Background/objectives: This study pioneers the application of the ViPLUS module, utilizing plane-wave ultrasound to measure breast tissue viscosity and elasticity. The primary goal was to establish normal reference values for viscosity in healthy women. Secondary objectives included exploring correlations between breast tissue viscosity and breast density categories, hormonal influences, and menstrual cycle phases.

Methods: A prospective study was conducted on 245 asymptomatic women. Viscosity and elasticity measurements were obtained using the ViPLUS module, ensuring high reliability with stringent quality control measures. Data were statistically analyzed to evaluate correlations and group differences.

Results: The median viscosity value for normal breast parenchyma was 1.7 Pa.s, with no significant variations based on breast density, menopausal status, or menstrual cycle phase. A strong correlation (rho = 0.866, < 0.001) was observed between elasticity and viscosity values.

Conclusions: The findings suggest that breast viscosity is consistent across diverse physiological states, indicating its potential as an independent diagnostic marker. This parameter could be pivotal in future breast cancer screening strategies, especially for younger women and those with dense breasts.

Download full-text PDF

Source
http://dx.doi.org/10.3390/cancers17020237DOI Listing

Publication Analysis

Top Keywords

plane-wave ultrasound
8
breast
8
viplus module
8
breast tissue
8
tissue viscosity
8
viscosity elasticity
8
breast density
8
menstrual cycle
8
viscosity
7
vi-plus pioneering
4

Similar Publications

Background/objectives: This study pioneers the application of the ViPLUS module, utilizing plane-wave ultrasound to measure breast tissue viscosity and elasticity. The primary goal was to establish normal reference values for viscosity in healthy women. Secondary objectives included exploring correlations between breast tissue viscosity and breast density categories, hormonal influences, and menstrual cycle phases.

View Article and Find Full Text PDF

Ultrasound blood flow imaging plays a crucial role in the diagnosis of cardiovascular and cerebrovascular diseases. Conventional ultrafast ultrasound plane-wave imaging techniques have limited capabilities in microvascular imaging. To enhance the quality of blood flow imaging, this study proposes a microbubble-based H-Scan ultrasound imaging technique.

View Article and Find Full Text PDF

Objective: Conventional coherent plane wave compounding (CPWC) and sum-of-square power Doppler (PD) estimation lead to low contrast and high noise level in ultrafast PD imaging when the number of plane-wave angle and the ensemble length is limited. The coherence-based PD estimation using temporal-multiply-and-sum (TMAS) of high-lag autocorrelation can effectively suppress the uncorrelated noises but at the cost of signal power due to the blood flow decorrelation.

Methods: In this study, the TMAS PD estimation is incorporated with complementary subset transmit in nonlinear compounding (DMAS-CST) to leverage the signal coherence in both angular and temporal dimensions for improvement of PD image quality.

View Article and Find Full Text PDF

Plane wave (PW) imaging is fast, but limited by poor imaging quality. Coherent PW compounding (CPWC) improves image quality but decrease frame rate. In this study, we propose a modified CycleGAN model that combines a residual attention module with a space-frequency dual-domain discriminator, termed RADD-CycleGAN, to rapidly reconstruct high-quality ultrasound images.

View Article and Find Full Text PDF
Article Synopsis
  • Ultrasound microvascular imaging (UMI) techniques like ultrafast power Doppler imaging (uPDI) and ultrasound localization microscopy (ULM) face challenges with low image quality due to noise from plane wave transmissions.
  • The study introduces a deep learning model called Yformer, which combines convolution and Transformer architectures to enhance UMI by effectively estimating noise and signal power, leading to improved image quality and lower computational costs.
  • In vivo tests on rat brains show that Yformer achieves high structural similarity (SSIM > 0.95) and significantly increases the resolution of liver ULM, demonstrating excellent adaptability across various datasets.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!