AIMC Topic: Magnetic Resonance Imaging

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Computer-Aided Detection AI Reduces Interreader Variability in Grading Hip Abnormalities With MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Accurate interpretation of hip MRI is time-intensive and difficult, prone to inter- and intrareviewer variability, and lacks a universally accepted grading scale to evaluate morphological abnormalities.

Discriminating stress from rest based on resting-state connectivity of the human brain: A supervised machine learning study.

Human brain mapping
Acute stress induces large-scale neural reorganization with relevance to stress-related psychopathology. Here, we applied a novel supervised machine learning method, combining the strengths of a priori theoretical insights with a data-driven approach...

Brain tumor classification of virtual NMR voxels based on realistic blood vessel-induced spin dephasing using support vector machines.

NMR in biomedicine
Remodeling of tissue microvasculature commonly promotes neoplastic growth; however, there is no imaging modality in oncology yet that noninvasively quantifies microvascular changes in clinical routine. Although blood capillaries cannot be resolved in...

Deep Learning Predicts Total Knee Replacement from Magnetic Resonance Images.

Scientific reports
Knee Osteoarthritis (OA) is a common musculoskeletal disorder in the United States. When diagnosed at early stages, lifestyle interventions such as exercise and weight loss can slow OA progression, but at later stages, only an invasive option is avai...

Brain Medical Image Fusion Based on Dual-Branch CNNs in NSST Domain.

BioMed research international
Computed tomography (CT) images show structural features, while magnetic resonance imaging (MRI) images represent brain tissue anatomy but do not contain any functional information. How to effectively combine the images of the two modes has become a ...

Evaluating severity of white matter lesions from computed tomography images with convolutional neural network.

Neuroradiology
PURPOSE: Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatica...

Perception-to-Image: Reconstructing Natural Images from the Brain Activity of Visual Perception.

Annals of biomedical engineering
The reappearance of human visual perception is a challenging topic in the field of brain decoding. Due to the complexity of visual stimuli and the constraints of fMRI data collection, the present decoding methods can only reconstruct the basic outlin...

Neural Message Passing for NMR Chemical Shift Prediction.

Journal of chemical information and modeling
Fast and accurate prediction of NMR spectra enables automatic structure validation and elucidation of molecules on a large scale. In this Article, we propose an improved method of learning from an NMR database to predict the chemical shifts of NMR-ac...

IOUC-3DSFCNN: Segmentation of Brain Tumors via IOU Constraint 3D Symmetric Full Convolution Network with Multimodal Auto-context.

Scientific reports
Accurate segmentation of brain tumors from magnetic resonance (MR) images play a pivot role in assisting diagnoses, treatments and postoperative evaluations. However, due to its structural complexities, e.g., fuzzy tumor boundaries with irregular sha...

Cardiac MR segmentation based on sequence propagation by deep learning.

PloS one
Accurate segmentation of myocardial in cardiac MRI (magnetic resonance image) is key to effective rapid diagnosis and quantitative pathology analysis. However, a low-quality CMR (cardiac magnetic resonance) image with a large amount of noise makes it...