Farhad Imani

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Ultrasound (US) radio-frequency (RF) time series is an effective tissue classification method that enables accurate cancer diagnosis, but the mechanisms underlying this method are not completely understood. This paper presents a model to describe the variations in tissue temperature and sound speed that take place during the RF time series scanning(More)
PURPOSE This paper is the first report on the monitoring of tissue ablation using ultrasound RF echo time series. METHODS We calcuate frequency and time domain features of time series of RF echoes from stationary tissue and transducer, and correlate them with ablated and non-ablated tissue properties. RESULTS We combine these features in a nonlinear(More)
In surface-based registration for image-guided interventions, the presence of missing data can be a significant issue. This often arises with real-time imaging modalities such as ultrasound, where poor contrast can make tissue boundaries difficult to distinguish from surrounding tissue. Missing data poses two challenges: ambiguity in establishing(More)
PURPOSE This paper presents the results of a large study involving fusion prostate biopsies to demonstrate that temporal ultrasound can be used to accurately classify tissue labels identified in multi-parametric magnetic resonance imaging (mp-MRI) as suspicious for cancer. METHODS We use deep learning to analyze temporal ultrasound data obtained from 255(More)
PURPOSE In recent years, fusion of multi-parametric MRI (mp-MRI) with transrectal ultrasound (TRUS)-guided biopsy has enabled targeted prostate biopsy with improved cancer yield. Target identification is solely based on information from mp-MRI, which is subsequently transferred to the subject coordinates through an image registration approach. mp-MRI has(More)
A discrete event simulation model was developed to represent the current vehicle axle and spring assembly lines and understand their dynamics for an automotive company in need of production increase to accommodate expected demand growth. The aim of this study is to provide viable manufacturing plans to improve productivity, and we propose several(More)
UNLABELLED This paper presents the results of a computer-aided intervention solution to demonstrate the application of RF time series for characterization of prostate cancer, in vivo. METHODS We pre-process RF time series features extracted from 14 patients using hierarchical clustering to remove possible outliers. Then, we demonstrate that the mean(More)