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.

True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation

PURPOSE

While sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement using a significantly reduced injected radiotracer protocol for amyloid PET/MRI.

METHODS

Eighteen participants underwent two separate ¹⁸F-florbetaben PET/MRI studies in which an ultra-low-dose (6.64 ± 3.57 MBq, 2.2 ± 1.3% of standard) or a standard-dose (300 ± 14 MBq) was injected. The PET counts from the standard-dose list-mode data were also undersampled to approximate an ultra-low-dose session. A pre-trained convolutional neural network was fine-tuned using MR images and either the injected or sampled ultra-low-dose PET as inputs. Image quality of the enhanced images was evaluated using three metrics (peak signal-to-noise ratio, structural similarity, and root mean square error), as well as the coefficient of variation (CV) for regional standard uptake value ratios (SUVRs). Mean cerebral uptake was correlated across image types to assess the validity of the sampled realizations. To judge clinical performance, four trained readers scored image quality on a five-point scale (using 15% non-inferiority limits for proportion of studies rated 3 or better) and classified cases into amyloid-positive and negative studies.

RESULTS

The deep learning-enhanced PET images showed marked improvement on all quality metrics compared with the low-dose images as well as having generally similar regional CVs as the standard-dose. All enhanced images were non-inferior to their standard-dose counterparts. Accuracy for amyloid status was high (97.2% and 91.7% for images enhanced from injected and sampled ultra-low-dose data, respectively) which was similar to intra-reader reproducibility of standard-dose images (98.6%).

CONCLUSION

Deep learning methods can synthesize diagnostic-quality PET images from ultra-low injected dose simultaneous PET/MRI data, demonstrating the general validity of sampled realizations and the potential to reduce dose significantly for amyloid imaging.

Neutrophil to lymphocyte ratio predicts disease progression following intra-arterial therapy of hepatocellular carcinoma

BACKGROUND

Prospectively predicting response to intra-arterial therapy for hepatocellular carcinoma (HCC) is challenging. Neutrophil/lymphocyte ratio (NLR) is a serum biomarker that is associated with survival for multiple malignancies. It was hypothesized that increased NLR would be associated with early disease progression after intra-arterial therapy of HCC.

METHODS

The outcomes of 86 treatment-naïve patients who had chemoembolization or radioembolization of HCC between July 2013-July 2014 were reviewed. Pre-treatment laboratory tests and imaging were used to measure NLR, Child-Pugh (CP) score, tumor number and tumor size. High/low NLR groups were defined as >3 and <3 respectively. Follow-up imaging at two months with assessed response using modified response criteria in solid tumors (mRECIST).

RESULTS

NLR >3 was seen in 25/86 patients (range 3.0-21.6). NLR >3 patients had a significantly higher baseline CP score. Comorbidities were otherwise similar between groups as was tumor number/size. Disease control was significantly worse (p = 0.014) with NLR >3. Logistic regression for tumor response revealed NLR >3 as the best predictor of early progression (p < 0.0001).

DISCUSSION

NLR may be a serologic biomarker of early progressive disease after intra-arterial therapy of HCC. Future research should focus on outcomes by treatment type or potentially combining arterial therapies with ablation and/or targeted biologic agents.