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.

Users can now run all or part of the processing chain automatically by providing a link to a reference processing. In steps where user-defined regions of interest are preferred, the processing pauses and waits for input.

In cooperation with Medoro s.r.o., we have implemented a secure connection between PerfLab and the hospital’s PACS. This allows DCE-MRI data to be sent to PerfLab with a single click from the MR system’s console. Once the perfusion map calculation is complete, the results are automatically sent back to the PACS.

Reference installation: Masaryk Memorial Cancer Institute

Jake Cray presented his pilot study on evaluation of PerfLab’s blind deconvolution on his datasets with direct measurements of AIF in the aorta:

CRAY, J L; VITOUŠ, J; JIŘÍK R, TITARENKO S, BUCKLEY D L. Blind Estimation Versus Direct Measurement of the Arterial Input Function in DCE-MRI of the Breast. ISMRM Workshop on Perfusion MRI. 2025. Pamplona, Spain

We have thoroughly redesigned PerfLab’s graphical user interface and restructured its internal architecture to improve usability and performance.

Online version of a detailed paper on the evaluation methodology and results of the OSIPI DCE Challenge finalized as an MRM Paper - published online:

Shalom ES, Kim H, van der Heijden RA, et al. The ISMRM Open Science Initiative for Perfusion Imaging (OSIPI): Results from the OSIPI–Dynamic Contrast-Enhanced challenge. Magn Reson Med. 2024; 91: 1803-1821. doi: 10.1002/mrm.29909

At the end of 2021, we participated in the international OSIPI DCE challenge (OSIPI - Open Science Initiative for Perfusion Imaging of the ISMRM Perfusion Study group). The competition compared the submitted software tools concerning repeatability, reproducibility, and accuracy of Ktrans estimation (focused only on the 1st generation PK model Extended Tofts) on real and simulated cancer DCE-MRI datasets (brain tumors). Out of 10 submitted software tools, PerfLab ranked 3rd (results presented at the ISMRM 2022 annual meeting in London).