We present results of a pilot study on clinical prostate MRI data acquired at the Masaryk Memorial Cancer Institute in Brno. Twenty-six patients with biopsy-verified prostate lesions were examined on a 3 T scanner with a dynamic T₁-weighted GRASP acquisition.

Quantitative perfusion parameters describe tumour vasculature directly and could help to separate clinically significant from clinically insignificant cancer, but their values depend on how the arterial input function (AIF) is obtained. We tested whether perfusion parameters estimated in PerfLab with the AATH model are associated with ISUP grade, and how this depends on the choice of AIF (blind deconvolution, Horsfield, Parker).

👉 [Read the detailed report here] to see the correlations with ISUP grade and the ROC analysis.

We present results on data originating from Stanford University (see the original study), processed earlier within the canSERV project, carried out in collaboration with the Luxembourg Institute of Health. Here, we present a follow-up analysis of the biological effects of focused ultrasound (FUS) on perfusion parameters in glioblastoma tumors.

In the original study, perfusion was measured by DCE-MRI in mice with implanted glioblastoma. We selected datasets where two measurements – one without FUS applied beforehand and one with FUS applied beforehand – were acquired within 1–4 days. FUS is expected to increase vascular permeability, which could, for example, improve the delivery of a drug into the tumor tissue.

We tested whether DCE-MRI perfusion parameters can detect the effect of FUS, and how robust this finding is with respect to the choice of denoising, regularization and pharmacokinetic model (ETK and AATH).

👉 [Read the detailed report here] to see the parameter maps and the quantitative comparison.

We are pleased to share a comprehensive validation of the latest version of PerfLab, conducted using the standardized datasets from the Open Source Initiative for Perfusion Imaging (OSIPI) DCE challenge (see the benchmark paper in Magnetic Resonance in Medicine).

In this rigorous assessment, we systematically evaluated the accuracy and repeatability of the key steps in our DCE-MRI analysis pipeline—AIF estimation, and pharmacokinetic modeling.

The results show that the current production version of PerfLab yields higher accuracy and lower parameter variability than our original submission to the OSIPI challenge. These optimizations translate directly into more robust and reproducible perfusion metrics for your imaging studies.

👉 [Read the detailed validation report here] to explore the benchmark results and see how PerfLab can enhance your research workflow.

Two collaborative projects supported within the canSERV framework, in partnership with RCSI and LIH, are currently in progress with active contributions from our team. As part of these projects, we are providing data processing services through PerfLab, enabling efficient and standardized analysis workflows for advanced imaging data.

In parallel, we are systematically collecting feedback from project users. Their input is being directly incorporated into ongoing development, helping us refine the software, improve usability, and address practical needs identified during deployment.

We look forward to continuing collaboration with our partners and further enhancing PerfLab based on user experience and emerging research requirements. For more information on the canSERV initiative, please visit the official website: canSERV - Cancer Survivorship Research

Users can now benefit from a new version of PerfLab computational core. The new core adds multiple new features, like automatic processing and report creation. In the backend the core runs on updated Python version and follows stricter coding rules to ensure better reproducibility and maintainability. With the release of V4 a V5_dev is now out for testing and open for feature additons. The V3 is now considered legacy and will no longer be maintained.