AIMC Topic: Magnetic Resonance Imaging
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Higher effect sizes for the detection of accelerated brain volume loss and disability progression in multiple sclerosis using deep-learning.
Computers in biology and medicine
Oct 18, 2024
PURPOSE: Clinical validation of "BrainLossNet", a deep learning-based method for fast and robust estimation of brain volume loss (BVL) from longitudinal T1-weighted MRI, for the detection of accelerated BVL in multiple sclerosis (MS) and for the disc...
Deep learning radiomic nomogram outperforms the clinical model in distinguishing intracranial solitary fibrous tumors from angiomatous meningiomas and can predict patient prognosis.
European radiology
Oct 16, 2024
OBJECTIVES: To evaluate the value of a magnetic resonance imaging (MRI)-based deep learning radiomic nomogram (DLRN) for distinguishing intracranial solitary fibrous tumors (ISFTs) from angiomatous meningioma (AMs) and predicting overall survival (OS...
TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.
Medical image analysis
Oct 16, 2024
Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Existing quantitative approaches, like radiomics and deep learning ...
Deep plug-and-play MRI reconstruction based on multiple complementary priors.
Magnetic resonance imaging
Oct 16, 2024
Magnetic resonance imaging (MRI) is widely used in clinical diagnosis as a safe, non-invasive, high-resolution medical imaging technology, but long scanning time has been a major challenge for this technology. The undersampling reconstruction method ...
Achieving accurate prostate auto-segmentation on CT in the absence of MR imaging.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Oct 16, 2024
BACKGROUND: Magnetic resonance imaging (MRI) is considered the gold standard for prostate segmentation. Computed tomography (CT)-based segmentation is prone to observer bias, potentially overestimating the prostate volume by ∼ 30 % compared to MRI. H...
Multiparametric MRI Radiomics With Machine Learning for Differentiating HER2-Zero, -Low, and -Positive Breast Cancer: Model Development, Testing, and Interpretability Analysis.
AJR. American journal of roentgenology
Oct 16, 2024
MRI radiomics has been explored for three-tiered classification of HER2 expression levels (i.e., HER2-zero, HER2-low, or HER2-positive) in patients with breast cancer, although an understanding of how such models reach their predictions is lacking. ...
Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation.
European journal of radiology
Oct 15, 2024
OBJECTIVE: This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the...
A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.
Journal of Alzheimer's disease : JAD
Oct 14, 2024
BACKGROUND: Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors.
A machine learning algorithm for creating isotropic 3D aortic segmentations from routine cardiac MR localizers.
Magnetic resonance imaging
Oct 12, 2024
BACKGROUND: The identification and measurement of aortic aneurysms is an important clinical problem. While specialized high-resolution 3D CMR sequences allow detailed aortic assessment, they are time-consuming which limits their use in screening rout...
Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study.
Journal of imaging informatics in medicine
Oct 10, 2024
The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR images and fine needle aspiration biopsy, but these diagnostic tools have low sensitivity and speci...
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