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Wavelet analysis (WA) decomposes laser Doppler (LD) microcirculatory signals into characteristic frequency intervals related to endothelial nitric oxide (NO)-independent, endothelial NO-dependent, neurogenic, myogenic, respiratory, and cardiac physiological influences. Since LD signals have a finite length, the WA results suffer from spectral leakage due to edge effects. The cone of influence (COI) delineates the regions of the wavelet scalogram where these effects become important. We aimed to determine whether accounting for the COI leads to significant differences in the WA results. Two typical patterns of LD signals were analysed: a baseline and a post-occlusive reactive hyperemia (PORH) signal. The WA spectra were constructed without and with excluding data affected by the COI. The relative power (RP = median power of each frequency interval/median power of the total spectrum) of the spectral components obtained without and with the COI was compared. Applying the COI correction did not significantly affect the baseline signals. On the contrary, in PORH, accounting for the COI resulted in significant differences in the RP of the endothelial NO-independent (p = 0.0005; Wilcoxon signed-rank test), endothelial NO-dependent (p = 0.0005), neurogenic (p = 0.0038), myogenic (p = 0.001), respiratory (p = 0.0002), and cardiac frequency bands (p = 0.0002). The results suggest that applying the COI correction to the WA results obtained from the LD signals is desirable, especially for transient signals. [ABSTRACT FROM AUTHOR]

Wavelet analysis (WA) provides superior time-frequency decomposition of complex signals than conventional spectral analysis tools. To illustrate its usefulness in assessing transient phenomena, we applied a custom-developed WA algorithm to laser-Doppler (LD) signals of the cutaneous microcirculation measured at glabrous (finger pulp) and nonglabrous (forearm) sites during early recovery after dynamic exercise. This phase, importantly contributing to the establishment of thermal homeostasis after exercise cessation, has not been adequately explored because of its complex, transient form. Using WA, we decomposed the LD signals measured during the baseline and early recovery into power spectra of characteristic frequency intervals corresponding to endothelial nitric oxide (NO)-dependent, neurogenic, myogenic, respiratory, and cardiac physiological influence. Assessment of relative power (RP), defined as the ratio between the median power in the frequency interval and the median power of the total spectrum, revealed that endothelial NO-dependent (5.87 early recovery; 1.53 baseline; P = 0.005; Wilcoxon signed-rank test) and respiratory (0.71 early recovery; 0.40 baseline; P = 0.001) components were significantly increased, and myogenic component (1.35 early recovery; 1.83 baseline; P = 0.02) significantly decreased during early recovery in the finger pulp. In the forearm, only the RP of the endothelial NO-dependent (1.90 early recovery; 0.94 baseline; P = 0.009) component was significantly increased. WA presents an irreplaceable tool for the assessment of transient phenomena. The relative contribution of the physiological mechanisms controlling the microcirculatory response in the early recovery phase appears to differ in glabrous and nonglabrous skin when compared with baseline; moreover, the endothelial NO-dependent influence seems to play an important role. [ABSTRACT FROM AUTHOR]

Featured Application: In this pilot study, we quantify the contribution of the late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) image analysis steps to the overall variability of the image analysis process in patients with myocarditis. Within the study protocol, we took an original approach and developed solutions to address this important but underexplored issue. We trust that our research represents a first step toward reducing the overall variability of the LGE-CMR image analysis process, which is important to improve the diagnosis and prognosis of myocarditis patients. Background: Myocardial damage in myocarditis is assessed through late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR). Variability in quantifying myocarditis extent results from imprecise image segmentation and inconclusive data on quantification method selection. To improve analysis precision, segmentation steps are systematically ranked based on their inherent risks of error. Additionally, data on two distinct quantification methods are presented. Methods: Using newly developed software, four experts analyzed five LGE-CMR left ventricular (LV) short-axis (SAx) images of myocarditis patients in three sessions. Regions of interest (ROIs) (myocardial (ROImyoc), reference (ROIref), and exclusion region (ROIexcl)) were identified and used to calculate LGE extent with 3σ (intensity above three standard deviations (σ) in reference) and the full width at half maximum (FWHM) method (intensity above 50% of maximum signal in reference). The reference LGE extent was calculated and the influence of the ROIs on LGE extent variability was determined. Interobserver and intraobserver variability were evaluated as 1-intraclass correlation coefficient (ICC). Results: LGE extent variability was 6.2 ± 0.6% for 3σ and 4.0 ± 0.6% for FWHM. The contributions of ROImyoc, ROIref, and ROIexcl were 1.5 ± 0.2%, 2.7 ± 0.4%, and 2 ± 0.3%, respectively, for 3σ, and 1.1 ± 0.1%, 1.6 ± 0.4%, and 1.3 ± 0.3%, respectively, for FWHM. LGE extent was lower in FWHM. Interobserver variability was 0.56 for 3σ and 0.43 for FWHM. The intraobserver variability was higher for the 3σ method in all four observers. Conclusion: ROIref selection contributed most to LGE extent variability. FWHM yielded lower LGE extent and lower inter- and intraobserver variability. Due to low statistical significance, the findings are only partially confirmed. [ABSTRACT FROM AUTHOR]