AIMC Topic: Magnetic Resonance Imaging
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Quantification of tissue stiffness with magnetic resonance elastography and finite difference time domain (FDTD) simulation-based spatiotemporal neural network.
Magnetic resonance imaging
Feb 4, 2025
Quantification of tissue stiffness with magnetic resonance elastography (MRE) is an inverse problem that is sensitive to noise. Conventional methods for the purpose include direct inversion (DI) and local frequency estimation (LFE). In this study, we...
Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 4, 2025
BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the efficacy of large brain metastases (BMs)(≥2 cm), whereas the workflow requires optimized. This study develops a two-stage, personalized deep learning aut...
Automated detection of early-stage osteonecrosis of the femoral head in adult using YOLOv10: Multi-institutional validation.
European journal of radiology
Feb 4, 2025
OBJECTIVES: To develop a deep learning model based on the You Only Look Once version 10 (YOLOv10) for detecting early-stage ONFH in adult using radiographs.
Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.
Radiation oncology (London, England)
Feb 4, 2025
RATIONALE AND OBJECTIVES: This study evaluated StarGAN, a deep learning model designed to generate synthetic computed tomography (sCT) images from magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT) data using a single model. Th...
Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.
Scientific reports
Feb 4, 2025
In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RA...
CQformer: Learning Dynamics Across Slices in Medical Image Segmentation.
IEEE transactions on medical imaging
Feb 4, 2025
Prevalent studies on deep learning-based 3D medical image segmentation capture the continuous variation across 2D slices mainly via convolution, Transformer, inter-slice interaction, and time series models. In this work, via modeling this variation b...
Leveraging Input-Level Feature Deformation With Guided-Attention for Sulcal Labeling.
IEEE transactions on medical imaging
Feb 4, 2025
The identification of cortical sulci is key for understanding functional and structural development of the cortex. While large, consistent sulci (or primary/secondary sulci) receive significant attention in most studies, the exploration of smaller an...
UTSRMorph: A Unified Transformer and Superresolution Network for Unsupervised Medical Image Registration.
IEEE transactions on medical imaging
Feb 4, 2025
Complicated image registration is a key issue in medical image analysis, and deep learning-based methods have achieved better results than traditional methods. The methods include ConvNet-based and Transformer-based methods. Although ConvNets can eff...
An Explainable Unified Framework of Spatio-Temporal Coupling Learning With Application to Dynamic Brain Functional Connectivity Analysis.
IEEE transactions on medical imaging
Feb 4, 2025
Time-series data such as fMRI and MEG carry a wealth of inherent spatio-temporal coupling relationship, and their modeling via deep learning is essential for uncovering biological mechanisms. However, current machine learning models for mining spatio...
M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder.
IEEE transactions on medical imaging
Feb 4, 2025
Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging...
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