Artifact-free partial volume correction for 177Lu-PSMA SPECT
Yibin Liu1, Zhonglin Lu1,2, Gefei Chen1,3
1Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Taipa, Macau, SAR, China.
This study developed a spatially variant point spread function (svPSF) enhanced partial volume correction (PVC) for 177Lu-PSMA SPECT imaging. The novel method significantly improves quantification accuracy without introducing Gibbs ringing artifacts or noise amplification.
Area of Science:
- Nuclear Medicine
- Medical Imaging
- Radiopharmaceutical Therapy
Background:
- Partial volume effect (PVE) in SPECT imaging reduces quantification accuracy.
- Traditional partial volume correction (PVC) methods often use spatially invariant point spread functions (PSFs) and can introduce Gibbs ringing artifacts.
Purpose of the Study:
- To develop a spatially variant PSF (svPSF) enhanced PVC for 177Lu-PSMA SPECT.
- To achieve improved quantification accuracy without Gibbs ringing or noise amplification.
Main Methods:
- Generated svPSFs via Monte Carlo simulations and linear interpolation.
- Enhanced Reblurred Van Cittert (RVC) and iterative Yang (IY) algorithms with svPSFs, voxel by voxel.
- Implemented non-negativity constraint (NC) in RVC to mitigate artifacts.
- Validated methods using simulated (cylindrical and XCAT phantoms) and real clinical 177Lu-PSMA-617 SPECT data.
Main Results:
- svPSF-enhanced PVC methods improved recovery coefficients (RC) for phantom spheres.
- Mean absolute errors (MAE) in kidneys, parotid glands, and tumors were significantly reduced, e.g., from 51.8% to 8.8% for tumors.
- The proposed svPSF+NC-RVC method showed improved background uniformity (CoV of 0.082).
- Gibbs ringing artifacts and noise were reduced with NC implementation in RVC.
Conclusions:
- The developed svPSF enhanced PVC methods enhance quantification accuracy in 177Lu-PSMA-617 SPECT.
- Non-negativity constraint (NC) effectively suppresses Gibbs ringing artifacts and noise in RVC images.
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