AIMC Topic: Magnetic Resonance Imaging
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Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges.
Journal of digital imaging
Nov 2, 2022
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incor...
Artificial Intelligence-Driven Ultra-Fast Superresolution MRI: 10-Fold Accelerated Musculoskeletal Turbo Spin Echo MRI Within Reach.
Investigative radiology
Nov 2, 2022
Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging; however, long pulse sequence acquisition times may restrict patient tolerability and access. Advances in MRI scanners, coil technology, and innovative pulse sequence ...
Deep Learning Reconstruction for Accelerated Spine MRI: Prospective Analysis of Interchangeability.
Radiology
Nov 1, 2022
Background Deep learning (DL)-based MRI reconstructions can reduce examination times for turbo spin-echo (TSE) acquisitions. Studies that prospectively employ DL-based reconstructions of rapidly acquired, undersampled spine MRI are needed. Purpose To...
Overcoming challenges of translating deep-learning models for glioblastoma: the ZGBM consortium.
The British journal of radiology
Nov 1, 2022
OBJECTIVE: To report imaging protocol and scheduling variance in routine care of glioblastoma patients in order to demonstrate challenges of integrating deep-learning models in glioblastoma care pathways. Additionally, to understand the most common i...
End-to-end deep learning model for segmentation and severity staging of anterior cruciate ligament injuries from MRI.
Diagnostic and interventional imaging
Oct 31, 2022
PURPOSE: The purpose of this study was to develop a semi-supervised segmentation and classification deep learning model for the diagnosis of anterior cruciate ligament (ACL) tears on MRI based on a semi-supervised framework, double-linear layers U-Ne...
Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.
European radiology
Oct 29, 2022
OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to ...
CerebNet: A fast and reliable deep-learning pipeline for detailed cerebellum sub-segmentation.
NeuroImage
Oct 27, 2022
Quantifying the volume of the cerebellum and its lobes is of profound interest in various neurodegenerative and acquired diseases. Especially for the most common spinocerebellar ataxias (SCA), for which the first antisense oligonculeotide-base gene s...
Benchmarking of Deep Architectures for Segmentation of Medical Images.
IEEE transactions on medical imaging
Oct 27, 2022
In recent years, there were many suggestions regarding modifications of the well-known U-Net architecture in order to improve its performance. The central motivation of this work is to provide a fair comparison of U-Net and its five extensions using ...
Qutrit-Inspired Fully Self-Supervised Shallow Quantum Learning Network for Brain Tumor Segmentation.
IEEE transactions on neural networks and learning systems
Oct 27, 2022
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bilevel quantum bits often describe quantum neural network models. In this article, a novel self-supervised s...
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