- Workshop presentation
- Open Access
A MRI-based open source tool for quantitative measurement of relaxation times and perfusion in cardiac tissues
© Yazdanparast et al. 2016
- Published: 27 January 2016
- Arterial Spin Label
- DICOM Viewer
- Magnetic Resonance Image Dataset
- Undeniable Importance
- Image Processing Feature
In cardiac Magnetic Resonance Imaging(MRI) studies, evaluating T1, T2 and T*2 relaxation times and hemodynamics assessment using perfusion analysis has an undeniable importance for understanding magnetic characteristics of many tissues. For this purpose, quantitative computational-based methods, implemented on top of the recognized clinical settings could be used as a robust and reproducible alternative to classical invasive techniques.
Osirix DICOM viewer(Pixmeo, Switzerland) was used as the platform for developing and validating the proposed tool. Set of images with varying acquisition parameters - Inversion Time(TI) for T1 mapping and Echo Time(TE) for T2(*) mapping - were acquired. Linear least squared curve fitting was performed on a pixel-by-pixel basis and calculated values were saved in a separate file as the resulted map. For T1 mapping, -exp(-TI / T1) and for T2(*) mapping, -exp(-TE/T2) were used as time axis inputs in the linear fitting process. Coefficient of determination (r2) value was calculated for each pixel and used as a measure to evaluate the goodness of fit. To get perfusion maps, two relaxation time maps were used and perfusion values were calculated for each pixel using the formula: perf = λ * (1/map1 - 1/map2), in which map1 and map2 corresponds to values obtained for two relaxation time maps and λ is tissue-blood partition coefficient.
Absolute quantitative relaxation time and perfusion maps for various MRI datasets were obtained using the developed open-source tool and on top of a well-known clinical setting. Pixel-based visualization of fitting process ensures end-users regarding the accuracy of the calculations. Finally, image processing features which has already been implemented in OsiriX will facilitate the visualization and processing of the generated maps.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.