[HTML][HTML] Deep learning based synthesis of MRI, CT and PET: Review and analysis
Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …
aiming to overcome the challenges associated with acquiring multiple image modalities for …
Deep learning for the harmonization of structural MRI scans: a survey
Medical imaging datasets for research are frequently collected from multiple imaging centers
using different scanners, protocols, and settings. These variations affect data consistency …
using different scanners, protocols, and settings. These variations affect data consistency …
Deep learning-based algorithm for postoperative glioblastoma MRI segmentation: a promising new tool for tumor burden assessment
Objective Clinical and surgical decisions for glioblastoma patients depend on a tumor
imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance …
imaging-based evaluation. Artificial Intelligence (AI) can be applied to magnetic resonance …
Neuralizer: General Neuroimage Analysis without Re-Training
Neuroimage processing tasks like segmentation, reconstruction, and registration are central
to the study of neuroscience. Robust deep learning strategies and architectures used to …
to the study of neuroscience. Robust deep learning strategies and architectures used to …
Synthesis of diffusion-weighted MRI scalar maps from FLAIR volumes using generative adversarial networks
K Chan, PJ Maralani, AR Moody… - Frontiers in …, 2023 - frontiersin.org
Introduction Acquisition and pre-processing pipelines for diffusion-weighted imaging (DWI)
volumes are resource-and time-consuming. Generating synthetic DWI scalar maps from …
volumes are resource-and time-consuming. Generating synthetic DWI scalar maps from …
Contrast‐enhanced MRI synthesis using dense‐dilated residual convolutions based 3D network toward elimination of gadolinium in neuro‐oncology
Recent studies have raised broad safety and health concerns about using of gadolinium
contrast agents during magnetic resonance imaging (MRI) to enhance identification of active …
contrast agents during magnetic resonance imaging (MRI) to enhance identification of active …
[HTML][HTML] Deep Learning for MRI Segmentation and Molecular Subtyping in Glioblastoma: Critical Aspects from an Emerging Field
M Bonada, LF Rossi, G Carone, F Panico… - …, 2024 - pmc.ncbi.nlm.nih.gov
Deep learning (DL) has been applied to glioblastoma (GBM) magnetic resonance imaging
(MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and …
(MRI) assessment for tumor segmentation and inference of molecular, diagnostic, and …
Leptomeningeal metastatic disease: new frontiers and future directions
Leptomeningeal metastatic disease (LMD), encompassing entities of 'meningeal
carcinomatosis', neoplastic meningitis' and 'leukaemic/lymphomatous meningitis', arises …
carcinomatosis', neoplastic meningitis' and 'leukaemic/lymphomatous meningitis', arises …
SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy
Numerous imaging techniques are available for observing and interrogating biological
samples, and several of them can be used consecutively to enable correlative analysis of …
samples, and several of them can be used consecutively to enable correlative analysis of …
A layer-wise fusion network incorporating self-supervised learning for multimodal MR image synthesis
Magnetic resonance (MR) imaging plays an important role in medical diagnosis and
treatment; different modalities of MR images can provide rich and complementary …
treatment; different modalities of MR images can provide rich and complementary …