63 citations · 100 across the 7 of their papers we have counts for
7 papers
GDCNet: Calibrationless geometric distortion correction of echo planar imaging data using deep learning
Marina Manso Jimeno, Keren Bachi, George Gardner +3
Functional magnetic resonance imaging techniques benefit from echo-planar imaging's fast image acquisition but are susceptible to inhomogeneities in the main magnetic field, result…
Automated detection of motion artifacts in brain MR images using deep learning and explainable artificial intelligence
Marina Manso Jimeno, Keerthi Sravan Ravi, Maggie Fung +2
Quality assessment, including inspecting the images for artifacts, is a critical step during MRI data acquisition to ensure data quality and downstream analysis or interpretation s…
Repeatability of image quality in very low field MRI
Pavan Poojar Kunal Aggarwal, Marina Manso Jimeno, Sairam Geethanath
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 measurem…
Virtual Scanner Games: expanding access to Magnetic Resonance (MR) education through interactive web tutorials
Gehua Tong, Rishi Ananth, John Thomas Vaughan +1
Background: Magnetic Resonance Imaging (MRI) is a medical imaging technique that combines principles of physics, math, and engineering to look inside humans and animals, hence deli…
Developing and deploying deep learning models in brain MRI: a review
Kunal Aggarwal, Marina Manso Jimeno, Keerthi Sravan Ravi +2
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 a…
Superconducting magnet designs and MRI accessibility: a review
Marina Manso Jimeno, John Thomas Vaughan, Sairam Geethanath
Presently, Magnetic Resonance Imaging (MRI) magnets must deliver excellent magnetic field (B0) uniformity to achieve optimum image quality. Long magnets can satisfy the homogeneity…
Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networks
Fabian Balsiger, Amaresha Shridhar Konar, Shivaprasad Chikop +4
Magnetic resonance fingerprinting (MRF) quantifies multiple nuclear magnetic resonance parameters in a single and fast acquisition. Standard MRF reconstructs parametric maps using…