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| http://dx.doi.org/10.1148/rycan.230095 | DOI Listing |
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Fast, Present and Future of the Concept of Spondyloarthritis.
Curr Rheumatol Rep
January 2025
Rheumatologisches Versorgungszentrum Steglitz, Ruhr Universität Bochum, Schloßstr.110, 12163, Berlin, Germany.
Purpose Of Review: Axial spondyloarthritis (axSpA) is a rather prevalent chronic inflammatory rheumatic disease that affects already relatively young patients. It has been known better since the end of the nineteenth century but quite a lot has been learned since the early 60ies when the first classification (diagnostic) criteria for ankylosing spondylitis (AS) were agreed on. I have been part of many developments in the last 30 years, and I'm happy to have been able to contribute to the scientific progress in terms of diagnosis, imaging, pathophysiology and therapy.
View Article and Find Full Text PDFHDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint.
Bioengineering (Basel)
December 2024
Center of Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY 10016, USA.
Non-linear least squares (NLS) methods are commonly used for quantitative magnetic resonance imaging (MRI), especially for multi-exponential T1ρ mapping, which provides precise parameter estimation for different relaxation models in tissues, such as mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models. However, NLS may suffer from problems like sensitivity to initial guesses, slow convergence speed, and high computational cost. While deep learning (DL)-based T1ρ fitting methods offer faster alternatives, they often face challenges such as noise sensitivity and reliance on NLS-generated reference data for training.
View Article and Find Full Text PDFSynthetic data in generalizable, learning-based neuroimaging.
Imaging Neurosci (Camb)
November 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.
View Article and Find Full Text PDFMachine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.
R Soc Open Sci
January 2025
University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham B15 2GW, UK.
Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS.
View Article and Find Full Text PDFENHANCING TRANSCRANIAL FOCUSED ULTRASOUND TREATMENT PLANNING WITH SYNTHETIC CT FROM ULTRA-SHORT ECHO TIME (UTE) MRI: A MULTI-TASK DEEP LEARNING APPROACH.
Proc IEEE Int Symp Biomed Imaging
May 2024
Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
Utilizing a multi-task deep learning framework, this study generated synthetic CT (sCT) images from a limited dataset of Ultrashort echo time (UTE) MRI for transcranial focused ultrasound (tFUS) planning. A 3D Transformer U-Net was employed to produce sCT images that closely replicated actual CT scans, demonstrated by an average Dice coefficient of 0.868 for morphological accuracy.
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