山口 匡

Abstract We conducted a fundamental study to elucidate the relationship between acoustic and electrical properties in the context of liver steatosis. The speed of sound, attenuation coefficient, conductivity, and relative permittivity were measured in rat livers with varying degrees of fat deposition. Fat deposition result in a decrease in the speed of sound, an increase in the attenuation coefficient, and reductions in conductivity and relative permittivity. However, no linear correlation was observed between these properties and fat content or droplet size individually. However, a notable correlation between changes in acoustic and electrical properties was identified when the structural and organizational effects of fat were considered in combination. Especially, attenuation changes were found to correlate with corresponding changes in electrical properties. These findings underscore the importance of comprehensively considering structural factors, such as fat droplet size and distribution, to better understand the physical mechanisms underlying the relationship between acoustic and electrical properties.

Hemorheological properties, such as erythrocyte aggregation can be assessed by ultrasonic backscatter coefficient analysis. In this study, a data-acquisition sequence with dual-frequency (dual-f) excitation was proposed to expand the ultrasonic frequency bandwidth with high-frame-rate imaging. The approach was experimentally validated using ex vivo porcine blood measurements and in vivo human imaging. The center frequency of the excitation wave was alternated between 7.8 (f1) and 12.5 (f2) MHz in the frequency spectral analysis using the reference phantom method. The frequency spectra revealed that the dual-f sequence achieved a bandwidth of 4.5-15 MHz at -20 dB, almost equivalent to those achieved with conventional single-frequency excitation (5.0-15 MHz) with a short-duration wave at 10 MHz (mono-f) in reference media with the sufficient condition of signal-to-noise ratio. The aggregation and disaggregation states of porcine blood suspended in high-molecular-weight dextran were determined by the isotropic diameter and packing factor using the structure factor size estimator. The discrimination performance of the dual-f approach increased, owing to the broadband frequency responses, in contrast with the limited performance of mono-f due to a low signal-to-noise ratio. This approach incorporating dual-f sequence is beneficial for obtaining robustly frequency spectra of hemorheological properties from in vivo scenarios.

Abstract Purpose Early detection and quantitative evaluation of liver steatosis are crucial. Therefore, this study investigated a method for classifying ultrasound images to fatty liver grades based on echo-envelope statistics (ES) and convolutional neural network (CNN) analyses. Methods Three fatty liver grades, i.e., normal, mild, and moderate-to-severe, were defined using the thresholds of the magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF). There were 10 cases of each grade, totaling 30 cases. To visualize the texture information affected by the deposition of fat droplets within the liver, the maps of first- and fourth-order moments and the heat maps formed from both moments were employed as parametric images derived from the ES. Several dozen to hundreds of regions of interest (ROIs) were extracted from the liver region in each parametric image. A total of 7680 ROIs were utilized for the transfer learning of a pretrained VGG-16 and classified using the transfer-learned VGG-16. Results The classification accuracies of the ROIs in all types of the parametric images were approximately 46%. The fatty liver grade for each case was determined by hard voting on the classified ROIs within the case. In the case of the fourth-order moment maps, the classification accuracy of the cases through hard voting mostly increased to approximately 63%. Conclusions The formation of parametric images derived from the ES and the CNN classification of the parametric images were proposed for the quantitative diagnosis of liver steatosis. In more than 60% of the cases, the fatty liver grade could be estimated solely using ultrasound images.

The objective of this work is to address the need for versatile and effective tissue characterization in abdominal ultrasound diagnosis using a simpler system. We evaluated the backscattering coefficient (BSC) of several tissue-mimicking phantoms utilizing three different ultrasonic probes: a single-element transducer, a linear array probe for clinical use, and a laboratory-made annular array probe. The single-element transducer, commonly used in developing fundamental BSC evaluation methods, served as a benchmark. The linear array probe provided a clinical comparison, while the annular array probe was tested for its potential in high-frequency and high-resolution ultrasonic observations. Our findings demonstrate that the annular array probe meets clinical demands by providing accurate BSC measurements, showcasing its capability for high-frequency and high-resolution imaging with a simpler, more versatile system.

Abstract Analysis of the envelope statistics of ultrasound echo signals contributes to quantitative tissue characterization in medical ultrasound. Many probability distribution model functions have been studied, and the model function that should be used for tissue characterization depends on the type of disease, even in the same organ. Thus, an appropriate model selection is important for an accurate diagnosis. In this study, we aimed to select a model using threshold processing for modeling errors instead of a simple selection by minimizing the modeling error. For this purpose, we compared several indicators of modeling errors using random number simulations, ultrasonic simulation, and phantom experiment. The results validated that the Mahalanobis distance of moments is an appropriate indicator because it enables the use of a constant threshold value, regardless of the type of model function and data length.