Performance Improvements in HYPR-OSEM

Ju-Chieh Kevin Cheng, Julian Matthews, Ronald Boellaard, Ian Janzen, Jose Anton-Rodriguez, Vesna Sossi

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

We describe methods which improve the performances in contrast recovery coefficient (CRC) versus noise trade-off and in convergence rate in CRC of the newly developed HYPR-OSEM algorithm. HYPR-OSEM is a reconstruction method which incorporates HighlY constrained back-PRojection (HYPR) de-noising directly within the widely used OSEM algorithm. 3 forms of HYPR-OSEM have been proposed. Previously, we have demonstrated that all forms of HYPR-OSEM can improve SNR without degrading accuracy in terms of resolution and contrast. However, slower convergence rate in CRC was observed from all forms of HYPR-OSEM. In this work, we investigated the effect of the filter kernel size used in the HYPR operator. Furthermore, we introduced the Iterative HYPR (IHYPR) operator as an effort to accelerate the convergence rate in CRC. Multiple independent noisy realizations of a simulated and an experimental contrast phantom with various sizes of hot and cold inserts were used for the evaluations. CRC vs voxel noise, image profile, and root-mean-squared error (RMSE) in CRC vs iteration were compared across standard and proposed reconstruction methods. Visual image quality assessment of a [11C]PK11195 patient scan was also conducted. It was observed that the noise reduction performance of HYPR-F(B)-OSEM is not very sensitive to the filter kernel size used in the HYPR operator, whereas better CRC vs noise trajectories and lower RMSE in CRC can be achieved by wider kernels for HYPR-AU-OSEM. On the other hand, the CRC convergence rate for HYPR-AU-OSEM becomes much slower with a wider kernel. When the IHYPR operator was introduced into the AU method (i.e. IHYPR-AU-OSEM), similar CRC convergence speed with respect to OSEM was attained without excessively degrading the CRC vs noise trajectories. In summary, the AU method has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision, and IHYPR-AU-OSEM can achieve better CRC vs noise trajectories with similar convergence speed as compared to OSEM with or without a post reconstruction filter.
Original languageEnglish
Title of host publication2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538622827
DOIs
Publication statusPublished - 2018
Event2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Atlanta, United States
Duration: 21 Oct 201728 Oct 2017

Publication series

Name2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings

Conference

Conference2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017
CountryUnited States
CityAtlanta
Period21/10/201728/10/2017

Cite this

Cheng, J-C. K., Matthews, J., Boellaard, R., Janzen, I., Anton-Rodriguez, J., & Sossi, V. (2018). Performance Improvements in HYPR-OSEM. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings [8532928] (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NSSMIC.2017.8532928
Cheng, Ju-Chieh Kevin ; Matthews, Julian ; Boellaard, Ronald ; Janzen, Ian ; Anton-Rodriguez, Jose ; Sossi, Vesna. / Performance Improvements in HYPR-OSEM. 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings).
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title = "Performance Improvements in HYPR-OSEM",
abstract = "We describe methods which improve the performances in contrast recovery coefficient (CRC) versus noise trade-off and in convergence rate in CRC of the newly developed HYPR-OSEM algorithm. HYPR-OSEM is a reconstruction method which incorporates HighlY constrained back-PRojection (HYPR) de-noising directly within the widely used OSEM algorithm. 3 forms of HYPR-OSEM have been proposed. Previously, we have demonstrated that all forms of HYPR-OSEM can improve SNR without degrading accuracy in terms of resolution and contrast. However, slower convergence rate in CRC was observed from all forms of HYPR-OSEM. In this work, we investigated the effect of the filter kernel size used in the HYPR operator. Furthermore, we introduced the Iterative HYPR (IHYPR) operator as an effort to accelerate the convergence rate in CRC. Multiple independent noisy realizations of a simulated and an experimental contrast phantom with various sizes of hot and cold inserts were used for the evaluations. CRC vs voxel noise, image profile, and root-mean-squared error (RMSE) in CRC vs iteration were compared across standard and proposed reconstruction methods. Visual image quality assessment of a [11C]PK11195 patient scan was also conducted. It was observed that the noise reduction performance of HYPR-F(B)-OSEM is not very sensitive to the filter kernel size used in the HYPR operator, whereas better CRC vs noise trajectories and lower RMSE in CRC can be achieved by wider kernels for HYPR-AU-OSEM. On the other hand, the CRC convergence rate for HYPR-AU-OSEM becomes much slower with a wider kernel. When the IHYPR operator was introduced into the AU method (i.e. IHYPR-AU-OSEM), similar CRC convergence speed with respect to OSEM was attained without excessively degrading the CRC vs noise trajectories. In summary, the AU method has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision, and IHYPR-AU-OSEM can achieve better CRC vs noise trajectories with similar convergence speed as compared to OSEM with or without a post reconstruction filter.",
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Cheng, J-CK, Matthews, J, Boellaard, R, Janzen, I, Anton-Rodriguez, J & Sossi, V 2018, Performance Improvements in HYPR-OSEM. in 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings., 8532928, 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017, Atlanta, United States, 21/10/2017. https://doi.org/10.1109/NSSMIC.2017.8532928

Performance Improvements in HYPR-OSEM. / Cheng, Ju-Chieh Kevin; Matthews, Julian; Boellaard, Ronald; Janzen, Ian; Anton-Rodriguez, Jose; Sossi, Vesna.

2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8532928 (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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N2 - We describe methods which improve the performances in contrast recovery coefficient (CRC) versus noise trade-off and in convergence rate in CRC of the newly developed HYPR-OSEM algorithm. HYPR-OSEM is a reconstruction method which incorporates HighlY constrained back-PRojection (HYPR) de-noising directly within the widely used OSEM algorithm. 3 forms of HYPR-OSEM have been proposed. Previously, we have demonstrated that all forms of HYPR-OSEM can improve SNR without degrading accuracy in terms of resolution and contrast. However, slower convergence rate in CRC was observed from all forms of HYPR-OSEM. In this work, we investigated the effect of the filter kernel size used in the HYPR operator. Furthermore, we introduced the Iterative HYPR (IHYPR) operator as an effort to accelerate the convergence rate in CRC. Multiple independent noisy realizations of a simulated and an experimental contrast phantom with various sizes of hot and cold inserts were used for the evaluations. CRC vs voxel noise, image profile, and root-mean-squared error (RMSE) in CRC vs iteration were compared across standard and proposed reconstruction methods. Visual image quality assessment of a [11C]PK11195 patient scan was also conducted. It was observed that the noise reduction performance of HYPR-F(B)-OSEM is not very sensitive to the filter kernel size used in the HYPR operator, whereas better CRC vs noise trajectories and lower RMSE in CRC can be achieved by wider kernels for HYPR-AU-OSEM. On the other hand, the CRC convergence rate for HYPR-AU-OSEM becomes much slower with a wider kernel. When the IHYPR operator was introduced into the AU method (i.e. IHYPR-AU-OSEM), similar CRC convergence speed with respect to OSEM was attained without excessively degrading the CRC vs noise trajectories. In summary, the AU method has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision, and IHYPR-AU-OSEM can achieve better CRC vs noise trajectories with similar convergence speed as compared to OSEM with or without a post reconstruction filter.

AB - We describe methods which improve the performances in contrast recovery coefficient (CRC) versus noise trade-off and in convergence rate in CRC of the newly developed HYPR-OSEM algorithm. HYPR-OSEM is a reconstruction method which incorporates HighlY constrained back-PRojection (HYPR) de-noising directly within the widely used OSEM algorithm. 3 forms of HYPR-OSEM have been proposed. Previously, we have demonstrated that all forms of HYPR-OSEM can improve SNR without degrading accuracy in terms of resolution and contrast. However, slower convergence rate in CRC was observed from all forms of HYPR-OSEM. In this work, we investigated the effect of the filter kernel size used in the HYPR operator. Furthermore, we introduced the Iterative HYPR (IHYPR) operator as an effort to accelerate the convergence rate in CRC. Multiple independent noisy realizations of a simulated and an experimental contrast phantom with various sizes of hot and cold inserts were used for the evaluations. CRC vs voxel noise, image profile, and root-mean-squared error (RMSE) in CRC vs iteration were compared across standard and proposed reconstruction methods. Visual image quality assessment of a [11C]PK11195 patient scan was also conducted. It was observed that the noise reduction performance of HYPR-F(B)-OSEM is not very sensitive to the filter kernel size used in the HYPR operator, whereas better CRC vs noise trajectories and lower RMSE in CRC can be achieved by wider kernels for HYPR-AU-OSEM. On the other hand, the CRC convergence rate for HYPR-AU-OSEM becomes much slower with a wider kernel. When the IHYPR operator was introduced into the AU method (i.e. IHYPR-AU-OSEM), similar CRC convergence speed with respect to OSEM was attained without excessively degrading the CRC vs noise trajectories. In summary, the AU method has been determined to be the more effective form of HYPR-OSEM in terms of accuracy and precision, and IHYPR-AU-OSEM can achieve better CRC vs noise trajectories with similar convergence speed as compared to OSEM with or without a post reconstruction filter.

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Cheng J-CK, Matthews J, Boellaard R, Janzen I, Anton-Rodriguez J, Sossi V. Performance Improvements in HYPR-OSEM. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8532928. (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings). https://doi.org/10.1109/NSSMIC.2017.8532928