AIMC Topic: Magnetic Resonance Imaging
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Predicting drug craving among ketamine-dependent users through machine learning based on brain structural measures.
Progress in neuro-psychopharmacology & biological psychiatry
Dec 9, 2024
BACKGROUND: Craving is a core factor driving drug-seeking and -taking, representing a significant risk factor for relapse. This study aims to identify neuroanatomical biomarkers for quantifying and predicting craving.
Enhancing brain tumor classification by integrating radiomics and deep learning features: A comprehensive study utilizing ensemble methods on MRI scans.
Journal of X-ray science and technology
Dec 9, 2024
BACKGROUND AND OBJECTIVE: This study aims to assess the effectiveness of combining radiomics features (RFs) with deep learning features (DFs) for classifying brain tumors-specifically Glioma, Meningioma, and Pituitary Tumor-using MRI scans and advanc...
Automated Segmentation of MRI White Matter Hyperintensities in 8421 Patients with Acute Ischemic Stroke.
AJNR. American journal of neuroradiology
Dec 9, 2024
BACKGROUND AND PURPOSE: To date, only a few small studies have attempted deep learning-based automatic segmentation of white matter hyperintensity (WMH) lesions in patients with cerebral infarction; this issue is complicated because stroke-related le...
Development and Validation of a Deep Learning System to Differentiate HER2-Zero, HER2-Low, and HER2-Positive Breast Cancer Based on Dynamic Contrast-Enhanced MRI.
Journal of magnetic resonance imaging : JMRI
Dec 6, 2024
BACKGROUND: Previous studies explored MRI-based radiomic features for differentiating between human epidermal growth factor receptor 2 (HER2)-zero, HER2-low, and HER2-positive breast cancer, but deep learning's effectiveness is uncertain.
Estimation of fatty acid composition in mammary adipose tissue using deep neural network with unsupervised training.
Magnetic resonance in medicine
Dec 6, 2024
PURPOSE: To develop a deep learning-based method for robust and rapid estimation of the fatty acid composition (FAC) in mammary adipose tissue.
Automated Classification of Body MRI Sequences Using Convolutional Neural Networks.
Academic radiology
Dec 6, 2024
RATIONALE AND OBJECTIVES: Multi-parametric MRI (mpMRI) studies of the body are routinely acquired in clinical practice. However, a standardized naming convention for MRI protocols and series does not exist currently. Conflicts in the series descripti...
Magnetic resonance image denoising for Rician noise using a novel hybrid transformer-CNN network (HTC-net) and self-supervised pretraining.
Medical physics
Dec 6, 2024
BACKGROUND: Magnetic resonance imaging (MRI) is a crucial technique for both scientific research and clinical diagnosis. However, noise generated during MR data acquisition degrades image quality, particularly in hyperpolarized (HP) gas MRI. While de...
A multi-view learning approach with diffusion model to synthesize FDG PET from MRI T1WI for diagnosis of Alzheimer's disease.
Alzheimer's & dementia : the journal of the Alzheimer's Association
Dec 6, 2024
INTRODUCTION: This study presents a novel multi-view learning approach for machine learning (ML)-based Alzheimer's disease (AD) diagnosis.
Automatic segmentation of pericardial adipose tissue from cardiac MR images via semi-supervised method with difference-guided consistency.
Medical physics
Dec 5, 2024
BACKGROUND: Accurate and automatic segmentation of pericardial adipose tissue (PEAT) in cardiac magnetic resonance (MR) images is essential for the diagnosis and treatment of cardiovascular diseases. Precise segmentation is challenging due to high co...
A prognostic and predictive model based on deep learning to identify optimal candidates for intensity-modulated radiotherapy alone in patients with stage II nasopharyngeal carcinoma: A retrospective multicenter study.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 5, 2024
PURPOSE: To develop and validate a prognostic and predictive model integrating deep learning MRI features and clinical information in patients with stage II nasopharyngeal carcinoma (NPC) to identify patients with a low risk of progression for whom i...
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