A three-dimensional (3D) transrectal ultrasound (TRUS) imaging system is presented in this paper. The 3D imaging system is used for diagnosing diseases of prostate. The 3D image is reconstructed by a series of two-dimensional image data which is obtained through rectum. It can be a guide to prostate needle biopsies. The system is built by two parts: hardware and software. In the hardware, the mechanical device, stepper motor, control circuit, B Mode TRUS and personal computer (PC) workshop are presented. The software includes the firmware of micro control unit and software of the PC workshop. In order to evaluate the performance of the 3D imaging system, we did experiments with water and agar phantoms, and the results demonstrated the system's ability of 3D imaging with high-precision.
Mitral valve disease is one of the most popular heart valve diseases. Precise positioning and displaying of the valve characteristics is necessary for the minimally invasive mitral valve repairing procedures. This paper presents a multi-resolution elastic registration method to compute the deformation functions constructed from cubic B-splines in three dimensional ultrasound images, in which the objective functional to be optimized was generated by maximum likelihood method based on the probabilistic distribution of the ultrasound speckle noise. The algorithm was then applied to register the mitral valve voxels. Numerical results proved the effectiveness of the algorithm.
In this paper, we propose an image-based key frame gating method to reduce motion artifacts in intravascular ultrasound (IVUS) longitudinal cuts. The artifacts are mainly caused by the periodic relative displacement between blood vessels and the IVUS catheter due to cardiac motion. The method is achieved in four steps as following. Firstly, we convert IVUS image sequences to polar coordinates to cut down the amount of calculation. Secondly, we extracted a one-dimensional signal cluster reflecting cardiac motion by spectral analysis and filtering techniques. Thirdly, we designed a Butterworth band-pass filter for filtering the one-dimensional signal clusters. Fourthly, we retrieved the extremes of the filtered signal clusters to seek key frames to compose key-frames gated sequences. Experimental results showed that our algorithm was fast and the average frame processing time was 17ms. Observing the longitudinal viewpictures, we found that comparing to the original ones, the gated sequences had similar trend, less saw tooth shape, and good continuity. We selected 12 groups of clinical IVUS sequences[images (876±65 frames), coronary segments length (14.61±1.08 mm)] to calculate vessel volume, lumen volume, mean plaque burden of the original and gated sequences. Statistical results showed that, on one hand, both vessel volume and lumen volume measured of the gated sequences were significantly smaller than those of the original ones, and there was no significant difference on mean plaque burden between original and gated sequences, which met the need of the clinical diagnosis and treatment. On the other hand, variances of vessel area and lumen area of the gated sequences were significantly smaller than those of the original sequences, indicating that the gated sequences would be more stable than the original ones.
This paper explored the feasibility of using ultrasonic Nakagami statistic parameter imaging to evaluate the thermal lesion induced by microwave ablation (MWA) in porcine models. In this paper, thermal lesions were induced in livers and kidneys in 5 swines using a clinical MWA system. During this treatment progress, ultrasonic radiofrequency (RF) data were collected. The dynamic changes of Nakagami parameter in the thermal lesion were calculated, and the ultrasonic B-mode images and Nakagami images were reconstructed simultaneously. The contrast-to-noise ratio (CNR) between the thermal lesion and the surrounding normal tissue was calculated over the MWA procedure. After MWA, a bright hyperechoic region appeared in the ultrasonic Nakagami image as an indicator of the thermal lesion and this bright spot enlarged with lesion development during MWA exposure. The mean value of Nakagami parameter in the liver and kidney increased from 0.78 and 0.79 before treatment to 0.91 and 0.92 after treatment, respectively. During MWA exposure, the mean values of CNR calculated from the Nakagami parameter increased from 0.49 to 1.13 in the porcine liver and increased from 0.51 to 0.85 in the kidney, which were both higher than those calculated from the B-mode images. This in vivo study on porcine models suggested that the ultrasonic Nakagami imaging may provide an alternative modality for monitoring MWA treatment.
Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.
Objective To explore the potential indicators of cervical lymph node metastasis in papillary thyroid microcarcinoma (PTMC) patients and to develop a nomogram model. Methods The clinicopathologic features of PTMC patients in the SEER database from 2004 to 2015 and PTMC patients who were admitted to the Center for Thyroid and Breast Surgery of Xuanwu Hospital from 2019 to 2020 were retrospectively analyzed. The records of SEER database were divided into training set and internal verification set according to 7∶3. The patients data of Xuanwu Hospital were used as the external verification set. Logistic regression and Lasso regression were used to analyze the potential indicators for cervical lymph node metastasis. A nomogram was developed and whose predictive value was verified in the internal and external validation sets. According to the preoperative ultrasound imaging characteristics, the risk scores for PTMC patients were further calculated. The consistency between the scores based on pathologic and ultrasound imaging characteristics was verified. Results The logistic regression analysis results illustrated that male, age<55 years old, tumor size, multifocality, and extrathyroidal extension were associated with cervical lymph node metastasis in PTMC patients (P<0.001). The C index of the nomogram was 0.722, and the calibration curve exhibited to be a fairly good consistency with the perfect prediction in any set. The ROC curve of risk score based on ultrasound characteristics for predicting lymph node metastasis in PTMC patients was 0.701 [95%CI was (0.637 4, 0.765 6)], which was consistent with the risk score based on pathological characteristics (Kappa value was 0.607, P<0.001). Conclusions The nomogram model for predicting the lymph node metastasis of PTMC patients shows a good predictive value, and the risk score based on the preoperative ultrasound imaging characteristics has good consistency with the risk score based on pathological characteristics.
In recent years, due to the emergence of ultrafast ultrasound imaging technology, the sensitivity of detecting slow and micro blood flow with ultrasound has been dramatically improved, and functional ultrasound imaging (fUSI) has been developed. fUSI is a novel technology for neurological imaging that utilizes neurovascular coupling to detect the functional activity of the central nervous system (CNS) with high spatiotemporal resolution and high sensitivity, which is dynamic, non-invasive or minimally invasive. fUSI fills the gap between functional magnetic resonance imaging (fMRI) and optical imaging with its high accessibility and portability. Moreover, it is compatible with electrophysiological recording and optogenetics. In this paper, we review the developments of fUSI and its applications in neuroimaging. To date, fUSI has been used in various animals ranging from mice to non-human primates, as well as in clinical surgeries and bedside functional brain imaging of neonates. In conclusion, fUSI has great potential in neuroscience research and is expected to become an important tool for neuroscientists, pathologists and pharmacologists.