A MR Guided De-noising for PET Using IHYPR-LR

Ju-Chieh Kevin Cheng, Julian Matthews, Ronald Boellaard, Vesna Sossi

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

Abstract

We describe a MR guided de-noising method for PET based on Iterative HighlY constrained back-PRojection Local Region (IHYPR-LR) post processing. IHYPR-LR is a modified version of HYPR-LR with the composite image updated iteratively, and HYPR-LR is a de-nosing method originally developed for time-resolved MRI. In this work, a co-registered T1-weighted MR image with high resolution and low noise was used as the initialization of the composite image in the IHYPRLR frame work for PET de-noising. A [11C]DASB Parkinsonian patient study conducted on the High Resolution Research Tomograph (HRRT) was used for the evaluations of the proposed method. The study was divided into high and low count frames with similar tracer distribution. The high count data were used to extract the optimal number of IHYPR iterations which minimizes the bias introduced by the MR composite without excessively degrading the level of noise reduction. The optimal number of iterations was then applied to low count PET data. The de-noised images were generated using the original HYPR-LR and IHYPRLR and compared with the OSEM images with a standard 2mm FWHM Gaussian post filter for the HRRT. As expected, since MR images do not always share the same contrast and structures with PET images, bias in contrast was observed from the denoised PET image using the original HYPR-LR. On the other hand, 3 iterations of IHYPR-LR successfully reduced the bias and outperformed the post filtered PET image in terms of noise reduction and structure boundary definitions. In summary, the proposed MR guided de-noising method achieves noise reduction and enhances structure boundary definitions without degrading the PET contrast.
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., & Sossi, V. (2018). A MR Guided De-noising for PET Using IHYPR-LR. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings [8532650] (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.2017.8532650
Cheng, Ju-Chieh Kevin ; Matthews, Julian ; Boellaard, Ronald ; Sossi, Vesna. / A MR Guided De-noising for PET Using IHYPR-LR. 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 = "A MR Guided De-noising for PET Using IHYPR-LR",
abstract = "We describe a MR guided de-noising method for PET based on Iterative HighlY constrained back-PRojection Local Region (IHYPR-LR) post processing. IHYPR-LR is a modified version of HYPR-LR with the composite image updated iteratively, and HYPR-LR is a de-nosing method originally developed for time-resolved MRI. In this work, a co-registered T1-weighted MR image with high resolution and low noise was used as the initialization of the composite image in the IHYPRLR frame work for PET de-noising. A [11C]DASB Parkinsonian patient study conducted on the High Resolution Research Tomograph (HRRT) was used for the evaluations of the proposed method. The study was divided into high and low count frames with similar tracer distribution. The high count data were used to extract the optimal number of IHYPR iterations which minimizes the bias introduced by the MR composite without excessively degrading the level of noise reduction. The optimal number of iterations was then applied to low count PET data. The de-noised images were generated using the original HYPR-LR and IHYPRLR and compared with the OSEM images with a standard 2mm FWHM Gaussian post filter for the HRRT. As expected, since MR images do not always share the same contrast and structures with PET images, bias in contrast was observed from the denoised PET image using the original HYPR-LR. On the other hand, 3 iterations of IHYPR-LR successfully reduced the bias and outperformed the post filtered PET image in terms of noise reduction and structure boundary definitions. In summary, the proposed MR guided de-noising method achieves noise reduction and enhances structure boundary definitions without degrading the PET contrast.",
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Cheng, J-CK, Matthews, J, Boellaard, R & Sossi, V 2018, A MR Guided De-noising for PET Using IHYPR-LR. in 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings., 8532650, 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.2017.8532650

A MR Guided De-noising for PET Using IHYPR-LR. / Cheng, Ju-Chieh Kevin; Matthews, Julian; Boellaard, Ronald; Sossi, Vesna.

2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8532650 (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 a MR guided de-noising method for PET based on Iterative HighlY constrained back-PRojection Local Region (IHYPR-LR) post processing. IHYPR-LR is a modified version of HYPR-LR with the composite image updated iteratively, and HYPR-LR is a de-nosing method originally developed for time-resolved MRI. In this work, a co-registered T1-weighted MR image with high resolution and low noise was used as the initialization of the composite image in the IHYPRLR frame work for PET de-noising. A [11C]DASB Parkinsonian patient study conducted on the High Resolution Research Tomograph (HRRT) was used for the evaluations of the proposed method. The study was divided into high and low count frames with similar tracer distribution. The high count data were used to extract the optimal number of IHYPR iterations which minimizes the bias introduced by the MR composite without excessively degrading the level of noise reduction. The optimal number of iterations was then applied to low count PET data. The de-noised images were generated using the original HYPR-LR and IHYPRLR and compared with the OSEM images with a standard 2mm FWHM Gaussian post filter for the HRRT. As expected, since MR images do not always share the same contrast and structures with PET images, bias in contrast was observed from the denoised PET image using the original HYPR-LR. On the other hand, 3 iterations of IHYPR-LR successfully reduced the bias and outperformed the post filtered PET image in terms of noise reduction and structure boundary definitions. In summary, the proposed MR guided de-noising method achieves noise reduction and enhances structure boundary definitions without degrading the PET contrast.

AB - We describe a MR guided de-noising method for PET based on Iterative HighlY constrained back-PRojection Local Region (IHYPR-LR) post processing. IHYPR-LR is a modified version of HYPR-LR with the composite image updated iteratively, and HYPR-LR is a de-nosing method originally developed for time-resolved MRI. In this work, a co-registered T1-weighted MR image with high resolution and low noise was used as the initialization of the composite image in the IHYPRLR frame work for PET de-noising. A [11C]DASB Parkinsonian patient study conducted on the High Resolution Research Tomograph (HRRT) was used for the evaluations of the proposed method. The study was divided into high and low count frames with similar tracer distribution. The high count data were used to extract the optimal number of IHYPR iterations which minimizes the bias introduced by the MR composite without excessively degrading the level of noise reduction. The optimal number of iterations was then applied to low count PET data. The de-noised images were generated using the original HYPR-LR and IHYPRLR and compared with the OSEM images with a standard 2mm FWHM Gaussian post filter for the HRRT. As expected, since MR images do not always share the same contrast and structures with PET images, bias in contrast was observed from the denoised PET image using the original HYPR-LR. On the other hand, 3 iterations of IHYPR-LR successfully reduced the bias and outperformed the post filtered PET image in terms of noise reduction and structure boundary definitions. In summary, the proposed MR guided de-noising method achieves noise reduction and enhances structure boundary definitions without degrading the PET contrast.

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Cheng J-CK, Matthews J, Boellaard R, Sossi V. A MR Guided De-noising for PET Using IHYPR-LR. In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8532650. (2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2017 - Conference Proceedings). https://doi.org/10.1109/NSSMIC.2017.8532650