<p>With the development of wireless communication technology, second-, third-, fourth-generation transmission systems based on electromagnetic (EM) waves are widely used. At the same time, public concerns have been raised about whether the use of such systems will have an impact on health and safety standards. The heating effect is the most prominent and scientifically verifiable biological effect. So, the influence of EM waves on human body is addressed by studying the heating characteristics on head models.</p> <p>The objective of this thesis is to study the effects of radio frequency (RF) waves radiation from mobile phones on the human head under different transmission generations. The six-layer head model is used to evaluate the specific absorption rate (SAR) distribution and thermal effect. This model allows to study the effects of SAR and temperature rise without the need for high computational resources. In order to find the effect of temperature rise and exposure time, the finite difference time domain (FDTD) method was used to solve the biothermal equation with appropriate boundary conditions.</p> <p>Also, different age-dependent head models are used to study the differences of SAR for children at different ages.</p> <p>In general, the results show that with the increase in frequency, the influence of the EM wave is more pronounced, as demonstrated by the SAR and temperature rise distribution. In addition, SAR distribution of younger ages show a significant increase, which indicates that children exposed to EM waves are subject to more harmed. </p>

<div>Magnetic resonance imaging (MRI) has become an invaluable tool in health care. Despite its popularity, there is still an ever-increasing need for faster scans and better image quality. Multi-coil MRI, which uses multiple transmit and/or receive coils, holds the potential to address many of these MRI challenges. Multi-coil MRI systems can utilize parallel transmission (pTx) technology using multi-dimensional radio-frequency (RF) pulses for parallel excitation. The pTx platform is shown to be superior in high-field MRI. Therefore, this dissertation is focused on the RF pulse design and optimization on an MRI system with multiple transceiver coils.</div><div> </div><div>This dissertation addresses three major research topics. First, we investigate the optimization of pTx RF pulses when considering both transmitters and receivers of the MRI system. We term this framework multiple-input multiple-output (MIMO) MRI. The RF pulse design method is modeled by minimizing the excitation error while simultaneously maximizing the signal-to-noise ratio (SNR) of the reconstructed MR image. It further allows a key trade-off between the SNR and the excitation accuracy. Additionally, multiple acceleration factors, different numbers of used receive coils, maximum excitation error tolerance, and different excitation patterns are simulated and analyzed within this model. For a given excitation pattern, our method is shown to improve the SNR by 18-130% under certain acceleration schemes, as compared to conventional parallel transmission methods, while simultaneously controlling the excitation error in a desired scope.</div><div> </div><div>Second, we propose a pTx RF pulse design method that controls the peak local specific absorption rates (SARs) using a compressed set of SAR matrices. RF power, peak local SARs, excitation accuracy, and SNR are simultaneously controlled in the designed pTx RF pulses. An alternative compression method using k-means clustering algorithm is proposed for an upper-bounded estimation of peak local SARs. The performance of the pTx design method is simulated using a human head model and an eight-channel transceiver coil array. The proposed method reduces the 10-g peak local SAR by 44.6-54.2%, as compared to the unconstrained pTx approach, when it has a pre-defined lower bound of SNR and an upper bound of excitation error tolerance. The k-means clustering-based SAR compression model shows its efficiency as it generates a narrower and more accurate overestimation bound than the conventional SAR compression model.</div><div> </div><div>Finally, we propose two machine learning based pTx RF pulse design methods and test them for the ultra-fast pTx RF pulse prediction. The two methods proposed are the kernelized ridge regression (KRR) based pTx RF pulse design and the feedforward neural network (FNN) based pTx RF pulse design. These two methods learn the training pTx RF pulses from the extracted key features of their corresponding B1+ fields. These methods are compared with other supervised learning methods (nearest-neighbor methods, etc.). All learned pTx RF pulses should be reasonably SAR-efficient because training pTx RF pulses are SAR-efficient. Longer computation time and pre-scan time are the drawbacks of the current pTx approach, and we address this issue by instantaneously predicting pTx RF pulses using well-trained machine learning models.</div>