Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging

Kuang Gong; Paul Kyu Han; Keith A Johnson; Georges El Fakhri; Chao Ma; Quanzheng Li

doi:10.1007/s00259-020-05061-w

Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging

Eur J Nucl Med Mol Imaging. 2021 May;48(5):1351-1361. doi: 10.1007/s00259-020-05061-w. Epub 2020 Oct 27.

Authors

Kuang Gong^#¹, Paul Kyu Han^#¹, Keith A Johnson^{1

2

3}, Georges El Fakhri¹, Chao Ma¹, Quanzheng Li⁴

Affiliations

¹ Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
² Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
³ Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA.
⁴ Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA. Li.Quanzheng@mgh.harvard.edu.

^# Contributed equally.

Abstract

Purpose: PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging.

Methods: Thirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon-based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy.

Results: The Dice coefficients of the deep learning methods based on MPRAGE, Dixon, and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal, and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices.

Conclusion: Deep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.

Keywords: Attenuation correction; Convolutional neural network; Deep learning; PET/MR; UTE/multi-echo Dixon.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Deep Learning*
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Multimodal Imaging
Positron-Emission Tomography
Tomography, X-Ray Computed

Abstract

Publication types

MeSH terms

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