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.

We address the applicability of wavelet analysis (WA) in evaluating transient phenomena on a model of early recovery to exercise, which is the only exercise-associated phase characterized by a distinct transient shape and as such cannot be assessed using conventional tools. Our WA-based algorithm provided a reliable spectral decomposition of laser-Doppler (LD) signals in early recovery, enabling us to speculate roughly on the mechanisms involved in the regulation of skin microcirculation in this phase.

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 (ROI_myoc), reference (ROI_ref), and exclusion region (ROI_excl)) 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, ROI_ref, and ROI_excl 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: ROI_ref 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.

Background: Acute myocarditis (AM) affects myocardial structure and function, assessed by cardiac magnetic resonance late gadolinium enhancement (CMR-LGE) and speckle tracking echocardiography (STE), respectively; however, the correlation between the two techniques at the segmental level is inconsistent. We studied natural heterogeneity and anatomical orientation mismatch as potential causes of correlation discrepancy. Methods: A total of 30 AM patients underwent left ventricle LGE-CMR and STE measurement, acquiring 18 segmental values depicting edema extent and peak longitudinal strain, respectively. Baseline segmental correlation was compared to average patient segmental correlation and to segmental correlation after spatial resolution reduction achieved by averaging adjacent segments in four successive iterations, where the degree of spatial resolution reduction was evaluated based on the relative decrease in segmental standard deviation. Results: Baseline segmental correlation was weak, i.e., r = 0.24 (p < 0.05) but improved in fitted SLGE and SpLS baseline correlation (r₀ = 0.44, p < 0.05) and in average patient correlation (r = 0.55, p < 0.05). Iterative spatial resolution reduction increased the correlation to r₁ = 0.49 and r₂ = 0.51 and then decreased it to r₃ = 0.11 (p < 0.05) and r₄ = 0.07 (p > 0.05), with corresponding decreases in segmental standard deviation relative to baseline from σ₀ = 12.87 to σ/σ₀ = 0.68, 0.51, 0.38, and 0.29 in SLGE values and σ₀ = 4.77 to σ/σ₀ = 0.57, 0.41, 0.31, and 0.23 in SpLS. Conclusions: Improved correlation in average patients is associated with natural heterogeneity, which indicates a need to develop more robust indicators of ventricular function. The improved correlation in moderate spatial resolution reduction indicates a potential solution for anatomic orientation mismatch between CMR-LGE and STE techniques.