ObjectiveTo introduce the new nomenclature scheme of the International Working Group (1995) on hepatic nodules, and summarize the imaging features of various hepatic nodules in light of their pathological characteristics, and evaluate the diagnostic values of various imaging facilities.MethodsUltrasound, computed tomography(CT), magnetic resonance imaging(MRI), and angiographic CT were reviewed and introduced.ResultsMany of these types of hepatic nodules play a role in the de novo and stepwise carcinogenesis of hepatocellular carcinoma(HCC) in the following steps: regenerative nodule, lowgrade dysplastic nodule, highgrade dysplastic nodule, small HCC, and large HCC. Accompanying such transformations, there are significant alterations in the blood supply and perfusion of these hepatic nodules.ConclusionModern stateoftheart medical imaging facilities can not only delineate and depict these hepatic nodules, but also provide important clues for the characterization of focal hepatic lesions in most cases, thus facilitating the early detection, diagnosis and management of HCC in its early stage.
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.
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.
The design of wall filter in ultrasonic microvascular imaging directly affects the resolution of blood flow imaging. We compared the traditional polynomial regression wall filter algorithm and two algorithms based on singular value decomposition (SVD), Full-SVD algorithm and RS-RSVD algorithm (random sampling based on random singular value decomposition) through experiments with simulated data and human renal entity data imaging experiments. The experimental results showed that the filtering effect of the traditional polynomial regression wall filter algorithm was limited, however, Full-SVD algorithm and RS-RSVD algorithm could better extract the micro blood flow signal from the tissue or noise signal. When RS-RSVD algorithm was randomly divided into 16 blocks, the signal-to-noise ratio was the same as that of Full-SVD algorithm, reduces the contrast-to-noise ratio by 2.05 dB, and reduces the execution time by 90.41%. RS-RSVD algorithm can improve the operation efficiency and is more conducive to the real-time imaging of high frame rate ultrasound microvessels.
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.