A1 Refereed original research article in a scientific journal

Tissue Probability-Based Attenuation Correction for Brain PET/MR by Using SPM8




AuthorsJarmo Teuho, Jani Linden, Jarkko Johansson, Jouni Tuisku, Terhi Tuokkola, Mika Teräs

PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Publication year2016

JournalIEEE Transactions on Nuclear Science

Journal name in sourceIEEE Transactions on Nuclear Science

Journal acronymIEEE Transactions on Nuclear Science

Volume63

Issue5

First page 2452

Last page2463

Number of pages12

ISSN0018-9499

eISSN1558-1578

DOIhttps://doi.org/10.1109/TNS.2015.2513064


Abstract

Bone attenuation remains a methodological challenge in hybrid PET/MR, as bone is hard to visualize via magnetic resonance imaging (MRI). Therefore, novel methods for taking into account bone attenuation in MR-based attenuation correction (MRAC) are needed. In this study, we propose a tissue-probability based attenuation correction (TPB-AC), which employs the commonly available neurological toolbox SPM8, to derive a subject-specific mu-map by segmentation of T1-weighted MR images. The procedures to derive a mu-map representing soft tissue, air and bone from the New Segment function in SPM8 and MATLAB are described. Visual and quantitative comparisons against CT-based attenuation correction (CTAC) data were performed using two mu-values (0.135 cm(-1) and 0.145 cm(-1)) for bone. Results show improvement of visual quality and quantitative accuracy of positron emission tomography (PET) images when TPB-AC mu-map is used in PET/MR image reconstruction. Underestimation in PET images was decreased by an average of 5+/-2 percent in the whole brain across all patients. In addition, the method performed well when compared to CTAC, with maximum differences (mean +/- standard deviation) of -3 +/- 2 percent and 2 +/- 4 percent in two regions out of 28. Finally, the method is simple and computationally efficient, offering a promising platform for further development. Therefore, a subject-specific MR-based mu-map can be derived only from the tissue probability maps from the New Segment function of SPM8.



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