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An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians.Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands.In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering.While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney's actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses.

Purpose This study evaluated the usefulness of intraprocedural contrast-enhanced ultrasound (CEUS)/ultrasound (US) fusion imaging using a perflubutane-based contrast agent combined with preprocedural auto sweep three-dimensional US to obtain volume data for guidance and evaluation of the therapeutic efficacy of radiofrequency ablation (RFA). Methods This uncontrolled clinical trial included 50 hepatocellular carcinomas (HCCs) with a mean diameter of 15.3 mm that had been treated by RFA. The efficacy of RFA was evaluated by CEUS/US fusion imaging during the procedure. If the ablation was deemed to be inadequate, further ablation was performed until adequate ablation was achieved. Contrast-enhanced computed tomography (CECT) or contrast-enhanced magnetic resonance imaging (CEMRI) was performed a month after RFA, and the images obtained using each modality were reviewed to evaluate the efficacy of RFA. Results Thirty-three of the 50 lesions were evaluated by CEUS/US fusion imaging as having been adequately ablated after the first RFA procedure. The ablation was evaluated as inadequate in the remaining 17 lesions, for which additional ablation was performed. Ninety-eight (49/50) of all HCCs were evaluated as having been eventually adequately ablated on intraprocedural CEUS/US fusion imaging. The concordance rate for evaluations between intraprocedural CEUS/US fusion imaging and CECT/CEMRI performed 1 month after RFA was 88% (44/50). The kappa value for agreement between the two methods of evaluation was 0.792. Conclusion Intraprocedural fusion imaging combining CEUS and auto sweep three-dimensional US appears to be a useful modality for RFA guidance and evaluation of therapeutic efficacy of RFA in patients with HCC.

High-intensity focused ultrasound (HIFU) was experimentally used for focal therapy for anti-cancer effects in prostate cancer (PC). Focal therapy is a diagnosis-based investigational treatment option for localized PC that cures clinically significant PC (csPC) while preserving the anatomical structures related to urinary and sexual function based on its spread observed using multi-parametric magnetic resonance imaging (mpMRI). The European Association of Urology indicated that the current status of focal therapy for localized PC was an investigational modality and encouraged prospective recording of outcomes and recruitment of suitable patients in 2018. During the last few years, large-population multi- and single-center prospective studies have investigated focal therapy as a treatment strategy for localized PC. In a multicenter prospective study with 5-year follow-up, failure-free survival, which was defined as avoidance of local salvage therapy (surgery or radiotherapy), systemic therapy, metastases, and prostate cancer-specific death, was 88%. In the previous studies, there was no significant influence on urinary function before and at 3 months after the treatment, although transient impairment was reported 1 month after the treatment. Pad- and leak-free continence was preserved in 80-100% of the patients after treatment. Erectile function was significantly impaired in the initial 3 months after treatment compared to the pretreatment values, but it improved 6 months after the focal therapy in the previous reports. Paired comparison studies and cohort studies with long-term follow-up will contribute to verifying this treatment's clinical outcomes for patients with localized PC.

Robotic medical ultrasound can support diagnostics and alleviate fatigues. However, state-of-the-art ultrasound devices are too large and complex for home healthcare. Moreover, organs move in accordance with respiration. This movement changes the ultrasound image pattern and it is difficult for the operator to diagnose easily and accurately. To cope with these problems, the authors newly developed a compact portable ultrasound diagnostic robot for home healthcare, which compensate the organ motion. It can support those who find it difficult to visit a hospital for temporal, spatial, or physical reasons. A robust template matching method to servo the target was applied. Specifically, this robot moves to the target, whose position is detected by finding and identifying the image position of the target in the real-time input images. The authors also applied a multi-threading algorithm with two threads to enhance the real-time performance. One is for image processing with template matching. Another is for robot control to servo the target. Experimental results show that their proposed robot and algorithms can be suppressed to 25.2% motion in pk-to-pk for the periodic phantom organ motion (period of 3 s and of pk-to-pk 40 mm).

Diagnosis of liver fibrosis is important for establishing treatment and assessing the risk of carcinogenesis. Ultrasound imaging is an excellent diagnostic method as a screening test in terms of non-invasiveness and simplicity. The purpose of this study was to automatically diagnose liver fibrosis using ultrasound images to reduce the burden on physicians. We proposed and implemented a system for extracting regions of liver parenchyma utilizing U-Net. Using regions of interest, the stage of fibrosis was classified as F0, F1, F2, F3, or F4 utilizing CORALNet, an ordinal regression model based on ResNet18. The effectiveness of the proposed system was verified. The system implemented using U-Net had a maximum mean Dice coefficient of 0.929. The results of classification of liver fibrosis utilizing CORALNet had a mean absolute error (MAE) of 1.22 and root mean square error (RMSE) of 1.60. The per-case results had a MAE of 1.55 and RMSE of 1.34. U-Net extracted regions of liver parenchyma from the images with high accuracy, and CORALNet showed effectiveness using ordinal information to classify fibrosis in the images. As a future task, we will study a model that is less dependent on teaching data.

Noise-free ultrasound images are essential for organ monitoring during regional ultrasound-guided therapy. When the affected area is located under the ribs, however, acoustic shadow is caused by the reflection of sound from hard tissues such as bone, and the image is output with missing information in this region. Therefore, in the present study, we attempt to complement the image in the missing area. The overall flow of the complementation method to generate a shadow-free composite image is as follows. First, we constructed a binary classification method for the presence or absence of acoustic shadow on a phantom kidney based on a convolutional neural network. Second, we created a composite shadow-free image by searching for a suitable image from a time-series database and superimposing the corresponding area without shadow onto the missing area of the target image. In addition, we constructed and verified an automatic kidney mask generation method utilizing U-Net. The complementation accuracy for kidney tracking could be enhanced by template matching. Zero-mean normalized cross-correlation (ZNCC) values after complementation were higher than that of before complementation under four different data generation conditions: (i) changing the position of the bed of the robotic ultrasound diagnostic system in the translational direction, (ii) changing the probe angle in the translational direction, (iii) with the addition of rotational motion of the probe to condition (ii). Although there was large variation in the shape of the kidney contour in condition (iii), the proposed method improved the ZNCC value from 0.5437 to 0.5807. The effectiveness of the proposed method was demonstrated in phantom experiments. Verification of its effectiveness in real organs is necessary in future study.

To estimate the role of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid-enhanced MRI (EOB-MRI) in predicting hypervascularization outcome of non-hypervascular hypointense hepatic lesions in high-risk patients for hepatocellular carcinoma (HCC). Under the premise of non-hyperenhance in arterial phase (AP) and hypointensity in hepatobiliary phase (HBP) of EOB-MRI, 29 fresh lesions from 22 patients with chronic viral hepatitis (median (range) age: 69(57–82) years) were prospectively enrolled. During continuously followed-up by EOB-MRI, lesional vascularity in AP, the signal intensity (SI) ratios of lesions-to-parenchyma in HBP images (post-contrast ratio) and adjusted enhancement with reference of unenhanced images (EOB enhancement ratio) were examined. After 644 (220–2912) days of follow-up, 20 lesions changed into hyperenhancement in AP of EOB-MRI (hypervascularized group), while nine remained non-hyperenhanced (maintained non-hypervascular group). There is no statistical difference of post-contrast ratio at the initial detection. The post-contrast ratios in hypervascularized group were different between each follow-up time point when followed-up ≥ three (P

Background & aims To explore the ability of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI)/ultrasound (US) fusion imaging (FI) to improve the prognosis of radiofrequency ablation (RFA) by ablating the characteristic findings of hepatocellular carcinoma (HCC) in hepatobiliary phase (HBP) imaging. Methods We retrospectively recruited 115 solitary HCC lesions with size of (15.9 ± 4.6) mm. They were all treated by RFA and preoperative EOB-MRI. According to the modalities guiding RFA performance, the lesions were grouped into contrast enhanced US (CEUS)/US guidance group and EOB-MRI/US FI guidance group. For the latter group, the ablation scope was set to cover the HBP findings (peritumoral hypointensity and irregular protruding margin). The presence of HBP findings, the modalities guided RFA, the recurrence rate were observed. Results After an average follow-up of 377 days, local tumor progression (LTP) and intrahepatic distant recurrence (IDR) were 14.8% and 38.4%, respectively. The lesions having HBP findings exhibited a higher recurrence rate (73.7%) than the lesions without HBP findings (42.9%) (p = 0.002) and a low overall recurrence-free curve using the Kaplan-Meier method (p = 0.038). Using EOB-MRI/US FI as guidance, there was no difference in the recurrence rate between the groups with and without HBP findings (p = 0.799). In lesions with HBP findings, RFA guided by EOB-MRI/US FI (53.8%) produced a lower recurrence rate than CEUS/US (84.0%) (p = 0.045). Conclusions The intraprocedurally application of EOB-MRI/US FI to determine ablation scope according to HBP findings is feasible and beneficial for prognosis of RFA.

INTRODUCTION AND OBJECTIVE:To evaluate the usefulness of a computer-aided diagnosis system (CAD) in evaluating the severity of benign prostatic hyperplasia (BPH) using a super-ellipse model to char...

Purpose This paper examines the feasibility of automated alignment in sagittal direction in MR-TRUS fusion biopsy of the prostate by comparing the prostate contour in axial images between different modalities. In the treatment of prostate cancer, an important factor affecting the prognosis of patients is focal therapy of cancer within the prostate. Therefore, MR-TRUS fusion biopsy of the prostate is attracting attention as one of the most effective localization techniques. Because the accuracy of this biopsy is highly dependent on the doctor performing it, automation should reduce variability in diagnostic performance. Method The MR image is scaled to the same scale as the TRUS image, and the contours of the prostate on the MR and TRUS images are compared in polar coordinates. In addition, this method makes it possible to perform a robust comparison against deformation by comparing specific angle ranges. It is also possible to improve the accuracy of error calculation by accumulating contour data. Result The axial image selected by the proposed method using the prostate contour obtained from the doctor-labeled segmentation image has an error of about 4 mm in the sagittal direction on average compared to the axial image selected by the doctor did. Furthermore, using the inaccurate prostate contours obtained by performing segmentation with U-Net only slightly reduced the accuracy. In addition, it was found that alignment accuracy is improved by using the angler weight. Conclusion It has been shown that sagittal alignment can be performed with some degree of accuracy using only axial images. Also, the angular weight values used indicate that when comparing axial images, it may be an important factor in determining the same axial cross section to compare the parts that deform due to probe pressure.

The main purpose of this study is to construct a system to track the tumor position during radiofrequency ablation (RFA) treatment. Existing tumor tracking systems are designed to track a tumor in a two-dimensional (2D) ultrasound (US) image. As a result, the three-dimensional (3D) motion of the organs cannot be accommodated and the ablation area may be lost. In this study, we propose a method for estimating the 3D movement of the liver as a preliminary system for tumor tracking. Additionally, in current 3D movement estimation systems, the motion of different structures during RFA could reduce the tumor visibility in US images. Therefore, we also aim to improve the estimation of the 3D movement of the liver by improving the liver segmentation. We propose a novel approach to estimate the relative 6-axial movement (x, y, z, roll, pitch, and yaw) between the liver and the US probe in order to estimate the overall movement of the liver. We used a convolutional neural network (CNN) to estimate the 3D displacement from two-dimensional US images. In addition, to improve the accuracy of the estimation, we introduced a segmentation map of the liver region as the input for the regression network. Specifically, we improved the extraction accuracy of the liver region by using a bi-directional convolutional LSTM U-Net with densely connected convolutions (BCDU-Net). By using BCDU-Net, the accuracy of the segmentation was dramatically improved, and as a result, the accuracy of the movement estimation was also improved. The mean absolute error for the out-of-plane direction was 0.0645 mm/frame. The experimental results show the effectiveness of our novel method to identify the movement of the liver by BCDU-Net and CNN. Precise segmentation of the liver by BCDU-Net also contributes to enhancing the performance of the liver movement estimation.