Conventional Ultrasound B-mode Imaging Research Articles

Detecting pulmonary nodules by using ultrasound multiple scattering.

Although X-Ray Computed Tomography (CT) is widely used for detecting pulmonary nodules inside the parenchyma, it cannot be used during video-assisted surgical procedures. Real-time, non-ionizing, ultrasound-based techniques are an attractive alternative for nodule localization to ensure safe resection margins during surgery. Conventional ultrasound B-mode imaging of the lung is challenging due to multiple scattering. However, the multiple scattering contribution can be exploited to detect regions inside the lung containing no scatterers. Pulmonary nodules are homogeneous regions in contrast to the highly scattering parenchyma containing millions of air-filled alveoli. We developed a method relying on mapping the multiple scattering contribution inside the highly scattering lung to detect and localize pulmonary nodules. Impulse response matrices were acquired in ex-vivo pig and dog lungs using a linear array transducer to semi-locally investigate the backscattered field. Extracting the multiple-scattering contribution using singular-value decomposition and combining it with a depression detection algorithm allowed us to detect and localize regions with less multiple scattering, associated with the nodules. The feasibility of this method was demonstrated in five ex-vivo lungs containing a total of 20 artificial nodules. Ninety-five percent of the nodules were detected. Nodule depth and diameter significantly correlated with their ex-vivo CT-estimated counterparts (R = 0.960, 0.563, respectively).

2D and 3D real-time passive cavitation imaging of pulsed cavitation ultrasound therapy in moving tissues

Pulsed cavitation ultrasound therapy (PCUT) is an effective non-invasive therapeutic approach in various medical indications that relies on the mechanical effects generated by cavitation bubbles. Even though limited by the poor contrast, conventional ultrasound B-Mode imaging has been widely used for the guidance and monitoring of the therapeutic procedure, allowing the visualization of the cavitation bubble cloud. However, the visualization of the bubble cloud is often limited in deep organs such as the liver and the heart and remains moreover completely subjective for the operator. Our goal is to develop a new imaging mode to better identify the cavitation cloud.Active and passive cavitation imaging methods have been developed but none of them has been able to locate the cavitation bubble created by PCUT in real-time and in moving organs. In this paper we propose a passive ultrasound imaging approach combined with a spatiotemporal singular value decomposition filter to detect and map the bubble cloud with high sensitivity and high contrast.In moving applications at a maximal motion speed of 10 mm s−1, the contrast-to-noise ratio for passive cavitation imaging is up to 10 times higher than for active cavitation imaging, with a temporal resolution of about 100 ms. The mapping of the bubble cloud can be overlaid in real-time to the conventional B-Mode, which permits to locate the cavitation phenomena in relation to the anatomic image. Finally, we extend the technique to volumetric imaging and show its feasibility on moving phantoms.

Ultrasonic Nakagami Imaging of High-intensity Focused Ultrasound-induced Thermal Lesions in Porcine Livers: Ex Vivo Study.

High-intensity focused ultrasound (HIFU) has demonstrated the capacity to be used for local thermal ablation in clinical surgery; however, relying solely on conventional ultrasound B-mode imaging to monitor HIFU thermal ablation and determine ablation levels remains a challenge. Here, we experimentally demonstrate the ability to use Nakagami imaging to monitor HIFU-induced thermal lesions in porcine livers ex vivo. Ultrasonic Nakagami imaging has been proven to be able to characterize tissues with different scatterer concentrations and distributions. The pathological sections from HIFU thermally ablated porcine liver tissues reveal that normal and denatured tissues significantly differ in scatterer concentration and distribution. Therefore, we believe that Nakagami imaging can be used to monitor thermal ablation by tracing Nakagami parameter changes in liver tissues. The ex vivo porcine liver experiments were performed using a homemade HIFU device synchronized with a commercial diagnostic ultrasound scanner to obtain the ultrasound envelope data before and after thermal ablation. These data were used to evaluate the performance of thermal lesion characterization using Nakagami imaging and were compared with those derived from conventional B-mode imaging. Experimental results showed that Nakagami imaging can be used to identify thermal lesions, which are difficult to visualize using conventional B-mode imaging because there is no apparent bubble formation. In cases with apparent bubble formation, Nakagami imaging could provide a more accurate estimation of lesion size and position. In addition, the Nakagami imaging algorithm is characterized by low computational complexity, which means it can be easily integrated as postprocessing for existing array imaging systems.

Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound.

Conventional medical imaging technologies, including ultrasound, have continued to improve over the years. For example, in oncology, medical imaging is characterized by high sensitivity, i.e., the ability to detect anomalous tissue features, but the ability to classify these tissue features from images often lacks specificity. As a result, a large number of biopsies of tissues with suspicious image findings are performed each year with a vast majority of these biopsies resulting in a negative finding. To improve specificity of cancer imaging, quantitative imaging techniques can play an important role. Conventional ultrasound B-mode imaging is mainly qualitative in nature. However, quantitative ultrasound (QUS) imaging can provide specific numbers related to tissue features that can increase the specificity of image findings leading to improvements in diagnostic ultrasound. QUS imaging can encompass a wide variety of techniques including spectral-based parameterization, elastography, shear wave imaging, flow estimation, and envelope statistics. Currently, spectral-based parameterization and envelope statistics are not available on most conventional clinical ultrasound machines. However, in recent years, QUS techniques involving spectral-based parameterization and envelope statistics have demonstrated success in many applications, providing additional diagnostic capabilities. Spectral-based techniques include the estimation of the backscatter coefficient (BSC), estimation of attenuation, and estimation of scatterer properties such as the correlation length associated with an effective scatterer diameter (ESD) and the effective acoustic concentration (EAC) of scatterers. Envelope statistics include the estimation of the number density of scatterers and quantification of coherent to incoherent signals produced from the tissue. Challenges for clinical application include correctly accounting for attenuation effects and transmission losses and implementation of QUS on clinical devices. Successful clinical and preclinical applications demonstrating the ability of QUS to improve medical diagnostics include characterization of the myocardium during the cardiac cycle, cancer detection, classification of solid tumors and lymph nodes, detection and quantification of fatty liver disease, and monitoring and assessment of therapy.

Bimodal Multiparameter-Based Approach for Benign–Malignant Classification of Breast Tumors

Proposed here is a breast tumor classification technique using conventional ultrasound B-mode imaging and a new elasticity imaging-based bimodal multiparameter index. A set of conventional ultrasound (US) and ultrasound elastography (UE) parameters are studied, and among those, the effective ones whose independent as well as combined performance is found satisfactory are selected. To improve the combined US performance, two new US parameters are proposed: edge diffusivity, which assesses edge blurriness to differentiate malignant from benign lesions, and the shape asymmetry factor, which quantifies tumor shape irregularity by comparing the tumor boundary with an ellipse fitted to the lesion. Then a new bimodal multiparameter characterization index is defined to discriminate 201 pathologically confirmed breast tumors of which 56 are malignant lesions, 79 are fibroadenomas, 42 are cysts and 24 are inflammatory lesions. The weights of the multiparameter bimodal index are optimally computed using a genetic algorithm (GA). To evaluate the performance variation of the index on different data sets, the tumors are categorized into three classes: malignant lesion versus fibroadenoma, malignant lesion versus fibroadenoma and cyst and malignant lesion versus fibroadenoma, cyst and inflammation. The test results reveal that the proposed bimodal index achieves satisfactory quality metrics (e.g., 94.64%–98.21% sensitivity, 97.24%–100.00% specificity and 96.52%–99.44% accuracy) for classification of the aforementioned three classes of breast tumors. Its performance is also observed to be better in totality of the quality metrics sensitivity, specificity, accuracy, positive predictive value and negative predictive value as compared with that of a conventional bimodal index as well as unimodal multiparameter indices based on US or UE. It is suggested that the proposed simple bimodal linear classifier may assist radiologists in better diagnosis of breast tumors and help reduce the number of unnecessary biopsies.

Comparison of contrast in brightness mode and strain ultrasonography of glial brain tumours

BackgroundImage contrast between normal tissue and brain tumours may sometimes appear to be low in intraoperative ultrasound. Ultrasound imaging of strain is an image modality that has been recently explored for intraoperative imaging of the brain. This study aims to investigate differences in image contrast between ultrasound brightness mode (B-mode) images and ultrasound strain magnitude images of brain tumours.MethodsUltrasound radiofrequency (RF) data was acquired during surgery in 15 patients with glial tumours. The data were subsequently processed to provide strain magnitude images. The contrast in the B-mode images and the strain images was determined in assumed normal brain tissue and tumour tissue at selected regions of interest (ROI). Three measurements of contrast were done in the ultrasound data for each patient. The B-mode and strain contrasts measurements were compared using the paired samples t- test.ResultsThe statistical analysis of a total of 45 measurements shows that the contrasts in the strain magnitude images are significantly higher than in the conventional ultrasound B-mode images (P < 0.0001).ConclusionsThe results indicate that ultrasound strain imaging provides better discrimination between normal brain tissue and glial tumour tissue than conventional ultrasound B-mode imaging. Ultrasound imaging of tissue strain therefore holds the potential of becoming a valuable adjunct to conventional intraoperative ultrasound imaging in brain tumour surgery.

Accuracy of kidney stone size in conventional ultrasound Bmode imaging

The objective of this study was to determine the accuracy of conventional ultrasound imaging in sizing kidney stones, since this can be a determining factor in the treatment protocol for fragments on the order of 5 mm. Ex-vivo human kidney stones 3 to 12 mm were imaged in a water bath at depths from 6 to 10 cm using a Philips HDI5000 and Versonics software-based ultrasound system with the C4-2 transducer. Stone sizes were estimated offline a) manually by a sonographer and b) through an automated contrast based edge detection algorithm. Stone size was consistently overestimated with both instruments and by both estimation methods. On average, size was overestimated by 1 to 2 mm for stones 6 cm deep, and the overestimation increased with increasing depth and system gain. The overestimation was independent of actual stone size. These results suggest there is an inherent error in conventional ultrasound that leads to overestimation of stone size. These results also validate the software-based instrument for future work toward 1) investigating the sources of overestimation and 2) testing new methods for improving the accuracy of size estimation. Work supported by NIH DK43881 and DK092197, and NSBRI through NASA NCC 9-58.

Oil-filled polymer microcapsules for ultrasound-mediated delivery of lipophilic drugs

The use of ultrasound contrast agents as local drug delivery systems continues to grow. Current limitations are the amount of drug that can be incorporated as well as the efficiency of drug release upon insonification. This study focuses on the synthesis and characterisation of novel polymeric microcapsules for ultrasound-triggered delivery of lipophilic drugs. Microcapsules with a shell of fluorinated end-capped poly( l-lactic acid) were made through pre-mix membrane emulsification and contained, apart from a gaseous phase, different amounts of hexadecane oil as a drug-carrier reservoir. Mean number weighted diameters were between 1.22 µm and 1.31 µm. High-speed imaging at ~ 10 million fames per second showed that for low acoustic pressures (1 MHz, 0.24 MPa) microcapsules compressed but remained intact. At higher diagnostic pressures of 0.51 MPa, microcapsules cracked, thereby releasing the encapsulated gas and model lipophilic drug. Using conventional ultrasound B-mode imaging at a frequency of 2.5 MHz, a marked enhancement of scatter intensity over a tissue-mimicking phantom was observed for all differently loaded microcapsules. The partially oil-filled microcapsules with high drug loads and well-defined acoustic activation thresholds have great potential for ultrasound-triggered local delivery of lipophilic drugs under ultrasound image-guidance.

Improved scatterer property estimates from ultrasound backscatter for small gate lengths using a gate-edge correction factor

Backscattered rf signals used to construct conventional ultrasound B-mode images contain frequency-dependent information that can be examined through the backscattered power spectrum. The backscattered power spectrum is found by taking the magnitude squared of the Fourier transform of a gated time segment corresponding to a region in the scattering volume. When a time segment is gated, the edges of the gated regions change the frequency content of the backscattered power spectrum due to truncating of the waveform. Tapered windows, like the Hanning window, and longer gate lengths reduce the relative contribution of the gate-edge effects. A new gate-edge correction factor was developed that partially accounted for the edge effects. The gate-edge correction factor gave more accurate estimates of scatterer properties at small gate lengths compared to conventional windowing functions. The gate-edge correction factor gave estimates of scatterer properties within 5% of actual values at very small gate lengths (less than 5 spatial pulse lengths) in both simulations and from measurements on glass-bead phantoms. While the gate-edge correction factor gave higher accuracy of estimates at smaller gate lengths, the precision of estimates was not improved at small gate lengths over conventional windowing functions.

Defining optimal axial and lateral resolution for estimating scatterer properties from volumes using ultrasound backscatter

The rf signals used to construct conventional ultrasound B-mode images contain frequency-dependent information that can be examined through the backscattered power spectrum. Typically, the backscattered power spectrum is calculated from a region of interest (ROI) within some larger volume. The dimensions of the ROI are defined axially by the spatial length corresponding to the time gate and laterally by the number of scan lines included in the ROI. Averaging the backscattered power spectra from several independent scan lines can reduce the presence of noise caused by electronics and by the random scatterer spacings, but also decreases the lateral resolution of the interrogation region. Furthermore, larger axial gate lengths can be used to reduce the effects of noise and improve the precision and accuracy of scatterer property estimates but also decreases the axial resolution. A trade-off exists between the size of the ROI (the number of scan lines used, the separation distance between each scan line, the axial gate length) and the accuracy and precision of scatterer property estimates. A series of simulations and measurements from physical phantoms were employed to examine these trade-offs. The simulations and phantom measurements indicated the optimal lateral and axial sizes of the ROI, where estimate accuracy and precision were better than 10% and 5%, respectively, occurred at 4 to 5 beamwidths laterally and 15 to 20 spatial pulse lengths axially.

703 Expression of the Ets-2 transcription factor in human breast cancer

Proposed here is a breast tumor classification technique using conventional ultrasound B-mode imaging and a new elasticity imaging-based bimodal multiparameter index. A set of conventional ultrasound (US) and ultrasound elastography (UE) parameters are studied, and among those, the effective ones whose independent as well as combined performance is found satisfactory are selected. To improve the combined US performance, two new US parameters are proposed: edge diffusivity, which assesses edge blurriness to differentiate malignant from benign lesions, and the shape asymmetry factor, which quantifies tumor shape irregularity by comparing the tumor boundary with an ellipse fitted to the lesion. Then a new bimodal multiparameter characterization index is defined to discriminate 201 pathologically confirmed breast tumors of which 56 are malignant lesions, 79 are fibroadenomas, 42 are cysts and 24 are inflammatory lesions. The weights of the multiparameter bimodal index are optimally computed using a genetic algorithm (GA). To evaluate the performance variation of the index on different data sets, the tumors are categorized into three classes: malignant lesion versus fibroadenoma, malignant lesion versus fibroadenoma and cyst and malignant lesion versus fibroadenoma, cyst and inflammation. The test results reveal that the proposed bimodal index achieves satisfactory quality metrics (e.g., 94.64%–98.21% sensitivity, 97.24%–100.00% specificity and 96.52%–99.44% accuracy) for classification of the aforementioned three classes of breast tumors. Its performance is also observed to be better in totality of the quality metrics sensitivity, specificity, accuracy, positive predictive value and negative predictive value as compared with that of a conventional bimodal index as well as unimodal multiparameter indices based on US or UE. It is suggested that the proposed simple bimodal linear classifier may assist radiologists in better diagnosis of breast tumors and help reduce the number of unnecessary biopsies.