Virtual monoenergetic dual-energy CT for evaluation of hepatic and splenic lacerations.

PURPOSE: To evaluate the utility of virtual monoenergetic imaging in assessing hepatic and splenic lacerations and to determine the optimal energy level to maximize injury contrast-to-noise ratio. METHODS: We retrospectively examined 49 contrast-enhanced abdominal CT studies performed on a dual-source dual-energy CT (DECT) scanner with reported liver and/or splenic lacerations. All studies included portal venous phase imaging acquired simultaneously at low (80 or 100 kVp) and high (140 kVp with tin filtration) energy levels. Conventional 120 kVp-equivalent images were generated for routine review by blending the low and high energy acquisitions. Virtual monoenergetic reconstructions were retrospectively generated in 10 keV increments from 40 to 90 keV. Liver or splenic laceration attenuation, background parenchymal attenuation, and noise were measured on each set of monoenergetic and conventional images. Injury-to-parenchyma contrast and contrast-to-noise ratios (CNR) were calculated. Differences between CNR of monoenergetic series and conventional images were assessed with a paired t test. RESULTS: Liver laceration was identified in 28 patients, and splenic laceration in 22 patients. Background noise was lower at higher monoenergetic levels, with the lowest noise seen at 90 keV, less than that of conventional images (stddev 8.0 for 90 keV and 8.5 for conventional based on noise of uninjured liver/spleen parenchyma, p < 0.001). For both liver and splenic lacerations, injury-to-parenchyma contrast was greater at lower monoenergetic levels, with maximum at 40 keV. Contrast at 40-70 keV was significantly greater than that of conventional images (p < 0.001). Injury-to parenchyma CNR was also greater at 40-70 keV than that of conventional images and with statistical significance. CNR was highest at 40 keV for both liver (6.5 for 40 keV and 5.4 for conventional, p < 0.001) and splenic lacerations (7.5 vs. 5.8, p < 0.001). CONCLUSIONS: DECT virtual monoenergetic imaging at low keV improves injury-to-parenchyma CNR of hepatic and splenic lacerations compared with traditional polyenergetic reconstructions. Specially, the optimal energy level for assessing both was 40 keV.

Biomedical blockchain with practical implementations and quantitative evaluations: a systematic review.

OBJECTIVE: Blockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics. MATERIALS AND METHODS: This systematic review adapts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to review biomedical blockchain papers published by August 2023 from 3 databases. Blockchain application, implementation, and evaluation metrics were collected and summarized. RESULTS: Following screening, 11 articles were included in this review. Articles spanned a range of biomedical applications including COVID-19 medical data sharing, decentralized internet of things (IoT) data storage, clinical trial management, biomedical certificate storage, electronic health record (EHR) data sharing, and distributed predictive model generation. Only one article demonstrated blockchain deployment at a medical facility. DISCUSSION: Ethereum was the most common blockchain platform. All but one implementation was developed with private network permissions. Also, 8 articles contained storage speed metrics and 6 contained query speed metrics. However, inconsistencies in presented metrics and the small number of articles included limit technological comparisons with each other. CONCLUSION: While blockchain demonstrates feasibility for adoption in healthcare, it is not as popular as currently existing technologies for biomedical data management. Addressing implementation and evaluation factors will better showcase blockchain's practical benefits, enabling blockchain to have a significant impact on the health sector.