Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
Artificial intelligence for MR image reconstruction: an overview for clinicians
DJ Lin, PM Johnson, F Knoll… - Journal of Magnetic …, 2021 - Wiley Online Library
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with
recent breakthroughs applying deep‐learning models for data acquisition, classification …
recent breakthroughs applying deep‐learning models for data acquisition, classification …
A survey on deep learning in medicine: Why, how and when?
New technologies are transforming medicine, and this revolution starts with data. Health
data, clinical images, genome sequences, data on prescribed therapies and results …
data, clinical images, genome sequences, data on prescribed therapies and results …
Emergence of deep learning in knee osteoarthritis diagnosis
Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing
significant disability in patients worldwide. Manual diagnosis, segmentation, and …
significant disability in patients worldwide. Manual diagnosis, segmentation, and …
Artificial intelligence–driven ultra-fast superresolution MRI: 10-fold accelerated musculoskeletal turbo spin echo MRI within reach
DJ Lin, SS Walter, J Fritz - Investigative Radiology, 2023 - journals.lww.com
Magnetic resonance imaging (MRI) is the keystone of modern musculoskeletal imaging;
however, long pulse sequence acquisition times may restrict patient tolerability and access …
however, long pulse sequence acquisition times may restrict patient tolerability and access …
[HTML][HTML] Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are
designed for data with near-isotropic voxels at approximately 1 mm resolution, and …
designed for data with near-isotropic voxels at approximately 1 mm resolution, and …
Prospective deployment of deep learning in MRI: a framework for important considerations, challenges, and recommendations for best practices
Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …
Improving robustness of deep learning based knee mri segmentation: Mixup and adversarial domain adaptation
Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA)
research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data …
research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data …
Osteoarthritis of the temporomandibular joint can be diagnosed earlier using biomarkers and machine learning
J Bianchi, AC de Oliveira Ruellas, JR Gonçalves… - Scientific reports, 2020 - nature.com
After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most
common musculoskeletal condition affecting 5 to 12% of the population, with an annual …
common musculoskeletal condition affecting 5 to 12% of the population, with an annual …
Low‐field MRI of stroke: challenges and opportunities
Stroke is a leading cause of death and disability worldwide. The reasons for increased
stroke burden in developing countries are inadequately controlled risk factors resulting from …
stroke burden in developing countries are inadequately controlled risk factors resulting from …