Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Sep;41(13):3667-3679.
doi: 10.1002/hbm.25039. Epub 2020 May 21.

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies

Hossein Arabi  1 , Karin Bortolin  1 , Nathalie Ginovart  2   3 , Valentina Garibotto  1   4 , Habib Zaidi  1   4   5   6
Affiliations

Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies

Hossein Arabi et al. Hum Brain Mapp. 2020 Sep.

Abstract

PET attenuation correction (AC) on systems lacking CT/transmission scanning, such as dedicated brain PET scanners and hybrid PET/MRI, is challenging. Direct AC in image-space, wherein PET images corrected for attenuation and scatter are synthesized from nonattenuation corrected PET (PET-nonAC) images in an end-to-end fashion using deep learning approaches (DLAC) is evaluated for various radiotracers used in molecular neuroimaging studies. One hundred eighty brain PET scans acquired using 18 F-FDG, 18 F-DOPA, 18 F-Flortaucipir (targeting tau pathology), and 18 F-Flutemetamol (targeting amyloid pathology) radiotracers (40 + 5, training/validation + external test, subjects for each radiotracer) were included. The PET data were reconstructed using CT-based AC (CTAC) to generate reference PET-CTAC and without AC to produce PET-nonAC images. A deep convolutional neural network was trained to generate PET attenuation corrected images (PET-DLAC) from PET-nonAC. The quantitative accuracy of this approach was investigated separately for each radiotracer considering the values obtained from PET-CTAC images as reference. A segmented AC map (PET-SegAC) containing soft-tissue and background air was also included in the evaluation. Quantitative analysis of PET images demonstrated superior performance of the DLAC approach compared to SegAC technique for all tracers. Despite the relatively low quantitative bias observed when using the DLAC approach, this approach appears vulnerable to outliers, resulting in noticeable local pseudo uptake and false cold regions. Direct AC in image-space using deep learning demonstrated quantitatively acceptable performance with less than 9% absolute SUV bias for the four different investigated neuroimaging radiotracers. However, this approach is vulnerable to outliers which result in large local quantitative bias.

Keywords: PET; attenuation correction; deep learning; neuroimaging tracers; quantification.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

FIGURE 1
FIGURE 1
Comparison of PET images corrected for attenuation using CT‐based, SegAC and DLAC approaches along with the reference CT image for the four different radiotracers. Difference SUV error maps are also shown for DLAC and SegAC approaches
FIGURE 2
FIGURE 2
Mean absolute relative bias (%) and mean bias (%) of tracer uptake of PET‐DLAC and PET‐SegAC with respect to reference PET‐CTAC for 18F‐Flortaucipir calculated for 70 brain regions
FIGURE 3
FIGURE 3
Mean absolute relative bias (%) and mean bias (%) of tracer uptake of PET‐DLAC and PET‐SegAC with respect to reference PET‐CTAC for 18F‐Flutemetamol calculated for 70 brain regions
FIGURE 4
FIGURE 4
Mean absolute relative bias (%) and mean bias (%) of tracer uptake of PET‐DLAC and PET‐SegAC with respect to reference PET‐CTAC for 18F‐FDG calculated for 63 brain regions
FIGURE 5
FIGURE 5
Mean absolute relative bias (%) and mean bias (%) of tracer uptake of PET‐DLAC and PET‐SegAC with respect to reference PET‐CTAC for 18F‐DOPA calculated for seven brain regions
FIGURE 6
FIGURE 6
Joint histogram analysis depicting the correlation between activity concentration of PET‐DLAC and PET‐SegAC images versus reference PET‐CTAC images for the four neuroimaging radiotracers
FIGURE 7
FIGURE 7
Outlier report: The DLAC approach resulted in considerable pseudo‐uptake in the posterior of a single 18F‐Flortaucipir PET study. Sagittal views of PET‐CTAC, PET‐SegAC, and PET‐DLAC together with their corresponding SUV bias maps along with the reference CT image are presented. The plot shows SUV profiles through the three PET images
See this image and copyright information in PMC

References

    1. Arabi, H. , Dowling, J. A. , Burgos, N. , Han, X. , Greer, P. B. , Koutsouvelis, N. , & Zaidi, H. (2018). Comparative study of algorithms for synthetic CT generation from MRI: Consequences for MRI‐guided radiation planning in the pelvic region. Medical Physics, 45, 5218–5233. - PubMed
    1. Arabi, H. , Koutsouvelis, N. , Rouzaud, M. , Miralbell, R. , & Zaidi, H. (2016). Atlas‐guided generation of pseudo‐CT images for MRI‐only and hybrid PET–MRI‐guided radiotherapy treatment planning. Physics in Medicine and Biology, 61, 6531–6552. - PubMed
    1. Arabi, H. , Rager, O. , Alem, A. , Varoquaux, A. , Becker, M. , & Zaidi, H. (2015). Clinical assessment of MR‐guided 3‐class and 4‐class attenuation correction in PET/MR. Molecular Imaging and Biology, 17, 264–276. - PubMed
    1. Arabi, H. , & Zaidi, H. (2016a). Magnetic resonance imaging‐guided attenuation correction in whole‐body PET/MRI using a sorted atlas approach. Medical Image Analysis, 31, 1–15. - PubMed
    1. Arabi, H. , & Zaidi, H. (2016b). One registration multi‐atlas‐based pseudo‐CT generation for attenuation correction in PET/MRI. European Journal of Nuclear Medicine and Molecular Imaging, 43, 2021–2035. - PubMed

Publication types