AJNR. American journal of neuroradiology
Sep 29, 2022
BACKGROUND AND PURPOSE: Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learni...
AJNR. American journal of neuroradiology
Jul 28, 2022
BACKGROUND AND PURPOSE: Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could pr...
AJNR. American journal of neuroradiology
Jul 28, 2022
BACKGROUND AND PURPOSE: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require manual lesion annotations for model development, while unsupervised deep learning methods such as gener...
AJNR. American journal of neuroradiology
Apr 28, 2022
BACKGROUND AND PURPOSE: Prioritizing reading of noncontrast head CT examinations through an automated triage system may improve time to care for patients with acute neuroradiologic findings. We present a natural language-processing approach for label...
AJNR. American journal of neuroradiology
Feb 17, 2022
BACKGROUND AND PURPOSE: MR imaging provides critical information about fetal brain growth and development. Currently, morphologic analysis primarily relies on manual segmentation, which is time-intensive and has limited repeatability. This work aimed...
AJNR. American journal of neuroradiology
Jan 27, 2022
BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-...
AJNR. American journal of neuroradiology
Jan 6, 2022
BACKGROUND AND PURPOSE: Accurate radiologic prediction of cavernous sinus invasion by pituitary adenoma remains challenging. We aimed to assess whether 1-mm-slice-thickness MRI with deep learning-based reconstruction can better predict cavernous sinu...
AJNR. American journal of neuroradiology
Dec 2, 2021
BACKGROUND AND PURPOSE: Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentatio...
AJNR. American journal of neuroradiology
Nov 25, 2021
BACKGROUND AND PURPOSE: In this prospective, multicenter, multireader study, we evaluated the impact on both image quality and quantitative image-analysis consistency of 60% accelerated volumetric MR imaging sequences processed with a commercially av...
AJNR. American journal of neuroradiology
Aug 19, 2021
BACKGROUND AND PURPOSE: Communication gaps exist between radiologists and referring physicians in conveying diagnostic certainty. We aimed to explore deep learning-based bidirectional contextual language models for automatically assessing diagnostic ...