AIMC Topic:
Magnetic Resonance Imaging
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Synthesis of gadolinium-enhanced glioma images on multisequence magnetic resonance images using contrastive learning.
Medical physics
Feb 29, 2024
BACKGROUND: Gadolinium-based contrast agents are commonly used in brain magnetic resonance imaging (MRI), however, they cannot be used by patients with allergic reactions or poor renal function. For long-term follow-up patients, gadolinium deposition...
Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks.
Science bulletin
Feb 29, 2024
Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex. Recent studies attempt to leverage the representation power of deep neural networks (DNNs) to predic...
Deep learning segmentation of the choroid plexus from structural magnetic resonance imaging (MRI): validation and normative ranges across the adult lifespan.
Fluids and barriers of the CNS
Feb 29, 2024
BACKGROUND: The choroid plexus functions as the blood-cerebrospinal fluid (CSF) barrier, plays an important role in CSF production and circulation, and has gained increased attention in light of the recent elucidation of CSF circulation dysfunction i...
Affine medical image registration with fusion feature mapping in local and global.
Physics in medicine and biology
Feb 28, 2024
. Medical image affine registration is a crucial basis before using deformable registration. On the one hand, the traditional affine registration methods based on step-by-step optimization are very time-consuming, so these methods are not compatible ...
Deep Learning Combined with Radiologist's Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images.
BioMed research international
Feb 28, 2024
PURPOSE: Segmentation of hepatocellular carcinoma (HCC) is crucial; however, manual segmentation is subjective and time-consuming. Accurate and automatic lesion contouring for HCC is desirable in clinical practice. In response to this need, our study...
Evaluating the impact of reinforcement learning on automatic deep brain stimulation planning.
International journal of computer assisted radiology and surgery
Feb 27, 2024
PURPOSE: Traditional techniques for automating the planning of brain electrode placement based on multi-objective optimization involving many parameters are subject to limitations, especially in terms of sensitivity to local optima, and tend to be re...
Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.
European radiology
Feb 26, 2024
OBJECTIVES: To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis.
Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy.
Medical physics
Feb 26, 2024
BACKGROUND: Deep learning-based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging-guided radiotherapy (MRgRT)Â workflow.
Intra and inter-regional functional connectivity of the human brain due to Task-Evoked fMRI Data classification through CNN & LSTM.
Journal of neuroradiology = Journal de neuroradiologie
Feb 25, 2024
BACKGROUND AND PURPOSE: Olfaction is an early marker of neurodegenerative disease. Standard olfactory function is essential due to the importance of olfaction in human life. The psychophysical evaluation assesses the olfactory function commonly. It i...
Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing.
Psychiatry research
Feb 25, 2024
Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using s...
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