AIMC Topic: Magnetic Resonance Imaging

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Interpretable deep learning as a means for decrypting disease signature in multiple sclerosis.

Journal of neural engineering Jul 19, 2021
The mechanisms driving multiple sclerosis (MS) are still largely unknown, calling for new methods allowing to detect and characterize tissue degeneration since the early stages of the disease. Our aim is to decrypt the microstructural signatures of t...
Humans Multiple Sclerosis Diffusion Tensor Imaging Multiple Sclerosis, Relapsing-Remitting Deep Learning Magnetic Resonance Imaging
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Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.

Journal of biomedical informatics Jul 18, 2021
The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and m...
Magnetic Resonance Imaging Humans Body Composition Neural Networks, Computer Tomography, X-Ray Computed Image Processing, Computer-Assisted
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The PHU-NET: A robust phase unwrapping method for MRI based on deep learning.

Magnetic resonance in medicine Jul 17, 2021
PURPOSE: This work was aimed at designing a deep-learning-based approach for MR image phase unwrapping to improve the robustness and efficiency of traditional methods.
Image Processing, Computer-Assisted Magnetic Resonance Imaging Signal-To-Noise Ratio Algorithms Humans Deep Learning
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B-Map: a fuzzy-based model to detect foreign objects in a brain.

Medical & biological engineering & computing Jul 17, 2021
To cope up with the medical complications, scientists and physicians rely more on digitized historical evidence. It helps them to identify the disease and to develop new drugs and strategies. The authors have designed a model called B-Map. It can det...
Foreign Bodies Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Algorithms Fuzzy Logic Brain
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Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction.

NeuroImage Jul 16, 2021
Quantitative susceptibility mapping (QSM) and R2* mapping are MRI post-processing methods that quantify tissue magnetic susceptibility and transverse relaxation rate distributions. However, QSM and R2* acquisitions are relatively slow, even with para...
Magnetic Resonance Imaging Brain Mapping Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Humans Brain
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Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation.

Acta oncologica (Stockholm, Sweden) Jul 15, 2021
BACKGROUND: Manual delineation of gross tumor volume (GTV) is essential for radiotherapy treatment planning, but it is time-consuming and suffers inter-observer variability (IOV). In clinics, CT, PET, and MRI are used to inform delineation accuracy d...
Positron Emission Tomography Computed Tomography Head and Neck Neoplasms Magnetic Resonance Imaging Tomography, X-Ray Computed Humans Deep Learning Retrospective Studies
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Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine.

Brain and behavior Jul 15, 2021
OBJECTIVE: Autism spectrum disorder (ASD) is a neurodevelopmental condition with a heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; cortical thickness has proved to be involved in the etiopathogenesis of ASD core...
Brain Mapping Support Vector Machine Magnetic Resonance Imaging Humans Autism Spectrum Disorder Brain Child
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Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations.

NeuroImage Jul 14, 2021
We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of b...
Humans Neural Networks, Computer Multimodal Imaging Deep Learning Databases, Factual Diffusion Tensor Imaging Connectome Image Processing, Computer-Assisted Autism Spectrum Disorder Magnetic Resonance Imaging
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Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma.

AJR. American journal of roentgenology Jul 14, 2021
The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival (RFS) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). This retrospective stu...
Female Image Interpretation, Computer-Assisted Middle Aged Neoplasm Recurrence, Local Magnetic Resonance Imaging Ovary Ovarian Neoplasms Humans Survival Analysis Support Vector Machine Retrospective Studies
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Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network.

Scientific reports Jul 14, 2021
Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. There have been several radiomics studies for noninvasive risk stratification of EC us...
Humans Neural Networks, Computer Adult Middle Aged Image Processing, Computer-Assisted Female Aged Magnetic Resonance Imaging Endometrial Neoplasms
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