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Author

Chun, Se Young
Bio-Medical Image Processing Lab (BMIPL)
Research Interests
  • Inverse problem, sparse signal, multimodal information, diffeomorphic alignment, statistical learning, medical imaging

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Bias atlases for segmentation-based pet attenuation correction using PET-CT and MR

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Title
Bias atlases for segmentation-based pet attenuation correction using PET-CT and MR
Author
Ouyang, JinsongChun, Se YoungPetibon, YoannBonab, Ali A.Alpert, NathanielEl Fakhri, Georges
Keywords
Attenuation correction; PET-MR
Issue Date
201310
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE TRANSACTIONS ON NUCLEAR SCIENCE, v.60, no.5, pp.3373 - 3382
Abstract
This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.
URI
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DOI
http://dx.doi.org/10.1109/TNS.2013.2278624
ISSN
0018-9499
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ECE_Journal Papers

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