Platelet-based MPLE denoising of SPECT images: phantom and patient study
N Riyahi-Alam1, N Alibabaei, A Takavar
1Dept. of Med. Phys. & Biomed. Eng., Tehran Univ. of Med. Sci., Iran. riahinad@sina.tums.ac.ir
Abstract:
In this study the evaluation of a Platelet-based Maximum Penalized Likelihood Estimation (MPLE) for denoising SPECT images was performed and compared with other denoising methods such as Wavelets or Butterworth filteration. Platelet-based MPLE factorization as a multiscale decomposition approach has been already proposed for better edges and surfaces representation due to Poisson noise and inherent smoothness of this kind of images. We applied this approach on both simulated and real SPECT images. For NEMA phantom images, the measured noise levels before (M(b)) and after (M(a)) denoising with Platelet-based MPLE approach were M(b)=2.1732, M(a)=0.1399. In patient study for 32 cardiac SPECT images, the difference between noise level and SNR before and after the approach were (M(b)=3.7607, SNR(b)=9.7762, M(a)=0.7374, SNR(a)=41.0848) respectively. Thus the Coefficient Variance (C.V) of SNR values for denoised images with this algorithm as compared with Butterworth filter, (145/33%) was found. For 32 brain SPECT images the Coefficient Variance of SNR values, (196/17%) was obtained. Our results shows that Platelet-based MPLE is a useful method for denoising SPECT images considering better homogenous image, improvements in SNR, better radioactive uptake in target organ and reduction of interfering activity from background radiation to compare to that of other conventional denoising methods.
Insights
Platelet-based Maximum Penalized Likelihood Estimation (MPLE) effectively denoises SPECT images, significantly improving signal-to-noise ratio (SNR) and image quality compared to traditional methods.
Area of Science:
- Medical Imaging
- Image Processing
- Nuclear Medicine
Background:
- Single Photon Emission Computed Tomography (SPECT) imaging is susceptible to noise, which can degrade image quality and diagnostic accuracy.
- Conventional denoising methods like Wavelets and Butterworth filters have limitations in preserving image details and reducing noise effectively.
Purpose of the Study:
- To evaluate the efficacy of a novel Platelet-based Maximum Penalized Likelihood Estimation (MPLE) method for denoising SPECT images.
- To compare the performance of Platelet-based MPLE against established denoising techniques.
Main Methods:
- Platelet-based MPLE was applied to both simulated and real SPECT datasets, including NEMA phantom, cardiac, and brain imaging.
- Quantitative analysis involved measuring noise levels (M) and signal-to-noise ratios (SNR) before and after denoising.
- Comparative analysis included calculating the Coefficient Variance (C.V) of SNR values against Butterworth filtration.
Main Results:
- Platelet-based MPLE significantly reduced noise levels in NEMA phantom images from M(b)=2.1732 to M(a)=0.1399.
- In cardiac SPECT, SNR improved from 9.7762 to 41.0848 with Platelet-based MPLE, with a lower C.V. of SNR (145/33%) compared to Butterworth.
- Brain SPECT images also showed improved SNR with a C.V. of 196/17%.
Conclusions:
- Platelet-based MPLE demonstrates superior performance in denoising SPECT images compared to Wavelets and Butterworth filters.
- The method enhances image homogeneity, improves SNR, optimizes radioactive uptake visualization, and reduces background interference.
- Platelet-based MPLE is a valuable tool for improving the diagnostic quality of SPECT imaging.
