ArticleConcepts in magnetic resonance. Part A, Bridging education and research2015
Edge Sharpness Assessment by Parametric Modeling: Application to Magnetic Resonance Imaging.
Article in Concepts in magnetic resonance. Part A, Bridging education and research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Performance of Free-Running Cardiac and Respiratory Motion-Resolved Whole-Heart 5D MRI in Congenital Heart Disease Patients Using a Gadolinium Enhanced Fast-Interrupted Steady-State Sequence.Journal of magnetic resonance imaging : JMRI · 2026Article
- Native myocardial T1 mapping using rapid 1.5T T1FLASH and T1 MOLLI during breath-hold and free-breathing.BMC cardiovascular disorders · 2026Article
- Design of Differential Single-Block and Multi-Block Presaturated Ultrashort Echo Time Pulse Sequences for Fast and Flexible Short-TMagnetic resonance in medicine · 2026Article
- EMORe: Motion-Robust 5D MRI Reconstruction via Expectation-Maximization-Guided Binning Correction and Outlier Rejection.IEEE transactions on medical imaging · 2026Article
- Quantification of edge-enhancing effects using accelerated deep learning reconstructed orbital MRI sequences.European journal of radiology open · 2026Article
- Deep Learning-Based Denoising for High-Resolution Carotid Vessel Wall MRI Using Standard Neurovascular Coils.Magnetic resonance in medicine · 2026Article
- Online Spatiotemporally Constrained Reconstruction for Real-Time Interactive MRI.Magnetic resonance in medicine · 2026Article
- Navigator motion-resolved MR fingerprinting using implicit neural representation: Feasibility for free-breathing three-dimensional whole-liver multiparametric mapping.Magnetic resonance in medicine · 2026Article
- Dual-branch residual encoder-decoder convolutional neural network (DB-REDCNN): a computed tomography-integrated multimodal network for positron emission tomography denoising.Quantitative imaging in medicine and surgery · 2025Article
- Simultaneous multi-slice cardiac real-time MRI at 0.55T.Magnetic resonance in medicine · 2025Article
- Self-gated free-running 5D whole-heart MRI using blind source separation for automated cardiac motion extraction.Magnetic resonance in medicine · 2025Article
- High on sparsity: Interbin compensation of cardiac motion for improved assessment of left-ventricular function using 5D whole-heart MRI.Magnetic resonance in medicine · 2025Article
- Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.Journal of imaging informatics in medicine · 2025Article
- Characterizing Off-center MRI with ZTE.Zeitschrift fur medizinische Physik · 2024Article
- Large-scale 3D non-Cartesian coronary MRI reconstruction using distributed memory-efficient physics-guided deep learning with limited training data.Magma (New York, N.Y.) · 2024Article
- Similarity-driven motion-resolved reconstruction for ferumoxytol-enhanced whole-heart MRI in congenital heart disease.PloS one · 2024Article
- Deep learning-based left ventricular segmentation demonstrates improved performance on respiratory motion-resolved whole-heart reconstructions.Frontiers in radiology · 2023Article
- Free-running cardiac and respiratory motion-resolved 5D whole-heart coronary cardiovascular magnetic resonance angiography in pediatric cardiac patients using ferumoxytol.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2022Article
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3 authors.
Funding
Abstract
In biomedical imaging, edge sharpness is an important yet often overlooked image quality metric. In this work, a semi-automatic method to quantify edge sharpness in the presence of significant noise is presented with application to magnetic resonance imaging (MRI). The method is based on parametric modeling of image edges. First, an edge map is automatically generated and one or more edges-of-interest (EOI) are manually selected using graphical user interface. Multiple exclusion criteria are then enforced to eliminate edge pixels that are potentially not suitable for sharpness assessment. Second, at each pixel of the EOI, an image intensity profile is read along a small line segment that runs locally normal to the EOI. Third, the profiles corresponding to all EOI pixels are individually fitted with a sigmoid function characterized by four parameters, including one that represents edge sharpness. Last, the distribution of the sharpness parameter is used to quantify edge sharpness. For validation, the method is applied to simulated data as well as MRI data from both phantom imaging and cine imaging experiments. This method allows for fast, quantitative evaluation of edge sharpness even in images with poor signal-to-noise ratio. Although the utility of this method is demonstrated for MRI, it can be adapted for other medical imaging applications.
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