AIMC Topic: Magnetic Resonance Imaging
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Rapid lumbar MRI protocol using 3D imaging and deep learning reconstruction.
Skeletal radiology
Jan 5, 2023
BACKGROUND AND PURPOSE: Three-dimensional (3D) imaging of the spine, augmented with AI-enabled image enhancement and denoising, has the potential to reduce imaging times without compromising image quality or diagnostic performance. This work evaluate...
4D segmentation of the thoracic aorta from 4D flow MRI using deep learning.
Magnetic resonance imaging
Jan 5, 2023
BACKGROUND: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure it...
A Comparison of Three Different Deep Learning-Based Models to Predict the MGMT Promoter Methylation Status in Glioblastoma Using Brain MRI.
Journal of digital imaging
Jan 5, 2023
Glioblastoma (GBM) is the most common primary malignant brain tumor in adults. The standard treatment for GBM consists of surgical resection followed by concurrent chemoradiotherapy and adjuvant temozolomide. O-6-methylguanine-DNA methyltransferase (...
Head CT deep learning model is highly accurate for early infarct estimation.
Scientific reports
Jan 5, 2023
Non-contrast head CT (NCCT) is extremely insensitive for early (< 3-6 h) acute infarct identification. We developed a deep learning model that detects and delineates suspected early acute infarcts on NCCT, using diffusion MRI as ground truth (3566 NC...
Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis.
PloS one
Jan 5, 2023
Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study proposes a new method to detect phase-encoding (PE) lines corrupted by motion and remove motion artifacts in MR images. 67 cases containing 8710 slices of axial T2...
Deep Learning With Radiogenomics Towards Personalized Management of Gliomas.
IEEE reviews in biomedical engineering
Jan 5, 2023
A state-of-the-art interdisciplinary survey on multi-modal radiogenomic approaches is presented involving applications to the diagnosis and personalized management of gliomas a common kind of brain tumors through noninvasive imaging integrated with g...
Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics.
Medical & biological engineering & computing
Jan 4, 2023
Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted ...
Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification.
The British journal of radiology
Jan 4, 2023
OBJECTIVES: To precisely predict prostate cancer (PCa) risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features.
Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation.
Computers in biology and medicine
Jan 4, 2023
BACKGROUND: The O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential clinical brain tumor biomarker for Glioblastoma Multiforme (GBM). Knowing the status of MGMT met...
Computer-aided diagnosis in prostate cancer: a retrospective evaluation of the Watson Elementary system for preoperative tumor characterization in patients treated with robot-assisted radical prostatectomy.
World journal of urology
Jan 3, 2023
PURPOSE: Computer-aided diagnosis (CAD) may improve prostate cancer (PCa) detection and support multiparametric magnetic resonance imaging (mpMRI) readers for better characterization. We evaluated Watson Elementary (WE) CAD system results referring t...
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