AIMC Topic: Magnetic Resonance Imaging

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Functional Alignment-Auxiliary Generative Adversarial Network-Based Visual Stimuli Reconstruction via Multi-Subject fMRI.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Functional Magnetic Resonance Imaging (fMRI) provides more precise spatial and temporal information to reconstruct stimulus images than other technologies that can be used to measure the human brain's neural responses. The fMRI scans, however, genera...

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Human brain mapping
Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) can potentially provide underlying pathophysiological information by detecting the iron distribution ...

Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL.

Magnetic resonance in medicine
PURPOSE: The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths.

An Optimized Deep Learning Model for Predicting Mild Cognitive Impairment Using Structural MRI.

Sensors (Basel, Switzerland)
Early diagnosis of mild cognitive impairment (MCI) with magnetic resonance imaging (MRI) has been shown to positively affect patients' lives. To save time and costs associated with clinical investigation, deep learning approaches have been used widel...

Bloch simulator-driven deep recurrent neural network for magnetization transfer contrast MR fingerprinting and CEST imaging.

Magnetic resonance in medicine
PURPOSE: To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects.

Bidirectional feature matching based on deep pairwise contrastive learning for multiparametric MRI image synthesis.

Physics in medicine and biology
Multi-parametric MR image synthesis is an effective approach for several clinical applications where specific modalities may be unavailable to reach a diagnosis. While technical and practical conditions limit the acquisition of new modalities for a p...

Automated deep learning auto-segmentation of air volumes for MRI-guided online adaptive radiation therapy of abdominal tumors.

Physics in medicine and biology
. In the current MR-Linac online adaptive workflow, air regions on the MR images need to be manually delineated for abdominal targets, and then overridden by air density for dose calculation. Auto-delineation of these regions is desirable for speed p...

Utility of accelerated T2-weighted turbo spin-echo imaging with deep learning reconstruction in female pelvic MRI: a multi-reader study.

European radiology
OBJECTIVES: To determine the clinical feasibility of T2-weighted turbo spin-echo (T2-TSE) imaging with deep learning reconstruction (DLR) in female pelvic MRI compared with conventional T2 TSE in terms of image quality and scan time.

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

Diagnostic and interventional radiology (Ankara, Turkey)
PURPOSE: Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. De...