AIMC Topic:
Magnetic Resonance Imaging
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ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.
NeuroImage
Jul 24, 2023
Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to their success in classification, segmentation, and detection challenges. A principal challenge in large-scale deployment of DNNs in neuroimage analysi...
Functional Outcome Prediction in Acute Ischemic Stroke Using a Fused Imaging and Clinical Deep Learning Model.
Stroke
Jul 24, 2023
BACKGROUND: Predicting long-term clinical outcome based on the early acute ischemic stroke information is valuable for prognostication, resource management, clinical trials, and patient expectations. Current methods require subjective decisions about...
Dissociable default-mode subnetworks subserve childhood attention and cognitive flexibility: Evidence from deep learning and stereotactic electroencephalography.
Neural networks : the official journal of the International Neural Network Society
Jul 23, 2023
Cognitive flexibility encompasses the ability to efficiently shift focus and forms a critical component of goal-directed attention. The neural substrates of this process are incompletely understood in part due to difficulties in sampling the involved...
Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline.
European radiology
Jul 22, 2023
OBJECTIVES: This study aims at a fully automatic pipeline for measuring the magnetic resonance parkinsonism index (MRPI) using deep learning methods.
Multiple instance ensembling for paranasal anomaly classification in the maxillary sinus.
International journal of computer assisted radiology and surgery
Jul 21, 2023
PURPOSE: Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately class...
Deep learning-based local SAR prediction using B maps and structural MRI of the head for parallel transmission at 7 T.
Magnetic resonance in medicine
Jul 19, 2023
PURPOSE: To predict subject-specific local specific absorption rate (SAR) distributions of the human head for parallel transmission (pTx) systems at 7 T.
Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data.
AJR. American journal of roentgenology
Jul 19, 2023
The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton density fat fraction (PDFF) for hepatic fat quantification is not widely available. As an alternative, hepatic fat can be assessed by a two-point Dixon m...
Differentiation between multiple sclerosis and neuromyelitis optica spectrum disorder using a deep learning model.
Scientific reports
Jul 19, 2023
Multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) are autoimmune inflammatory disorders of the central nervous system (CNS) with similar characteristics. The differential diagnosis between MS and NMOSD is critical for initiat...
MRI-based deep learning model for differentiation of hepatic hemangioma and hepatoblastoma in early infancy.
European journal of pediatrics
Jul 18, 2023
UNLABELLED: Hepatic hemangioma (HH) and hepatoblastoma (HBL) are common pediatric liver tumors and present with similar clinical manifestations with limited distinguishing value of serum AFP in early infancy. An accurate differentiation diagnostic to...
Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Jul 18, 2023
BACKGROUND: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT fro...
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