AIM： To evaluate the feasibility and utility of confocal laser endomicroscopy （CLE） in the description of normal gastrointestinal （GI） mucosa and in the diagnosis of GI disorders in children, in comparison to his...AIM： To evaluate the feasibility and utility of confocal laser endomicroscopy （CLE） in the description of normal gastrointestinal （GI） mucosa and in the diagnosis of GI disorders in children, in comparison to histology.METHODS： Forty-four patients （19 female） median age 10.9 years （range 0.7-16.6 years） with suspected or known GI pathology underwent esophago-gastro- duodenoscopy （OGD） （n = 36） and/or ileocolonoscopy （IC） （n = 31） with CLE using sodium fluorescein and acriflavine as contrast agents. Histological sections were compared with same site confocal images by two experienced pediatric and GI histopathologists and endoscopists, respectively.RESULTS： Duodenum and ileum were intubated in all but one patient undergoing OGD and IC. The median procedure time was 16.4 min （range 7-25 rain） for OGD and 27.9 min （range 15-45 min） for IC. A total of 4798 confocal images were compared with 153 biopsies from the upper GI tract from 36 procedures, and 4661 confocal images were compared with 188 biopsies from the ileocolon from 31 procedures.Confocal images were comparable to conventional histology both in normal and in pathological conditions such as esophagitis, Helicobacter pylori gastritis, celiac disease, inflammatory bowel disease, colonic heterotopia, and graft versus host disease.CONCLUSION： CLE offers the prospect of targeting biopsies to abnormal mucosa, thereby increasing diagnostic yield, reducing the number of biopsies, decreasing the burden on the histopathological services, and reducing costs.展开更多
The local defect in rotating machine always gives rise to repetitive transients in the collected vibration signal. However, the transient signature is prone to be contaminated by strong background noises, thus it is a...The local defect in rotating machine always gives rise to repetitive transients in the collected vibration signal. However, the transient signature is prone to be contaminated by strong background noises, thus it is a challenging task to detect the weak transients for machine fault diagnosis. In this paper, a novel adaptive tunable Q-factor wavelet transform(TQWT) filter based feature extraction method is proposed to detect repetitive transients. The emerging TQWT possesses distinct advantages over the classical constant-Q wavelet transforms, whose Q-factor can be tuned to match the oscillatory behavior of different signals, but the parameter selection for TQWT heavily relies on prior knowledge. Within our adaptive TQWT filter algorithm, the automatic optimization techniques for three TQWT parameters are implemented to achieve an optimal TQWT basis that matches the transient components. Specifically, the decomposition level is selected according to a center frequency ratio based stopping criterion, and the Q-factor and redundancy are optimized based on the minimum energy-weighted normalized wavelet entropy.Then, the adaptive TQWT decomposition can be achieved in a sparse way and result in subband signals at various wavelet scales.Further, the optimum subband signal which carries transient feature information, is identified using a normalized energy to bandwidth ratio index. Finally, the single branch reconstruction signal from the optimum subband is obtained with transient signatures via inverse TQWT, and the frequency of repetitive transients is detected using Hilbert envelope demodulation. It has been verified via numerical simulation that the proposed adaptive TQWT filter based feature extraction method can adaptively select TQWT parameters and the optimum subband for repetitive transient detection without prior knowledge. The proposed method is also applied to faulty bearing vibration signals and its effectiveness is validated.展开更多
基金Supported by Peel Research Foundation and Yorkshire Cancer ResearchThe Egyptian Cultural Bureau
文摘AIM： To evaluate the feasibility and utility of confocal laser endomicroscopy （CLE） in the description of normal gastrointestinal （GI） mucosa and in the diagnosis of GI disorders in children, in comparison to histology.METHODS： Forty-four patients （19 female） median age 10.9 years （range 0.7-16.6 years） with suspected or known GI pathology underwent esophago-gastro- duodenoscopy （OGD） （n = 36） and/or ileocolonoscopy （IC） （n = 31） with CLE using sodium fluorescein and acriflavine as contrast agents. Histological sections were compared with same site confocal images by two experienced pediatric and GI histopathologists and endoscopists, respectively.RESULTS： Duodenum and ileum were intubated in all but one patient undergoing OGD and IC. The median procedure time was 16.4 min （range 7-25 rain） for OGD and 27.9 min （range 15-45 min） for IC. A total of 4798 confocal images were compared with 153 biopsies from the upper GI tract from 36 procedures, and 4661 confocal images were compared with 188 biopsies from the ileocolon from 31 procedures.Confocal images were comparable to conventional histology both in normal and in pathological conditions such as esophagitis, Helicobacter pylori gastritis, celiac disease, inflammatory bowel disease, colonic heterotopia, and graft versus host disease.CONCLUSION： CLE offers the prospect of targeting biopsies to abnormal mucosa, thereby increasing diagnostic yield, reducing the number of biopsies, decreasing the burden on the histopathological services, and reducing costs.
基金supported by the National Natural Science Foundation of China (Grant Nos. 51335006 & 51605244)
文摘The local defect in rotating machine always gives rise to repetitive transients in the collected vibration signal. However, the transient signature is prone to be contaminated by strong background noises, thus it is a challenging task to detect the weak transients for machine fault diagnosis. In this paper, a novel adaptive tunable Q-factor wavelet transform(TQWT) filter based feature extraction method is proposed to detect repetitive transients. The emerging TQWT possesses distinct advantages over the classical constant-Q wavelet transforms, whose Q-factor can be tuned to match the oscillatory behavior of different signals, but the parameter selection for TQWT heavily relies on prior knowledge. Within our adaptive TQWT filter algorithm, the automatic optimization techniques for three TQWT parameters are implemented to achieve an optimal TQWT basis that matches the transient components. Specifically, the decomposition level is selected according to a center frequency ratio based stopping criterion, and the Q-factor and redundancy are optimized based on the minimum energy-weighted normalized wavelet entropy.Then, the adaptive TQWT decomposition can be achieved in a sparse way and result in subband signals at various wavelet scales.Further, the optimum subband signal which carries transient feature information, is identified using a normalized energy to bandwidth ratio index. Finally, the single branch reconstruction signal from the optimum subband is obtained with transient signatures via inverse TQWT, and the frequency of repetitive transients is detected using Hilbert envelope demodulation. It has been verified via numerical simulation that the proposed adaptive TQWT filter based feature extraction method can adaptively select TQWT parameters and the optimum subband for repetitive transient detection without prior knowledge. The proposed method is also applied to faulty bearing vibration signals and its effectiveness is validated.
