Purpose We design parallel transmission (pTx) spokes pulses with time-shifted sub-pulse profiles for joint mitigation of intensity variations due to B1+ effects, signal loss due to through-plane dephasing and the specific absorption rate (SAR) at 7 Tesla. Methods We derive a slice-averaged small tip-angle (SA-STA) approximation of the magnetization signal at echo time that depends on the B1+ transmit profiles, the through-plane B0 gradient and the amplitude and time-shifts of the spoke waveforms. We minimize a magnitude least-squares (MLS) objective based on this signal equation using a fast interior-point approach with analytical expressions of the Jacobian and Hessian. Results Our algorithm runs in less than three minutes for the design of 2-spoke pulses subject to hundreds of local SAR constraints (single CPU, Matlab implementation). On a B0/B1+ head phantom, joint optimization of the channel-dependent time-shifts and spoke amplitudes allowed signal recovery in high-B0 regions at no increase of SAR. Although the method creates uniform magnetization profiles (i.e., uniform intensity), the flip-angle varies across the image which makes it ill-suited to T1-weighted applications. Conclusions The SA-STA approach presented in this work is best suited to T2*-weighted applications with long echo times that require signal recovery around B0 “hotspots”.

T his article presents results from the integration of a noncontact physiological radar monitoring system (PRMS) with a type I polysomnography (PSG) system to perform sleep monitoring. The PRMS system consists of two continuous-wave Doppler radars operating at the industrial, scientific, and medical (ISM) band of 2.45 GHz. The system can acquire data, perform digital processing, and output appropriate conventional analog outputs with a latency of approximately 130 ms, which can be recorded and displayed by a gold standard sleep monitoring sys-tem along with other standard sensor measurements. Radar data was also used to successfully detect paradoxical motion that was simulated using linear movers and to categorize normal breathing, apnea, and hypopnea in sleeping subjects with obstructive sleep apnea (OSA). OSA Using PSGWith about 15 million Americans suffering from OSA [1], it is one of the most common health disorders. Studies show a relationship between sleep apnea and cardiovascular diseases. Many patients with heart

Proposed is a detection algorithm for physiological monitoring with Ultra Wide Band (UWB) radar. This new algorithm is based on detection of movement energy in a specified band of frequency using wavelet and filter banks. One of the advantages of this algorithm is its ability to detect heart and respiration rates of a subject in an environment containing other motion. The heart movement is detected with the accuracy of 95% and respiration with the 100%. This algorithm has a repeatability of 93% which is a significant characteristic of the method.

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Recently, Ultra-wide band signals have become attractive for their particular advantage of having high spatial resolution and good penetration ability which makes them suitable in medical applications. One of these applications is wireless detection of heart rate and respiration rate. Two hypothesis of static environment and fixed patient are considered in the method presented in previous literatures which are not valid for long term monitoring of ambulant patients. In this article, a new method to detect the respiration rate of a moving target is presented. The first algorithm is applied to the simulated and experimental data for detecting respiration rate of a fixed target. Then, the second algorithm is developed to detect respiration rate of a moving target. The proposed algorithm uses correlation for body movement cancellation, and then detects the respiration rate based on energy in frequency domain. The results of algorithm prove an accuracy of 98.4 and 97% in simulated and experimental data, respectively.

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