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The Use of Wavelet Filters to De-noise µPET Data

The Use of Wavelet Filters to De-noise µPET Data. Joe Grudzinski. Motivation. Theoretically, FBP is best algorithm for determining distribution of radioactivity Ramp filter amplify high frequency noise. Possible Solutions. Fourier filters

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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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