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. 2019 Mar;290(3):649-656.
doi: 10.1148/radiol.2018180940. Epub 2018 Dec 11.

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

Kevin T Chen  1 , Enhao Gong  1 , Fabiola Bezerra de Carvalho Macruz  1 , Junshen Xu  1 , Athanasia Boumis  1 , Mehdi Khalighi  1 , Kathleen L Poston  1 , Sharon J Sha  1 , Michael D Greicius  1 , Elizabeth Mormino  1 , John M Pauly  1 , Shyam Srinivas  1 , Greg Zaharchuk  1
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Ultra-Low-Dose 18F-Florbetaben Amyloid PET Imaging Using Deep Learning with Multi-Contrast MRI Inputs

Kevin T Chen et al. Radiology. 2019 Mar.

Erratum in

Abstract

Purpose To reduce radiotracer requirements for amyloid PET/MRI without sacrificing diagnostic quality by using deep learning methods. Materials and Methods Forty data sets from 39 patients (mean age ± standard deviation [SD], 67 years ± 8), including 16 male patients and 23 female patients (mean age, 66 years ± 6 and 68 years ± 9, respectively), who underwent simultaneous amyloid (fluorine 18 [18F]-florbetaben) PET/MRI examinations were acquired from March 2016 through October 2017 and retrospectively analyzed. One hundredth of the raw list-mode PET data were randomly chosen to simulate a low-dose (1%) acquisition. Convolutional neural networks were implemented with low-dose PET and multiple MR images (PET-plus-MR model) or with low-dose PET alone (PET-only) as inputs to predict full-dose PET images. Quality of the synthesized images was evaluated while Bland-Altman plots assessed the agreement of regional standard uptake value ratios (SUVRs) between image types. Two readers scored image quality on a five-point scale (5 = excellent) and determined amyloid status (positive or negative). Statistical analyses were carried out to assess the difference of image quality metrics and reader agreement and to determine confidence intervals (CIs) for reading results. Results The synthesized images (especially from the PET-plus-MR model) showed marked improvement on all quality metrics compared with the low-dose image. All PET-plus-MR images scored 3 or higher, with proportions of images rated greater than 3 similar to those for the full-dose images (-10% difference [eight of 80 readings], 95% CI: -15%, -5%). Accuracy for amyloid status was high (71 of 80 readings [89%]) and similar to intrareader reproducibility of full-dose images (73 of 80 [91%]). The PET-plus-MR model also had the smallest mean and variance for SUVR difference to full-dose images. Conclusion Simultaneously acquired MRI and ultra-low-dose PET data can be used to synthesize full-dose-like amyloid PET images. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Catana in this issue.

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Figures

Figure 1:
Figure 1:
A schematic of the encoder-decoder convolutional neural network used in this work. The arrows denote computational operations and the tensors are denoted by boxes with the number of channels indicated above each box. Conv = convolution, BN = batch normalization, ReLU = rectified linear unit activation.
Figure 2:
Figure 2:
The input and output channels of the convolutional neural network. For the PET+MR model, the low-dose PET and the three MRI contrasts are used as inputs and trained on the full-dose PET image as the ground truth to synthesize an image resembling the full-dose PET image. For the PET-only model, only the low-dose PET image is used as input.
Figure 3:
Figure 3:
Image quality metrics compare images from low-dose PET, the PET-only model, and the PET+MR model. For the three metrics, comparison is to the ground truth full-dose PET images. All pair-wise t tests had P values less than .001. The image generated from the PET+MR model is superior for all three metrics: higher peak signal-to-noise ratio (PSNR), higher structural similarity (SSIM), and lower root mean square error (RMSE).
Figure 4:
Figure 4:
Amyloid-positive PET image in a 58-year-old male patient with Alzheimer disease, with the T1-weighted MR image (left) shown as reference. The region within the red box in the images in the top row is enlarged and shown in the bottom row. The synthesized PET images show significantly reduced noise compared with the low-dose PET images, while the images generated from the PET+MR model were superior in reflecting the underlying anatomic patterns of the amyloid tracer uptake compared with the images generated from the PET-only model.
Figure 5:
Figure 5:
Confusion matrices for amyloid status readings between full-dose and PET+MR model images and between full-dose and PET-only model images.
Figure 6:
Figure 6:
Clinical image quality scores (1 = uninterpretable/low, 5 = excellent/high; mean scores and standard deviation of all readings presented at top of each bar) as independently assigned by the two readers.
Figure 7:
Figure 7:
Bland-Altman two-dimensional histograms of regional standard uptake value ratios (SUVRs) compared between methods (ground truth to low dose, ground truth to PET+MR model, ground truth to PET-only model) across all data sets. The scale bar denotes the number of data points in each pixel; the solid and dashed lines denote the mean and 95% confidence interval (CI) of the SUVR differences, respectively. The images synthesized from the PET+MR model had the lowest bias (0.04) and smallest 95% CI (−0.26, +0.33) relative to the ground truth full-dose images.
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