A1 Journal article – refereed
Quantitative Evaluation of 2 Scatter-Correction Techniques for F-18-FDG Brain PET/MRI in Regard to MR-Based Attenuation Correction




List of Authors: Teuho J, Saunavaara V, Tolvanen T, Tuokkola T, Karlsson A, Tuisku J, Teras M
Publisher: SOC NUCLEAR MEDICINE INC
Publication year: 2017
Journal: Journal of Nuclear Medicine
Journal name in source: JOURNAL OF NUCLEAR MEDICINE
Journal acronym: J NUCL MED
Volume number: 58
Issue number: 10
Number of pages: 8
ISSN: 0161-5505
eISSN: 1535-5667

Abstract
In PET, corrections for photon scatter and attenuation are essential for visual and quantitative consistency. MR attenuation correction (MRAC) is generally conducted by image segmentation and assignment of discrete attenuation coefficients, which offer limited accuracy compared with CT attenuation correction. Potential inaccuracies in MRAC may affect scatter correction, because the attenuation image (mu-map) is used in single scatter simulation (SSS) to calculate the scatter estimate. We assessed the impact of MRAC to scatter correction using 2 scatter-correction techniques and 3 mu-maps for MRAC. Methods: The tail-fitted SSS (TF-SSS) and a Monte Carlo-based single scatter simulation (MC-SSS) algorithm implementations on the Philips Ingenuity TF PET/MR were used with 1 CT-based and 2 MR-based mu-maps. Data from 7 subjects were used in the clinical evaluation, and a phantom study using an anatomic brain phantom was conducted. Scatter-correction sinograms were evaluated for each scatter correction method and mu-map. Absolute image quantification was investigated with the phantom data. Quantitative assessment of PET images was performed by volume-of-interest and ratio image analysis. Results: MRAC did not result in large differences in scatter algorithm performance, especially with TF-SSS. Scatter sinograms and scatter fractions did not reveal large differences regardless of the mu-map used. TF-SSS showed slightly higher absolute quantification. The differences in volume-of-interest analysis between TF-SSS and MC-SSS were 3% at maximum in the phantom and 4% in the patient study. Both algorithms showed excellent correlation with each other with no visual differences between PET images. MC-SSS showed a slight dependency on the mu-map used, with a difference of 2% on average and 4% at maximum when a mu-map without bone was used. Conclusion: The effect of different MR-based mu-maps on the performance of scatter correction was minimal in non-time-of-flight F-18-FDG PET/MR brain imaging. The SSS algorithm was not affected significantly by MRAC. The performance of the MC-SSS algorithm is comparable but not superior to TF-SSS, warranting further investigations of algorithm optimization and performance with different radiotracers and time-of-flight imaging.

Last updated on 2019-21-08 at 23:10