An Efficient and Accurate Super-Resolution Approach to Low-Field MRI via U-Net Architecture With Logarithmic Loss and L2 Regularization
Low-field (LF) MRI scanners have the power to revolutionize medical imaging by provid- 27 ing a portable and cheaper alternative to high-field MRI scanners. However, such scanners are usu- 28 ally significantly noisier and lower quality than their high-field counterparts. This prevents them 29 from appealing to global markets. The aim of this paper is to improve the SNR and overall image quality of low-field MRI scans (called super-resolution) to improve diagnostic capability and, as a result, make it more accessible. To address this issue, we propose a Nested U-Net neural network architecture super-resolution algorithm that outperforms previously suggested super-resolution deep learning methods with an average PSNR of 78.83 ± 0.01 and SSIM of 0.9551 ± 0.01. Our ANOVA paired t-test and Post-Hoc Tukey test demonstrate significance with a p-value < 0.0001 and no other network demonstrating significance higher than 0.1. We tested our network on artificial noisy downsampled synthetic data from 1500 T1 weighted MRI images through the dataset called the T1- mix. Four board-certified radiologists scored 25 images (100 image ratings total) on the Likert scale (1-5) assessing overall image quality, anatomical structure, and diagnostic confidence across our architecture and other published works (SR DenseNet, Generator Block, SRCNN, etc.). Our algo- rithm outperformed all other works with the highest MOS, 4.4 ± 0.3. We also introduce a new type of loss function called natural log mean squared error (NLMSE), outperforming MSE, MAE, and MSLE on this specific SR task. Additionally, we ran inference on actual Hyperfine scan images with successful qualitative results using a Generator RRDB block. In conclusion, we present a more ac- curate deep learning method for single image super-resolution applied to low-field MRI via a 45 Nested U-Net architecture.
Analysis on a New Electric Field Measurement Method Using Ionic Propulsion Propeller
Given the high sensitivity of electronic instruments, electromagnetic field intensity measuring is now becoming an essential part of the industry. Current electric field intensity meters are unfit for individual use and focus mainly on electromagnetic radiation rather than the field itself. In ionic propulsion, the propulsion force is proportional to electric field intensity but the use of this property on measurement remains largely unexplored. Here, our team investigates ionic propulsion in electric fields generated by electro-static methods and then systematically varies the point of measurement inside the field, thereby altering the intensity of the field without focusing on electromagnetic radiation. By combining the Van de graaff generator with an adjustable ionic thrust propeller, we find that the propeller speed which is proportional to the electric field is directly determined by the electric field intensity. Furthermore, we applied stroboscopy to the system to measure RPM, and have achieved the direct interaction between field intensity and RPM, which could be a new meter for field intensity measurement.
Study of regenerative and ontogenetic processes under the influence of EHF EMR.
The increased sensitivity of aquatic organisms to the effects of EMF has been proven by numerous experimental studies. It has been repeatedly noted that exposure to EMF of certain frequencies and intensities leads to disruption of physiological functions, orientation in time and space, changes in the behavior of organisms, suppression of motor activity. Other ranges of electromagnetic radiation, on the contrary, can cause the effects of increased regeneration, growth rate and survival. In connection with these trends, the purpose of our research is to analyze the effects of the influence of electromagnetic radiation of extremely high frequency on the development of the Xenopus laevis and the regeneration of newts and planarians