple Sclerosis Lesions: A Time to Peak Analysis

doi:10.33696/neurol.2.049

Abstract

The purpose of the present study was to evaluate, in multiple sclerosis (MS) lesions, the diagnostic performance of two different commercial post-processing MR perfusion software. These two different algorithms for processing Dynamic susceptibility contrast (DSC) perfusion images have been used to differentiate perfusion values among white matter (WM) lesions, normal appearing white matter (NAWM), and grey matter (GM) in MS.

Highlights

Cerebral circulation is essential to guarantee the proper functioning of the entire system and represents the movement of blood through the network of cerebral arteries and veins supplying the brain with the necessary oxygen and energy substrates [1]
A statistically significant difference between the two algorithms was demonstrated by comparing Time to Peak (TTP) values among recent, stable lesions and normal appearing white matter (NAWM)
TTP has been able to 1) examine the variability in the quantitative results of Dynamic susceptibility contrast (DSC) Magnetic Resonance (MR) perfusion imaging generated from identical source data of multiple sclerosis (MS) patients, 2) to identify the variables between two commercial post-processing algorithm and 3) to focus on the crucial role of post-processing inter-vendor differences

Summary

Introduction

Cerebral circulation is essential to guarantee the proper functioning of the entire system and represents the movement of blood through the network of cerebral arteries and veins supplying the brain with the necessary oxygen and energy substrates [1] In this regard, abnormal perfusion levels have been associated with a wide range of brain disorders such as stroke [2], brain tumors [3], Alzheimer’s disease [4], and multiple sclerosis (MS) [5]. From this curve (Figure 1), multiple hemodynamic parameters such as time to peak (TTP), mean transit time (MTT), cerebral blood flow (CBF), and cerebral blood volume (CBV) can be determined for each pixel and perfusion maps could be generated [7,8]

Methods

Discussion

Conclusion