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Title: Noise reduction in spectral CT: Reducing dose and breaking the trade-off between image noise and energy bin selection

Abstract

Purpose: Our purpose was to reduce image noise in spectral CT by exploiting data redundancies in the energy domain to allow flexible selection of the number, width, and location of the energy bins. Methods: Using a variety of spectral CT imaging methods, conventional filtered backprojection (FBP) reconstructions were performed and resulting images were compared to those processed using a Local HighlY constrained backPRojection Reconstruction (HYPR-LR) algorithm. The mean and standard deviation of CT numbers were measured within regions of interest (ROIs), and results were compared between FBP and HYPR-LR. For these comparisons, the following spectral CT imaging methods were used:(i) numerical simulations based on a photon-counting, detector-based CT system, (ii) a photon-counting, detector-based micro CT system using rubidium and potassium chloride solutions, (iii) a commercial CT system equipped with integrating detectors utilizing tube potentials of 80, 100, 120, and 140 kV, and (iv) a clinical dual-energy CT examination. The effects of tube energy and energy bin width were evaluated appropriate to each CT system. Results: The mean CT number in each ROI was unchanged between FBP and HYPR-LR images for each of the spectral CT imaging scenarios, irrespective of bin width or tube potential. However, image noise, as represented bymore » the standard deviation of CT numbers in each ROI, was reduced by 36%-76%. In all scenarios, image noise after HYPR-LR algorithm was similar to that of composite images, which used all available photons. No difference in spatial resolution was observed between HYPR-LR processing and FBP. Dual energy patient data processed using HYPR-LR demonstrated reduced noise in the individual, low- and high-energy images, as well as in the material-specific basis images. Conclusions: Noise reduction can be accomplished for spectral CT by exploiting data redundancies in the energy domain. HYPR-LR is a robust method for reducing image noise in a variety of spectral CT imaging systems without losing spatial resolution or CT number accuracy. This method improves the flexibility to select energy bins in the manner that optimizes material identification and separation without paying the penalty of increased image noise or its corollary, increased patient dose.« less

Authors:
; ; ; ; ;  [1];  [2];  [2]
  1. Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota 55905 (United States)
  2. (United States)
Publication Date:
OSTI Identifier:
22098608
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 38; Journal Issue: 9; Other Information: (c) 2011 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; ACCURACY; ALGORITHMS; CHARGES; COMPUTERIZED SIMULATION; COMPUTERIZED TOMOGRAPHY; FLEXIBILITY; IMAGE PROCESSING; MATHEMATICAL SOLUTIONS; NOISE; PATIENTS; PHOTONS; POTASSIUM CHLORIDES; RADIATION DOSES; REDUNDANCY; RUBIDIUM; SPATIAL RESOLUTION

Citation Formats

Leng, Shuai, Yu, Lifeng, Wang, Jia, Fletcher, Joel G., Mistretta, Charles A., McCollough, Cynthia H., Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53792, and Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota 55905. Noise reduction in spectral CT: Reducing dose and breaking the trade-off between image noise and energy bin selection. United States: N. p., 2011. Web. doi:10.1118/1.3609097.
Leng, Shuai, Yu, Lifeng, Wang, Jia, Fletcher, Joel G., Mistretta, Charles A., McCollough, Cynthia H., Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53792, & Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota 55905. Noise reduction in spectral CT: Reducing dose and breaking the trade-off between image noise and energy bin selection. United States. doi:10.1118/1.3609097.
Leng, Shuai, Yu, Lifeng, Wang, Jia, Fletcher, Joel G., Mistretta, Charles A., McCollough, Cynthia H., Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53792, and Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota 55905. Thu . "Noise reduction in spectral CT: Reducing dose and breaking the trade-off between image noise and energy bin selection". United States. doi:10.1118/1.3609097.
@article{osti_22098608,
title = {Noise reduction in spectral CT: Reducing dose and breaking the trade-off between image noise and energy bin selection},
author = {Leng, Shuai and Yu, Lifeng and Wang, Jia and Fletcher, Joel G. and Mistretta, Charles A. and McCollough, Cynthia H. and Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin 53792 and Department of Radiology, Mayo Clinic College of Medicine, Rochester, Minnesota 55905},
abstractNote = {Purpose: Our purpose was to reduce image noise in spectral CT by exploiting data redundancies in the energy domain to allow flexible selection of the number, width, and location of the energy bins. Methods: Using a variety of spectral CT imaging methods, conventional filtered backprojection (FBP) reconstructions were performed and resulting images were compared to those processed using a Local HighlY constrained backPRojection Reconstruction (HYPR-LR) algorithm. The mean and standard deviation of CT numbers were measured within regions of interest (ROIs), and results were compared between FBP and HYPR-LR. For these comparisons, the following spectral CT imaging methods were used:(i) numerical simulations based on a photon-counting, detector-based CT system, (ii) a photon-counting, detector-based micro CT system using rubidium and potassium chloride solutions, (iii) a commercial CT system equipped with integrating detectors utilizing tube potentials of 80, 100, 120, and 140 kV, and (iv) a clinical dual-energy CT examination. The effects of tube energy and energy bin width were evaluated appropriate to each CT system. Results: The mean CT number in each ROI was unchanged between FBP and HYPR-LR images for each of the spectral CT imaging scenarios, irrespective of bin width or tube potential. However, image noise, as represented by the standard deviation of CT numbers in each ROI, was reduced by 36%-76%. In all scenarios, image noise after HYPR-LR algorithm was similar to that of composite images, which used all available photons. No difference in spatial resolution was observed between HYPR-LR processing and FBP. Dual energy patient data processed using HYPR-LR demonstrated reduced noise in the individual, low- and high-energy images, as well as in the material-specific basis images. Conclusions: Noise reduction can be accomplished for spectral CT by exploiting data redundancies in the energy domain. HYPR-LR is a robust method for reducing image noise in a variety of spectral CT imaging systems without losing spatial resolution or CT number accuracy. This method improves the flexibility to select energy bins in the manner that optimizes material identification and separation without paying the penalty of increased image noise or its corollary, increased patient dose.},
doi = {10.1118/1.3609097},
journal = {Medical Physics},
issn = {0094-2405},
number = 9,
volume = 38,
place = {United States},
year = {2011},
month = {9}
}