AIMC Topic: Diffusion Magnetic Resonance Imaging
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Multi channel fusion diffusion models for brain tumor MRI data augmentation.
Scientific reports
Jul 2, 2025
The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for diagnosing brain tumors. However, high-quality medical image data for brain tumors is often scarce a...
Preoperative MRI-based deep learning reconstruction and classification model for assessing rectal cancer.
BMC medical imaging
Jul 1, 2025
BACKGROUND: To determine whether deep learning reconstruction (DLR) could improve the image quality of rectal MR images, and to explore the discrimination of the TN stage of rectal cancer by different readers and deep learning classification models, ...
Signal-To-Noise Ratio
Diffusion Magnetic Resonance Imaging
Neoplasm Staging
Rectal Neoplasms
Image Interpretation, Computer-Assisted
Female
Adult
Aged
Humans
Male
Retrospective Studies
Middle Aged
Aged, 80 and over
Image Processing, Computer-Assisted
Deep Learning
Magnetic Resonance Imaging
ROC Curve
Regional free-water diffusion is more strongly related to neuroinflammation than neurodegeneration.
Journal of neurology
Jun 25, 2025
INTRODUCTION: Recent research has suggested that neuroinflammation may be important in the pathogenesis of neurodegenerative diseases. Free-water diffusion (FWD) has been proposed as a non-invasive neuroimaging-based biomarker for neuroinflammation.
Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks.
PloS one
Jun 9, 2025
Stroke is the second leading cause of death, accounting for 11% of deaths worldwide. Comparing diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images is important for stroke diagnosis, but most studies have focused on lesion...
Toward diffusion MRI in the diagnosis and treatment of pancreatic cancer.
Medical oncology (Northwood, London, England)
May 28, 2025
Pancreatic cancer is a highly aggressive malignancy with rising incidence and mortality rates, often diagnosed at advanced stages. Conventional imaging methods, such as computed tomography (CT) and magnetic resonance imaging (MRI), struggle to assess...
Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.
Medical image analysis
Apr 20, 2025
Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI frame...
Quantitative Ischemic Lesions of Portable Low-Field Strength MRI Using Deep Learning-Based Super-Resolution.
Stroke
Apr 16, 2025
BACKGROUND: Deep learning-based synthetic super-resolution magnetic resonance imaging (SynthMRI) may improve the quantitative lesion performance of portable low-field strength magnetic resonance imaging (LF-MRI). The aim of this study is to evaluate ...
Deep learning-based automatic segmentation of cerebral infarcts on diffusion MRI.
Scientific reports
Apr 16, 2025
We explored effects of (1) training with various sample sizes of multi-site vs. single-site training data, (2) cross-site domain adaptation, and (3) data sources and features on the performance of algorithms segmenting cerebral infarcts on Magnetic R...
FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI.
NeuroImage
Apr 10, 2025
Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neu...
Diffusion MRI GAN synthesizing fibre orientation distribution data using generative adversarial networks.
Communications biology
Mar 28, 2025
Machine learning may enhance clinical data analysis but requires large amounts of training data, which are scarce for rare pathologies. While generative neural network models can create realistic synthetic data such as 3D MRI volumes and, thus, augme...
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