AIMC Topic: Magnetic Resonance Imaging
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Endovascular robotics: technical advances and future directions.
Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
Jan 21, 2025
Endovascular interventions excel in treating cardiovascular diseases in a minimally invasive manner, showing improved outcomes over open techniques. However, challenges related to precise navigation - still relying on 2D fluoroscopy - persist. This r...
Improved Image Quality Through Deep Learning Acceleration of Gradient-Echo Acquisitions in Uterine MRI: First Application with the Female Pelvis.
Academic radiology
Jan 21, 2025
RATIONALE AND OBJECTIVES: The aim of this study was to compare the image quality of a deep learning (DL)-accelerated volumetric interpolated breath-hold examination (VIBE) sequence with a standard (ST) VIBE sequence in assessing the uterus.
[Application and considerations of artificial intelligence and neuroimaging in the study of brain effect mechanisms of acupuncture and moxibustion].
Zhongguo zhen jiu = Chinese acupuncture & moxibustion
Jan 21, 2025
Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain...
Towards contrast-agnostic soft segmentation of the spinal cord.
Medical image analysis
Jan 21, 2025
Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring of cord compression or neurodegenerative diseases such as multiple sclerosis. While several semi an...
Unpaired Dual-Modal Image Complementation Learning for Single-Modal Medical Image Segmentation.
IEEE transactions on bio-medical engineering
Jan 21, 2025
OBJECTIVE: Multi-modal MR/CT image segmentation is an important task in disease diagnosis and treatment, but it is usually difficult to acquire aligned multi-modal images of a patient in clinical practice due to the high cost and specific allergic re...
Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy.
Cancer imaging : the official publication of the International Cancer Imaging Society
Jan 21, 2025
BACKGROUND: Radiomic analysis of quantitative features extracted from segmented medical images can be used for predictive modeling of prognosis in brain tumor patients. Manual segmentation of the tumor components is time-consuming and poses significa...
Coati optimization algorithm for brain tumor identification based on MRI with utilizing phase-aware composite deep neural network.
Electromagnetic biology and medicine
Jan 21, 2025
Brain tumors can cause difficulties in normal brain function and are capable of developing in various regions of the brain. Malignant tumours can develop quickly, pass through neighboring tissues, and extend to further brain regions or the central ne...
A Machine Learning Model for Predicting the HER2 Positive Expression of Breast Cancer Based on Clinicopathological and Imaging Features.
Academic radiology
Jan 20, 2025
RATIONALE AND OBJECTIVES: To develop a machine learning (ML) model based on clinicopathological and imaging features to predict the Human Epidermal Growth Factor Receptor 2 (HER2) positive expression (HER2-p) of breast cancer (BC), and to compare its...
Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review.
International journal of colorectal disease
Jan 20, 2025
PURPOSE: This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). The primary goal is to evaluate the perform...
Deep self-representation learning with hyper-laplacian regularization for brain imaging genetics association analysis.
Methods (San Diego, Calif.)
Jan 19, 2025
Brain imaging genetics aims to explore the association between genetic factors such as single nucleotide polymorphisms (SNPs) and brain imaging quantitative traits (QTs). However, most existing methods do not consider the nonlinear correlations betwe...
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