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Repeatability protocol. The study was performed over ten days. Three imaging sessions were performed daily, each containing temperature and humidity measurements before and after the scan. The imaging protocol measured transmit frequency, off-resonance mapping, and 3D imaging. The acquisition details are listed in Table 1
Source publication
We investigated the repeatability of image quality metrics such as SNR, image uniformity, and geometrical distortion at 0.05T over ten days and three sessions per day. The measurements included temperature, humidity, transmit frequency, off-resonance maps, and 3D turbo spin echo (TSE) images of an in vitro phantom. This resulted in a protocol with...
Contexts in source publication
Context 1
... measured temperature (°C) and humidity (%RH) before and after each Session at three locations. Figure 1 presents a schematic diagram outlining the details of the study, including the Session and time information. Supplementary Figure 1 shows a picture of the 0.05T scanner and the locations where temperature and humidity were measured using a portable digital thermo-hygrometer. ...
Context 2
... 1 presents a schematic diagram outlining the details of the study, including the Session and time information. Supplementary Figure 1 shows a picture of the 0.05T scanner and the locations where temperature and humidity were measured using a portable digital thermo-hygrometer. The three locations were: in front of the phantom within the bore (location 1), in front of the bore (location 2), and in the center of the room (location 3), shown with orange, yellow, and green circles, respectively. ...
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... The phantom dataset comprises T1-weighted axial Pro-MRI phantom images acquired using a single-coil 0.05T Multiwave MGNTQ MRI scanner during a repeatability study (Aggarwal P. P. K. et al., 2023). The images used were not corrected for geometric distortion. ...
Low-field MRI is gaining interest, especially in low-resource settings, due to its low cost, portability, small footprint, and low power consumption. However, it suffers from significant noise, limiting its clinical utility. This study introduces native noise denoising (NND), which leverages the inherent noise characteristics of the acquired low-field data. By obtaining the noise characteristics from corner patches of low-field images, we iteratively added similar noise to high-field images to create a paired noisy-clean dataset. A U-Net based denoising autoencoder was trained on this dataset and evaluated on three low-field datasets: the M4Raw dataset (0.3T), in vivo brain MRI (0.05T), and phantom images (0.05T). The NND approach demonstrated improvements in signal-to-noise ratio (SNR) of 32.76%, 19.02%, and 8.16% across the M4Raw, in vivo and phantom datasets, respectively. Qualitative assessments, including difference maps, line intensity plots, and effective receptive fields, suggested that NND preserves structural details and edges compared to random noise denoising (RND), indicating potential enhancements in visual quality. This substantial improvement in low-field imaging quality addresses the fundamental challenge of diagnostic confidence in resource-constrained settings. By mitigating the primary technical limitation of these systems, our approach expands the clinical utility of low-field MRI scanners, potentially facilitating broader access to diagnostic imaging across resource-limited healthcare environments globally.
