Dynamic Susceptibility Contrast MRI in Small Animals

Abstract

The use of magnetic resonance imaging (MRI) for studying the cerebral perfusion mechanisms is well proved and contrasted in the clinical and research setups. This methodology is a promising tool in assessing numerous brain diseases like intracranial tumors, neurodegeneration processes, mental disorders, injuries and so on. In the preclinical environment, perfusion MRI offers a powerful resource for characterizing pathological models and specially identifying biomarkers to monitor the illness and validate the efficacy of therapeutical approaches. This chapter presents the theoretical bases and experimental protocols of dynamic susceptibility contrast MRI acquisitions for developing perfusion MRI studies in small animals.

Perfusion-weighted software written in Python for DSC-MRI analysis

“…Regarding preclinical MRI data processing, there is variability in the programs for computing the perfusion variables that use different methods for the analysis, both regarding the mathematical modeling and the protocols of image acquisition. Moreover, most of them have closed access, limited to the institution responsible for the software development (Gordaliza et al, 2015 ; Huhndorf et al, 2016 ; López-Larrubia, 2018 ; Hartmann et al, 2020 ; Tsai et al, 2021 ). This issue is present even in human health, causing concerns about the accuracy of software quantitative perfusion parameters.…”

“…or normalization of the obtained data. There are also more complex techniques that automatically determine AIF based on, among other things, the detection of pixels with the greatest contrast enhancement in various regions of interest, the use of clustering techniques, or the creation of physical models for the echo-planar signal intensity from an artery (Ostergaard et al, 1996 ; Wu et al, 2003 ; López-Larrubia, 2018 ). In this study, we provide the possibility of semi-automatic AIF or VOF adjustment with real curve monitoring for an accurate AIF-pixel selection.…”

“…Most of the post-processing software used in preclinical studies are home-built and custom-designed, its use being, in most cases, limited to the institution responsible for the development. While there do exist some commercial software tools, they are expensive and specifically designed for clinical use (Gordaliza et al, 2015 ; López-Larrubia, 2018 ; Hartmann et al, 2020 ; Tsai et al, 2021 ). These tools often only provide relative values (no absolute tissue hemodynamic parameters), do not compute all of the main perfusion parameters (CBF, CBV, and MTT), and overlook direct parameters such as signal recovery (SR) and percentage signal recovery (PSR), which may add valuable diagnostic information without requiring additional measurements (Huhndorf et al, 2016 ).…”

Preclinical models of middle cerebral artery occlusion: new imaging approaches to a classic technique

“…Further, Perfusion-Weighted Imaging (PWI) MRI can identify areas with decreased perfusion in order to estimate tissue at risk of ischemia, the penumbra. The more sensitive perfusion technique to measure penumbra requires contrast and is known as the dynamic susceptibility contrast-enhanced (DSC) MR perfusion ( 52 ). This is a contrasted T1 sequence that measures relative cerebral blood volume, relative cerebral blood flow and mean transit time.…”