Publications by authors named "Marina Manso Jimeno"
Article Synopsis
- * A 2D convolutional neural network (CNN) was trained on synthesized data and tested on various datasets, achieving notable precision (85%) and recall (80%) in identifying motion issues.
- * The model demonstrated excellent agreement with a radiologist's assessments (93%) and correlates strongly with an image quality metric, aiming to streamline the quality assessment process, especially in low-resource environments.
Background: Very low-field MR has emerged as a promising complementary device to high-field MRI scanners, offering several advantages. One of the key benefits is that very low-field scanners are generally more portable and affordable to purchase and maintain, making them an attractive option for medical facilities looking to reduce costs. Very low-field MRI systems also have lower RF power deposition, making them safer and less likely to cause tissue heating or other safety concerns.
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- Magnetic Resonance Imaging (MRI) is essential for diagnosing Alzheimer's Disease (AD) but is often slow due to the need for expertise in optimizing imaging protocols, which affects its clinical value.
- The study shows how a deep learning-based approach can enhance MRI efficiency by improving image quality and increasing imaging throughput by 1.94 times using an accelerated protocol known as the Gold Standard (GS) protocol.
- The GS protocol is a significant part of comprehensive AD imaging, and the study also explores its potential for automating volumetric measurements of relevant brain structures related to AD diagnosis.
Magnetic resonance imaging (MRI) of the brain has benefited from deep learning (DL) to alleviate the burden on radiologists and MR technologists, and improve throughput. The easy accessibility of DL tools has resulted in a rapid increase of DL models and subsequent peer-reviewed publications. However, the rate of deployment in clinical settings is low.
View Article and Find Full Text PDFPresently, magnetic resonance imaging (MRI) magnets must deliver excellent magnetic field (B ) uniformity to achieve optimum image quality. Long magnets can satisfy the homogeneity requirements but require considerable superconducting material. These designs result in large, heavy, and costly systems that aggravate as field strength increases.
View Article and Find Full Text PDFLow-field MR scanners are more accessible in resource-constrained settings where skilled personnel are scarce. Images acquired in such scenarios are prone to artifacts such as wrap-around and Gibbs ringing. Such artifacts negatively affect the diagnostic quality and may be confused with pathology or reduce the region of interest visibility.
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