AIMC Topic: Magnetic Resonance Imaging

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MRI-based deep learning techniques for the prediction of isocitrate dehydrogenase and 1p/19q status in grade 2-4 adult gliomas.

Journal of medical imaging and radiation oncology
Molecular biomarkers are becoming increasingly important in the classification of intracranial gliomas. While tissue sampling remains the gold standard, there is growing interest in the use of deep learning (DL) techniques to predict these markers. T...

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification.

Human brain mapping
The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing eff...

HGM-cNet: Integrating hippocampal gray matter probability map into a cascaded deep learning framework improves hippocampus segmentation.

European journal of radiology
A robust cascaded deep learning framework with integrated hippocampal gray matter (HGM) probability map was developed to improve the hippocampus segmentation (called HGM-cNet) due to its significance in various neuropsychiatric disorders such as Alzh...

Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI.

European radiology
OBJECTIVES: To evaluate a deep learning model for automated and interpretable classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy from lumbar spine MRI.

Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI.

NeuroImage. Clinical
The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy when predicting the course of neurological conditions, including multiple sclerosis, by means of ext...

Automated Prediction of Early Recurrence in Advanced Sinonasal Squamous Cell Carcinoma With Deep Learning and Multi-parametric MRI-based Radiomics Nomogram.

Academic radiology
RATIONALE AND OBJECTIVES: Preoperative prediction of the recurrence risk in patients with advanced sinonasal squamous cell carcinoma (SNSCC) is critical for individualized treatment. To evaluate the predictive ability of radiomics signature (RS) base...

Deep learning-based dominant index lesion segmentation for MR-guided radiation therapy of prostate cancer.

Medical physics
BACKGROUND: Dose escalation radiotherapy enables increased control of prostate cancer (PCa) but requires segmentation of dominant index lesions (DIL). This motivates the development of automated methods for fast, accurate, and consistent segmentation...

Deep learning-based decision forest for hereditary clear cell renal cell carcinoma segmentation on MRI.

Medical physics
BACKGROUND: von Hippel-Lindau syndrome (VHL) is an autosomal dominant hereditary syndrome with an increased predisposition of developing numerous cysts and tumors, almost exclusively clear cell renal cell carcinoma (ccRCC). Considering the lifelong s...

Deep Learning Algorithm Enables Cerebral Venous Thrombosis Detection With Routine Brain Magnetic Resonance Imaging.

Stroke
BACKGROUND: Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose CVT. This study aimed to develop and evaluate a novel deep learning (DL) algorithm for detecting CVT...

Inter-individual deep image reconstruction via hierarchical neural code conversion.

NeuroImage
The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. Although anatomical and fu...