Probabilistic air segmentation and sparse regression estimated pseudo CT for PET/MR attenuation correction

Yasheng Chen, Meher Juttukonda, Yi Su, Tammie Benzinger, Brian G. Rubin, Yueh Z. Lee, Weili Lin, Dinggang Shen, David Lalush, Hongyu An

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Purpose: To develop a positron emission tomography (PET) attenuation correction method for brain PET/magnetic resonance (MR) imaging by estimating pseudo computed tomographic (CT) images from T1-weighted MR and atlas CT images. Materials and Methods: In this institutional review board-approved and HIPAAcompliant study, PET/MR/CT images were acquired in 20 subjects after obtaining written consent. A probabilistic air segmentation and sparse regression (PASSR) method was developed for pseudo CT estimation. Air segmentation was performed with assistance from a probabilistic air map. For nonair regions, the pseudo CT numbers were estimated via sparse regression by using atlas MR patches. The mean absolute percentage error (MAPE) on PET images was computed as the normalized mean absolute difference in PET signal intensity between a method and the reference standard continuous CT attenuation correction method. Friedman analysis of variance and Wilcoxon matched-pairs tests were performed for statistical comparison of MAPE between the PASSR method and Dixon segmentation, CT segmentation, and population averaged CT atlas (mean atlas) methods. Results: The PASSR method yielded a mean MAPE ± standard deviation of 2.42% ± 1.0, 3.28% ± 0.93, and 2.16% ± 1.75, respectively, in the whole brain, gray matter, and white matter, which were significantly lower than the Dixon, CT segmentation, and mean atlas values (P < .01). Moreover, 68.0% 6 16.5, 85.8% 6 12.9, and 96.0% 6 2.5 of whole-brain volume had within 62%, 65%, and 610% percentage error by using PASSR, respectively, which was significantly higher than other methods (P < .01). Conclusion: PASSR outperformed the Dixon, CT segmentation, and mean atlas methods by reducing PET error owing to attenuation correction.

Original languageEnglish
Pages (from-to)562-569
Number of pages8
JournalRadiology
Volume275
Issue number2
DOIs
Publication statusPublished - 2015 May 1
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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    Chen, Y., Juttukonda, M., Su, Y., Benzinger, T., Rubin, B. G., Lee, Y. Z., Lin, W., Shen, D., Lalush, D., & An, H. (2015). Probabilistic air segmentation and sparse regression estimated pseudo CT for PET/MR attenuation correction. Radiology, 275(2), 562-569. https://doi.org/10.1148/radiol.14140810