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1.
Opt Express ; 27(23): 33027-33039, 2019 Nov 11.
Artigo em Inglês | MEDLINE | ID: mdl-31878377

RESUMO

An atomic magnetometer operated with elliptically polarized light is investigated theoretically and experimentally. To explore the potential of this magnetometric configuration, the analytical form of the outgoing signal is derived. Parameters that significantly influence the performance are optimized, which lead to a sensitivity of 300 fT/Hz at 45 ∘C with a 2×2×2 cm uncoated Rb vapor cell. It is remarkable that a sensitivity of 690 fT/Hz is achieved at room temperature of 24 ∘C, which is improved by an order of magnitude compared with the conventional Mx magnetometer under its own optimized condition. The elliptically polarized approach offers attractive features for developing compact, low-power magnetometers, which are available without heating the uncoated vapor cell.

2.
Comput Med Imaging Graph ; 57: 29-39, 2017 04.
Artigo em Inglês | MEDLINE | ID: mdl-28062170

RESUMO

Intravascular ultrasound (IVUS) has been well recognized as one powerful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. In this paper, we use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features were used as the input data to the ANN method, and then the different vascular layers were distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results showed that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error between two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images.


Assuntos
Túnica Adventícia/citologia , Túnica Adventícia/diagnóstico por imagem , Vasos Coronários/citologia , Vasos Coronários/diagnóstico por imagem , Interpretação de Imagem Assistida por Computador/métodos , Ultrassonografia/métodos , Túnica Adventícia/patologia , Vasos Coronários/patologia , Humanos , Patologia Clínica/métodos , Placa Aterosclerótica/diagnóstico por imagem , Placa Aterosclerótica/patologia , Projetos de Pesquisa
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