AIMC Topic: Magnetic Resonance Imaging
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Single-breath-hold T2WI liver MRI with deep learning-based reconstruction: A clinical feasibility study in comparison to conventional multi-breath-hold T2WI liver MRI.
Magnetic resonance imaging
Jun 18, 2021
OBJECTIVE: To investigate the clinical feasibility of single-breath-hold (SBH) T2-weighted (T2WI) liver MRI with deep learning-based reconstruction in the evaluation of image quality and lesion delineation, compared with conventional multi-breath-hol...
Automated cardiac segmentation of cross-modal medical images using unsupervised multi-domain adaptation and spatial neural attention structure.
Medical image analysis
Jun 17, 2021
Accurate cardiac segmentation of multimodal images, e.g., magnetic resonance (MR), computed tomography (CT) images, plays a pivot role in auxiliary diagnoses, treatments and postoperative assessments of cardiovascular diseases. However, training a we...
Multispectral co-occurrence of wavelet coefficients for malignancy assessment of brain tumors.
PloS one
Jun 17, 2021
Brain tumor is not most common, but truculent type of cancer. Therefore, correct prediction of its aggressiveness nature at an early stage would influence the treatment strategy. Although several diagnostic methods based on different modalities exist...
Assessing the Accuracy and Reproducibility of PARIETAL: A Deep Learning Brain Extraction Algorithm.
Journal of magnetic resonance imaging : JMRI
Jun 16, 2021
BACKGROUND: Manual brain extraction from magnetic resonance (MR) images is time-consuming and prone to intra- and inter-rater variability. Several automated approaches have been developed to alleviate these constraints, including deep learning pipeli...
Deep learning for noninvasive liver fibrosis classification: A systematic review.
Liver international : official journal of the International Association for the Study of the Liver
Jun 16, 2021
BACKGROUND AND AIMS: While biopsy is the gold standard for liver fibrosis staging, it poses significant risks. Noninvasive assessment of liver fibrosis is a growing field. Recently, deep learning (DL) technology has revolutionized medical image analy...
Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models.
Scientific reports
Jun 16, 2021
Side experiments are performed on radiomics models to improve their reproducibility. We measure the impact of myocardial masks, radiomic side experiments and data augmentation for information transfer (DAFIT) approach to differentiate patients with a...
Image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance imaging.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jun 15, 2021
INTRODUCTION: To develop an image-based deep learning model for predicting pathological response in rectal cancer using post-chemoradiotherapy magnetic resonance (MR) imaging.
Brain MRI Deep Learning and Bayesian Inference System Augments Radiology Resident Performance.
Journal of digital imaging
Jun 15, 2021
Automated quantitative and probabilistic medical image analysis has the potential to improve the accuracy and efficiency of the radiology workflow. We sought to determine whether AI systems for brain MRI diagnosis could be used as a clinical decision...
Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.
eLife
Jun 15, 2021
Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective...
A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections.
International journal of computer assisted radiology and surgery
Jun 13, 2021
PURPOSE: Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique, but require large annotat...
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