RESUMO
Sonoelastography is an ultrasonic technique that uses Kasai's autocorrelation algorithms to generate qualitative images of tissue elasticity using external mechanical vibrations. In the absence of synchronization between the mechanical vibration device and the ultrasound system, the random initial phase and finite ensemble length of the data packets result in temporal artifacts in the sonoelastography frames and, consequently, in degraded image quality. In this work, the analytic derivation of an optimal selection of acquisition parameters (i.e., pulse repetition frequency, vibration frequency, and ensemble length) is developed in order to minimize these artifacts, thereby eliminating the need for complex device synchronization. The proposed rule was verified through experiments with heterogeneous phantoms, where the use of optimally selected parameters increased the average contrast-to-noise ratio (CNR) by more than 200% and reduced the CNR standard deviation by 400% when compared to the use of arbitrarily selected imaging parameters. Therefore, the results suggest that the rule for specific selection of acquisition parameters becomes an important tool for producing high quality sonoelastography images.
RESUMO
Currently, the evaluation of thyroid cancer relies on the use of fine-needle aspiration biopsy, as non-invasive imaging methods do not provide sufficient levels of accuracy for the diagnosis of this disease. In this study, the potential of quantitative ultrasound methods for characterization of thyroid tissues was studied using a rodent model ex vivo. A high-frequency ultrasonic scanning system (40 MHz) was used to scan thyroids extracted from mice that had spontaneously developed thyroid lesions (cancerous or benign). Three sets of mice were acquired having different predispositions to developing three thyroid anomalies: C-cell adenoma, papillary thyroid carcinoma (PTC) and follicular variant papillary thyroid carcinoma (FV-PTC). A fourth set of mice that did not develop thyroid anomalies (normal mice) were used as controls. The backscatter coefficient was estimated from excised thyroid lobes the different mice. From the backscatter coefficient versus frequency (25-45 MHz), the effective scatterer diameter (ESD) and effective acoustic concentration (EAC) were estimated. From the envelope of the backscattered signal, the homodyned K distribution was used to estimate the k parameter (ratio of coherent to incoherent signal energy) and the µ parameter (number of scatterers per resolution cell). Statistically significant differences were observed between cancerous thyroids and normal thyroids based on the ESD, EAC and µ parameters. The mean ESD values were 18.0 ± 0.92, 15.9 ± 0.81 and 21.5 ± 1.80 µm for the PTC, FV-PTC and normal thyroids, respectively. The mean EAC values were 59.4 ± 1.74, 62.7 ± 1.61 and 52.9 ± 3.42 dB (mm(-3)) for the PTC, FV-PTC and normal thyroids, respectively. The mean µ values were 2.55 ± 0.37, 2.59 ± 0.43 and 1.56 ± 0.99 for the PTC, FV-PTC and normal thyroids, respectively. Statistically significant differences were observed between cancerous thyroids and C-cell adenomas based on the ESD and EAC parameters, with an estimated ESD value of 21.3 ± 1.50 µm and EAC value of 54.7 ± 2.24 dB mm(-3) for C-cell adenomas. These results suggest that high-frequency quantitative ultrasound may enhance the ability to detect and classify diseased thyroid tissues.