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- Hellström, MaxUmeå universitet,Institutionen för strålningsvetenskaper (author)
Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors
- Article/chapterEnglish2023
Publisher, publication year, extent ...
- John Wiley & Sons,2023
- electronicrdacarrier
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- LIBRIS-ID:oai:DiVA.org:umu-213711
- https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-213711URI
- https://doi.org/10.1002/mrm.29823DOI
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- Language:English
- Summary in:English
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- Purpose: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.Methods: We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping.Results: We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior.Conclusion: DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.
Subject headings and genre
- TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
- ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
- NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
- deep image prior
- denoising
- parameter mapping
- quantitative MRI
- uncertainty estimation
Added entries (persons, corporate bodies, meetings, titles ...)
- Löfstedt, TommyUmeå universitet,Institutionen för strålningsvetenskaper,Institutionen för datavetenskap(Swepub:umu)toklot02 (author)
- Garpebring, AndersUmeå universitet,Institutionen för strålningsvetenskaper(Swepub:umu)anga0014 (author)
- Umeå universitetInstitutionen för strålningsvetenskaper (creator_code:org_t)
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- In:Magnetic Resonance in Medicine: John Wiley & Sons90:6, s. 2557-25710740-31941522-2594
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