PET-ki-proc: An Open-Source Toolbox for Automated F-DOPA PET Data Analysis
Poster No:
1628
Submission Type:
Abstract Submission
Authors:
Julia Schulz1, Melissa Thalhammer1, Viktor Neumaier1, Moritz Bonhoeffer1, Felix Brandl2, Christian Sorg1
Institutions:
1Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM), Munich, Bavaria, 2Department of Psychiatry, School of Medicine, Technical University of Munich (TUM), Munich, Germany
First Author:
Julia Schulz
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Co-Author(s):
Melissa Thalhammer
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Viktor Neumaier
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Moritz Bonhoeffer
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Felix Brandl
Department of Psychiatry, School of Medicine, Technical University of Munich (TUM)
Munich, Germany
Department of Psychiatry, School of Medicine, Technical University of Munich (TUM)
Munich, Germany
Christian Sorg
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Introduction:
Accurate estimation of the F-DOPA influx constant ki from PET imaging is critical for investigating brain dopamine function. In neuroimaging research, F-DOPA PET provides a robust measure of dopaminergic transmission (Garnett, 1983) and is widely used to study neurological and psychiatric disorders, including schizophrenia (McCutcheon, 2018) and Parkinson's disease (Depierreux, 2021). Despite the importance of accurate ki estimation, the processing of F-DOPA PET images lacks standardization, and no open-source toolboxes are available for ki calculation. To address this, we developed PET-ki-proc, an automated, open-source toolbox for F-DOPA PET processing and ki estimation.
Methods:
PET-ki-proc provides a framework for automated processing of dynamic F-DOPA PET images running in Python (Figure 1). Attenuation corrected PET data are motion corrected using FSL mcflirt (https://open.win.ox.ac.uk/pages/fsl/fslpy/), followed by Chambolle's total variation denoising (https://scikit-image.org/docs/0.24.x/api/skimage.restoration.html). Striatum and cerebellum masks from the Oxford-GSK-Imanova and Harvard-Oxford atlases (Desikan, 2006; Tziortzi, 2014) are registered into native PET space using FSL FNIRT and FLIRT, with thresholding applied to these masks. The F-DOPA influx constant is calculated for the time frame 20-60 minutes post-F-DOPA injection using Gjedde-Patlak linear analysis (Patlak & Blasberg, 1985). Ki is estimated for the whole striatum as well as for executive, limbic, and sensorimotor subcluster, with the cerebellum serving as the reference region.
The pipeline is run with the function:
PET_ki_proc -subject=subject_id -session=session_id -ref_frame=30 -weight=100 -num_iter=100 -thr_striatum=0.4 -thr_cerebellum=0.9.
Input:
• decay-corrected dynamic F-DOPA PET images in BIDS format.
• T1-weighted (T1w) image in BIDS format.
Output:
• Summary PDF: containing graphs for Time-Activity Curves (TACs) and Patlak fit, motion parameters, and screenshots of the masks overlayed on the mean PET and T1w image (Figure 2).
• Results Sheet: with mean ki and r² values for the striatum and subclusters.
• ki map and r² map: for voxel-wise and ROI-wise analysis of ki and r² values in the striatum and its subclusters.
Additional arguments are provided to tailor the analysis to specific needs. These include the reference frame for motion correction (ref_frame), weight (weigh), and iteration numbers (num_iter) for denoising, as well as options for thresholding methods (absolute thr_striatum and percentage thrp_striatum) for regions of interest.
An additional script is provided to generate a subject list and loop through it. Before running any processes, the tool checks whether the required data are available, providing an notification if not. It is also ensured that each step is only executed if the output has not been already generated.
The pipeline typically takes around 25 minutes per subject to complete.
The pipeline is run with the function:
PET_ki_proc -subject=subject_id -session=session_id -ref_frame=30 -weight=100 -num_iter=100 -thr_striatum=0.4 -thr_cerebellum=0.9.
Input:
• decay-corrected dynamic F-DOPA PET images in BIDS format.
• T1-weighted (T1w) image in BIDS format.
Output:
• Summary PDF: containing graphs for Time-Activity Curves (TACs) and Patlak fit, motion parameters, and screenshots of the masks overlayed on the mean PET and T1w image (Figure 2).
• Results Sheet: with mean ki and r² values for the striatum and subclusters.
• ki map and r² map: for voxel-wise and ROI-wise analysis of ki and r² values in the striatum and its subclusters.
Additional arguments are provided to tailor the analysis to specific needs. These include the reference frame for motion correction (ref_frame), weight (weigh), and iteration numbers (num_iter) for denoising, as well as options for thresholding methods (absolute thr_striatum and percentage thrp_striatum) for regions of interest.
An additional script is provided to generate a subject list and loop through it. Before running any processes, the tool checks whether the required data are available, providing an notification if not. It is also ensured that each step is only executed if the output has not been already generated.
The pipeline typically takes around 25 minutes per subject to complete.
·Workflow of PET-ki-proc
Results:
The toolbox was validated on two independent datasets, comprising patients with schizophrenia and controls.
The first dataset, recently acquired by our group, included 21 controls and 28 patients with schizophrenia during psychosis, along with 18 follow-up scans for controls and 19 for patients in remission. Intraclass correlation coefficient analysis of the striatum showed a good to strong reproducibility (ICCcontrols = 0.84, p < 0.001, ICCpatients = 0.61, p = 0.002).
The second dataset comprised of 24 healthy controls and 26 patients with schizophrenia in psychotic remission (Avram et al., 2019). Using PET-ki-proc, we successfully reproduced the results from this study, further validating the robustness of our approach.
The first dataset, recently acquired by our group, included 21 controls and 28 patients with schizophrenia during psychosis, along with 18 follow-up scans for controls and 19 for patients in remission. Intraclass correlation coefficient analysis of the striatum showed a good to strong reproducibility (ICCcontrols = 0.84, p < 0.001, ICCpatients = 0.61, p = 0.002).
The second dataset comprised of 24 healthy controls and 26 patients with schizophrenia in psychotic remission (Avram et al., 2019). Using PET-ki-proc, we successfully reproduced the results from this study, further validating the robustness of our approach.
·Example of the summary PDF of PET-ki-proc
Conclusions:
By offering a robust and user-friendly solution, PET-ki-proc facilitates the reproducible analysis of F-DOPA PET data and is freely available for the research community. PET-ki-proc will be provided under the following link: https://github.com/juliasbrain/PET_ki_proc.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
Methods Development
PET Modeling and Analysis 1
Neuroinformatics and Data Sharing:
Workflows 2
Novel Imaging Acquisition Methods:
PET
Keywords:
Computational Neuroscience
Data analysis
Dopamine
Modeling
Open-Source Software
Positron Emission Tomography (PET)
Schizophrenia
Workflows
Other - F-DOPA PET
1|2Indicates the priority used for review
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