Publications

@article{527,

title = {Toward a real-time system for temporal enhanced ultrasound-guided prostate biopsy},

author = {Shekoofeh Azizi and Nathan Van~Woudenberg and Samira Sojoudi and Ming Li and Sheng Xu and Emran Anas and Pingkun Yan and Amir Tahmasebi and Jin Tae Kwak and Baris Turkbey and Peter Choyke and Peter Pinto and Bradford Wood and Parvin Mousavi and Purang Abolmaesumi},

doi = {10.1007/s11548-018-1749-z},

year  = {2018},

date = {2018-01-01},

journal = {International Journal of Computer Assisted Radiology and Surgery},

volume = {13},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Jamzad2018,

title = {Noninvasive Prediction of Renal Stone Surface Irregularities by Numerical Analysis of the Color Doppler Twinkling Artifact: An Ex Vivo Study},

author = {Amoon Jamzad and Seyed Kamaledin Setarehdan},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jum.14465},

doi = {10.1002/jum.14465},

year  = {2018},

date = {2018-01-01},

journal = {Journal of Ultrasound in Medicine},

volume = {37},

number = {5},

pages = {1211-1224},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{517,

title = {Investigation of Physical Phenomena Underlying Temporal-Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations.},

author = {Sharareh Bayat and Shekoofeh Azizi and Mohammad I Daoud and Guy Nir and Farhad Imani and Carlos D Gerardo and Pingkun Yan and Amir Tahmasebi and Francois Vignon and Samira Sojoudi and Storey Wilson and Kenneth A Iczkowski and Scott M Lucia and Larry Goldenberg and Septimiu E Salcudean and Purang Abolmaesumi and Parvin Mousavi},

doi = {10.1109/TUFFC.2017.2785230},

issn = {1525-8955},

year  = {2017},

date = {2017-12-01},

journal = {IEEE Trans Ultrason Ferroelectr Freq Control},

volume = {65},

pages = {400-410},

abstract = {\<p\>Temporal-enhanced ultrasound (TeUS) is a novel noninvasive imaging paradigm that captures information from a temporal sequence of backscattered US radio frequency data obtained from a fixed tissue location. This technology has been shown to be effective for classification of various in vivo and ex vivo tissue types including prostate cancer from benign tissue. Our previous studies have indicated two primary phenomena that influence TeUS: 1) changes in tissue temperature due to acoustic absorption and 2) micro vibrations of tissue due to physiological vibration. In this paper, first, a theoretical formulation for TeUS is presented. Next, a series of simulations are carried out to investigate micro vibration as a source of tissue characterizing information in TeUS. The simulations include finite element modeling of micro vibration in synthetic phantoms, followed by US image generation during TeUS imaging. The simulations are performed on two media, a sparse array of scatterers and a medium with pathology mimicking scatterers that match nuclei distribution extracted from a prostate digital pathology data set. Statistical analysis of the simulated TeUS data shows its ability to accurately classify tissue types. Our experiments suggest that TeUS can capture the microstructural differences, including scatterer density, in tissues as they react to micro vibrations.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{516,

title = {Broadband Vibration Detection in Tissue Phantoms Using a Fiber Fabry-Perot Cavity.},

author = {Jack Barnes and Sijia Li and Apoorv Goyal and Purang Abolmaesumi and Parvin Mousavi and Hans-Peter Loock},

doi = {10.1109/TBME.2017.2731663},

issn = {1558-2531},

year  = {2017},

date = {2017-07-24},

journal = {IEEE Trans Biomed Eng},

volume = {65},

pages = {921-927},

abstract = {\<p\>\textbf{OBJECTIVE: }A fiber optic vibration sensor is developed and characterized with an ultrawide dynamic sensing range, from less than 1 Hz to clinical ultrasound frequencies near 6 MHz. The vibration sensor consists of a matched pair of fiber Bragg gratings coupled to a custom-built signal processing circuit. The wavelength of a laser diode is locked to one of the many cavity resonances using the Pound-Drever-Hall scheme.\</p\>\<p\>\textbf{METHODS: }A calibrated piezoelectric vibration element was used to characterize the sensortextquoterights strain, temperature, and noise responses. To demonstrate its sensing capability, an ultrasound phantom with built-in low frequency vibration actuation was constructed.\</p\>\<p\>\textbf{RESULTS: }The fiber optic senor was shown to simultaneously capture the low frequency vibration and the clinical ultrasound transmission waveforms with nanostrain sensitivity.\</p\>\<p\>\textbf{CONCLUSION: }This miniaturized and sensitive vibration sensor can provide comprehensive information regarding strain response and the resultant ultrasound waveforms.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{551,

title = {Model-based registration of preprocedure MR and intraprocedure US of the lumbar spine.},

author = {Delaram Behnami and Alireza Sedghi and Emran Mohammad Abu Anas and Abtin Rasoulian and Alexander Seitel and Victoria Lessoway and Tamas Ungi and David Yen and Jill Osborn and Parvin Mousavi and Robert Rohling and Purang Abolmaesumi},

doi = {10.1007/s11548-017-1552-2},

issn = {1861-6429},

year  = {2017},

date = {2017-06-01},

journal = {Int J Comput Assist Radiol Surg},

volume = {12},

pages = {973-982},

abstract = {\<p\>\textbf{PURPOSE: }Epidural and spinal needle insertions, as well as facet joint denervation and injections are widely performed procedures on the lumbar spine for delivering anesthesia and analgesia. Ultrasound (US)-based approaches have gained popularity for accurate needle placement, as they use a non-ionizing, inexpensive and accessible modality for guiding these procedures. However, due to the inherent difficulties in interpreting spinal US, they yet to become the clinical standard-of-care.\</p\>\<p\>\textbf{METHODS: }A novel statistical shape~[Formula: see text]~pose~[Formula: see text]~scale (s~[Formula: see text]~p~[Formula: see text]~s) model of the lumbar spine is jointly registered to preoperative magnetic resonance (MR) and US images. An instance of the model is created for each modality. The shape and scale model parameters are jointly computed, while the pose parameters are estimated separately for each modality.\</p\>\<p\>\textbf{RESULTS: }The proposed method is successfully applied to nine pairs of preoperative clinical MR volumes and their corresponding US images. The results are assessed using the target registration error (TRE) metric in both MR and US domains. The s~[Formula: see text]~p~[Formula: see text]~s model in the proposed joint registration framework results in a mean TRE of 2.62 and 4.20~mm for MR and US images, respectively, on different landmarks.\</p\>\<p\>\textbf{CONCLUSION: }The joint framework benefits from the complementary features in both modalities, leading to significantly smaller TREs compared to a model-to-US registration approach. The s~[Formula: see text]~p~[Formula: see text]~s model also outperforms our previous shape~[Formula: see text]~pose model of the lumbar spine, as separating scale from pose allows to better capture pose and guarantees equally-sized vertebrae in both modalities. Furthermore, the simultaneous visualization of the patient-specific models on the MR and US domains makes it possible for clinicians to better evaluate the local registration accuracy.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{550,

title = {Classification of Clinical Significance of MRI Prostate Findings Using 3D Convolutional Neural Networks.},

author = {Alireza Mehrtash and Alireza Sedghi and Mohsen Ghafoorian and Mehdi Taghipour and Clare M Tempany and William M Wells and Tina Kapur and Parvin Mousavi and Purang Abolmaesumi and Andriy Fedorov},

doi = {10.1117/12.2277123},

issn = {0277-786X},

year  = {2017},

date = {2017-02-01},

journal = {Proc SPIE Int Soc Opt Eng},

volume = {10134},

abstract = {\<p\>Prostate cancer (PCa) remains a leading cause of cancer mortality among American men. Multi-parametric magnetic resonance imaging (mpMRI) is widely used to assist with detection of PCa and characterization of its aggressiveness. Computer-aided diagnosis (CADx) of PCa in MRI can be used as clinical decision support system to aid radiologists in interpretation and reporting of mpMRI. We report on the development of a convolution neural network (CNN) model to support CADx in PCa based on the appearance of prostate tissue in mpMRI, conducted as part of the SPIE-AAPM-NCI PROSTATEx challenge. The performance of different combinations of mpMRI inputs to CNN was assessed and the best result was achieved using DWI and DCE-MRI modalities together with the zonal information of the finding. On the test set, the model achieved an area under the receiver operating characteristic curve of 0.80.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{481,

title = {Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations},

author = {Shekoofeh Azizi and Sharareh Bayat and Pingkun Yan and Amir Tahmasebi and Guy Nir and Jin Tae Kwak and Sheng Xu and Storey Wilson and Kenneth A Iczkowski and Scott M Lucia and others},

year  = {2017},

date = {2017-01-01},

journal = {International journal of computer assisted radiology and surgery},

volume = {12},

pages = {1293\textendash1305},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{479,

title = {Investigation of Physical Phenomena Underlying Temporal Enhanced Ultrasound as a New Diagnostic Imaging Technique: Theory and Simulations},

author = {Sharareh Bayat and Shekoofeh Azizi and Mohammad I Daoud and Guy Nir and Farhad Imani and Carlos D Gerardo and Pingkun Yan and Amir Tahmasebi and Francois Vignon and Samira Sojoudi and others},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{480,

title = {Model-based registration of preprocedure MR and intraprocedure US of the lumbar spine},

author = {Delaram Behnami and Alireza Sedghi and Emran Mohammad Abu Anas and Abtin Rasoulian and Alexander Seitel and Victoria Lessoway and Tamas Ungi and David Yen and Jill Osborn and Parvin Mousavi and others},

year  = {2017},

date = {2017-01-01},

journal = {International journal of computer assisted radiology and surgery},

volume = {12},

pages = {973\textendash982},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{483,

title = {Reduction of Assessment Time for Stroke-Related Impairments Using Robotic Evaluation},

author = {Sayyed Mostafa Mostafavi and Stephen Scott and Sean Dukelow and Parvin Mousavi},

year  = {2017},

date = {2017-01-01},

journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},

volume = {25},

pages = {945\textendash955},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{440,

title = {Applications of Peptide Nucleic Acid Probes in Cytometry},

author = {P M Lansdorp and V Dragowska and N Rufer and T Brummendorf and S S S Poon and P Mousavi and T Duncan and U Martens},

year  = {1998},

date = {1998-01-01},

publisher = {proceedings of ISAC XIX International Congress},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{445,

title = {Control of a Cybernetic Forearm by Artificial Neural Networks},

author = {Hashemi M R Golpaighani and A Fallah and P Mousavi},

year  = {1993},

date = {1993-01-01},

journal = {Pajoohesh Journal},

volume = {13(27)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{519,

title = {A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy.},

author = {Emran Mohammad Abu Anas and Parvin Mousavi and Purang Abolmaesumi},

doi = {10.1016/j.media.2018.05.010},

issn = {1361-8423},

journal = {Med Image Anal},

volume = {48},

pages = {107-116},

abstract = {\<p\>Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi-automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real-time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp-MRI and ultrasound data. In addition to using convolutional networks for extracting spatial features, the proposed approach employs recurrent networks to exploit the temporal information among a series of ultrasound images. One of the key contributions in the architecture is to use residual convolution in the recurrent networks to improve optimization. We also exploit recurrent connections within and across different layers of the deep networks to maximize the utilization of the temporal information. Furthermore, we perform dense and sparse sampling of the input ultrasound sequence to make the network robust to ultrasound artifacts. Our architecture is trained on 2,238 labeled transrectal ultrasound images, with an additional 637 and 1,017 unseen images used for validation and testing, respectively. We obtain a mean Dice similarity coefficient of 93%, a mean surface distance error of 1.10~mm and a mean Hausdorff distance error of 3.0~mm. A comparison of the reported results with those of a state-of-the-art technique indicates statistically significant improvement achieved by the proposed approach.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{524,

title = {Deep Recurrent Neural Networks for Prostate Cancer Detection: Analysis of Temporal Enhanced Ultrasound.},

author = {Shekoofeh Azizi and Sharareh Bayat and Pingkun Yan and Amir Tahmasebi and Jin Tae Kwak and Sheng Xu and Baris Turkbey and Peter Choyke and Peter Pinto and Bradford Wood and Parvin Mousavi and Purang Abolmaesumi},

doi = {10.1109/TMI.2018.2849959},

issn = {1558-254X},

journal = {IEEE Trans Med Imaging},

volume = {37},

pages = {2695-2703},

abstract = {\<p\>Temporal enhanced ultrasound (TeUS), comprising the analysis of variations in backscattered signals from a tissue over a sequence of ultrasound frames, has been previously proposed as a new paradigm for tissue characterization. In this paper, we propose to use deep recurrent neural networks (RNN) to explicitly model the temporal information in TeUS. By investigating several RNN models, we demonstrate that long short-term memory (LSTM) networks achieve the highest accuracy in separating cancer from benign tissue in the prostate. We also present algorithms for in-depth analysis of LSTM networks. Our in vivo study includes data from 255 prostate biopsy cores of 157 patients. We achieve area under the curve, sensitivity, specificity, and accuracy of 0.96, 0.76, 0.98, and 0.93, respectively. Our result suggests that temporal modeling of TeUS using RNN can significantly improve cancer detection accuracy over previously presented works.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{518,

title = {Stochastic Modeling of Temporal Enhanced Ultrasound: Impact of Temporal Properties on Prostate Cancer Characterization.},

author = {Layan Nahlawi and Caroline Goncalves and Farhad Imani and Mena Gaed and Jose A Gomez and Madeleine Moussa and Eli Gibson and Aaron Fenster and Aaron Ward and Purang Abolmaesumi and Hagit Shatkay and Parvin Mousavi},

doi = {10.1109/TBME.2017.2778007},

issn = {1558-2531},

journal = {IEEE Trans Biomed Eng},

volume = {65},

pages = {1798-1809},

abstract = {\<p\>\textbf{OBJECTIVES: }Temporal enhanced ultrasound (TeUS) is a new ultrasound-based imaging technique that provides tissue-specific information. Recent studies have shown the potential of TeUS for improving tissue characterization in prostate cancer diagnosis. We study the temporal properties of TeUS-temporal order and length-and present a new framework to assess their impact on tissue information.\</p\>\<p\>\textbf{METHODS: }We utilize a probabilistic modeling approach using hidden Markov models (HMMs) to capture the temporal signatures of malignant and benign tissues from TeUS signals of nine patients. We model signals of benign and malignant tissues (284 and 286 signals, respectively) in their original temporal order as well as under order permutations. We then compare the resulting models using the Kullback-Liebler divergence and assess their performance differences in characterization. Moreover, we train HMMs using TeUS signals of different durations and compare their model performance when differentiating tissue types.\</p\>\<p\>\textbf{RESULTS: }Our findings demonstrate that models of order-preserved signals perform statistically significantly better (85% accuracy) in tissue characterization compared to models of order-altered signals (62% accuracy). The performance degrades as more changes in signal order are introduced. Additionally, models trained on shorter sequences perform as accurately as models of longer sequences.\</p\>\<p\>\textbf{CONCLUSION: }The work presented here strongly indicates that temporal order has substantial impact on TeUS performance; thus, it plays a significant role in conveying tissue-specific information. Furthermore, shorter TeUS signals can relay sufficient information to accurately distinguish between tissue types.\</p\>\<p\>\textbf{SIGNIFICANCE: }Understanding the impact of TeUS properties facilitates the process of its adopting in diagnostic procedures and provides insights on improving its acquisition.\</p\>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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{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}

}

@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}

}