ABSTRACT
Objective To explore the value of multiple non-gaussian distribution models DWI in evaluating hepatic ischemia reperfusion injury (HIRI) model in rats.Methods Forty-two SD rats were divided into 7 groups by random numeric table method.Each group had 6 rats.The 7 groups were 6 h,12 h,1 d,3 d,7 d,14 d group after HIRI and control group,respectively.The experimental groups underwent right hepatic portal vein and hepatic artery ligation,and received reperfusion 3 h after operation.MRI scanning (T1WI,T2WI,single b-values DWI and 15 b-values DWI) was performed at 6 h,12 h,1 d,3 d,7 d and 14 d after reperfusion.The control group underwent sham operation and MRI scanning.According to monoexponential model,biexponential model,threxponential model,stretched-exponential model DWI and diffusion kurtosis imaging,many parametres were obtained and their dynamic changes at each time point were observed.The parameters included standard apparent diffusion coefficient (ADCs),pure diffusion coefficients (D),pseudodiffusion coefficients (D*),perfusion fraction (f),ultra-high apparent diffusion coefficient (ADCu),distributed diffusion coefficient (DDC),water diffusion heterogeneity index (or),mean diffusion coefficient (MD) and mean diffusion kurtosis (MK).One way ANOVA was used to compare the differences of parameters among different groups.Results MRI examination and pathological examination were successfully completed in all rats.The right hepatic lobe in the experimental groups appeared hypointense on T1WI and hyperintense on T2WI relative to control group from 6 h after operation.The infarcted liver lobe in the experimental groups became significantly smaller at 1 week after HIRI and almost disappeared at 2 weeks after HIRI.The findings of DWI at different b-values were consistent with those at T2WI.There were significant differences in parameters among 6 h,12 h,1 d,3 d,7 d,14 d groups after HIRI and control group (P＜0.05).There were significant differences among 12 h after HIRI,1 d after HIRI,3 d after HIRI and 6 h after HIRI,7 d after HIRI,14 d after HIRI,control group concerning ADCs values respectively (P＜0.05).There were significant differences among 6 h after HIRI,12 h after HIRI,1 d after HIRI,3 d after HIRI,7 d after HIRI and 14 d after HIRI,control group concerning D,D*,f,ADCu,α,DDC,MK values respectively (P＜0.05).In addition,there were significant differences among 1 d after HIRI,3 d after HIRI and 6 h after HIRI,12 h after HIRI,7 d after HIRI and 14 d after HIRI,control group concerning D values respectively (P＜0.05).There were significant differences among 6 h after HIRI,12 h after HIRI,1 d after HIRI,3 d after HIRI and 7 d after HIRI,14 d after HIRI,control group concerning f values respectively (P＜0.05).There were significant differences among 12 h after HIRI and 14 d after HIRI,control group concerning MD values respectively (P＜0.05).There were significant differences between 1 d after HIRI and 14 d after HIRI concerning MD values (P＜0.05).Conclusion Multiple non-gaussian distribution models DWI is superior to conventional DWI in evaluating HIRI model in rats.
ABSTRACT
Oral teeth image segmentation plays an important role in teeth orthodontic surgery and implant surgery. As the tooth roots are often surrounded by the alveolar, the molar's structure is complex and the inner pulp chamber usually exists in tooth, it is easy to over-segment or lead to inner edges in teeth segmentation process. In order to further improve the segmentation accuracy, a segmentation algorithm based on local Gaussian distribution fitting and edge detection is proposed to solve the above problems. This algorithm combines the local pixels' variance and mean values, which improves the algorithm's robustness by incorporating the gradient information. In the experiment, the root is segmented precisely in cone beam computed tomography (CBCT) teeth images. Segmentation results by the proposed algorithm are then compared with the classical algorithms' results. The comparison results show that the proposed method can distinguish the root and alveolar around the root. In addition, the split molars can be segmented accurately and there are no inner contours around the pulp chamber.
Subject(s)
Humans , Algorithms , Computers , Cone-Beam Computed Tomography , Image Processing, Computer-Assisted , Normal Distribution , Tooth , Diagnostic Imaging , Tooth Root , Diagnostic ImagingABSTRACT
OBJECTIVE: A previous study suggested that the p-value of normality test applied to RR intervals is an index able to quantify Heart Rate Variability (HRV) through correlation of traditional time and frequency domain indices. We investigate the association between the p-value of normality test applied in RR intervals and symbolic analysis of HRV. METHOD: We evaluated 32 healthy women between 18 and 30 years old. RR intervals were used for HRV analysis and we performed symbolic analysis, where RR intervals are joined by symbols. Sets of three consecutive symbols (RR intervals) were grouped into four types of clusters, as follows: 1. Three equal symbols (zero variation); 2: two equal, one divergent symbol (one variation); 3. Three different symbols monotonically ascending or descending (two like variations); 4. Three different symbols, forming a peak or a trough (two unlike variations). Frequency of occurrence of each type of cluster was calculated. Normality tests were applied to all RR intervals and the p-value was calculated. We computed the correlations between the p-value of normality test and symbolic analysis of HRV. RESULTS: Correlation coefficients between the p-value of normality test from Kolmorogov-Smirnov test and the four types of clusters showed no correlation for any of them. Likewise, the correlation coefficient index between the p-value of normality test calculated from the Shapiro-Wilk test and symbolic analysis produced no significant results for any of the four types of clusters. CONCLUSION: There was no significant correlation between the p-value of normality test and HRV symbolic analysis. The physiological significance of this result is that the PNT is not related to chaotic behavior of HRV.
OBJETIVO: Um estudo anterior sugeriu que o valor "p" do teste de normalidade (PNT) aplicado a intervalos RR é um índice capaz de quantificar variabilidade da frequência cardíaca através da correlação tradicional dos índices de tempo e frequência. Investigamos a associação entre o teste PNT aplicado a intervalos RR e a análise simbólica da variabilidade da frequência cardíaca. MÉTODO: Foram avaliadas 32 mulheres saudáveis entre 18 e 30 anos de idade. Os intervalos RR foram utilizados para análise da variabilidade da frequência cardíaca; foi realizada uma análise simbólica, onde intervalos RR são unidos por símbolos. Grupos de três símbolos consecutivos foram agrupados em quatro tipos de aglomerados, a saber: 1: três símbolos iguais (variação zero); 2: dois símbolos iguais, um divergente (uma variação); 3: três símbolos diferentes, monotonicamente crescentes ou decrescentes (duas variações iguais); 4: três símbolos diferentes, formando um pico ou uma calha (duas variações desiguais). Foi calculada a frequência de ocorrência de cada tipo de cluster. Testes de normalidade foram aplicados a todos os intervalos RR e o valor de "p" foi calculado. Calculamos as correlações entre o PNT e análise simbólica de HRV. RESULTADOS: Os coeficientes de correlação entre PNT de teste Kolmorogov-Smirnov e os quatro tipos de clusters não mostraram correlação com qualquer um deles. Da mesma forma, o coeficiente de correlação entre o índice PNT calculado a partir do teste de Shapiro-Wilk e a análise simbólica não produziu resultados significativos para qualquer dos quatro tipos de aglomerados. CONCLUSÃO: Não houve correlação significativa entre o PNT e análise simbólica da variabilidade da frequência cardíaca. O significado fisiológico deste resultado é que o PNT não está relacionado ao comportamento caótico da VFC.
Subject(s)
Humans , Female , Adult , Autonomic Nervous System/physiology , Cardiovascular System , Cardiovascular Physiological Phenomena , Normal Distribution , Heart Rate/physiologyABSTRACT
Lambda-Mu-Sigma and Box-Cox Power Exponential are popular methods for constructing centile curves but are difficult to understand for medical professionals. As a result, the methods are used by experts only. Non-experts use software as a blackbox that can lead to wrong curves. This article explains these methods in a simple non-mathematical language so that medical professionals can use them correctly and confidently.
ABSTRACT
Chromatin texture, which partly reflects nuclear organization, is evolving as an important parameter indicating cell activation or transformation. In this study, chromatin pattern was evaluated by image analysis of the electron micrographs of follicular and papillary carcinoma cells of the thyroid gland and tested for discrimination of the two neoplasms. Digital grey images were converted from the electron micrographs; nuclear images, excluding nucleolus and intranuclear cytoplasmic inclusions, were obtained by segmentation; grey levels were standardized; and grey level histograms were generated. The histograms in follicular carcinoma showed Gaussian or near-Gaussian distribution and had a single peak, whereas those in papillary carcinoma had two peaks(bimodal), one at the black zone and the other at the white zone. In papillary carcinoma. the peak in the black zone represented an increased amount of heterochromatin particles and that at the white zone represented decreased electron density of euchromatin or nuclear matrix. These results indicate that the nuclei of follicular and papillary carcinoma cells differ intheir chromatin pattern and the difference may be due to decondensed chromatin and/or matrix substances.