Erratum: Ultra-low-dose 18F-florbetaben amyloid PET imaging using deep learning with multi-contrast MRI inputs (Radiology (2019) 290:3 (649-656) DOI: 10.1148/radiol.2018180940)
Journal
Radiology
Journal Volume
296
Journal Issue
3
Date Issued
2020
Author(s)
Chen K.T.
Gong E.
De Carvalho Macruz F.B.
Xu J.
Boumis A.
Khalighi M.
Poston K.L.
Sha S.J.
Greicius M.D.
Mormino E.
Pauly J.M.
Srinivas S.
Zaharchuk G.
Abstract
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 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 3 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 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 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 3 2 up-sampling and the output of its corresponding encoder set. A 1 31 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. ? 2020 Radiological Society of North America Inc.. All rights reserved.
Subjects
erratum
Type
corrigendum