Development and validation of an open source quantification tool for DSC-MRI studies. (1st March 2015)

• Record Type: Journal Article
• Authors: Gordaliza, P.M.; Mateos-Pérez, J.M.; Montesinos, P.; Guzmán-de-Villoria, J.A.; Desco, M.; Vaquero, J.J.
• Is Part Of: Computers in biology and medicine. Volume 58(2015)
• Page Start: 56
• Page End: 62
• Publication Date: 2015-03-01
• Subjects: Magnetic resonance imaging -- Perfusion -- ImageJ -- Open source -- Software tool; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.01.002
• Languages: English
• ISSNs: 0010-4825

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 >0.8 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. Highlights: We have developed an open source tool for DSC-MRI quantification. The software runs as a plugin for the multiplatform public domain software ImageJ. It contains an open API that allows external users to implement new methods. It was validated, with good results, against a clinical tool, Philips IntelliSpace.