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The Use of Wavelet Filters to De-noise µPET Data

Explore how wavelet filters can effectively de-noise µPET data in medical imaging applications. Learn how these filters are perfectly localized in both frequency and spatial domains, allowing for noise removal while preserving signal integrity. Compare results of applying wavelet filters versus traditional methods to see improved image quality and lesion detectability.

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The Use of Wavelet Filters to De-noise µPET Data

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  1. The Use of Wavelet Filters to De-noise µPET Data Joe Grudzinski

  2. Motivation • Theoretically, FBP is best algorithm for determining distribution of radioactivity • Ramp filter amplify high frequency noise

  3. Possible Solutions • Fourier filters • Only perfectly localized in frequency domain and not spatial domain • PET signals are non-stationary and do not exhibit global, periodic behavior • Wavelet filters • Perfectly localized in frequency and spatial domain • Possible to examine signals at differing resolutions

  4. ‘A Trous’ Filter • ‘With holes’ – add zeroes during up sample • Noise is distributed through all coefficients • Signal is concentrated in a few coefficients • With proper threshold, possible to remove noise • Noise is only in first 3 scales

  5. Results µPET/CT 124I-NM404 Removed Noise

  6. Results Ramp Hamm Shepp Denoised Image Original Removed Noise

  7. Results

  8. Results

  9. Conclusion • Wavelets have provided benefits in post-processing • Higher resolution images provide better detectability of lesions in clinical applications when contrast is conserved

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