AIMC Topic: Magnetic Resonance Imaging

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Electromagnetic interference elimination via active sensing and deep learning prediction for radiofrequency shielding-free MRI.

NMR in biomedicine
At present, MRI scans are typically performed inside fully enclosed radiofrequency (RF) shielding rooms, posing stringent installation requirements and causing patient discomfort. We aim to eliminate electromagnetic interference (EMI) for MRI with no...

Direct synthesis of multi-contrast brain MR images from MR multitasking spatial factors using deep learning.

Magnetic resonance in medicine
PURPOSE: To develop a deep learning method to synthesize conventional contrast-weighted images in the brain from MR multitasking spatial factors.

Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine.

Skeletal radiology
OBJECTIVES: To validate the subjective image quality and lesion detectability of deep learning-accelerated Dixon (DL-Dixon) imaging of the cervical spine compared with routine Dixon imaging.

Non-invasive grading of brain tumors using online support vector machine with dynamic fuzzy rule-based parameters optimization.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine
Non-invasive grading of brain tumors provides a valuable understanding of tumor growth that helps choose the proper treatment. In this paper, an online method with an innovative optimization approach as well as a new and fast tumor segmentation metho...

Evaluation of motion artefact reduction depending on the artefacts' directions in head MRI using conditional generative adversarial networks.

Scientific reports
Motion artefacts caused by the patient's body movements affect magnetic resonance imaging (MRI) accuracy. This study aimed to compare and evaluate the accuracy of motion artefacts correction using a conditional generative adversarial network (CGAN) w...

Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?

The British journal of radiology
OBJECTIVES: Current state-of-the-art natural language processing (NLP) techniques use transformer deep-learning architectures, which depend on large training datasets. We hypothesized that traditional NLP techniques may outperform transformers for sm...

Graphic Intelligent Diagnosis of Hypoxic-Ischemic Encephalopathy Using MRI-Based Deep Learning Model.

Neonatology
INTRODUCTION: Heterogeneous MRI manifestations restrict the efficiency and consistency of neuroradiologists in diagnosing hypoxic-ischemic encephalopathy (HIE) due to complex injury patterns. This study aimed to develop and validate an intelligent HI...

Prenatal Diagnosis of Placenta Accreta Spectrum Disorders: Deep Learning Radiomics of Pelvic MRI.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potential to quantify the MRI features of PAS.

Development of a deep-learning model for classification of LI-RADS major features by using subtraction images of MRI: a preliminary study.

Abdominal radiology (New York)
PURPOSE: Liver Imaging Reporting and Data System (LI-RADS) is limited by interreader variability. Thus, our study aimed to develop a deep-learning model for classifying LI-RADS major features using subtraction images using magnetic resonance imaging ...