Journal of Medical Ultrasonics最新文献

Purpose: Renal circulation evaluation is essential in understanding the cardiorenal relationship in heart failure (HF), and there is a growing interest in imaging techniques that visualize renal circulation. This study aimed to assess the effectiveness of superb microvascular imaging (SMI) in evaluating renal circulation in HF patients.

Method: The study included 71 HF patients undergoing cardiac catheterization. Prior to catheterization, renal ultrasound examinations were performed. A control group of 18 subjects without HF was also included. SMI was used to measure the vascular index (VI), which was calculated as the percentage of blood flow signal area in the region of interest. The intrarenal perfusion index (IRPI) was determined as a fluctuation index of VI, reflecting variations in the number of blood cells moving through renal tissue during the cardiac cycle.

Results: Using the upper 95% confidence interval of IRPI (0.6) from the control group, HF patients were classified into two groups. Patients with IRPI > 0.6 showed a more congestive profile. Right atrial pressure and biphasic or monophasic Doppler intrarenal flow pattern were independent determinants of IRPI > 0.6. In addition, IRPI remained a significant predictor of estimated glomerular filtration rate (eGFR).

Conclusion: The parameter IRPI as variations in SMI signal during the cardiac cycle may be a useful evaluation method for renal perfusion impairment in HF.

Endoscopic ultrasound-guided fine-needle aspiration (EUS-FNA) is the first-choice procedure for obtaining pathological tissue samples from gastrointestinal (GI) subepithelial lesions (SELs). However, its diagnostic accuracy is lower than that for pancreatic masses owing to puncture difficulty and the need for immunostaining for definitive diagnosis. The advent of fine-needle biopsy needles, which have become well known in recent years, improves the diagnostic accuracy of EUS-FNA for GI SELs. The forward-viewing echoendoscope and rapid on-site evaluation (ROSE) have also helped to improve diagnostic accuracy. Furthermore, in facilities where ROSE is not available, endosonographers perform a macroscopic on-site evaluation. With these procedural innovations, EUS-FNA is now performed aggressively even for SELs smaller than 20 mm. The incidence of procedure-related adverse events such as bleeding and infection is low, and thus, EUS-FNA can be safely performed to diagnose SELs.

Purpose: Renal congestion is a therapeutic target in congestive heart failure. However, its detailed evaluation in a clinical setting is challenging. This study sought to assess renal congestion impairment using superb microvascular imaging (SMI), a simple and accessible method.

Methods: Dahl salt-sensitive rats, used as a model for congestive heart failure, underwent central venous pressure (CVP) measurements. Renal congestion was evaluated through measurements of renal medullary pressure (RMP) and assessment of renal perfusion using contrast-enhanced ultrasonography at both the early (control group) and heart failure phases (HF group). All rats were assessed with SMI. The region of interest (ROI) was set in interlobular vessels, interlobar vessels, and a combination of these areas. The area ratio was calculated from the color pixel count in the ROI divided by the total pixel count in the ROI. Intrarenal perfusion index (IRPI) was defined as (maximum area ratio-minimum area ratio) / maximum area ratio.

Results: There were no significant differences in renal function and left ventricular ejection fraction between the two groups. CVP, time-to-peak (TTP) in the medulla, and RMP were higher in the HF group than in the control group. In the HF group, IRPI, evaluated in the interlobular vessels, was significantly higher than in the control group. IRPI was positively correlated with TTP in the medulla (p = 0.028, R = 0.60) and RMP (p < 0.001, R = 0.84), indicating that IRPI reflected renal congestion.

Conclusions: IRPI is a useful tool for assessing renal congestion in rats with congestive heart failure.

Purpose: Panoramic ultrasound is one of the recently introduced ultrasound evaluation techniques. We herein examined the relationship between the cross-sectional area of the rectus femoris muscle on panoramic ultrasound and its volume based on the gold standard computed tomography (CT) evaluation.

Methods: This was a single-center prospective observational study. A panoramic ultrasound assessment of the cross-sectional area of the rectus femoris muscle and a simple CT evaluation of its volume were performed on days 1 and 7 of hospitalization. Physical functions were assessed at discharge.

Results: Twenty patients were examined. The rate of change in the cross-sectional area of the rectus femoris muscle on panoramic ultrasound correlated with that in its volume on CT (correlation coefficient 0.59, p = 0.0061). In addition, a correlation was observed between the absolute value for the rectus femoris muscle cross-sectional area on panoramic ultrasound and physical functions at discharge. Rectus femoris muscle distances did not correlate with either.

Conclusion: In the acute phase of critical illness, the cross-sectional area of the rectus femoris muscle on panoramic images correlated with its volume on CT and, thus, it is a valid method for assessing muscle mass.

Purpose: Vascular distribution is important information for diagnosing diseases and supporting surgery. Photoacoustic imaging is a technology that can image blood vessels noninvasively and with high resolution. In photoacoustic imaging, a hemispherical array sensor is especially suitable for measuring blood vessels running in various directions. However, as a hemispherical array sensor, a sparse array sensor is often used due to technical and cost issues, which causes artifacts in photoacoustic images. Therefore, in this study, we reduce these artifacts using deep learning technology to generate signals of virtual dense array sensors.

Methods: Generating 2D virtual array sensor signals using a 3D convolutional neural network (CNN) requires huge computational costs and is impractical. Therefore, we installed virtual sensors between the real sensors along the spiral pattern in three different directions and used a 2D CNN to generate signals of the virtual sensors in each direction. Then we reconstructed a photoacoustic image using the signals from both the real sensors and the virtual sensors.

Results: We evaluated the proposed method using simulation data and human palm measurement data. We found that these artifacts were significantly reduced in the images reconstructed using the proposed method, while the artifacts were strong in the images obtained only from the real sensor signals.

Conclusion: Using the proposed method, we were able to significantly reduce artifacts, and as a result, it became possible to recognize deep blood vessels. In addition, the processing time of the proposed method was sufficiently applicable to clinical measurement.