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.
Thibault, Jean-Baptiste;
Sauer, Ken D;
Bouman, Charles A;
Hsieh, Jiang;
[1]
Department of Electrical Engineering, 275 Fitzpatrick, University of Notre Dame, Notre Dame, Indiana 46556-5637 (United States);
School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501 (United States);
Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188 (United States)]
- Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188 (United States)
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 (United States), School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501 (United States), and Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188 (United States)].
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 (United States), School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501 (United States), & Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188 (United States)].
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 (United States), School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501 (United States), and Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188 (United States)].
2007.
"A three-dimensional statistical approach to improved image quality for multislice helical CT."
United States.
https://doi.org/10.1118/1.2789499.
@misc{etde_21032861,
title = {A three-dimensional statistical approach to improved image quality for multislice helical CT}
author = {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 (United States), School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501 (United States), and Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188 (United States)]}
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}
journal = []
issue = {11}
volume = {34}
place = {United States}
year = {2007}
month = {Nov}
}
title = {A three-dimensional statistical approach to improved image quality for multislice helical CT}
author = {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 (United States), School of Electrical Engineering, Purdue University, West Lafayette, Indiana 47907-0501 (United States), and Applied Science Laboratory, GE Healthcare, 3000 N. Grandview Boulevard, W-1180, Waukesha, Wisconsin 53188 (United States)]}
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}
journal = []
issue = {11}
volume = {34}
place = {United States}
year = {2007}
month = {Nov}
}