AIMC Topic: Diffusion Magnetic Resonance Imaging

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Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR.

Journal of translational medicine
BACKGROUND: This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast le...

Prediction of dMRI signals with neural architecture search.

Journal of neuroscience methods
BACKGROUND: There is growing interest in the neuroscience community in estimating and mapping microscopic properties of brain tissue non-invasively using magnetic resonance measurements. Machine learning methods are actively investigated to predict t...

Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study.

The Lancet. Digital health
BACKGROUND: Gadolinium-based contrast agents (GBCAs) are widely used to enhance tissue contrast during MRI scans and play a crucial role in the management of patients with cancer. However, studies have shown gadolinium deposition in the brain after r...

Deep learning based multiplexed sensitivity-encoding (DL-MUSE) for high-resolution multi-shot DWI.

NeuroImage
PURPOSE: A phase correction method for high-resolution multi-shot (MSH) diffusion weighted imaging (DWI) is proposed. The efficacy and generalization capability of the method were validated on both healthy volunteers and patients.

A deep learning-based model for prediction of hemorrhagic transformation after stroke.

Brain pathology (Zurich, Switzerland)
Hemorrhagic transformation (HT) is one of the most serious complications after endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) patients. The purpose of this study is to develop and validate deep-learning (DL) models based on multiparam...

Combining multi-site magnetic resonance imaging with machine learning predicts survival in pediatric brain tumors.

Scientific reports
Brain tumors represent the highest cause of mortality in the pediatric oncological population. Diagnosis is commonly performed with magnetic resonance imaging. Survival biomarkers are challenging to identify due to the relatively low numbers of indiv...

Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter.

NeuroImage
Specific features of white matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur...

Training data distribution significantly impacts the estimation of tissue microstructure with machine learning.

Magnetic resonance in medicine
PURPOSE: Supervised machine learning (ML) provides a compelling alternative to traditional model fitting for parameter mapping in quantitative MRI. The aim of this work is to demonstrate and quantify the effect of different training data distribution...

An attention-based deep learning model for predicting microvascular invasion of hepatocellular carcinoma using an intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging.

Physics in medicine and biology
The intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging (IVIM-DWI) with a series of images with different-values has great potential as a tool for detecting, diagnosing, staging, and monitoring disease progression or ...

Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms.

European radiology
OBJECTIVE: To develop and validate deep learning (DL) methods for diagnosing autism spectrum disorder (ASD) based on conventional MRI (cMRI) and apparent diffusion coefficient (ADC) images.