AIMC Topic: Magnetic Resonance Imaging
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Deep learning-based reconstruction and superresolution for MR-guided thermal ablation of malignant liver lesions.
Cancer imaging : the official publication of the International Cancer Imaging Society
Apr 2, 2025
OBJECTIVE: This study evaluates the impact of deep learning-enhanced T1-weighted VIBE sequences (DL-VIBE) on image quality and procedural parameters during MR-guided thermoablation of liver malignancies, compared to standard VIBE (SD-VIBE).
Machine learning fusion for glioma tumor detection.
Scientific reports
Apr 2, 2025
The early detection of brain tumors is very important for treating them and improving the quality of life for patients. Through advanced imaging techniques, doctors can now make more informed decisions. This paper introduces a framework for a tumor d...
Deep Learning-Based Reconstruction for Accelerated Cervical Spine MRI: Utility in the Evaluation of Myelopathy and Degenerative Diseases.
AJNR. American journal of neuroradiology
Apr 2, 2025
BACKGROUND AND PURPOSE: Deep learning (DL)-based reconstruction enables improving the quality of MR images acquired with a short scan time. We aimed to prospectively compare the image quality and diagnostic performance in evaluating cervical degenera...
Leveraging Physics-Based Synthetic MR Images and Deep Transfer Learning for Artifact Reduction in Echo-Planar Imaging.
AJNR. American journal of neuroradiology
Apr 2, 2025
BACKGOUND AND PURPOSE: This study utilizes a physics-based approach to synthesize realistic MR artifacts and train a deep learning generative adversarial network (GAN) for use in artifact reduction on EPI, a crucial neuroimaging sequence with high ac...
Comprehensive Segmentation of Gray Matter Structures on T1-Weighted Brain MRI: A Comparative Study of Convolutional Neural Network, Convolutional Neural Network Hybrid-Transformer or -Mamba Architectures.
AJNR. American journal of neuroradiology
Apr 2, 2025
BACKGROUND AND PURPOSE: Recent advances in deep learning have shown promising results in medical image analysis and segmentation. However, most brain MRI segmentation models are limited by the size of their data sets and/or the number of structures t...
Multiparametric MRI-based Interpretable Machine Learning Radiomics Model for Distinguishing Between Luminal and Non-luminal Tumors in Breast Cancer: A Multicenter Study.
Academic radiology
Apr 1, 2025
RATIONALE AND OBJECTIVES: To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtyp...
A public benchmark for human performance in the detection of focal cortical dysplasia.
Epilepsia open
Apr 1, 2025
OBJECTIVE: This study aims to report human performance in the detection of Focal Cortical Dysplasias (FCDs) using an openly available dataset. Additionally, it defines a subset of this data as a "difficult" test set to establish a public baseline ben...
Enhancing brain age estimation under uncertainty: A spectral-normalized neural gaussian process approach utilizing 2.5D slicing.
NeuroImage
Apr 1, 2025
Brain age gap, the difference between estimated brain age and chronological age via magnetic resonance imaging, has emerged as a pivotal biomarker in the detection of brain abnormalities. While deep learning is accurate in estimating brain age, the a...
Patient-specific MRI super-resolution via implicit neural representations and knowledge transfer.
Physics in medicine and biology
Apr 1, 2025
Magnetic resonance imaging (MRI) is a non-invasive imaging technique that provides high soft tissue contrast, playing a vital role in disease diagnosis and treatment planning. However, due to limitations in imaging hardware, scan time, and patient co...
Impact of harmonization on predicting complications in head and neck cancer after radiotherapy using MRI radiomics and machine learning techniques.
Medical physics
Mar 31, 2025
BACKGROUND: Variations in medical images specific to individual scanners restrict the use of radiomics in both clinical practice and research. To create reproducible and generalizable radiomics-based models for outcome prediction and assessment, data...
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