Iterative SPECT Reconstruction Using Matched Filtering for Improved Image Quality

SPECT images reconstructed from low count studies suffer either from high noise or poor resolution. We have developed an iterative reconstruction with matched filtering (IRMF) to control image noise while maintaining higher image resolution. IRMF involves filtering the measured projection and re-pro...

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Hauptverfasser: Jinghan Ye, Xiyun Song, Zuo Zhao, Da Silva, A.J., Wiener, J.S., Lingxiong Shao
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creator Jinghan Ye
Xiyun Song
Zuo Zhao
Da Silva, A.J.
Wiener, J.S.
Lingxiong Shao
description SPECT images reconstructed from low count studies suffer either from high noise or poor resolution. We have developed an iterative reconstruction with matched filtering (IRMF) to control image noise while maintaining higher image resolution. IRMF involves filtering the measured projection and re-projection during iterative reconstruction with the same low-pass filter before the two are compared to generate an error projection. Another low-pass filter can be applied to the error projection before it is backprojected to update the current activity distribution estimate. The method is validated with a cardiac phantom filled with a clinical distribution of Tc-99m. A 1-second-per-frame scan was acquired to mimic a single gated segment. The image was reconstructed using ordered-subset expectation-maximization (OSEM) algorithm with depth-dependent resolution recovery. Reconstructions of similar spatial resolution with post-reconstruction Butterworth filtering (OSEM+F) and with matched filtering are compared visually and via standard deviation (SD) and signal-to-noise ratio (SNR) measurements. Results: Images reconstructed with IRMF show strong noise suppression in both the myocardium and background areas as compared to those reconstructed with OSEM+F. The SD in the background is reduced by ~30%, and the SNR is improved by ~100%. IRMF significantly improves image quality by suppressing noise in low count SPECT studies while maintaining higher image resolution.
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We have developed an iterative reconstruction with matched filtering (IRMF) to control image noise while maintaining higher image resolution. IRMF involves filtering the measured projection and re-projection during iterative reconstruction with the same low-pass filter before the two are compared to generate an error projection. Another low-pass filter can be applied to the error projection before it is backprojected to update the current activity distribution estimate. The method is validated with a cardiac phantom filled with a clinical distribution of Tc-99m. A 1-second-per-frame scan was acquired to mimic a single gated segment. The image was reconstructed using ordered-subset expectation-maximization (OSEM) algorithm with depth-dependent resolution recovery. Reconstructions of similar spatial resolution with post-reconstruction Butterworth filtering (OSEM+F) and with matched filtering are compared visually and via standard deviation (SD) and signal-to-noise ratio (SNR) measurements. Results: Images reconstructed with IRMF show strong noise suppression in both the myocardium and background areas as compared to those reconstructed with OSEM+F. The SD in the background is reduced by ~30%, and the SNR is improved by ~100%. 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We have developed an iterative reconstruction with matched filtering (IRMF) to control image noise while maintaining higher image resolution. IRMF involves filtering the measured projection and re-projection during iterative reconstruction with the same low-pass filter before the two are compared to generate an error projection. Another low-pass filter can be applied to the error projection before it is backprojected to update the current activity distribution estimate. The method is validated with a cardiac phantom filled with a clinical distribution of Tc-99m. A 1-second-per-frame scan was acquired to mimic a single gated segment. The image was reconstructed using ordered-subset expectation-maximization (OSEM) algorithm with depth-dependent resolution recovery. Reconstructions of similar spatial resolution with post-reconstruction Butterworth filtering (OSEM+F) and with matched filtering are compared visually and via standard deviation (SD) and signal-to-noise ratio (SNR) measurements. Results: Images reconstructed with IRMF show strong noise suppression in both the myocardium and background areas as compared to those reconstructed with OSEM+F. The SD in the background is reduced by ~30%, and the SNR is improved by ~100%. IRMF significantly improves image quality by suppressing noise in low count SPECT studies while maintaining higher image resolution.</abstract><pub>IEEE</pub><doi>10.1109/NSSMIC.2006.354369</doi><tpages>3</tpages></addata></record>
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subjects Error correction
Filtering
Image quality
Image reconstruction
Image resolution
Imaging phantoms
Low pass filters
Matched filters
Signal to noise ratio
Spatial resolution
title Iterative SPECT Reconstruction Using Matched Filtering for Improved Image Quality
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