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Oelze M.L.,University of Illinois at Urbana - Champaign | Mamou J.,zzi Center For Biomedical Engineering
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control | Year: 2016

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. © 2015 IEEE. Source


Mamou J.,zzi Center For Biomedical Engineering | Oelze M.L.,University of Illinois at Urbana - Champaign
Quantitative Ultrasound in Soft Tissues | Year: 2013

Due to parallel advances in signal processing and computer hardware in the last 15 years, quantitative ultrasound techniques have reached maturity, allowing for the construction of quantitative maps or images of soft tissues. This book will focus on 5 modern research topics related to quantitative ultrasound of soft tissues: - Spectral-based methods for tissue characterization, tissue typing, cancer detection, etc.; - Envelope statistics analysis as a means of quantifying and imaging tissue properties; - Ultrasound elastography for quantifying elastic properties of tissues (several clinical ultrasound scanners now display elastography images); - Scanning acoustic microscopy for forming images of mechanical properties of soft tissues with micron resolution (desktop size scanners are now available); and - Ultrasound computer tomography for breast cancer imaging (new ultrasound tomography systems have been developed and are currently under evaluation clinically). © Springer Science+Business Media Dordrecht 2013. Source


Mamou J.,zzi Center For Biomedical Engineering
Proceedings of Meetings on Acoustics | Year: 2013

Histology performed to assess lymph nodes excised during node-dissection surgeries from cancer patients suffers an unsatisfactory rate of false-negative determinations due to labor and time constraints. In this study, more than 300 lymph nodes were scanned in 3D using a 26-MHz high-frequency ultrasound transducer. Following scanning, individual nodes underwent a special histology procedure that involved step-sectioning each node at 50-μm intervals to guarantee that no significant cancer foci were missed. The 3D radio-frequency ultrasound dataset was analyzed using overlapping 3D regions-of-interests that were individually processed to yield thirteen quantitative ultrasound (QUS) estimates associated with tissue microstructure and were hypothesized to show contrast between normal and cancerous regions in lymph nodes. Step-wise linear discriminant analyses were performed to yield an optimal QUS-based classifier. ROC curves and areas under the ROC curves (AUCs) were obtained to assess cancer-detection performance. The AUC for the linear combination of four QUS estimates was 0.83 for a dataset of 110 axillary nodes of breast-cancer patients. Similarly, using five QUS estimates, an AUC of 0.97 was obtained for a dataset of 180 nodes of gastrointestinal-cancer patients. These studies demonstrate that QUS methods may provide an effective tool to guide pathologist towards suspicious regions in lymph nodes. © 2013 Acoustical Society of America. Source


Silverman R.H.,Columbia University | Silverman R.H.,zzi Center For Biomedical Engineering | Urs R.,Columbia University | Roychoudhury A.,Columbia University | And 5 more authors.
Investigative Ophthalmology and Visual Science | Year: 2014

PURPOSE. To develop and evaluate automated computerized algorithms for differentiation of normal and keratoconus corneas based solely on epithelial and stromal thickness data. METHODS. Maps of the corneal epithelial and stromal thickness were generated from Artemis-1 very high-frequency ultrasound arc-scans of 130 normal and 74 keratoconic subjects diagnosed by combined topography and tomography examination. Keratoconus severity was graded based on anterior curvature, minimum corneal thickness, and refractive error. Computer analysis of maps produced 161 features for one randomly selected eye per subject. Stepwise linear discriminant analysis (LDA) and neural network (NN) analysis were then performed to develop multivariate models based on combinations of selected features to correctly classify cases. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined for each classifier. RESULTS. Stepwise LDA resulted in a six-variable model that provided an AUC of 100%, indicative of complete separation of keratoconic from normal corneas. Leave-one-out analysis resulted in 99.2% specificity and 94.6% sensitivity. Neural network analysis using the same six variables resulted in an AUC of 100% for the training set. Test set performance averaged over 10 trials gave a specificity of 99.5 6 1.5% and sensitivity of 98.9 6 1.9%. The LDA function values correlated with keratoconus severity grade. CONCLUSIONS. The results demonstrate that epithelial remodeling in keratoconus represents an independent means for differentiation of normal from advanced keratoconus corneas. © 2014 The Association for Research in Vision and Ophthalmology, Inc. Source


Zenbutsu S.,Chiba University | Igarashi T.,Chiba University | Mamou J.,zzi Center For Biomedical Engineering | Yamaguchi T.,Chiba University
Japanese Journal of Applied Physics | Year: 2012

Laparoscopic surgery is one of the most challenging surgical operations, because inside information about the target organ cannot be fully understood from the laparoscopic image. Therefore, a fusion technique of laparoscopic and ultrasonic images is proposed for guidance during laparoscopic surgery. The proposed technique can display the internal organ structure by overlaying a three-dimensional (3D) ultrasonic image over a 3D laparoscopic image, which is acquired using a stereo laparoscope. The registration of the 3D images is performed by registering the surface of the target organ, which is found in the two 3D images without requiring the use of an external position detecting device. The proposed technique was evaluated experimentally using a tissue-mimicking phantom. Results obtained led to registration accuracy better than 2 cm. The total computation time was 3.1 min on a personal computer (Xeon processor, 3 GHz CPU). The structural information permits the visualization of target organs during laparoscopic surgery. © 2012 The Japan Society of Applied Physics. Source

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