ArticleMagnetic resonance in medicine2021
Optimized bias and signal inference in diffusion-weighted image analysis (OBSIDIAN).
Article in Magnetic resonance in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Self-Supervised Deep Learning Framework for Rician Distribution Based Denoising and Modeling of Multi-b Prostate Diffusion MRI.Magnetic resonance in medicine · 2026Article
- Optimization of Diffusion MRI With Consideration of the Signal Decay in Biological Tissues.Magnetic resonance in medicine · 2026Article
- Quantitative diffusion MRI in prostate cancer: Image quality, what we can measure and how it improves clinical assessment.European journal of radiology · 2023Review
- Truly reproducible uniform estimation of the ADC with multi-b diffusion data- Application in prostate diffusion imaging.Magnetic resonance in medicine · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
purposeCorrection of Rician signal bias in magnitude MR images.
methodsA model-based, iterative fitting procedure is used to simultaneously estimate true signal and underlying Gaussian noise with standard deviation
resultsModel simulations show that the Rician bias correction algorithm is fast, with an accuracy and precision that is on par to model-based MLE and direct fitting in the case of pure Gaussian noise. Increased accuracy in parameter prediction in a low signal-to-noise ratio (SNR) scenario is ideally achieved by using a composite of multiple signal decays from neighboring voxels as input for the algorithm. For patient data, good agreement with high SNR reference data of diffusion in prostate is achieved.
conclusionsOBSIDIAN is a novel, alternative, simple to implement approach for rapid Rician bias correction applicable in any case where differences between true signal decay and underlying model function can be considered negligible in comparison to noise. The proposed composite fitting approach permits accurate parameter estimation even in typical clinical scenarios with low SNR, which significantly simplifies comparison of complex diffusion parameters among studies.
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