1,720,994 research outputs found
Bayesian MRI reconstruction with joint uncertainty estimation using diffusion models
No full textPurpose
We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction.
Method
Samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method, different from conventional deep learning-based MRI reconstruction techniques. In addition to the maximum a posteriori estimate for the image, which can be obtained by maximizing the log-likelihood indirectly or directly, the minimum mean square error estimate and uncertainty maps can also be computed from those drawn samples. The data-driven Markov chains are constructed with the score-based generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement.
Results
We numerically investigate the framework from these perspectives: (1) the interpretation of the uncertainty of the image reconstructed from undersampled k-space; (2) the effect of the number of noise scales used to train the generative models; (3) using a burn-in phase in MCMC sampling to reduce computation; (4) the comparison to conventional
-wavelet regularized reconstruction; (5) the transferability of learned information; and (6) the comparison to fastMRI challenge.
Conclusion
A framework is described that connects the diffusion process and advanced generative models with Markov chains. We demonstrate its flexibility in terms of contrasts and sampling patterns using advanced generative priors and the benefits of also quantifying the uncertainty for every pixel
Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models
No full textWe introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. Different from conventional deep learning-based MRI reconstruction techniques, samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method. In addition to the maximum a posteriori (MAP) estimate for the image, which can be obtained with conventional methods, the minimum mean square error (MMSE) estimate and uncertainty maps can also be computed. The data-driven Markov chains are constructed from the generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement. This provides flexibility because the method can be applied to k-space acquired with different sampling schemes or receive coils using the same pre-trained models. Furthermore, we use a framework based on a reverse diffusion process to be able to utilize advanced generative models. The performance of the method is evaluated on an open dataset using 10-fold undersampling in k-space
Self‐supervised learning for improved calibrationless radial MRI with NLINV‐Net
Full text linkAbstract Purpose To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. Methods NLINV‐Net is a model‐based neural network architecture that directly estimates images and coil sensitivities from (radial) k‐space data via nonlinear inversion (NLINV). Combined with a training strategy using self‐supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real‐time cardiac imaging and (2) single‐shot subspace‐based quantitative T1 mapping. Furthermore, region‐optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k‐space‐based SSDU loss on the region of interest. NLINV‐Net‐based reconstructions were compared with conventional NLINV and PI‐CS (parallel imaging + compressed sensing) reconstruction and the effect of the region‐optimized virtual coils and the type of training loss was evaluated qualitatively. Results NLINV‐Net‐based reconstructions contain significantly less noise than the NLINV‐based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir‐based focused loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real‐time imaging. For quantitative imaging, T1‐maps reconstructed using NLINV‐Net show similar quality as PI‐CS reconstructions, but NLINV‐Net does not require slice‐specific tuning of the regularization parameter. Conclusion NLINV‐Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.National Institutes of Health https://doi.org/10.13039/100000002Volkswagen Foundation https://doi.org/10.13039/501100001663Deutsches Zentrum für Herz-Kreislaufforschung https://doi.org/10.13039/10001044
Free-Breathing Liver Fat and R2 Mapping: Multi-Echo Radial FLASH and Model-based Reconstruction (MERLOT)
No full textDeep, Deep Learning with BART
No full textPurpose: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods: The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a non-linear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the non-uniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network [1] and MoDL [2], were implemented. Results: State-of-the-art deep image-reconstruction networks can be constructed and trained using BART's gradient based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow. Conclusion: By integrating non-linear operators and neural networks into BART, we provide a general framework for deep-learning-based reconstruction in MRI
Generative priors for MRI reconstruction trained from magnitude-only images using phase augmentation
No full textIn this work, we present a workflow to construct generic and robust generative image priors from magnitude-only images. The priors can then be used for regularization in reconstruction to improve image quality. The workflow begins with the preparation of training datasets from magnitude-only magnetic resonance (MR) images. This dataset is then augmented with phase information and used to train generative priors of complex images. Finally, trained priors are evaluated using both linear and nonlinear reconstruction for compressed sensing parallel imaging with various undersampling schemes. The results of our experiments demonstrate that priors trained on complex images outperform priors trained only on magnitude images. In addition, a prior trained on a larger dataset exhibits higher robustness. Finally, we show that the generative priors are superior to ℓ 1 -wavelet regularization for compressed sensing parallel imaging with high undersampling. These findings stress the importance of incorporating phase information and leveraging large datasets to raise the performance and reliability of the generative priors for MR imaging (MRI) reconstruction. Phase augmentation makes it possible to use existing image databases for training. This article is part of the theme issue ‘Generative modelling meets Bayesian inference: a new paradigm for inverse problems’.Niedersächsisches Ministerium für Wissenschaft und Kultur http://dx.doi.org/10.13039/50110001057
Deep, Deep Learning with BART
No full textPurpose: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods: The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a non-linear operator framework that provides automatic differentiation to allow computation of gradients. Existing MRI-specific operators of BART, such as the non-uniform fast Fourier transform, are directly integrated into this framework and are complemented by common building blocks used in neural networks. To evaluate the use of the framework for advanced deep-learning-based reconstruction, two state-of-the-art unrolled reconstruction networks, namely the Variational Network [1] and MoDL [2], were implemented. Results: State-of-the-art deep image-reconstruction networks can be constructed and trained using BART's gradient based optimization algorithms. The BART implementation achieves a similar performance in terms of training time and reconstruction quality compared to the original implementations based on TensorFlow. Conclusion: By integrating non-linear operators and neural networks into BART, we provide a general framework for deep-learning-based reconstruction in MRI
Model-Based Reconstruction for Joint Estimation of , and Field Maps Using Single-Shot Inversion-Recovery Multi-Echo Radial FLASH
No full textPurpose: To develop a model-based nonlinear reconstruction for simultaneous water-specific , , field and/or fat fraction (FF) mapping using single-shot inversion-recovery (IR) multi-echo radial FLASH.
Methods: The proposed model-based reconstruction jointly estimates water-specific , , field and/or FF maps, as well as a set of coil sensitivities directly from -space obtained with a single-shot IR multi-echo radial FLASH sequence using blip gradients across echoes. Joint sparsity constraints are exploited on multiple quantitative maps to improve precision. Validations are performed on numerical and NIST phantoms and with in vivo studies of the human brain and liver at 3 T.
Results: Numerical phantom studies demonstrate the effects of fat signals in estimation and confirm good quantitative accuracy of the proposed method for all parameter maps. NIST phantom results confirm good quantitative and accuracy in comparison to Cartesian references. Apart from good quantitative accuracy and precision for multiple parameter maps, in vivo studies show improved image details utilizing the proposed joint estimation. The proposed method can achieve simultaneous water-specific , , field and/or FF mapping for brain (0.81 0.81 5 mm) and liver (1.6 1.6 6 mm) imaging within four seconds.
Conclusion: The proposed model-based nonlinear reconstruction, in combination with a single-shot IR multi-echo radial FLASH acquisition, enables joint estimation of accurate water-specific , , field and/or FF maps within four seconds. The present work is of potential value for specific clinical applications
Free-Breathing Liver Fat and R2 Mapping: Multi-Echo Radial FLASH and Model-based Reconstruction (MERLOT)
No full text
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