Modeling and Pre-Treatment of Photon-Starved CT Data for Iterative Reconstruction
An increasing number of X-ray CT procedures are being conducted with drastically reduced dosage, due at least in part to advances in statistical reconstruction methods that can deal more effectively with noise than can traditional techniques. As data become photon-limited, more detailed models are necessary to deal with count rates that drop to the levels of system electronic noise. We present two options for sinogram pre-treatment that can improve the performance of photon-starved measurements, with the intent of following with model-based image reconstruction. Both the local linear minimum mean-squared error (LLMMSE) filter and pointwise Bayesian restoration (PBR) show promise in extracting useful, quantitative information from very low-count data by reducing local bias while maintaining the lower noise variance of statistical methods. Results from clinical data demonstrate the potential of both techniques.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:36 |
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| Enthalten in: |
IEEE transactions on medical imaging - 36(2017), 1 vom: 24. Jan., Seite 277-287 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chang, Zhiqian [VerfasserIn] |
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| Anmerkungen: |
Date Completed 21.12.2017 Date Revised 02.12.2018 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1109/TMI.2016.2606338 |
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| 520 | |a An increasing number of X-ray CT procedures are being conducted with drastically reduced dosage, due at least in part to advances in statistical reconstruction methods that can deal more effectively with noise than can traditional techniques. As data become photon-limited, more detailed models are necessary to deal with count rates that drop to the levels of system electronic noise. We present two options for sinogram pre-treatment that can improve the performance of photon-starved measurements, with the intent of following with model-based image reconstruction. Both the local linear minimum mean-squared error (LLMMSE) filter and pointwise Bayesian restoration (PBR) show promise in extracting useful, quantitative information from very low-count data by reducing local bias while maintaining the lower noise variance of statistical methods. Results from clinical data demonstrate the potential of both techniques | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Thibault, Jean-Baptiste |e verfasserin |4 aut | |
| 700 | 1 | |a Pal, Debashish |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Lin |e verfasserin |4 aut | |
| 700 | 1 | |a Sauer, Ken |e verfasserin |4 aut | |
| 700 | 1 | |a Bouman, Charles |e verfasserin |4 aut | |
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