Noise Reduction and Image Quality Improvement of Low Dose and Ultra Low Dose Brain Perfusion CT by HYPR-LR Processing

To evaluate image quality and signal characteristics of brain perfusion CT (BPCT) obtained by low-dose (LD) and ultra-low-dose (ULD) protocols with and without post-processing by highly constrained back-projection (HYPR)–local reconstruction (LR) technique.Simultaneous BPCTs were acquired in 8 patients on a dual-source-CT by applying LD (80 kV,200 mAs,14×1.2 mm) on tube A and ULD (80 kV,30 mAs,14×1.2 mm) on tube B. Image data from both tubes was reconstructed with identical parameters and post-processed using the HYPR-LR. Correlation coefficients between mean and maximum (MAX) attenuation values within corresponding ROIs, area under attenuation curve (AUC), and signal to noise ratio (SNR) of brain parenchyma were assessed. Subjective image quality was assessed on a 5-point scale by two blinded observers (1:excellent, 5:non-diagnostic).Radiation dose of ULD was more than six times lower compared to LD. SNR was improved by HYPR: ULD vs. ULD+HYPR: 1.9±0.3 vs. 8.4±1.7, LD vs. LD+HYPR: 5.0±0.7 vs. 13.4±2.4 (both p<0.0001). There was a good correlation between the original datasets and the HYPR-LR post-processed datasets: r = 0.848 for ULD and ULD+HYPR and r = 0.933 for LD and LD+HYPR (p<0.0001 for both). The mean values of the HYPR-LR post-processed ULD dataset correlated better with the standard LD dataset (r = 0.672) than unprocessed ULD (r = 0.542), but both correlations were significant (p<0.0001). There was no significant difference in AUC or MAX. Image quality was rated excellent (1.3) in LD+HYPR and non-diagnostic (5.0) in ULD. LD and ULD+HYPR images had moderate image quality (3.3 and 2.7).SNR and image quality of ULD-BPCT can be improved to a level similar to LD-BPCT when using HYPR-LR without distorting attenuation measurements. This can be used to substantially reduce radiation dose. Alternatively, LD images can be improved by HYPR-LR to higher diagnostic quality

A Systematic Review of Cost-of-Illness Studies of Multimorbidity

Objectives: The economic burden of multimorbidity is considerable. This review analyzed the methods of cost-of-illness (COI) studies and summarized the economic outcomes of multimorbidity. Methods: A systematic review (2000–2016) was performed, which was registered with Prospero, reported according to PRISMA, and used a quality checklist adapted for COI studies. The inclusion criteria were peer-reviewed COI studies on multimorbidity, whereas the exclusion criterion was studies focusing on an index disease. Extracted data included the definition, measure, and prevalence of multimorbidity; the number of included health conditions; the age of study population; the variables used in the COI methodology; the percentage of multimorbidity vs. total costs; and the average costs per capita. Results: Among the 26 included articles, 14 defined multimorbidity as a simple count of 2 or more conditions. Methodologies used to derive the costs were markedly different. Given different healthcare systems, OOP payments of multimorbidity varied across countries. In the 17 and 12 studies with cut-offs of ≥2 and ≥3 conditions, respectively, the ratios of multimorbidity to non-multimorbidity costs ranged from 2–16 to 2–10. Among the ten studies that provided cost breakdowns, studies with and without a societal perspective attributed the largest percentage of multimorbidity costs to social care and inpatient care/medicine, respectively. Conclusion: Multimorbidity was associated with considerable economic burden. Synthesising the cost of multimorbidity was challenging due to multiple definitions of multimorbidity and heterogeneity in COI methods. Count method was most popular to define multimorbidity. There is consistent evidence that multimorbidity was associated with higher costs