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.

This paper presents a framework for the design of a haptic-based medical ultrasound training simulator. The proposed simulator is composed of a PHANToM haptic device and a modular software package that allows for visual feedback and kinesthetic interactions between an operator and multimodality image databases. The system provides real-time ultrasound images in the same fashion as a typical ultrasound machine, enhanced with corresponding augmented computerized tomographic (CT) and/or MRI images. The proposed training system allows trainees to develop radiology techniques and knowledge of the patient's anatomy with minimum practice on live patients, or in places or at times when radiology devices or patients with rare cases may not be available. Low-level details of the software structure that can be migrated to other similar medical simulators are described. A preliminary human factors study, conducted on the prototype of the developed simulator, demonstrates the potential usage of the system for clinical training.

This work presents a haptic-based medical ultrasound diagnostic simulator that can be used as an ultrasound training tool for radiology residents as well as an examination system for remote applications. The proposed system allows to develop radiology expertise with minimum practice on live patients, or in places or at times when radiology devices or patients with rare cases may not be available. The proposed simulator consists of a PC workstation with dual monitors, a PHANToMtrade haptic device and a modular software package that allows for visual feedback and kinesthetic interactions between the operator and multi-modality image databases. The system helps emulate a real ultrasound examination condition at hospital, which is enhanced with augmented CT and/or MRI images. The haptic interface creates position correspondence between the operator's hand and a virtual probe. Preliminary human factors studies have demonstrated significant potential of the developed system for scientific and commercial applications