AIMC Topic: Magnetic Resonance Imaging

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Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging based on machine learning.

Magnetic resonance imaging
Accurate measuring of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) is vital for the research of many diseases. The localization and quantification of SAT and VAT by computed tomography (CT) expose patients to harmful ionizing r...

Open-source pipeline for multi-class segmentation of the spinal cord with deep learning.

Magnetic resonance imaging
This paper presents an open-source pipeline to train neural networks to segment structures of interest from MRI data. The pipeline is tailored towards homogeneous datasets and requires relatively low amounts of manual segmentations (few dozen, or les...

Machine Learning for Diagnosis of Hematologic Diseases in Magnetic Resonance Imaging of Lumbar Spines.

Scientific reports
We aimed to assess feasibility of a support vector machine (SVM) texture classifier to discriminate pathologic infiltration patterns from the normal bone marrows in MRI. This retrospective study included 467 cases, which were split into a training (n...

MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: This study assessed the dosimetric accuracy of synthetic CT images generated from magnetic resonance imaging (MRI) data for focal brain radiation therapy, using a deep learning approach.

Differential diagnosis of multiple system atrophy with predominant parkinsonism and Parkinson's disease using neural networks.

Journal of the neurological sciences
Differential diagnosis between Parkinson's disease (PD) and atypical parkinsonism, such as multiple system atrophy (MSA), can be difficult, especially in the early stages of the disease. Deep learning using neural networks (NNs) makes possible the pr...

Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.

Magnetic resonance in medicine
PURPOSE: Radiomics allows for powerful data-mining and feature extraction techniques to guide clinical decision making. Image segmentation is a necessary step in such pipelines and different techniques can significantly affect results. We demonstrate...

Synthesizing T1 weighted MPRAGE image from multi echo GRE images via deep neural network.

Magnetic resonance imaging
For quantitative neuroimaging studies using multi-echo gradient echo (mGRE) images, additional T-weighted magnetization prepared rapid gradient echo (MPRAGE) images are often acquired to supplement the insufficient morphometric information of mGRE fo...

Oncologic outcomes in patients treated with endoscopic robot assisted simple enucleation (ERASE) for renal cell carcinoma: Results from a tertiary referral center.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
INTRODUCTION: Open Simple Enucleation (OSE) has been demonstrated to be an oncologically safe alternative to standard partial nephrectomy. We assessed the mid-term oncologic outcomes and predictors of disease recurrence in patients treated with Endos...

A simulation-based approach to improve decoded neurofeedback performance.

NeuroImage
The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain acti...