Nucl Med Mol Imaging.  2019 Oct;53(5):340-348. 10.1007/s13139-019-00610-0.

Amyloid PET Quantification Via End-to-End Training of a Deep Learning

• Kim JY
• Suh HY
• Ryoo HG
• Oh D
• Choi H
• Paeng JC
• Cheon GJ
• Kang KW
• Lee DS
• Alzheimer's Disease Neuroimaging Initiative

• KMID: 2465870
• DOI: http://doi.org/10.1007/s13139-019-00610-0

Abstract

PURPOSE
Although quantification of amyloid positron emission tomography (PET) is important for evaluating patients with cognitive impairment, its routine clinical use is hampered by complicated preprocessing steps and required MRI. Here, we suggested a one-step quantification based on deep learning using native-space amyloid PET images of different radiotracers acquired from multiple centers.
METHODS
Amyloid PET data of the Alzheimer Disease Neuroimaging Initiative (ADNI) were used for this study. A training/validation consists of 850 florbetapir PET images. Three hundred sixty-six florbetapir and 89 florbetaben PET images were used as test sets to evaluate the model. Native-space amyloid PET images were used as inputs, and the outputs were standardized uptake value ratios (SUVRs) calculated by the conventional MR-based method.
RESULTS
The mean absolute errors (MAEs) of the composite SUVR were 0.040, 0.060, and 0.050 of training/validation and test sets for florbetapir PETand a test set for florbetaben PET, respectively. The agreement of amyloid positivity measured by Cohen's kappa for test sets of florbetapir and florbetaben PET were 0.87 and 0.89, respectively.
CONCLUSION
We suggest a one-step quantification method for amyloid PET via a deep learning model. The model is highly reliable to quantify the amyloid PET regardless of multicenter images and various radiotracers.

Keyword

Alzheimer's disease; Quantification; Amyloid PET; Deep learning; Convolutional neural network

MeSH Terms

Alzheimer Disease
Amyloid*
Cognition Disorders
Humans
Learning*
Magnetic Resonance Imaging
Methods
Neuroimaging
Positron-Emission Tomography
Amyloid