Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

Laves, Max-Heinrich; Tölle, Malte; Ortmaier, Tobias

doi:10.48550/arxiv.2008.08837

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…In addition, due to the lack of ground truth, we were unable to make concrete conclusions about the performance of our CNN on test patient data. But the uncertainty of our CNN can be quantified by generating confidence maps [34][35][36] using Bayesian networks, 37 an ensemble of multiple networks, 38 or an extension of the probabilistic U-Net, 39 which can be one direction to investigate in the future.…”

Section: Discussionmentioning

confidence: 99%

DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single‐photon emission computerized tomography imaging resolution

Fessler

Mikell

et al. 2021

Medical Physics

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Purpose: Current methods for patient specific voxel-level dosimetry in radionuclide therapy suffer from a trade-off between accuracy and computational efficiency. Monte Carlo (MC) radiation transport is considered as the gold standard but is computationally expensive, while faster dose voxel kernel (DVK) convolution can be sub-optimal in the presence of tissue heterogeneities.Furthermore, the accuracies of both these methods are limited by the spatial resolution of the reconstructed emission image. To overcome these limitations, this paper takes a novel approach of constructing a single deep convolutional neural network (CNN) named as DblurDoseNet that learns to produce dose-rate maps while compensating for the limited resolution of SPECT images. Methods:To mitigate the effects of poor SPECT resolution and reconstruction artifacts on dosimetry, we trained our CNN using MC-generated dose-rate maps that directly corresponded to the true activity maps in virtual patient phantoms. We applied residual learning such that our CNN only learned the difference between the true dose-rate map and DVK dose-rate map with density scaling. The network consists of a depth feature extractor and a 2D U-Net, where the input was 11 slices (3.3 cm) of Lu-177 SPECT/CT images and the output was the dose-rate map corresponding to the center slice. In addition to phantoms, 42 SPECT/CT scans of patients who underwent Lu-177 DOTATATE therapy were also used for testing. Results: In test phantoms, the lesion/organ mean dose-rate error and the normalized root mean square error (NRMSE) relative to ground-truth for the CNN method was consistently lower than DVK and MC. In particular, for CNN compared to DVK/MC, the average improvement in mean dose error was 55%/53% and 66%/56%; and in NRMSE was 18%/17% and 10%/11% for lesion and kidney, respectively. Line profiles and dose-volume histograms demonstrated compensation for SPECT resolution effects in the CNN generated dose-rate maps. Noise, determined from multiple Poisson realizations, showed an average improvement of 21%/27% compared to DVK/MC. In patients, a high concordance was observed between CNN and MC in joint histogram analysis. The trained residual CNN took ~30 seconds on GPU to generate a (512 × 512 × 130) dose-rate map for a patient. Conclusion: The proposed CNN is well-suited for real-time patient-specific dosimetry for clinical treatment planning due to its demonstrated improvement in accuracy, resolution, noise and speed over the current gold-standard.

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Section: Discussionmentioning

confidence: 99%

DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single‐photon emission computerized tomography imaging resolution

Fessler

Mikell

et al. 2021

Medical Physics

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“…Recent efforts in this area include the Bayes by Backprop (Blundell et al 2015), its closely related mean-field variational inference by assuming a Gaussian prior distribution (Tölle et al 2021), stochastic batch normalization (Atanov et al 2018), and Monte-Carlo (MC) dropout (Gal and Ghahramani 2016;Kendall and Gal 2017). The applications of Bayesian deep learning in medical imaging expands on image denoising (Tölle et al 2021;Laves et al 2020b) and image segmentation (DeVries and Taylor 2018; Baumgartner et al 2019;Mehrtash et al 2020). In deep-learning-based image registration, the majority of methods provide a single, deterministic solution of the unknown geometric transformation.…”

Section: Bayesian Deep Learningmentioning

confidence: 99%

TransMorph: Transformer for unsupervised medical image registration

Chen

et al. 2021

Preprint

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In the last decade, convolutional neural networks (ConvNets) have dominated the field of medical image analysis. However, it is found that the performances of ConvNets may still be limited by their inability to model long-range spatial relations between voxels in an image. Numerous vision Transformers have been proposed recently to address the shortcomings of ConvNets, demonstrating state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their self-attention mechanism enables a more precise comprehension of the spatial correspondence between moving and fixed images. In this paper, we present TransMorph, a hybrid Transformer-ConvNet model for volumetric medical image registration. We also introduce three variants of TransMorph, with two diffeomorphic variants ensuring the topology-preserving deformations and a Bayesian variant producing a well-calibrated registration uncertainty estimate. The proposed models are extensively validated against a variety of existing registration methods and Transformer architectures using volumetric medical images from two applications: inter-patient brain MRI registration and phantom-to-CT registration. Qualitative and quantitative results demonstrate that TransMorph and its variants lead to a substantial performance improvement over the baseline methods, demonstrating the effectiveness of Transformers for medical image registration.

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A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T

Hamilton

Truesdell

Galizia

et al. 2023

Magn Reson Mater Phy

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Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior

Cited by 3 publications

References 18 publications

DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single‐photon emission computerized tomography imaging resolution

DblurDoseNet: A deep residual learning network for voxel radionuclide dosimetry compensating for single‐photon emission computerized tomography imaging resolution

TransMorph: Transformer for unsupervised medical image registration

A low-rank deep image prior reconstruction for free-breathing ungated spiral functional CMR at 0.55 T and 1.5 T

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