MRPET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

Baowei Fei; Xiaofeng Yang; Jonathon A. Nye; John N. Aarsvold; Nivedita Raghunath; Morgan Cervo; Rebecca Stark; Carolyn C. Meltzer; John R. Votaw

doi:10.1118/1.4754796

MRPET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

Baowei Fei, Xiaofeng Yang, Jonathon A. Nye, John N. Aarsvold, Nivedita Raghunath, Morgan Cervo, Rebecca Stark, Carolyn C. Meltzer, John R. Votaw

Research output: Contribution to journal › Article › peer-review

47 Scopus citations

Abstract

Purpose: Combined MRPET is a relatively new, hybrid imaging modality. A human MRPET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MRPET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MRPET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with ¹¹CPIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was 6.5. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MRPET.

Original language	English (US)
Pages (from-to)	6443-6454
Number of pages	12
Journal	Medical physics
Volume	39
Issue number	10
DOIs	https://doi.org/10.1118/1.4754796
State	Published - Oct 2012
Externally published	Yes

Keywords

attenuation correction
classification
combined MRPET
image registration
neuroimaging
segmentation

ASJC Scopus subject areas

Biophysics
Radiology Nuclear Medicine and imaging

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10.1118/1.4754796

Cite this

@article{d1e260f634404bb595d79d444650ff0f,

title = "MRPET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction",

abstract = "Purpose: Combined MRPET is a relatively new, hybrid imaging modality. A human MRPET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MRPET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MRPET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with 11CPIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was 6.5. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MRPET.",

keywords = "attenuation correction, classification, combined MRPET, image registration, neuroimaging, segmentation",

author = "Baowei Fei and Xiaofeng Yang and Nye, {Jonathon A.} and Aarsvold, {John N.} and Nivedita Raghunath and Morgan Cervo and Rebecca Stark and Meltzer, {Carolyn C.} and Votaw, {John R.}",

note = "Funding Information: This research was supported in part by National Institutes of Health (NIH) Grant No. R01CA156775 (PI: Fei), The Georgia Cancer Coalition Distinguished Clinicians and Scientists Award (PI: Fei), The Emory Molecular and Translational Imaging Center (NIH P50CA128301), SPORE in Head and Neck Cancer (NIH P50CA128613), and The Atlanta Clinical and Translational Science Institute (ACTSI) that is supported by Public Health Service (PHS) Grant No. UL1 RR025008 from the Clinical and Translational Science Award program. The authors thank Larry Byars, Matthias Schmand, and Christian Michel from Siemens Healthcare for the technical support of the combined MR/PET system. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.",

year = "2012",

month = oct,

doi = "10.1118/1.4754796",

language = "English (US)",

volume = "39",

pages = "6443--6454",

journal = "Medical physics",

issn = "0094-2405",

publisher = "AAPM - American Association of Physicists in Medicine",

number = "10",

}

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T1 - MRPET quantification tools

T2 - Registration, segmentation, classification, and MR-based attenuation correction

AU - Fei, Baowei

AU - Yang, Xiaofeng

AU - Nye, Jonathon A.

AU - Aarsvold, John N.

AU - Raghunath, Nivedita

AU - Cervo, Morgan

AU - Stark, Rebecca

AU - Meltzer, Carolyn C.

AU - Votaw, John R.

N1 - Funding Information: This research was supported in part by National Institutes of Health (NIH) Grant No. R01CA156775 (PI: Fei), The Georgia Cancer Coalition Distinguished Clinicians and Scientists Award (PI: Fei), The Emory Molecular and Translational Imaging Center (NIH P50CA128301), SPORE in Head and Neck Cancer (NIH P50CA128613), and The Atlanta Clinical and Translational Science Institute (ACTSI) that is supported by Public Health Service (PHS) Grant No. UL1 RR025008 from the Clinical and Translational Science Award program. The authors thank Larry Byars, Matthias Schmand, and Christian Michel from Siemens Healthcare for the technical support of the combined MR/PET system. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.

PY - 2012/10

Y1 - 2012/10

N2 - Purpose: Combined MRPET is a relatively new, hybrid imaging modality. A human MRPET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MRPET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MRPET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with 11CPIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was 6.5. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MRPET.

AB - Purpose: Combined MRPET is a relatively new, hybrid imaging modality. A human MRPET prototype system consisting of a Siemens 3T Trio MR and brain PET insert was installed and tested at our institution. Its present design does not offer measured attenuation correction (AC) using traditional transmission imaging. This study is the development of quantification tools including MR-based AC for quantification in combined MRPET for brain imaging. Methods: The developed quantification tools include image registration, segmentation, classification, and MR-based AC. These components were integrated into a single scheme for processing MRPET data. The segmentation method is multiscale and based on the Radon transform of brain MR images. It was developed to segment the skull on T1-weighted MR images. A modified fuzzy C-means classification scheme was developed to classify brain tissue into gray matter, white matter, and cerebrospinal fluid. Classified tissue is assigned an attenuation coefficient so that AC factors can be generated. PET emission data are then reconstructed using a three-dimensional ordered sets expectation maximization method with the MR-based AC map. Ten subjects had separate MR and PET scans. The PET with 11CPIB was acquired using a high-resolution research tomography (HRRT) PET. MR-based AC was compared with transmission (TX)-based AC on the HRRT. Seventeen volumes of interest were drawn manually on each subject image to compare the PET activities between the MR-based and TX-based AC methods. Results: For skull segmentation, the overlap ratio between our segmented results and the ground truth is 85.2 ± 2.6. Attenuation correction results from the ten subjects show that the difference between the MR and TX-based methods was 6.5. Conclusions: MR-based AC compared favorably with conventional transmission-based AC. Quantitative tools including registration, segmentation, classification, and MR-based AC have been developed for use in combined MRPET.

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MRPET quantification tools: Registration, segmentation, classification, and MR-based attenuation correction

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