Evaluation of a 2D UNet-Based Attenuation Correction Methodology for PET/MR Brain Studies

Deep learning (DL) strategies applied to magnetic resonance (MR) images in positron emission tomography (PET)/MR can provide synthetic attenuation correction (AC) maps, and consequently PET images, more accurate than segmentation or atlas-registration strategies. As first objective, we aim to investigate the best MR image to be used and the best point of the AC pipeline to insert the synthetic map in. Sixteen patients underwent a 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) and a PET/MR brain study in the same day. PET/CT images were reconstructed with attenuation maps obtained: (1) from CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with a 2D UNet trained on MR image/attenuation map pairs. As for MR, T1-weighted and Zero Time Echo (ZTE) images were considered; as for attenuation maps, CTs and 511 keV low-resolution attenuation maps were assessed. As second objective, we assessed the ability of DL strategies to provide proper AC maps in presence of cranial anatomy alterations due to surgery. Three 11C-methionine (METH) PET/MR studies were considered. PET images were reconstructed with attenuation maps obtained: (1) from diagnostic coregistered CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with 2D UNets trained on the sixteen FDG anatomically normal patients. Only UNets taking ZTE images in input were considered. FDG and METH PET images were quantitatively evaluated. As for anatomically normal FDG patients, UNet AC models generally provide an uptake estimate with lower bias than atlas-based or segmentation-based methods. The intersubject average bias on images corrected with UNet AC maps is always smaller than 1.5%, except for AC maps generated on too coarse grids. The intersubject bias variability is the lowest (always lower than 2%) for UNet AC maps coming from ZTE images, larger for other methods. UNet models working on MR ZTE images and generating synthetic CT or 511 keV low-resolution attenuation maps therefore provide the best results in terms of both accuracy and variability. As for METH anatomically altered patients, DL properly reconstructs anatomical alterations. Quantitative results on PET images confirm those found on anatomically normal FDG patients.

Nicotine self-administration and withdrawal: modulation of anxiety in the social interaction test in rats

RATIONALE

Most smokers report smoking has an anxiolytic effect, which may contribute to nicotine dependence.

OBJECTIVE

To examine effects in the social interaction test (SI) of anxiety after 4 weeks' self-administered nicotine (15 infusions of 0.03 mg/kg, totalling 0.45 mg/kg per day), and after 24 and 72 h of withdrawal. The effect of exposure to the operant chamber on withdrawal responses was also examined.

METHODS

Animals were trained to self-administer saline or nicotine and after 4 weeks they were tested in SI after their daily self-administration session. Animals were retested after 24 and 72 h withdrawal, when they were either taken directly from the home cage or were tested 5 min after a 30-min exposure to the operant chamber.

RESULTS

Compared with the saline control group, the animals that had been self-administering nicotine for 4 weeks showed decreased social interaction with no decrease in locomotor activity, indicating a significant anxiogenic effect of the nicotine infusions. There was no change in social interaction after 24 and 72 h withdrawal from chronic nicotine, regardless of whether or not the rats were exposed to the operant chamber just prior to being tested.

CONCLUSIONS

Nicotine self-administration is not maintained because of its anxiolytic effect, but despite, or because of, its anxiogenic effect. There was no evidence of an anxiogenic response after either 24 or 72 h of withdrawal and thus increased anxiety on withdrawal from nicotine does not seem to contribute to nicotine self-administration.