AIMC Topic: Magnetic Resonance Imaging
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Deep learning-accelerated T2-weighted imaging of the prostate: Impact of further acceleration with lower spatial resolution on image quality.
European journal of radiology
Oct 30, 2021
PURPOSE: To compare image quality in prostate MRI among standard T2-weighted imaging (T2-std), accelerated T2-weighted imaging (T2WI) with high resolution (T2-HR) and more accelerated T2WI with lower resolution (T2-LR) using both conventional reconst...
Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease.
Neuroscience letters
Oct 29, 2021
Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI) based CAD methods can help reveal structural changes in brain. Classifier is a key component in CAD...
Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm.
Magnetic resonance imaging
Oct 29, 2021
OBJECTIVE: To assess a new deep learning-based MR reconstruction method, "DLRecon," for clinical evaluation of peripheral nerves.
Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.
Computers in biology and medicine
Oct 29, 2021
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation pro...
Subchondral Bone Length in Knee Osteoarthritis: A Deep Learning-Derived Imaging Measure and Its Association With Radiographic and Clinical Outcomes.
Arthritis & rheumatology (Hoboken, N.J.)
Oct 29, 2021
OBJECTIVE: To develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis (OA) and test it against estimates of disease severity.
Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma.
Scientific reports
Oct 29, 2021
Even a tiny functioning pituitary adenoma could cause symptoms; hence, accurate diagnosis and treatment are crucial for management. However, it is difficult to diagnose a small pituitary adenoma using conventional MR sequence. Deep learning-based rec...
Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm.
Scientific data
Oct 28, 2021
Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a...
Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.
BMC psychiatry
Oct 28, 2021
BACKGROUND: Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-state functional neuroimaging have revealed alterations in graph-based brain topological network metri...
White matter hyperintensities segmentation using an ensemble of neural networks.
Human brain mapping
Oct 27, 2021
White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network ...
Automated post-operative brain tumour segmentation: A deep learning model based on transfer learning from pre-operative images.
Magnetic resonance imaging
Oct 27, 2021
Automated brain tumour segmentation from post-operative images is a clinically relevant yet challenging problem. In this study, an automated method for segmenting brain tumour into its subregions has been developed. The dataset consists of multimodal...
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