UMMPerfusion: an open source software tool towards quantitative MRI perfusion analysis in clinical routine

Abstract

To develop a generic Open Source MRI perfusion analysis tool for quantitative parameter mapping to be used in a clinical workflow and methods for quality management of perfusion data. We implemented a classic, pixel-by-pixel deconvolution approach to quantify T1-weighted contrast-enhanced dynamic MR imaging (DCE-MRI) perfusion data as an OsiriX plug-in. It features parallel computing capabilities and an automated reporting scheme for quality management. Furthermore, by our implementation design, it could be easily extendable to other perfusion algorithms. Obtained results are saved as DICOM objects and directly added to the patient study. The plug-in was evaluated on ten MR perfusion data sets of the prostate and a calibration data set by comparing obtained parametric maps (plasma flow, volume of distribution, and mean transit time) to a widely used reference implementation in IDL. For all data, parametric maps could be calculated and the plug-in worked correctly and stable. On average, a deviation of 0.032 ± 0.02 ml/100 ml/min for the plasma flow, 0.004 ± 0.0007 ml/100 ml for the volume of distribution, and 0.037 ± 0.03 s for the mean transit time between our implementation and a reference implementation was observed. By using computer hardware with eight CPU cores, calculation time could be reduced by a factor of 2.5. We developed successfully an Open Source OsiriX plug-in for T1-DCE-MRI perfusion analysis in a routine quality managed clinical environment. Using model-free deconvolution, it allows for perfusion analysis in various clinical applications. By our plug-in, information about measured physiological processes can be obtained and transferred into clinical practice.

Fig. 1

Calibration data set of size of 32 pixels. Sixteen pixels were taken from an artery (C1–D8) and 16 pixels from the prostate (A1–B8) to obtain intensity values. Color coding of the pixel is for visualization purpose only

Fig. 2

Graphical user interface of the perfusion plug-in. The arterial input function (green curve) selected by the user is shown in display window. The plot is updated in real-time when the user changes the corresponding ROI in the image. Also the current setting of the baseline (red line) is depicted

Fig. 3

Visualization of perfusion analysis results by our plug-in using. Top left the original data set (3D + time), top right plasma flow, lower left volume of distribution, lower right mean transit time. Data originates from a prostate MR exam

Fig. 4

Illustration of image fusion of a plasma flow map calculated by our plug-in to a high-resolution T1-weighted MRI of the same patient data set. The T1-weighted image is given in grayscale, the plasma flow map in a rainbow colormap

Fig. 5

Report generated during perfusion analysis. It shows location and shape of the arterial input function and parameter settings like the chosen regularization for the perfusion calculation