PET image filtering
Reducing image noise by spatial filtering
Computation of parametric image the noise in dynamic PET image may need to be reduced. Filtering may also allow patient dose reduction (Axelsson & Sörensen, 2013). There are different methods for producing smoother images:
1. Image reconstruction
PET image reconstruction parameters can be modified, either by increasing the level of filtering, or by changing the filter type. FBP reconstruction gives the same result whether filtering is done in the reconstruction or after reconstruction. Iterative reconstruction methods may give variable results depending on the filter applied during the reconstruction. Measured PSF can be applied in the reconstruction to reduce both noise and partial volume effect.
2. Image thresholding
PET images can be thresholded to remove reconstruction artifacts, especially outside the body. Several tools can be used for thresholding dynamic and static images, including our own tools imgthrs and imgcutof for ECAT, Analyze, and NIfTI image format.
Traditional image filtering methods, not related to PET, do not take advantage of the increased signal-to-noise ratio (SNR) of the entire time series. A few methods that apply the time information have been proposed, for example HYPR method (Christian et al., 2010), flexible segmentation method (Bentourkia, 2001), factor analysis (Tsartsalis et el. 2018), and spectral analysis (Veronese et al., 2018). Currently there are no software in TPC for using these methods, although some preliminary programs for testing purposes are available (imgfsegm, imgdysmo).
Based on simulations, Huang et al (2003) suggested that noise level in parametric images could be reduced using total variance (TV) de-noising.
Volume-based (3D) smoothing in the brain increases partial volume effects. Cortical surface-based smoothing can reduce the bias and inter-subject variance (Greve et al., 2014). Denoising, partial volume correction, and image segmentation are dependent on each other, and a joint solution can provide better results than performing each step separately (Xu et al., 2018).
- Partial volume effect
- Calculating parametric images
- Simulating noise
- Image clustering
- Factor analysis
- Correlation coefficient constrained parametric images
- Comparing PET TACs
- Tools for processing image data
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Bentourkia M. A flexible image segmentation prior to parametric estimation. Comput Med Imaging Graph. 2001; 25: 501-506. doi: 10.1016/S0895-6111(01)00016-7.
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Updated at: 2019-12-01
Created at: 2013-10-24
Written by: Vesa Oikonen