- Meeting abstract
- Open Access
Masamune: a tool for automatic dynamic PET data processing, image reconstruction and integrated PET/MRI data analysis
© Chonde et al; licensee Springer 2014
- Published: 29 July 2014
- Partial Volume Effect Correction
- Automate Bolus
- Advanced Data Analysis
- Bolus Arrival Time
- Single Count Rate
We describe a novel semi-automated pipeline which integrates advanced data analysis tools for MR and PET with advanced PET reconstruction correction methods (partial volume effect correction [PVC], motion correction [MC], attenuation correction [AC]) in a user-friendly Matlab graphical user interface (GUI).
The reconstruction and analysis GUI is written in Matlab. Computationally intensive tasks in the pipeline are automatically transferred to a high-performance computing cluster and retrieved.
Descriptions of the commercial packages used can be found in their corresponding references. SPM8  is used in MC and AC processing. Comkat  and PMOD  are used for kinetic modeling. FSL  and SPM8 are used for group analysis. Freesurfer  is used for regions-of-interest (ROI) definition and smoothing.
Data preprocessing: Head-motion is derived from a number of sources: echo-planar MR images, MR-based motion navigators, and directly from the PET data when MR data is unavailable (e.g. during shimming). Subsequently, the ME-MPRAGE is reoriented to the reference position. Cortical and subcortical ROIs are labeled using FreeSurfer; similarly, the MPRAGE is registered to MNI-space for generating subject-specific atlases.
Image reconstruction: An OP-OSEM algorithm is used for PET reconstruction . MC  and PVC  can be performed using the results from data preprocessing. AC can be imported directly from CT, using MR-images , or through atlas-based methods.
Automated Bolus Arrival Time (BAT) & Image-Derived Input Function: The singles count rate is recorded during PET acquisition. The BAT is determined by fitting a trilinear piecewise function and used as the reference time. Time-of-Flight MR can then be used to segment the arteries of the head and an image-derived input function can be determined using short frames.
We presented a novel pipeline which interfaces with a number of different commercial software to provide improved PET data quantification.
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