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- Kuczera, StefanGothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper,Institute of Clinical Sciences (author)
Optimized bias and signal inference in diffusion-weighted image analysis (OBSIDIAN)
- Article/chapterEnglish2021
Publisher, publication year, extent ...
- 2021-07-18
- Wiley,2021
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- LIBRIS-ID:oai:gup.ub.gu.se/307721
- https://gup.ub.gu.se/publication/307721URI
- https://doi.org/10.1002/mrm.28773DOI
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- Language:English
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- Purpose Correction of Rician signal bias in magnitude MR images. Methods A model-based, iterative fitting procedure is used to simultaneously estimate true signal and underlying Gaussian noise with standard deviation sigma g on a pixel-by-pixel basis in magnitude MR images. A precomputed function that relates absolute residuals between measured signals and model fit to sigma g is used to iteratively estimate sigma g. The feasibility of the method is evaluated and compared to maximum likelihood estimation (MLE) for diffusion signal decay simulations and diffusion-weighted images of the prostate considering 21 linearly spaced b-values from 0 to 3000 s/mm(2). A multidirectional analysis was performed with publically available brain data. Results Model 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. Conclusions OBSIDIAN 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.
Subject headings and genre
- MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Radiologi och bildbehandling hsv//swe
- MEDICAL AND HEALTH SCIENCES Clinical Medicine Radiology, Nuclear Medicine and Medical Imaging hsv//eng
- diffusion MRI
- noise
- prostate
- Rician bias correction
- different mathematical-models
- maximum-likelihood-estimation
- rician
- noise
- water diffusion
- prostate
- mri
- Radiology
- Nuclear Medicine & Medical Imaging
Added entries (persons, corporate bodies, meetings, titles ...)
- Alipoor, Mohammad,1983Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper,Institute of Clinical Sciences(Swepub:gu)xalipm (author)
- Langkilde, Fredrik,1990Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper,Institute of Clinical Sciences(Swepub:gu)xlafrp (author)
- Maier, Stephan E,1959Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper,Institute of Clinical Sciences(Swepub:gu)xmaist (author)
- Göteborgs universitetInstitutionen för kliniska vetenskaper (creator_code:org_t)
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- In:Magnetic Resonance in Medicine: Wiley86:5, s. 2716-27320740-31941522-2594
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