Development and validation of an open source quantification tool for DSC-MRI studies

Abstract

Abstract:Motivation: This work presents the development of an open source tool for the quantification of dynamic susceptibility weighted contrast enhanced (DSC) perfusion studies. The development of this tool is motivated by the lack of open source tools implemented on open platforms to allow external developers to implement their own quantification methods easily and without the need of paying for a development license. Materials and methods: This quantification tool was developed as a plugin for the ImageJ image analysis platform using the Java programming language. A modular approach was used in the implementation of the components, in such a way that the addition of new methods can be done without breaking any of the existing functionalities. For the validation process, images from seven patients with brain tumors were acquired and quantified with the presented tool and with a widely used clinical software package. The resulting perfusion parameters were then compared. Results: Perfusion parameters and the corresponding parametric images were obtained. When no gamma fitting is used, an excellent agreement with the tool used as a gold standard was obtained (R 2 40.8 and values are within 95% CI limits in Bland Altman plots).Conclusion: An open source tool that performs quantification of perfusion studies using magnetic resonance imaging has been developed and validated using a clinical software package. It works as an ImageJ plugin and the source code has been published with an open source license.

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.…”

“…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 ).…”