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A hardware and software system for MRI applications requiring external device data
Author(s) -
Isaieva Karyna,
Fauvel Marc,
Weber Nicolas,
Vuissoz PierreAndré,
Felblinger Jacques,
Oster Julien,
Odille Freddy
Publication year - 2022
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.29280
Subject(s) - computer science , software , computer hardware , data acquisition , usb , synchronizing , real time computing , embedded system , artificial intelligence , transmission (telecommunications) , telecommunications , programming language , operating system
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.