AIMC Topic:
Magnetic Resonance Imaging
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A motion-corrected deep-learning reconstruction framework for accelerating whole-heart magnetic resonance imaging in patients with congenital heart disease.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Mar 22, 2024
BACKGROUND: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment and management of adult patients with congenital heart disease (CHD). However, conventional techniques for three-dimensional (3D) whole-heart acqu...
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling and Annotation Redundancy Restriction.
Journal of imaging informatics in medicine
Mar 21, 2024
Deep learning models have demonstrated great potential in medical imaging but are limited by the expensive, large volume of annotations required. To address this, we compared different active learning strategies by training models on subsets of the m...
Deep learning-based image reconstruction for the multi-arterial phase images: improvement of the image quality to assess the small hypervascular hepatic tumor on gadoxetic acid-enhanced liver MRI.
Abdominal radiology (New York)
Mar 21, 2024
PURPOSE: To evaluated the impact of a deep learning (DL)-based image reconstruction on multi-arterial-phase magnetic resonance imaging (MA-MRI) for small hypervascular hepatic masses in patients who underwent gadoxetic acid-enhanced liver MRI.
Abnormal Brachial Plexus Differentiation from Routine Magnetic Resonance Imaging: An AI-based Approach.
Neuroscience
Mar 21, 2024
Automatic abnormality identification of brachial plexus (BP) from normal magnetic resonance imaging to localize and identify a neurologic injury in clinical practice (MRI) is still a novel topic in brachial plexopathy. This study developed and evalua...
The features associated with mammography-occult MRI-detected newly diagnosed breast cancer analysed by comparing machine learning models with a logistic regression model.
La Radiologia medica
Mar 21, 2024
PURPOSE: To compare machine learning (ML) models with logistic regression model in order to identify the optimal factors associated with mammography-occult (i.e. false-negative mammographic findings) magnetic resonance imaging (MRI)-detected newly di...
Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods.
Abdominal radiology (New York)
Mar 21, 2024
OBJECTIVE: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients wit...
Diagnosing schizophrenia using deep learning: Novel interpretation approaches and multi-site validation.
Brain research
Mar 20, 2024
Schizophrenia is a profound and enduring mental disorder that imposes significant negative impacts on individuals, their families, and society at large. The development of more accurate and objective diagnostic tools for schizophrenia can be expedite...
Financial impact of incorporating deep learning reconstruction into magnetic resonance imaging routine.
European journal of radiology
Mar 20, 2024
PURPOSE: Artificial intelligence and deep learning solutions are increasingly utilized in healthcare and radiology. The number of studies addressing their enhancement of productivity and monetary impact is, however, still limited. Our hospital has fa...
Unpaired deep learning for pharmacokinetic parameter estimation from dynamic contrast-enhanced MRI without AIF measurements.
NeuroImage
Mar 20, 2024
DCE-MRI provides information about vascular permeability and tissue perfusion through the acquisition of pharmacokinetic parameters. However, traditional methods for estimating these pharmacokinetic parameters involve fitting tracer kinetic models, w...
An Analysis of Loss Functions for Heavily Imbalanced Lesion Segmentation.
Sensors (Basel, Switzerland)
Mar 20, 2024
Heavily imbalanced datasets are common in lesion segmentation. Specifically, the lesions usually comprise less than 5% of the whole image volume when dealing with brain MRI. A common solution when training with a limited dataset is the use of specifi...
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