Work Package 4

MR assisted IDIF on MR/BrainPET

With the advances in PET and PET/MR technology allowing higher quantification accuracy, dynamic PET studies using modeling for full quantification are becoming more prevalent. The gold standard requires arterial blood sampling to estimate the arterial input function (AIF). However, this method can be complex and is invasive due to arterial cannulation. Conversely, the image-derived input function (IDIF) method is a non-invasive alternative enabling estimation with no or minimal blood sampling. This work aimed to develop a fully automated method for IDIF estimation in PET/MR and dedicated PET studies, especially where no anatomical information is available.

Our method was implemented with Python, comprising the following steps:

  1. Open MR and PET images in Nifti format
  2. Creates an average image of the dynamic PET
  3. Perform MR bias correction in T1 MPRAGE
  4. Rigid co-registration of PET average image to T1 MPRAGE bias corrected image
  5. Save transformation matrices for further needs
  6. Remove skull from T1 MPRAGE MR bias corrected image
  7. Apply median filter to T1 MPRAGE MR corrected image without skull
  8. Select internal carotids (ICA) in MR image using probability map created from atlas
  9. Binary mask created from segmented ICA
  10. Dynamic (for each frame) binary ICA mask created
  11. Partial volume correction (PVC) was applied to PET dynamic image
  12. Dynamic ICA mask applied to PET dynamic image
  13. Select and average the 4 hottest5 pixels values per plane
  14. Get time from PET framing scheme
  15. Get arterial blood curve information (gold standard) from the study for comparison/validation
  16. Plot image-derived and arterial blood curves
  17. Fit blood curves with Gaussian
  18. Calibrate IDIF curve based on AIF curve
  19. Calculate the area under the curve (AUC) for the peak (P) and tail (T) for validation – ratio between IDIF and AIF AUCs

The developed automated tool appears to be feasible for PET/MR and dedicated PET applications and the need for interaction/manual operations from the user is minimal – only the input image path and blood data file need to be changed.

The segmentation using the brain vessel atlas had yet some limitations. Notably, the vessel atlas used for creating the ICA probability maps only contained the upper part of the ICA, resulting in an incomplete representation that disregarded individual anatomical variations. Ongoing assessment aims to determine the influence of different segmentation methods, partial volume correction methods, and fitting functions. Further evaluation of the general robustness of the method and its performance with other radiotracers and in the presence of volunteer motion will be made, including the test of other PVC methods available for PET image correction and the evaluation of different fit functions.

In any case and in summary, the project has succeeded in optimizing the determination of the IDIF, ensuring that it provides a good approximation. This is a significant accomplishment, especially in applications where precision is essential. A reliable IDIF contributes to the overall robustness and reliability of the project and its dual focus on user-friendliness and IDIF accuracy demonstrates a well-rounded approach, addressing both usability and functionality.