An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study

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6 Citations (Scopus)

Abstract

Purpose: To study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed. Materials and methods: The RSEMD method was tested on in-silico, Catphan<sup>®</sup>500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver. Results: In all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms. Conclusions: In this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.

Original languageEnglish (US)
JournalPhysica Medica
DOIs
StateAccepted/In press - Feb 3 2015

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recovery
Noise
Four-Dimensional Computed Tomography
Feasibility Studies
lesions
Computer Simulation
Liver
liver
low noise
set theory
iteration
plateaus
projection
dosage
high resolution

Keywords

  • CNR and SNR improvement
  • Noisy CT image recovery
  • Rapid converging
  • Resolution subsets-based iterative method

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging
  • Physics and Astronomy(all)

Cite this

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title = "An iterative deconvolution algorithm for image recovery in clinical CT: A phantom study",
abstract = "Purpose: To study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed. Materials and methods: The RSEMD method was tested on in-silico, Catphan{\circledR}500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver. Results: In all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms. Conclusions: In this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.",
keywords = "CNR and SNR improvement, Noisy CT image recovery, Rapid converging, Resolution subsets-based iterative method",
author = "Slavine, {Nikolai V.} and Jeffrey Guild and McColl, {Roderick W.} and Anderson, {Jon A.} and Oz, {Orhan K.} and Lenkinski, {Robert E.}",
year = "2015",
month = "2",
day = "3",
doi = "10.1016/j.ejmp.2015.06.009",
language = "English (US)",
journal = "Physica Medica",
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T1 - An iterative deconvolution algorithm for image recovery in clinical CT

T2 - A phantom study

AU - Slavine, Nikolai V.

AU - Guild, Jeffrey

AU - McColl, Roderick W.

AU - Anderson, Jon A.

AU - Oz, Orhan K.

AU - Lenkinski, Robert E.

PY - 2015/2/3

Y1 - 2015/2/3

N2 - Purpose: To study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed. Materials and methods: The RSEMD method was tested on in-silico, Catphan®500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver. Results: In all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms. Conclusions: In this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.

AB - Purpose: To study the feasibility of using an iterative reconstruction algorithm to improve previously reconstructed CT images which are judged to be non-diagnostic on clinical review. A novel rapidly converging, iterative algorithm (RSEMD) to reduce noise as compared with standard filtered back-projection algorithm has been developed. Materials and methods: The RSEMD method was tested on in-silico, Catphan®500, and anthropomorphic 4D XCAT phantoms. The method was applied to noisy CT images previously reconstructed with FBP to determine improvements in SNR and CNR. To test the potential improvement in clinically relevant CT images, 4D XCAT phantom images were used to simulate a small, low contrast lesion placed in the liver. Results: In all of the phantom studies the images proved to have higher resolution and lower noise as compared with images reconstructed by conventional FBP. In general, the values of SNR and CNR reached a plateau at around 20 iterations with an improvement factor of about 1.5 for in noisy CT images. Improvements in lesion conspicuity after the application of RSEMD have also been demonstrated. The results obtained with the RSEMD method are in agreement with other iterative algorithms employed either in image space or with hybrid reconstruction algorithms. Conclusions: In this proof of concept work, a rapidly converging, iterative deconvolution algorithm with a novel resolution subsets-based approach that operates on DICOM CT images has been demonstrated. The RSEMD method can be applied to sub-optimal routine-dose clinical CT images to improve image quality to potentially diagnostically acceptable levels.

KW - CNR and SNR improvement

KW - Noisy CT image recovery

KW - Rapid converging

KW - Resolution subsets-based iterative method

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