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Published on: June 16, 2014
Extraction of an input function from dynamic micro-PET images using wavelet packet based sub-band decomposition
Jhih-Shian Lee1, Kuan-Hao Su, Wen-Yuan Chang
1Department of Biomedical Imaging & Radiological Sciences, National Yang-Ming University, No. 155, Sec. 2, Li-Nong Street, Taipei 112, Taiwan.
Neuroimage
|August 16, 2012
Summary
Accurately measuring physiological parameters with Positron Emission Tomography (PET) requires a plasma time activity curve (TAC). This study introduces a wavelet packet based sub-band decomposition independent component analysis (WP SDICA) method for improved accuracy using minimal blood samples.
Area of Science:
- Nuclear medicine
- Medical imaging
- Physiological modeling
Background:
- Positron Emission Tomography (PET) enables physiological parameter quantification.
- Accurate quantification necessitates measuring the plasma time activity curve (TAC).
- Image-derived input functions (IDIFs) offer a noninvasive alternative but suffer from low PET image resolution and signal-to-noise ratio (SNR), impacting accuracy.
Purpose of the Study:
- To develop and validate a method for extracting accurate input functions from microPET images.
- To assess the efficacy of wavelet packet based sub-band decomposition independent component analysis (WP SDICA) with minimal plasma samples (zero or one).
- To compare the performance of WP SDICA against population-based and fuzzy c-means clustering approaches.
Main Methods:
- Utilized simulated dynamic rat PET images with varying spatial resolutions and SNRs.
- Applied WP SDICA to dynamic PET images from eight Sprague-Dawley rats.
- Evaluated accuracy using normalized root mean square errors, area under curve errors, and correlation coefficients.
Main Results:
- The one-sample WP SDICA approach demonstrated superior accuracy compared to other methods in both simulated and realistic scenarios.
- WP SDICA achieved better accuracy in deriving input functions from microPET images.
- Metabolic rate estimations using the one-sample WP SDICA method exhibited the smallest errors.
Conclusions:
- Wavelet packet based sub-band decomposition independent component analysis (WP SDICA) with a single plasma sample provides accurate input functions for PET quantification.
- This noninvasive method significantly improves the accuracy of physiological parameter estimation.
- WP SDICA offers a promising advancement for quantitative PET imaging, reducing the need for extensive blood sampling.
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