Quantification of intramyocardial blood volume using 99mTc-RBC SPECT/CT: a pilot human study.

BACKGROUND: Quantification of intramyocardial blood volume (IMBV), the fraction of myocardium that is occupied by blood, is a promising Index to measure microcirculatory functions. In previous large animal SPECT/CT studies injected with 99mTc-labeled Red Blood Cell (RBC) and validated by ex vivo microCT, we have demonstrated that accurate IMBV can be measured. In this study, we report the data processing methods and results of the first-in-human pilot study. METHODS: Data from three subjects have been included to date. Each subject underwent rest and adenosine-induced stress 99mTc-RBC SPECT/CT on a dedicated cardiac system with both non-contrast and contrast-enhanced CT acquired. Corrections of attenuation (AC) and scatter (SC), respiratory and cardiac gating, and partial volume correction (PVC) were applied. We also performed automatic segmentation and registration approach based on the blood pool topology in both SPECT and CT images. RESULTS: The quantified IMBV across all subjects under resting conditions were 35.0% ± 3.3% for the end-diastolic phase and 24.1% ± 2.7% for the end-systolic phase. The cycle-dependent change in IMBV (ΔIMBV) between diastolic and systolic phases was 31.5% ± 3.0%. Under stress, IMBV were 40.6% ± 4.2% for the end-diastolic phase and 26.5% ± 2.8% for the end-systolic phase, and ΔIMBV was 34.7% ± 7.4%. CONCLUSIONS: It is feasible to quantify IMBV in resting and stress conditions in human studies using SPECT/CT with 99mTc-RBC.

Geographical diversification using ETFs: Multinational evidence from COVID-19 pandemic.

We examine the relations between dollar flows of U.S. listed ETFs with exposure to the U.S., Europe, Asia, and the rest of the world following an emergency like the COVID-19 crisis. Using a Markov Switching Model (MSVAR), we find evidence that investors use ETFs to gain exposure to foreign markets and swiftly adjust their portfolio's allocation in response to the change in the number of COVID-19 infected people in every location. We further extend our study to ETFs listed in the U.S., Europe, and Asia and investigate the change in foreign and domestic money flow, before and after the pandemic. We show that investors around the world rebalance their portfolios by monitoring the countries' performance in controlling the pandemic. Our findings show that while investors in the U.S. and Asian countries direct their money to domestic funds and reduce their foreign investment following the pandemic, European investors increase foreign investment and reduce home bias. This is consistent with the flight-to-safety effect when investors shift their asset allocation away from riskier investments (here riskier locations) and into safer ones during the adverse economic shock.

PET Image Denoising using a Deep-Learning Method for Extremely Obese Patients.

The image quality in clinical PET scan can be severely degraded due to high noise levels in extremely obese patients. Our work aimed to reduce the noise in clinical PET images of extremely obese subjects to the noise level of lean subject images, to ensure consistent imaging quality. The noise level was measured by normalized standard deviation (NSTD) derived from a liver region of interest. A deep learning-based noise reduction method with a fully 3D patch-based U-Net was used. Two U-Nets, U-Nets A and B, were trained on datasets with 40% and 10% count levels derived from 100 lean subjects, respectively. The clinical PET images of 10 extremely obese subjects were denoised using the two U-Nets. The results showed the noise levels of the images with 40% counts of lean subjects were consistent with those of the extremely obese subjects. U-Net A effectively reduced the noise in the images of the extremely obese patients while preserving the fine structures. The liver NSTD improved from 0.13±0.04 to 0.08±0.03 after noise reduction (p = 0.01). After denoising, the image noise level of extremely obese subjects was similar to that of lean subjects, in terms of liver NSTD (0.08±0.03 vs. 0.08±0.02, p = 0.74). In contrast, U-Net B over-smoothed the images of extremely obese patients, resulting in blurred fine structures. In a pilot reader study comparing extremely obese patients without and with U-Net A, the difference was not significant. In conclusion, the U-Net trained by datasets from lean subjects with matched count level can provide promising denoising performance for extremely obese subjects while maintaining image resolution, though further clinical evaluation is needed.