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TOF MLEM Adaptation for the Total-Body J-PET with a Realistic Analytical System Response Matrix
- Source :
- IEEE Transactions on Radiation and Plasma Medical Sciences 7(5) (2023) 509-520
- Publication Year :
- 2023
-
Abstract
- We report a study of the original image reconstruction algorithm based on the time-of-flight maximum likelihood expectation maximisation (TOF MLEM), developed for the total-body (TB) Jagiellonian PET (J-PET) scanners. The method is applicable to generic cylindrical or modular multi-layer layouts and is extendable to multi-photon imaging. The system response matrix (SRM) is represented as a set of analytical functions, uniquely defined for each pair of plastic scintillator strips used for the detection. A realistic resolution model (RM) in detector space is derived from fitting the Monte Carlo simulated emissions and detections of annihilation photons on oblique transverse planes. Additional kernels embedded in SRM account for TOF, parallax effect and axial smearing. The algorithm was tested on datasets, simulated in GATE for the NEMA IEC and static XCAT phantoms inside a 24-module 2-layer TB J-PET. Compared to the reference TOF MLEM with none or a shift-invariant RM, an improvement was observed, as evaluated by the analysis of image quality, difference images and ground truth metrics. We also reconstructed the data with additive contributions, pre-filtered geometrically and with non-TOF scatter correction applied. Despite some deterioration, the obtained results still capitalise on the realistic RM with better edge preservation and superior ground truth metrics. The envisioned prospects of the TOF MLEM with analytical SRM include its application in multi-photon imaging and further upgrade to account for the non-collinearity, positron range and other factors.
Details
- Database :
- arXiv
- Journal :
- IEEE Transactions on Radiation and Plasma Medical Sciences 7(5) (2023) 509-520
- Publication Type :
- Report
- Accession number :
- edsarx.2302.02967
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/TRPMS.2023.3243735