Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Nuclear medicine and molecular imaging ; h5-index 0.0

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 PET and 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.

Kim Ji-Young, Suh Hoon Young, Ryoo Hyun Gee, Oh Dongkyu, Choi Hongyoon, Paeng Jin Chul, Cheon Gi Jeong, Kang Keon Wook, Lee Dong Soo


Alzheimer’s disease, Amyloid PET, Convolutional neural network, Deep learning, Quantification