AIMC Topic:
Magnetic Resonance Imaging
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Editorial for "Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping".
Journal of magnetic resonance imaging : JMRI
Dec 29, 2023
Evaluation of deep learning reconstruction on diffusion-weighted imaging quality and apparent diffusion coefficient using an ice-water phantom.
Radiological physics and technology
Dec 28, 2023
This study assessed the influence of deep learning reconstruction (DLR) on the quality of diffusion-weighted images (DWI) and apparent diffusion coefficient (ADC) using an ice-water phantom. An ice-water phantom with known diffusion properties (true ...
Discrimination of benign and malignant breast lesions on dynamic contrast-enhanced magnetic resonance imaging using deep learning.
Journal of cancer research and therapeutics
Dec 28, 2023
PURPOSE: To evaluate the capability of deep transfer learning (DTL) and fine-tuning methods in differentiating malignant from benign lesions in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).
Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping.
Journal of magnetic resonance imaging : JMRI
Dec 27, 2023
BACKGROUND: Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were...
Fusion Radiomics-Based Prediction of Response to Neoadjuvant Chemotherapy for Osteosarcoma.
Academic radiology
Dec 27, 2023
RATIONALE AND OBJECTIVES: Neoadjuvant chemotherapy (NAC) is the most crucial prognostic factor for osteosarcoma (OS), it significantly prolongs progression-free survival and improves the quality of life. This study aims to develop a deep learning rad...
Predictive Value of Tests
Chemotherapy, Adjuvant
Nomograms
Treatment Outcome
Neoadjuvant Therapy
Contrast Media
Middle Aged
Magnetic Resonance Imaging
Osteosarcoma
Bone Neoplasms
Deep Learning
Humans
Adolescent
Sensitivity and Specificity
Child
Retrospective Studies
Radiomics
Female
Male
Young Adult
Adult
Machine learning based on magnetic resonance imaging and clinical parameters helps predict mesenchymal-epithelial transition factor expression in oral tongue squamous cell carcinoma: a pilot study.
Oral surgery, oral medicine, oral pathology and oral radiology
Dec 27, 2023
OBJECTIVES: This study aimed to develop machine learning models to predict phosphorylated mesenchymal-epithelial transition factor (p-MET) expression in oral tongue squamous cell carcinoma (OTSCC) using magnetic resonance imaging (MRI)-derived textur...
Exploring the impact of super-resolution deep learning on MR angiography image quality.
Neuroradiology
Dec 27, 2023
PURPOSE: The aim of this study is to assess the effect of super-resolution deep learning-based reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T.
Automatic liver segmentation and assessment of liver fibrosis using deep learning with MR T1-weighted images in rats.
Magnetic resonance imaging
Dec 24, 2023
OBJECTIVES: To validate the performance of nnU-Net in segmentation and CNN in classification for liver fibrosis using T1-weighted images.
Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.
Operative neurosurgery (Hagerstown, Md.)
Dec 22, 2023
BACKGROUND AND OBJECTIVES: In high-grade glioma (HGG) surgery, intraoperative MRI (iMRI) has traditionally been the gold standard for maximizing tumor resection and improving patient outcomes. However, recent Level 1 evidence juxtaposes the efficacy ...
Automated bone age assessment from knee joint by integrating deep learning and MRI-based radiomics.
International journal of legal medicine
Dec 22, 2023
Bone age assessment (BAA) is a crucial task in clinical, forensic, and athletic fields. Since traditional age estimation methods are suffered from potential radiation damage, this study aimed to develop and evaluate a deep learning radiomics method b...
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