Purpose: Numerous MRI applications require data from external devices. Such devices are often independent of the MRI system, so synchronizing these data with the MRI data is often tedious and limited to offline use. In this work, a hardware and software system is proposed for acquiring data from external devices during MR imaging, for use online (in real-time) or offline.
Methods:The hardware includes a set of external devices -electrocardiography (ECG) devices, respiration sensors, microphone, electronics of the MR system etc. -using various channels for data transmission (analog, digital, optical fibers), all connected to a server through a universal serial bus (USB) hub. The software is based on a flexible client-server architecture, allowing real-time processing pipelines to be configured and executed. Communication protocols and data formats are proposed, in particular for transferring the external device data to an open-source reconstruction software (Gadgetron), for online image reconstruction using external physiological data. The system performance is evaluated in terms of accuracy of the recorded signals and delays involved in the real-time processing tasks. Its flexibility is shown with various applications.
Results:The real-time system had low delays and jitters (on the order of 1 ms). Example MRI applications using external devices included: prospectively gated cardiac cine imaging, multi-modal acquisition of the vocal tract (image, sound, and respiration) and online image reconstruction with nonrigid motion correction.
Conclusion:The performance of the system and its versatile architecture make it suitable for a wide range of MRI applications requiring online or offline use of external device data.
K E Y W O R D Shardware, physiological data, real-time, signal processing, software Karyna Isaieva and Marc Fauvel contributed equally to this work.
Results: Several themes were generated from the interviews which highlighted the difference in learning transvaginal ultrasound (TVUS) between clinical and simulation environments. The participants reported that the simulation learning environment was relaxed when compared to training during clinical practice, due to the opportunity to focus on the TV scans and to discuss the findings with colleagues in an unlimited length of time and without concerns about the patient's presence. The clinical training was reported as a stressful environment for learning due to the need to perform an invasive scan with limited experience, time constraints and limited privacy to perform learning attitudes in front of patients. Conclusions: Simulation-based training was found to be a friendly learning environment that provides a relaxed format for learning TVUS skills when compared to clinical-based training. The opportunity to train freely without time constraints in a private environment was of considerable importance to novice practitioners and was found to potentially induce efficient learning experiences once employed within the context of limited pressure. VP34.23 Usefulness of an intrapartum ultrasound simulator for midwives training: results from a RCT
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