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Title: A three-dimensional statistical approach to improved image quality for multislice helical CT

Abstract

Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.

Authors:
; ; ;  [1]
  1. Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188 (United States)
Publication Date:
OSTI Identifier:
21032861
Resource Type:
Journal Article
Journal Name:
Medical Physics
Additional Journal Information:
Journal Volume: 34; Journal Issue: 11; Other Information: DOI: 10.1118/1.2789499; (c) 2007 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; ALGORITHMS; BEAMS; COMPUTERIZED TOMOGRAPHY; IMAGE PROCESSING; IMAGES; ITERATIVE METHODS; OPTIMIZATION; PATIENTS; PHANTOMS; SIMULATION; SPATIAL RESOLUTION

Citation Formats

Thibault, Jean-Baptiste, Sauer, Ken D, Bouman, Charles A, Hsieh, Jiang, Department of Electrical Engineering, 275 Fitzpatrick, University of Notre Dame, Notre Dame, Indiana 46556-5637, School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501, and Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188. A three-dimensional statistical approach to improved image quality for multislice helical CT. United States: N. p., 2007. Web. doi:10.1118/1.2789499.
Thibault, Jean-Baptiste, Sauer, Ken D, Bouman, Charles A, Hsieh, Jiang, Department of Electrical Engineering, 275 Fitzpatrick, University of Notre Dame, Notre Dame, Indiana 46556-5637, School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501, & Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188. A three-dimensional statistical approach to improved image quality for multislice helical CT. United States. https://doi.org/10.1118/1.2789499
Thibault, Jean-Baptiste, Sauer, Ken D, Bouman, Charles A, Hsieh, Jiang, Department of Electrical Engineering, 275 Fitzpatrick, University of Notre Dame, Notre Dame, Indiana 46556-5637, School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501, and Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188. 2007. "A three-dimensional statistical approach to improved image quality for multislice helical CT". United States. https://doi.org/10.1118/1.2789499.
@article{osti_21032861,
title = {A three-dimensional statistical approach to improved image quality for multislice helical CT},
author = {Thibault, Jean-Baptiste and Sauer, Ken D and Bouman, Charles A and Hsieh, Jiang and Department of Electrical Engineering, 275 Fitzpatrick, University of Notre Dame, Notre Dame, Indiana 46556-5637 and School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501 and Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188},
abstractNote = {Multislice helical computed tomography scanning offers the advantages of faster acquisition and wide organ coverage for routine clinical diagnostic purposes. However, image reconstruction is faced with the challenges of three-dimensional cone-beam geometry, data completeness issues, and low dosage. Of all available reconstruction methods, statistical iterative reconstruction (IR) techniques appear particularly promising since they provide the flexibility of accurate physical noise modeling and geometric system description. In this paper, we present the application of Bayesian iterative algorithms to real 3D multislice helical data to demonstrate significant image quality improvement over conventional techniques. We also introduce a novel prior distribution designed to provide flexibility in its parameters to fine-tune image quality. Specifically, enhanced image resolution and lower noise have been achieved, concurrently with the reduction of helical cone-beam artifacts, as demonstrated by phantom studies. Clinical results also illustrate the capabilities of the algorithm on real patient data. Although computational load remains a significant challenge for practical development, superior image quality combined with advancements in computing technology make IR techniques a legitimate candidate for future clinical applications.},
doi = {10.1118/1.2789499},
url = {https://www.osti.gov/biblio/21032861}, journal = {Medical Physics},
issn = {0094-2405},
number = 11,
volume = 34,
place = {United States},
year = {Thu Nov 15 00:00:00 EST 2007},
month = {Thu Nov 15 00:00:00 EST 2007}
}