Generative Adversarial Network-Enhanced Ultra-Low-Dose [18F]-PI-2620 τ PET/MRI in Aging and Neurodegenerative Populations.

BACKGROUND AND PURPOSE: With the utility of hybrid τ PET/MR imaging in the screening, diagnosis, and follow-up of individuals with neurodegenerative diseases, we investigated whether deep learning techniques can be used in enhancing ultra-low-dose [18F]-PI-2620 τ PET/MR images to produce diagnostic-quality images. MATERIALS AND METHODS: Forty-four healthy aging participants and patients with neurodegenerative diseases were recruited for this study, and [18F]-PI-2620 τ PET/MR data were simultaneously acquired. A generative adversarial network was trained to enhance ultra-low-dose τ images, which were reconstructed from a random sampling of 1/20 (approximately 5% of original count level) of the original full-dose data. MR images were also used as additional input channels. Region-based analyses as well as a reader study were conducted to assess the image quality of the enhanced images compared with their full-dose counterparts. RESULTS: The enhanced ultra-low-dose τ images showed apparent noise reduction compared with the ultra-low-dose images. The regional standard uptake value ratios showed that while, in general, there is an underestimation for both image types, especially in regions with higher uptake, when focusing on the healthy-but-amyloid-positive population (with relatively lower τ uptake), this bias was reduced in the enhanced ultra-low-dose images. The radiotracer uptake patterns in the enhanced images were read accurately compared with their full-dose counterparts. CONCLUSIONS: The clinical readings of deep learning-enhanced ultra-low-dose τ PET images were consistent with those performed with full-dose imaging, suggesting the possibility of reducing the dose and enabling more frequent examinations for dementia monitoring.

Application of Deep Learning to Predict Standardized Uptake Value Ratio and Amyloid Status on 18F-Florbetapir PET Using ADNI Data.

BACKGROUND AND PURPOSE: Cortical amyloid quantification on PET by using the standardized uptake value ratio is valuable for research studies and clinical trials in Alzheimer disease. However, it is resource intensive, requiring co-registered MR imaging data and specialized segmentation software. We investigated the use of deep learning to automatically quantify standardized uptake value ratio and used this for classification. MATERIALS AND METHODS: Using the Alzheimer's Disease Neuroimaging Initiative dataset, we identified 2582 18F-florbetapir PET scans, which were separated into positive and negative cases by using a standardized uptake value ratio threshold of 1.1. We trained convolutional neural networks (ResNet-50 and ResNet-152) to predict standardized uptake value ratio and classify amyloid status. We assessed performance based on network depth, number of PET input slices, and use of ImageNet pretraining. We also assessed human performance with 3 readers in a subset of 100 randomly selected cases. RESULTS: We have found that 48% of cases were amyloid positive. The best performance was seen for ResNet-50 by using regression before classification, 3 input PET slices, and pretraining, with a standardized uptake value ratio root-mean-square error of 0.054, corresponding to 95.1% correct amyloid status prediction. Using more than 3 slices did not improve performance, but ImageNet initialization did. The best trained network was more accurate than humans (96% versus a mean of 88%, respectively). CONCLUSIONS: Deep learning algorithms can estimate standardized uptake value ratio and use this to classify 18F-florbetapir PET scans. Such methods have promise to automate this laborious calculation, enabling quantitative measurements rapidly and in settings without extensive image processing manpower and expertise.

Intracerebral periventricular pseudocysts in a fetus with mitochondrial depletion syndrome: an association or coincidence.

We report the prenatal ultrasound and magnetic resonance imaging finding of periventricular, large subependymal pseudocysts (SEPCs) in a patient who was later diagnosed as having mitochondrial depletion syndrome (MDS). To our knowledge, this is the first report of fetal SEPCs in a patient with MDS. These findings may provide an important diagnostic tool for prenatal diagnosis of MDS in at risk pregnancies when the gene mutation causing the condition has not been delineated. It may also direct the neonatologist in the postnatal care of the newborn detected prenatally with SEPCs in view of the association of this finding with infection, chromosome abnormalities, metabolic disorders and other abnormalities, when such findings are identified serendipitously. Further research is needed to find if the SEPCs detected in our patient is an association or a coincidental finding.

[Improved method of equilibrium radionuclide ventriculography]. / Usovershenstvovannyi sposob ravnovesnoi radionuklidnoi ventrikulografii.

The commonly used method of static radionuclide ventriculography with a cardiosynchronizer does not permit obtaining a curve of ventricular function over a complete cardiac cycle, especially using computers with a small memory volume. Besides, the final segment of a curve is usually insignificant as a result of variations in a duration of the R-R interval. A method of 2-stage recording of radionuclide ventriculography has been proposed, the second stage being shifted to a certain time interval, i. e. a delay line is introduced between a synchronizing device and computer. Two methods of delay--in the software and hardware--are proposed, the latter being more preferable as it makes unnecessary extra technical devices. Particular attention is drawn to a choice of delay time depending on a computer memory volume: in a small memory volume delay time must be approximately one-third of the patient's R-R interval.

[Background correction in inhalation scintigraphy of the lungs using 133Xe]. / Korrektsiia fona pri ingaliatsionnoi stsintigrafii legkikh s 133Xe.

Based on the regularity of decreasing the blood volumes from the internal edge of the lung to its periphery the authors proposed a method of gradient background correction in inhalation scintigraphy of the lungs with 133Xe. The background is detached on each image line separately for the right and left lungs. Software was developed for the implementation of this technique of background correction for any computer with a FORTRAN-IV translator.