Optimization of imaging parameters in chest CT for COVID-19 patients: an experimental phantom study.

BACKGROUND: With the global outbreak of coronavirus disease 2019 (COVID-19), chest computed tomography (CT) is vital for diagnosis and follow-up. The increasing contribution of CT to the population-collected dose has become a topic of interest. Radiation dose optimization for chest CT of COVID-19 patients is of importance in clinical practice. The present study aimed to investigate the factors affecting the detection of ground-glass nodules and exudative lesions in chest CT among COVID-19 patients and to find an appropriate combination of imaging parameters that optimize detection while effectively reducing the radiation dose. METHODS: The anthropomorphic thorax phantom, with 9 spherical nodules of different diameters and CT values of -800, -630, and 100 HU, was used to simulate the lesions of COVID-19 patients. Four custom-simulated lesions of porcine fat and ethanol were also scanned at 3 tube potentials (120, 100, and 80 kV) and corresponding milliampere-seconds (mAs) (ranging from 10 to 100). Separate scans were performed at pitches of 0.6, 0.8, 1.0, 1.15, and 1.49, and at collimations of 10, 20, 40, and 80 mm at 80 kV and 100 mAs. CT values and standard deviations of simulated nodules and lesions were measured, and radiation dose quantity (volume CT dose index; CTDIvol) was collected. Contrast-to-noise ratio (CNR) and figure of merit (FOM) were calculated. All images were subjectively evaluated by 2 radiologists to determine whether the nodules were detectable and if the overall image quality met diagnostic requirements. RESULTS: All simulated lesions, except -800 HU nodules, were detected at all scanning conditions. At a fixed voltage of 120 or 100 kV, with increasing mAs, image noise tended to decrease, and the CNR tended to increase (F=9.694 and P=0.033 for 120 kV; F=9.028 and P=0.034 for 100 kV). The FOM trend was the same as that of CNR (F=2.768 and P=0.174 for 120 kV; F=1.915 and P=0.255 for 100 kV). At 80 kV, the CNRs and FOMs had no significant change with increasing mAs (F=4.522 and P=0.114 for CNRs; F=1.212 and P=0.351 for FOMs). For the 4 nodules of -800 and -630 HU, CNRs had no statistical differences at each of the 5 pitches (F=0.673, P=0.476). The CNRs and FOMs at each of the 4 collimations had no statistical differences (F=2.509 and P=0.125 for CNRs; F=1.485 and P=0.309 for FOMs) for each nodule. CNRs and subjective evaluation scores increased with increasing parameter values for each imaging iteration. The CNRs of 4 -800 HU nodules in the qualified images at the thresholds of scanning parameters of 120 kV/20 mAs, 100 kV/40 mAs, and 80 kV/80 mAs, had statistical differences (P=0.038), but the FOMs had no statistical differences (P=0.085). Under the 3 threshold conditions, the CNRs and FOMs of the 4 nodules were highest at 100 kV and 40 mAs (1.6 mGy CTDIvol). CONCLUSIONS: For chest CT among COVID-19 patients, it is recommended that 100 kV/40 mAs is used for average patients; the radiation dose can be reduced to 1.6 mGy with qualified images to detect ground-glass nodules and exudation lesions.

An application of RASER technique in the treatment of chronic total occlusion accompanied with stent fracture in right coronary artery: a case report.

BACKGROUND: The interventional treatment of chronic total occlusion (CTO) with stent fracture as well as severe calcification was extremely difficult and no effective technique has been reported. CASE PRESENTATION: A 50-year-old woman was hospitalized for angina, angiography revealed triple vessel disease, CTO accompanied with stent fracture in right coronary artery (RCA). Treatment using conventional coronary intervention was expected to be difficult. Therefore, we performed RASER technique, which was a combination of excimer laser coronary atherectomy (ELCA) with rotational atherectomy (RA), followed by the deployment of drug-eluting stents. Intravascular ultrasound (IVUS) revealed well attachment of the stents, the patient was discharged 3 days after the procedure and no recurrent chest discomfort was reported in a follow-up time of 10 months. CONCLUSION: This case report provided a first report of RASER technique in the treatment of CTO with stent fracture and severe calcification.

Efficient State Management for Scaling Out Stateful Operators in Stream Processing Systems.

Many big data applications require real-time analysis of continuous data streams. Stream Processing Systems (SPSs) are designed to act on real-time streaming data using continuous queries consisting of interconnected operators. The dynamic nature of data streams, for example, fluctuation in data arrival rates and uneven data distribution, can cause an operator to be a bottleneck one. Scalability is an important factor in SPS, but detecting bottleneck operator correctly and scaling it without affecting application execution are challenging. A stateful operator such as aggregation or join makes scaling operation more difficult as it involves state management. Current research does not address the issue of scaling stateful operators efficiently as mostly stop application for handling state, which results in significant overheads to the performance. In this article, the key idea is to detect bottleneck operator correctly using the runtime bottleneck detection approach and then scale out this operator and manage its internal state in a way that we can achieve almost zero latency. During the bottleneck detection process, we have defined alarming_threshold, a parameter for the operators that can be bottleneck operators in the future and scale_out_threshold, when the operator is bottleneck. To scale out, we have presented two techniques, active backup and checkpointing, the former one will start a Secondary Execution (SE) in back end by partitioning state and input streams to multiple nodes at alarming_threshold; this SE will replace primary node at scale_out_threshold. In the latter technique, a State Manager (SM) module will start state checkpointing at alarming_threshold to external store and perform scale out by managing state and input stream at scale_out_threshold. The first approach will help us to achieve almost zero latency goal, while the latter one is a resource efficient technique. Our results show that both techniques are working while providing desired goals of reducing overall latency during scale out and improving resource utilization.