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. 2020 Aug 17;296(3):E195. doi: 10.1148/radiol.2020202527

Ultra–Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs

Kevin T Chen, Enhao Gong, Fabiola Bezerra de Carvalho Macruz, Junshen Xu, Athanasia Boumis, Mehdi Khalighi, Kathleen L Poston, Sharon J Sha, Michael D Greicius, Elizabeth Mormino, John M Pauly, Shyam Srinivas, Greg Zaharchuk
PMCID: PMC8906337  PMID: 32804601

Originally published in:

Radiology 2019;290(3):649–656

https://doi.org/10.1148/radiol.2018180940

Erratum in:

Online only: DOI:10.1148/radiol.2020202527

This erratum corrects the network schematic in Figure 1. The network structure in Figure 1 should be as follows: The encoder portion consists of three sets of three 3 × 3 convolution (conv)-batch normalization (BN)-rectified linear unit activation (ReLU) operations, with 32, 32, and 64 tensors for the convolutions in each set respectively; 2 × 2 max-pooling is performed on the output of each set and fed into the next set. The center connection consists of one set of three 3 × 3 conv (64 tensors)-BN-ReLU operations; the result is added with the input of the center connection (residual connection) and passed on to the decoder portion. The decoder portion consists of three sets of three 3 × 3 conv-BN-ReLU operations, with 64, 32, and 32 tensors for the convolutions in each set respectively. The inputs of each set are a concatenation of the output of the previous set after 2 × 2 up-sampling and the output of its corresponding encoder set. A 1 ×1 convolution with hyperbolic tangent activation is performed on the output of the final decoder set and added to the original low-dose PET input to obtain the output image.


Articles from Radiology are provided here courtesy of Radiological Society of North America

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