Assessment of the low-field magnetic resonance imaging for the brain scan imaging of the infant hydrocephalus.

Objective: In today's global clinical settings, low-field magnetic resonance imaging (MRI) technology is becoming increasingly prevalent. Ensuring high-quality image acquisition is crucial for accurate disease diagnosis and treatment and for evaluating the impact of poor-quality images. In this study, we explored the potential of deep learning as a diagnostic tool for improving image quality in hydrocephalus analysis planning. This could include discussions on the diagnostic accuracy, cost-effectiveness, and practicality of using low-field MRI as an alternative.

Methods: There are many reasons which are going to affect infant computed tomography images. These are spatial resolution, noise, and contrast between the brain and cerebrospinal fluid (CSF). Now, we can enhance using the application of deep learning algorithms. Both improved and down quality were situated to the three qualified pediatric neurosurgeons comfortable with working in poor- to middle-level income countries for the analysis of clinical tools in the planning of the treatment of hydrocephalus.

Results: The results predict that a picture will be classified as beneficial for hydrocephalus treatment planning, according to image resolution and the contrast-to-noise ratio (CNR) between the brain and CSF. The CNR is significantly improved by deep learning enhancement, which also improves the apparent likelihood of the image.

Conclusion: However, poor-quality images might be desirable to image improved by deep learning, since those images will not offer the risk of confusing facts which could misguide the decision of the analysis of patients. Such findings support the newly introduced measurement standards in estimating the acceptable quality of images for clinical use.