OBJECTIVES: The aim of this study was to investigate the feasibility and impact of a novel deep learning superresolution algorithm tailored to partial Fourier allowing retrospectively theoretical acquisition time reduction in 1.5 T T1-weighted gradie...
PURPOSE: Deep learning (DL) models have rapidly become a popular and cost-effective tool for image classification within oncology. A major limitation of DL models is their vulnerability to adversarial images, manipulated input images designed to caus...
Magnetic resonance imaging clinics of North America
Feb 1, 2022
Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and di...
MATERIALS AND METHODS: This monocentric retrospective study leveraged 200 multiparametric brain MRIs acquired between November 2019 and February 2020 at Gustave Roussy Cancer Campus (Villejuif, France). A total of 145 patients were included: 107 form...
BACKGROUND: Longitudinal measurement of tumor burden with magnetic resonance imaging (MRI) is an essential component of response assessment in pediatric brain tumors. We developed a fully automated pipeline for the segmentation of tumors in pediatric...
Cerebral cortex (New York, N.Y. : 1991)
Jan 22, 2022
Mild cognitive impairment (MCI) is often considered the precursor of Alzheimer's disease. However, MCI is associated with substantially variable progression rates, which are not well understood. Attempts to identify the mechanisms that underlie MCI p...
Theaim of this study was to determine the effect formedby pelvic diameters preoperatively measured throughmultiparametric magnetic resonance imaging (mp-MRI) and different surgical positions on anesthesiaparameters used during perineal robot-assisted...
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