Zhuhuang Zhou

Monitoring Radiofrequency Ablation Using Real-Time Ultrasound Nakagami Imaging Combined with Frequency and Temporal Compounding Techniques

Gas bubbles induced during the radiofrequency ablation (RFA) of tissues can affect the detection of ablation zones (necrosis zone or thermal lesion) during ultrasound elastography. To resolve this problem, our previous study proposed ultrasound Nakagami imaging for detecting thermal-induced bubble formation to evaluate ablation zones. To prepare for future applications, this study (i) created a novel algorithmic scheme based on the frequency and temporal compounding of Nakagami imaging for enhanced ablation zone visualization, (ii) integrated the proposed algorithm into a clinical scanner to develop a real-time Nakagami imaging system for monitoring RFA, and (iii) investigated the applicability of Nakagami imaging to various types of tissues. The performance of the real-time Nakagami imaging system in visualizing RFA-induced ablation zones was validated by measuring porcine liver (n = 18) and muscle tissues (n = 6). The experimental results showed that the proposed algorithm can operate on a standard clinical ultrasound scanner to monitor RFA in real time. The Nakagami imaging system effectively monitors RFA-induced ablation zones in liver tissues. However, because tissue properties differ, the system cannot visualize ablation zones in muscle fibers. In the future, real-time Nakagami imaging should be focused on the RFA of the liver and is suggested as an alternative monitoring tool when advanced elastography is unavailable or substantial bubbles exist in the ablation zone.

Window-modulated compounding Nakagami imaging for ultrasound tissue characterization

Ultrasound Nakagami parametric imaging is a useful tool for tissue characterization. Previous literature has suggested using a square with side lengths corresponding to 3 times the transducer pulse length as the minimum window for constructing the Nakagami image. This criterion does not produce sufficiently smooth images for the Nakagami image to characterize homogeneous tissues. To improve image smoothness, we proposed window-modulated compounding (WMC) Nakagami imaging based on summing and averaging the Nakagami images formed using sliding windows with varying window side lengths from 1 to N times the transducer pulse length in 1 pulse length step. Simulations (the number densities of scatterers: 2-16 scatterers/mm(2)) and experiments on fully developed speckle phantoms (the scatterer diameters: 20-106 μm) were conducted to suggest an appropriate number of frames N and to evaluate the image smoothness and resolution by analyzing the full width at half maximum (FWHM) of the parameter distribution and the widths of the image autocorrelation function (ACF), respectively. In vivo ultrasound measurements on rat livers without and with cirrhosis were performed to validate the practical performance of the WMC Nakagami image in tissue characterization. The simulation results showed that using a range of N from 7 to 10 as the number of frames for image compounding reduces the estimation error to less than 5%. Based on this criterion, the Nakagami parameter obtained from the WMC Nakagami image increased from 0.45 to 0.95 after increasing the number densities of scatterers from 2 to 16 scatterers/mm(2). The FWHM of the parameter distribution (bins=40) was 13.5±1.4 for the Nakagami image and 9.1±1.43 for the WMC Nakagami image, respectively (p-value<.05). The widths of the ACF for the Nakagami and WMC Nakagami images were 454±5.36 and 458±4.33, respectively (p-value>.05). In the phantom experiments, we also found that the FWHM of the parameter distribution for the WMC Nakagami image was smaller than that of the conventional Nakagami image (p-value<.05), and there was no significant difference of the ACF width between the Nakagami and WMC Nakagami images (p-value>.05). In the animal experiments, the Nakagami parameters obtained from the WMC Nakagami image for normal and cirrhotic rat livers were 0.62±0.08 and 0.92±0.07, respectively (p-value<.05). The results demonstrated that the WMC technique significantly improved the image smoothness of Nakagami imaging without resolution degradation, giving Nakagami model-based imaging the ability to visualize scatterer properties with enhanced image quality.

Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts

Accurate segmentation of liver from abdominal CT scans is critical for computer-assisted diagnosis and therapy. Despite many years of research, automatic liver segmentation remains a challenging task. In this paper, a novel method was proposed for automatic delineation of liver on CT volume images using supervoxel-based graph cuts. To extract the liver volume of interest (VOI), the region of abdomen was firstly determined based on maximum intensity projection (MIP) and thresholding methods. Then, the patient-specific liver VOI was extracted from the region of abdomen by using a histogram-based adaptive thresholding method and morphological operations. The supervoxels of the liver VOI were generated using the simple linear iterative clustering (SLIC) method. The foreground/background seeds for graph cuts were generated on the largest liver slice, and the graph cuts algorithm was applied to the VOI supervoxels. Thirty abdominal CT images were used to evaluate the accuracy and efficiency of the proposed algorithm. Experimental results show that the proposed method can detect the liver accurately with significant reduction of processing time, especially when dealing with diseased liver cases.

Semi-automatic Breast Ultrasound Image Segmentation Based on Mean Shift and Graph Cuts

Computerized tumor segmentation on breast ultrasound (BUS) images remains a challenging task. In this paper, we proposed a new method for semi-automatic tumor segmentation on BUS images using Gaussian filtering, histogram equalization, mean shift, and graph cuts. The only interaction required was to select two diagonal points to determine a region of interest (ROI) on an input image. The ROI image was shrunken by a factor of 2 using bicubic interpolation to reduce computation time. The shrunken image was smoothed by a Gaussian filter and then contrast-enhanced by histogram equalization. Next, the enhanced image was filtered by pyramid mean shift to improve homogeneity. The object and background seeds for graph cuts were automatically generated on the filtered image. Using these seeds, the filtered image was then segmented by graph cuts into a binary image containing the object and background. Finally, the binary image was expanded by a factor of 2 using bicubic interpolation, and the expanded image was processed by morphological opening and closing to refine the tumor contour. The method was implemented with OpenCV 2.4.3 and Visual Studio 2010 and tested for 38 BUS images with benign tumors and 31 BUS images with malignant tumors from different ultrasound scanners. Experimental results showed that our method had a true positive rate (TP) of 91.7%, a false positive (FP) rate of 11.9%, and a similarity (SI) rate of 85.6%. The mean run time on Intel Core 2.66 GHz CPU and 4 GB RAM was 0.49 ± 0.36 s. The experimental results indicate that the proposed method may be useful in BUS image segmentation.

Ultrasonic evaluation of microwave-induced thermal lesions based on wavelet analysis of mean scatterer spacing

The microwave ablation has become an important manner for tumor treatment. In this paper, we proposed a new method for evaluation of microwave-induced thermal lesions using the wavelet analysis of the mean scatterer spacing (MSS). First, the ultrasonic radiofrequency (RF) data of normal and coagulated porcine liver tissues was collected through the temperature-controlled water bath heating experiments. The convex array ultrasound probe with a center frequency of 3.5 MHz was used. Second, the wavelet analysis was used to compute the MSS of normal and coagulated porcine liver tissues, respectively. Finally, the microwave-induced thermal lesions were detected based on the differences in the MSS between normal tissues and thermal lesions. Eighteen cases of microwave ablation experiments and 20 cases of water bath heating experiments were conducted on fresh porcine liver samples. The MSS of normal porcine liver tissues was 1.15±0.12 mm, and the MSS of coagulated porcine liver tissues was 0.93±0.07 mm. Six cases of thermal lesions were compared between the MSS-detected area and the caliper-measured area, and the MSS-detected area had an error of (13.55±5.29) %. The experimental results indicated that the proposed method could be used in preliminary detection and evaluation of microwave-induced thermal lesions.

A survey of ultrasound elastography approaches to percutaneous ablation monitoring

Percutaneous thermal ablation has been widely used as a minimally invasive treatment for tumors. Treatment monitoring is essential for preventing complications while ensuring treatment efficacy. Mechanical testing measurements on tissue reveal that tissue stiffness increases with temperature and ablation duration. Different types of imaging methods can be used to monitor ablation procedures, including temperature or thermal strain imaging, strain imaging, modulus imaging, and shear modulus imaging. Ultrasound elastography demonstrates the potential to become the primary imaging modality for monitoring percutaneous ablation. This review briefly presented the state-of-the-art ultrasound elastography approaches for monitoring radiofrequency ablation and microwave ablation. These techniques were divided into four groups: quasi-static elastography, acoustic radiation force elastography, sonoelastography, and applicator motion elastography. Their advantages and limitations were compared and discussed. Future developments were proposed with respect to heat-induced bubbles, tissue inhomogeneities, respiratory motion, three-dimensional monitoring, multi-parametric monitoring, real-time monitoring, experimental data center for percutaneous ablation, and microwave ablation monitoring.

Entropic Imaging of Cataract Lens: An In Vitro Study

Phacoemulsification is a common surgical method for treating advanced cataracts. Determining the optimal phacoemulsification energy depends on the hardness of the lens involved. Previous studies have shown that it is possible to evaluate lens hardness via ultrasound parametric imaging based on statistical models that require data to follow a specific distribution. To make the method more system-adaptive, nonmodel-based imaging approach may be necessary in the visualization of lens hardness. This study investigated the feasibility of applying an information theory derived parameter – Shannon entropy from ultrasound backscatter to quantify lens hardness. To determine the physical significance of entropy, we performed computer simulations to investigate the relationship between the signal-to-noise ratio (SNR) based on the Rayleigh distribution and Shannon entropy. Youngs modulus was measured in porcine lenses, in which cataracts had been artificially induced by the immersion in formalin solution in vitro. A 35-MHz ultrasound transducer was used to scan the cataract lenses for entropy imaging. The results showed that the entropy is 4.8 when the backscatter data form a Rayleigh distribution corresponding to an SNR of 1.91. The Youngs modulus of the lens increased from approximately 8 to 100 kPa when we increased the immersion time from 40 to 160 min (correlation coefficient r = 0.99). Furthermore, the results indicated that entropy imaging seemed to facilitate visualizing different degrees of lens hardening. The mean entropy value increased from 2.7 to 4.0 as the Youngs modulus increased from 8 to 100 kPa (r = 0.85), suggesting that entropy imaging may have greater potential than that of conventional statistical parametric imaging in determining the optimal energy to apply during phacoemulsification.

Comparison of ultrasound temperature imaging with infrared thermometry during radio frequency ablation

Radio frequency ablation (RFA) is a widely used alternative modality in the treatment of tumors. During RFA, temperature monitoring is essential to ensure accurate and appropriate thermal dosage. Ultrasound temperature imaging based on the detection of echo time-shift has been demonstrated to have good ability to monitor the temperature distribution. However, no study has proven that the region of ultrasound temperature imaging can correspond well to the practical temperature distribution in the tissue. In this study, we aim to combine ultrasound and infrared systems to clarify the correlation between ultrasound temperature imaging and the practical temperature distribution in a tissue. Five porcine livers (n = 5) were ablated using an RFA system and monitored with an ultrasound system to acquire raw backscattered data for temperature imaging. Meanwhile, an infrared imaging system was used to obtain the practical temperature map of the tissue. The results showed that the temperature distribution detected by ultrasound echo time-shift agreed with those obtained from the infrared image. When the tissue temperature was higher than 45°C, ultrasound temperature imaging is difficult to describe the behavior of the heat transfer in a homogeneous medium. In this study, we used the experimental setup based on combining ultrasound and infrared systems to confirm the reliability and limitations of ultrasound temperature imaging in RFA monitoring. Such an experimental design may be considered as an indispensable platform for the development and optimization of ultrasound temperature imaging techniques in RFA monitoring.

3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy C-Means and Graph Cuts

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy C-means (FCM) and graph cuts. With a single seed point, the tumor volume of interest (VOI) was extracted using confidence connected region growing algorithm to reduce computational cost. Then, initial foreground/background regions were labeled automatically, and a kernelized FCM with spatial information was incorporated in graph cuts segmentation to increase segmentation accuracy. The proposed method was evaluated on the public clinical dataset (3Dircadb), which included 15 CT volumes consisting of various sizes of liver tumors. We achieved an average volumetric overlap error (VOE) of 29.04% and Dice similarity coefficient (DICE) of 0.83, with an average processing time of 45 s per tumor. The experimental results showed that the proposed method was accurate for 3D liver tumor segmentation with a reduction of processing time.

Effect of ultrasound frequency on the Nakagami statistics of human liver tissues

The analysis of the backscattered statistics using the Nakagami parameter is an emerging ultrasound technique for assessing hepatic steatosis and fibrosis. Previous studies indicated that the echo amplitude distribution of a normal liver follows the Rayleigh distribution (the Nakagami parameter m is close to 1). However, using different frequencies may change the backscattered statistics of normal livers. This study explored the frequency dependence of the backscattered statistics in human livers and then discussed the sources of ultrasound scattering in the liver. A total of 30 healthy participants were enrolled to undergo a standard care ultrasound examination on the liver, which is a natural model containing diffuse and coherent scatterers. The liver of each volunteer was scanned from the right intercostal view to obtain image raw data at different central frequencies ranging from 2 to 3.5 MHz. Phantoms with diffuse scatterers only were also made to perform ultrasound scanning using the same protocol for comparisons with clinical data. The Nakagami parameter–frequency correlation was evaluated using Pearson correlation analysis. The median and interquartile range of the Nakagami parameter obtained from livers was 1.00 (0.98–1.05) for 2 MHz, 0.93 (0.89–0.98) for 2.3 MHz, 0.87 (0.84–0.92) for 2.5 MHz, 0.82 (0.77–0.88) for 3.3 MHz, and 0.81 (0.76–0.88) for 3.5 MHz. The Nakagami parameter decreased with the increasing central frequency (r = −0.67, p < 0.0001). However, the effect of ultrasound frequency on the statistical distribution of the backscattered envelopes was not found in the phantom results (r = −0.147, p = 0.0727). The current results demonstrated that the backscattered statistics of normal livers is frequency-dependent. Moreover, the coherent scatterers may be the primary factor to dominate the frequency dependence of the backscattered statistics in a liver.