AIMC Topic: Magnetic Resonance Imaging

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A least square generative network based on invariant contrastive feature pair learning for multimodal MR image synthesis.

International journal of computer assisted radiology and surgery
PURPOSE: During MR-guided neurosurgical procedures, several factors may limit the acquisition of additional MR sequences, which are needed by neurosurgeons to adjust surgical plans or ensure complete tumor resection. Automatically synthesized MR cont...

Deep Learning of Time-Signal Intensity Curves from Dynamic Susceptibility Contrast Imaging Enables Tissue Labeling and Prediction of Survival in Glioblastoma.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: An autoencoder can learn representative time-signal intensity patterns to provide tissue heterogeneity measures using dynamic susceptibility contrast MR imaging. The aim of this study was to investigate whether such an autoenc...

Artificial intelligence assisted whole organ pancreatic fat estimation on magnetic resonance imaging and correlation with pancreas attenuation on computed tomography.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]
BACKGROUND: Fatty pancreas is associated with inflammatory and neoplastic pancreatic diseases. Magnetic resonance imaging (MRI) is the diagnostic modality of choice for measuring pancreatic fat. Measurements typically use regions of interest limited ...

[Feasibility Study of the Prediction of Radiologist's Instructions with the Bi-LSTM Model Trained with Descriptions of MR Imaging Order-statement].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: Magnetic resonance (MR) images provide essential diagnostic information; however, it is also a very burdensome examination for patients. At our hospital, radiologists make imaging instructions for all MR examination orders, but this is a tim...

Adrenal lesion classification with abdomen caps and the effect of ROI size.

Physical and engineering sciences in medicine
Accurate classification of adrenal lesions on magnetic resonance (MR) images are very important for diagnosis and treatment planning. The detection and classification of lesions in medical imaging heavily rely on several key factors, including the sp...

The potential for clinical application of automatic quantification of olfactory bulb volume in MRI scans using convolutional neural networks.

NeuroImage. Clinical
The olfactory bulbs (OBs) play a key role in olfactory processing; their volume is important for diagnosis, prognosis and treatment of patients with olfactory loss. Until now, measurements of OB volumes have been limited to quantification of manually...

Cerebrovascular super-resolution 4D Flow MRI - Sequential combination of resolution enhancement by deep learning and physics-informed image processing to non-invasively quantify intracranial velocity, flow, and relative pressure.

Medical image analysis
The development of cerebrovascular disease is tightly coupled to regional changes in intracranial flow and relative pressure. Image-based assessment using phase contrast magnetic resonance imaging has particular promise for non-invasive full-field ma...

A deep learning-based reconstruction approach for accelerated magnetic resonance image of the knee with compressed sense: evaluation in healthy volunteers.

The British journal of radiology
OBJECTIVES: To evaluate the feasibility of combining compressed sense (CS) with a newly developed deep learning-based algorithm (CS-AI) using convolutional neural networks to accelerate 2D MRI of the knee.

Left ventricle segmentation combining deep learning and deformable models with anatomical constraints.

Journal of biomedical informatics
Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required from the expert, many automatic segmentation methods have been proposed, in...