Today we have two papers that are probably very far from each other on the Technology Readiness Level scale. The first one deals with an improvement over traditional MRI by accelerating the signal acquisition and the other is barely at the embryonic stage of taking as many data as possible to see how new sensors could be developed and lead to actual dedicated compressive sensors:
Non-Contrast-Enhanced 4D MRA Using Compressed Sensing Reconstruction by T-C. Chang, M. S. Nadar, J. Guehring, M. O. Zenge, K. T. Block, P. Schmitt, and E. Mueller. The introduction reads:
In the recent years, non-contrast magnetic resonance angiography (NCE MRA) has been an emerging tool in the diagnosis of degenerative vascular disease [1]. In this context, a novel time-resolved data acquisition method was proposed [2]. This technique uses two ECG-triggered CINE-like bSSFP acquisitions of multiple 3D phases: One after selective and another one after non-selective inversion. With a subtraction of the two datasets, the background signal can be eliminated, and the difference between the signals of differently labeled inflowing blood yields time-resolved vascular information. Desired improvements include a reduction of the total scan time to reduce the impact of unintentional motion as well as an increase of the spatial and/or temporal resolution. Thus, the compressed sensing (CS) technique, which aims at reconstructing high quality signal from largely undersampled acquisitions, is a natural fit to further enhance time-resolved NCE-MRA applications.
Wearable Assistive System Design for Fall Prevention by Wenyao Xu, Fang Gong, Lei He, Majid Sarrafzadeh. The abstract reads:
Fall is the prevalent issue among the elderly, and fall risk assessment and prevention are very important. Recent research discovers that bio-signal can be used to forecast falls via the pre-warning information. However, assistive devices for fall prevention are fully customized and difficult to implement in terms of wearability. In the paper, we will introduce the framework to design and implement wearable systems. Also we will present three case studies: smart insole, smart cane and smart headset to verify the feasibility of our proposed method. To the best of our knowledge, it is the first comprehensive literature for the discussion about fall prevention technology.
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