Publications

@article{75,

title = {Ultrasound-based characterization of prostate cancer: an in vivo clinical feasibility study.},

author = {Farhad Imani and Purang Abolmaesumi and Eli Gibson and Amir Khojaste Galesh-Khale and Mena Gaed and Madeleine Moussa and Jose A Gomez and Cesare Romagnoli and Robert D Siemens and Michael Leviridge and Silvia Chang and Aaron Fenster and Aaron D Ward and Parvin Mousavi},

journal = {Med Image Comput Comput Assist Interv},

volume = {16},

pages = {279-86},

abstract = {\<p\>\textbf{UNLABELLED: }This paper presents the results of an in vivo clinical study to accurately characterize prostate cancer using new features of ultrasound RF time series.\</p\>\<p\>\textbf{METHODS: }The mean central frequency and wavelet features of ultrasound RF time series from seven patients are used along with an elaborate framework of ultrasound to histology registration to identify and verify cancer in prostate tissue regions as small as 1.7 mm x 1.7 mm.\</p\>\<p\>\textbf{RESULTS: }In a leave-one-patient-out cross-validation strategy, an average classification accuracy of 76% and the area under ROC curve of 0.83 are achieved using two proposed RF time series features. The results statistically significantly outperform those achieved by previously reported features in the literature. The proposed features show the clinical relevance of RF time series for in vivo characterization of cancer.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{30,

title = {Enhanced multi-site EMG-force estimation using contact pressure.},

author = {Javad Hashemi and Evelyn Morin and Parvin Mousavi and Keyvan Hashtrudi-Zaad},

doi = {10.1109/EMBC.2012.6346619},

issn = {1557-170X},

journal = {Conf Proc IEEE Eng Med Biol Soc},

volume = {2012},

pages = {3098-101},

abstract = {\<p\>A modification method based on integrated contact pressure and surface electromyogram (SEMG) recordings over the biceps brachii muscle is presented. Multi-site sEMGs are modified by pressure signals recorded at the same locations for isometric contractions. The resulting pressure times SEMG signals are significantly more correlated to the force induced at the wrist (FW), yielding SEMG-force models with superior performance in force estimation. A sensor patch, combining six SEMG and six contact pressure sensors was designed and built. SEMG, and contact pressure data over the biceps brachii and induced wrist force data were collected from 5 subjects. Polynomial fitting was used to find a mapping between biceps SEMG and wrist force. Comparison between evaluation values from models trained with modified and non-modified SEMG signals revealed a statistically significant superiority of models trained with the modified SEMG.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{39e,

title = {iCTNet: a Cytoscape plugin to produce and analyze integrative complex traits networks.},

author = {Lili Wang and Pouya Khankhanian and Sergio E Baranzini and Parvin Mousavi},

doi = {10.1186/1471-2105-12-380},

issn = {1471-2105},

journal = {BMC Bioinformatics},

volume = {12},

pages = {380},

abstract = {\<p\>\textbf{BACKGROUND: }The speed at which biological datasets are being accumulated stands in contrast to our ability to integrate them meaningfully. Large-scale biological databases containing datasets of genes, proteins, cells, organs, and diseases are being created but they are not connected. Integration of these vast but heterogeneous sources of information will allow the systematic and comprehensive analysis of molecular and clinical datasets, spanning hundreds of dimensions and thousands of individuals. This integration is essential to capitalize on the value of current and future molecular- and cellular-level data on humans to gain novel insights about health and disease.\</p\>\<p\>\textbf{RESULTS: }We describe a new open-source Cytoscape plugin named iCTNet (integrated Complex Traits Networks). iCTNet integrates several data sources to allow automated and systematic creation of networks with up to five layers of omics information: phenotype-SNP association, protein-protein interaction, disease-tissue, tissue-gene, and drug-gene relationships. It facilitates the generation of general or specific network views with diverse options for more than 200 diseases. Built-in tools are provided to prioritize candidate genes and create modules of specific phenotypes.\</p\>\<p\>\textbf{CONCLUSIONS: }iCTNet provides a user-friendly interface to search, integrate, visualize, and analyze genome-scale biological networks for human complex traits. We argue this tool is a key instrument that facilitates systematic integration of disparate large-scale data through network visualization, ultimately allowing the identification of disease similarities and the design of novel therapeutic approaches.The online database and Cytoscape plugin are freely available for academic use at: http://www.cs.queensu.ca/ictnet.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{22b,

title = {Towards an augmented ultrasound guided spinal needle insertion system.},

author = {Colin Sutherland and Keyvan Hashtrudi-Zaad and Purang Abolmaesumi and Parvin Mousavi},

doi = {10.1109/IEMBS.2011.6090935},

issn = {1557-170X},

journal = {Conf Proc IEEE Eng Med Biol Soc},

volume = {2011},

pages = {3459-62},

abstract = {\<p\>We propose a haptic-based simulator for ultrasound-guided percutaneous spinal interventions. The system is composed of a haptic device to provide force feedback, a camera system to display video and augmented computed tomography (CT) overlay, a finite element model for tissue deformation and US simulation from a CT volume. The proposed system is able to run a large finite element model at the required haptic rate for smooth force feedback, and uses haptic device position measurements for a steady response. The simulated US images from CT closely resemble the vertebrae images captured in vivo. This is the first report of a system that provides a training environment to couple haptic feedback with a tracked mannequin, and a CT volume overlaid on a visual feed of the mannequin.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{7_38,

title = {Discrete Fourier analysis of ultrasound RF time series for detection of prostate cancer.},

author = {M Moradi and P Mousavi and D R Siemens and E E Sauerbrei and P Isotalo and A Boag and P Abolmaesumi},

doi = {10.1109/IEMBS.2007.4352545},

issn = {1557-170X},

journal = {Conf Proc IEEE Eng Med Biol Soc},

volume = {2007},

pages = {1339-42},

abstract = {\<p\>In this paper, we demonstrate that a set of six features extracted from the discrete Fourier transform of ultrasound Radio-Frequency (RF) time series can be used to detect prostate cancer with high sensitivity and specificity. Ultrasound RF time series refer to a series of echoes received from one spatial location of tissue while the imaging probe and the tissue are fixed in position. Our previous investigations have shown that at least one feature, fractal dimension, of these signals demonstrates strong correlation with the tissue microstructure. In the current paper, six new features that represent the frequency spectrum of the RF time series have been used, in conjunction with a neural network classification approach, to detect prostate cancer in regions of tissue as small as 0.03 cm2. Based on pathology results used as gold standard, we have acquired mean accuracy of 91%, mean sensitivity of 92% and mean specificity of 90% on seven human prostates.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10b,

title = {Tissue characterization using fractal dimension of high frequency ultrasound RF time series.},

author = {Mehdi Moradi and Parvin Mousavi and Purang Abolmaesumi},

journal = {Med Image Comput Comput Assist Interv},

volume = {10},

pages = {900-8},

abstract = {\<p\>This paper is the first report on the analysis of ultrasound RF echo time series acquired using high frequency ultrasound. We show that variations in the intensity of one sample of RF echo over time is correlated with tissue microstructure. To form the RF time series, a high frequency probe and a tissue sample were fixed in position and RF signals backscattered from the tissue were continuously recorded. The fractal dimension of RF time series was used as a feature for tissue classification. Feature values acquired from different areas of one tissue type were statistically similar. For animal tissues with different cellular microstructure, we successfully used the fractal dimension of RF time series to distinguish segments as small as 20 microns with accuracies as high as 98%. The results of this study demonstrate that the analysis of RF time series is a promising approach for distinguishing tissue types with different cellular microstructure.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{8_35,

title = {Detection of prostate cancer from RF ultrasound echo signals using fractal analysis.},

author = {Mehdi Moradi and Purang Abolmaesumi and Phillip A Isotalo and David R Siemens and Eric E Sauerbrei and Parvin Mousavi},

doi = {10.1109/IEMBS.2006.259325},

issn = {1557-170X},

journal = {Conf Proc IEEE Eng Med Biol Soc},

volume = {1},

pages = {2400-3},

abstract = {\<p\>In this paper we propose a new feature, average Higuchi dimension of RF time series (AHDRFT), for detection of prostate cancer using ultrasound data. The proposed feature is extracted from RF echo signals acquired from prostate tissue in an in vitro setting and is used in combination with texture features extracted from the corresponding B-scan images. In a novel approach towards RF data collection, we continuously recorded backscattered echoes from the prostate tissue to acquire time series of the RF signals. We also collected B-scan images and performed a detailed histopathologic analysis on the tissue. To compute AHDRFT, the Higuchi fractal dimensions of the RF time series were averaged over a region of interest. AHDRFT and texture features extracted from corresponding B-scan images were used to classify regions of interest, as small as 0.028 cm of the prostate tissue in cancerous and normal classes. We validated the results based on our histopathologic maps. A combination of image statistical moments and features extracted from co-occurrence matrices of the B-scan images resulted in classification accuracy of around 87%. When AHDRFT was added to the feature vectors, the classification accuracy was consistently over 95% with best results of over 99% accuracy. Our results show that the RF time series backscattered from prostate tissues contain information that can be used for detection of prostate cancer.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}