» Articles » PMID: 23081993

Comparison of Segmentation-based Attenuation Correction Methods for PET/MRI: Evaluation of Bone and Liver Standardized Uptake Value with Oncologic PET/CT Data

Overview
Journal J Nucl Med
Specialty Nuclear Medicine
Date 2012 Oct 20
PMID 23081993
Citations 30
Authors
Affiliations
Soon will be listed here.
Abstract

Unlabelled: For attenuation correction (AC) in PET/MRI systems, segmentation-based methods are most often used. However, the standardized uptake value (SUV) of lesions in the bone and liver, which have higher attenuation coefficients than other organs, can be underestimated, potentially leading to misinterpretation of clinical cases. Errors in SUV estimation are also dependent on the segmentation schemes used in the segmentation-based AC. In this study, this potential bias in SUV estimation using 4 different segmentation-based AC methods was evaluated for the PET/CT data of cancer patients with bone and liver lesions.

Methods: Forty patients who had spine or liver lesions and underwent (18)F-FDG PET/CT participated (18 women and 22 men; 20 spine lesions and 20 liver lesions; mean age (± SD), 60.5 ± 11.4 y; mean body weight, 57.7 ± 10.4 kg). The patient body region was extracted from the CT image and categorized into 5 tissue groups (air, lungs, fat, water, and bone) using Hounsfield unit thresholds, which were determined from the CT histogram. Four segmentation-based AC methods (SLA [soft-tissue/lung/air], WFLA [water/fat/lung/air], SLAB [soft-tissue/lung/air/bone], and WFLAB [water/fat/lung/air/bone]) were compared with CT-based AC. The mean attenuation coefficient for each group was calculated from 40 CT images and assigned to the attenuation maps. PET sinograms were reconstructed using segmentation- and CT-based AC maps, and mean SUV in the lesions was compared.

Results: Mean attenuation coefficients for air, lungs, fat, water, and bone were 0.0058, 0.0349, 0.0895, 0.0987, and 0.1178 cm(-1), respectively. In the spine lesions, the SUVs were underestimated by 16.4% ± 8.5% (SLA AC) and 14.7% ± 7.5% (WFLA AC) but not to a statistically significant extent for SLAB and WFLAB AC relative to CT AC. In the liver lesions, the SUVs were underestimated by 11.1% ± 2.6%, 8.1% ± 3.0%, 6.8% ± 3.8%, and 4.1% ± 3.8% with SLA, SLAB, WFLA, and WFLAB AC, respectively.

Conclusion: Without bone segmentation, the SUVs of spine lesions were considerably underestimated; however, the bias was acceptable with bone segmentation. In liver lesions, the segmentation-based AC methods yielded a negative bias in SUV; however, inclusion of the bone and fat segments reduced the SUV bias. The results of this study will be useful for understanding organ-dependent bias in SUV between PET/CT and PET/MRI.

Citing Articles

Learning CT-free attenuation-corrected total-body PET images through deep learning.

Li W, Huang Z, Chen Z, Jiang Y, Zhou C, Zhang X Eur Radiol. 2024; 34(9):5578-5587.

PMID: 38355987 DOI: 10.1007/s00330-024-10647-1.


Comparison of deep learning-based emission-only attenuation correction methods for positron emission tomography.

Hwang D, Kang S, Kim K, Choi H, Lee J Eur J Nucl Med Mol Imaging. 2021; 49(6):1833-1842.

PMID: 34882262 DOI: 10.1007/s00259-021-05637-0.


A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea.

Lee J, Kim K, Choi Y, Kim H Nucl Med Mol Imaging. 2021; 55(6):265-284.

PMID: 34868376 PMC: 8602621. DOI: 10.1007/s13139-021-00721-7.


The impact of MR-based attenuation correction in spinal cord FDG-PET/MR imaging for neurological studies.

Brancato V, Borrelli P, Alfano V, Picardi M, Mascalchi M, Nicolai E Med Phys. 2021; 48(10):5924-5934.

PMID: 34369590 PMC: 9293017. DOI: 10.1002/mp.15149.


Impact of Tissue Classification in MRI-Guided Attenuation Correction on Whole-Body Patlak PET/MRI.

Zhuang M, Karakatsanis N, Dierckx R, Zaidi H Mol Imaging Biol. 2019; 21(6):1147-1156.

PMID: 30838550 DOI: 10.1007/s11307-019-01338-1.