Purpose Our goal was to use a generative adversarial network (GAN) with feature matching and task‐specific perceptual loss to synthesize standard‐dose amyloid Positron emission tomography (PET) images of high quality and including accurate pathological features from ultra‐low‐dose PET images only. Methods Forty PET datasets from 39 participants were acquired with a simultaneous PET/MRI scanner following injection of 330 ± 30 MBq of the amyloid radiotracer 18F‐florbetaben. The raw list‐mode PET data were reconstructed as the standard‐dose ground truth and were randomly undersampled by a factor of 100 to reconstruct 1% low‐dose PET scans. A 2D encoder‐decoder network was implemented as the generator to synthesize a standard‐dose image and a discriminator was used to evaluate them. The two networks contested with each other to achieve high‐visual quality PET from the ultra‐low‐dose PET. Multi‐slice inputs were used to reduce noise by providing the network with 2.5D information. Feature matching was applied to reduce hallucinated structures. Task‐specific perceptual loss was designed to maintain the correct pathological features. The image quality was evaluated by peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) metrics with and without each of these modules. Two expert radiologists were asked to score image quality on a 5‐point scale and identified the amyloid status (positive or negative). Results With only low‐dose PET as input, the proposed method significantly outperformed Chen et al.'s method (Chen et al. Radiology. 2018;290:649–656) (which shows the best performance in this task) with the same input (PET‐only model) by 1.87 dB in PSNR, 2.04% in SSIM, and 24.75% in RMSE. It also achieved comparable results to Chen et al.'s method which used additional magnetic resonance imaging (MRI) inputs (PET‐MR model). Experts' reading results showed that the proposed method could achieve better overall image quality and maintain better pathological features indicating amyloid status than both PET‐only and PET‐MR models proposed by Chen et al. Conclusion Standard‐dose amyloid PET images can be synthesized from ultra‐low‐dose images using GAN. Applying adversarial learning, feature matching, and task‐specific perceptual loss are essential to ensure image quality and the preservation of pathological features.

IMPORTANCE Predicting infarct size and location is important for decision-making and prognosis in patients with acute stroke. OBJECTIVES To determine whether a deep learning model can predict final infarct lesions using magnetic resonance images (MRIs) acquired at initial presentation (baseline) and to compare the model with current clinical prediction methods. DESIGN, SETTING, AND PARTICIPANTS In this multicenter prognostic study, a specific type of neural network for image segmentation (U-net) was trained, validated, and tested using patients from the Imaging Collaterals in Acute Stroke (iCAS) study from April 14, 2014, to April 15, 2018, and the Diffusion Weighted Imaging Evaluation for Understanding Stroke Evolution Study-2 (DEFUSE-2) study from July 14, 2008, to September 17, 2011 (reported in October 2012). Patients underwent baseline perfusion-weighted and diffusion-weighted imaging and MRI at 3 to 7 days after baseline. Patients were grouped into unknown, minimal, partial, and major reperfusion status based on 24-hour imaging results. Baseline images acquired at presentation were inputs, and the final true infarct lesion at 3 to 7 days was considered the ground truth for the model. The model calculated the probability of infarction for every voxel, which can be thresholded to produce a prediction. Data were analyzed from July 1, 2018, to March 7, 2019. MAIN OUTCOMES AND MEASURES Area under the curve, Dice score coefficient (DSC) (a metric from 0-1 indicating the extent of overlap between the prediction and the ground truth; a DSC of Ն0.5 represents significant overlap), and volume error. Current clinical methods were compared with model performance in subgroups of patients with minimal or major reperfusion. RESULTS Among the 182 patients included in the model (97 women [53.3%]; mean [SD] age, 65 [16] years), the deep learning model achieved a median area under the curve of 0.92 (interquartile range [IQR], 0.87-0.96), DSC of 0.53 (IQR, 0.31-0.68), and volume error of 9 (IQR, −14 to 29) mL. In subgroups with minimal (DSC, 0.58 [IQR, 0.31-0.67] vs 0.55 [IQR, 0.40-0.65]; P = .37) or major (DSC, 0.48 [IQR, 0.29-0.65] vs 0.45 [IQR, 0.15-0.54]; P = .002) reperfusion for which comparison with existing clinical methods was possible, the deep learning model had comparable or better performance. CONCLUSIONS AND RELEVANCE The deep learning model appears to have successfully predicted infarct lesions from baseline imaging without reperfusion information and achieved comparable performance to existing clinical methods. Predicting the subacute infarct lesion may help clinicians prepare for decompression treatment and aid in patient selection for neuroprotective clinical trials.

mTORC1 activation stimulates articular chondrocyte proliferation and differentiation to initiate OA, in part by downregulating FGFR3 and PPR.

Vascular-invasion-mediated interactions between activated articular chondrocytes and subchondral bone are essential for osteoarthritis (OA) development. Here, we determined the role of nutrient sensing mechanistic target of rapamycin complex 1 (mTORC1) signaling in the crosstalk across the bone cartilage interface and its regulatory mechanisms. Then mice with chondrocyte-specific mTORC1 activation (Tsc1 CKO and Tsc1 CKO ) or inhibition (Raptor CKO ) and their littermate controls were subjected to OA induced by destabilization of the medial meniscus (DMM) or not. DMM or Tsc1 CKO mice were treated with bevacizumab, a vascular endothelial growth factor (VEGF)-A antibody that blocks angiogenesis. Articular cartilage degeneration was evaluated using the Osteoarthritis Research Society International score. Immunostaining and Western blotting were conducted to detect H-type vessels and protein levels in mice. Primary chondrocytes from mutant mice and ADTC5 cells were treated with interleukin-1β to investigate the role of chondrocyte mTORC1 in VEGF-A secretion and in vitro vascular formation. Clearly, H-type vessels were increased in subchondral bone in DMM-induced OA and aged mice. Cartilage mTORC1 activation stimulated VEGF-A production in articular chondrocyte and H-type vessel formation in subchondral bone. Chondrocyte mTORC1 promoted OA partially through formation of VEGF-A-stimulated subchondral H-type vessels. In particular, vascular-derived nutrients activated chondrocyte mTORC1, and stimulated chondrocyte activation and production of VEGF, resulting in further angiogenesis in subchondral bone. Thus a positive-feedback regulation of H-type vessel formation in subchondral bone by articular chondrocyte nutrient-sensing mTORC1 signaling is essential for the pathogenesis and progression of OA. © 2018 American Society for Bone and Mineral Research.