8,062 research outputs found
MR image reconstruction using deep density priors
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled
measurements exploit prior information to compensate for missing k-space data.
Deep learning (DL) provides a powerful framework for extracting such
information from existing image datasets, through learning, and then using it
for reconstruction. Leveraging this, recent methods employed DL to learn
mappings from undersampled to fully sampled images using paired datasets,
including undersampled and corresponding fully sampled images, integrating
prior knowledge implicitly. In this article, we propose an alternative approach
that learns the probability distribution of fully sampled MR images using
unsupervised DL, specifically Variational Autoencoders (VAE), and use this as
an explicit prior term in reconstruction, completely decoupling the encoding
operation from the prior. The resulting reconstruction algorithm enjoys a
powerful image prior to compensate for missing k-space data without requiring
paired datasets for training nor being prone to associated sensitivities, such
as deviations in undersampling patterns used in training and test time or coil
settings. We evaluated the proposed method with T1 weighted images from a
publicly available dataset, multi-coil complex images acquired from healthy
volunteers (N=8) and images with white matter lesions. The proposed algorithm,
using the VAE prior, produced visually high quality reconstructions and
achieved low RMSE values, outperforming most of the alternative methods on the
same dataset. On multi-coil complex data, the algorithm yielded accurate
magnitude and phase reconstruction results. In the experiments on images with
white matter lesions, the method faithfully reconstructed the lesions.
Keywords: Reconstruction, MRI, prior probability, machine learning, deep
learning, unsupervised learning, density estimationComment: Published in IEEE TMI. Main text and supplementary material, 19 pages
tota
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
Deep MRI reconstruction is commonly performed with conditional models that
de-alias undersampled acquisitions to recover images consistent with
fully-sampled data. Since conditional models are trained with knowledge of the
imaging operator, they can show poor generalization across variable operators.
Unconditional models instead learn generative image priors decoupled from the
imaging operator to improve reliability against domain shifts. Recent diffusion
models are particularly promising given their high sample fidelity.
Nevertheless, inference with a static image prior can perform suboptimally.
Here we propose the first adaptive diffusion prior for MRI reconstruction,
AdaDiff, to improve performance and reliability against domain shifts. AdaDiff
leverages an efficient diffusion prior trained via adversarial mapping over
large reverse diffusion steps. A two-phase reconstruction is executed following
training: a rapid-diffusion phase that produces an initial reconstruction with
the trained prior, and an adaptation phase that further refines the result by
updating the prior to minimize reconstruction loss on acquired data.
Demonstrations on multi-contrast brain MRI clearly indicate that AdaDiff
outperforms competing conditional and unconditional methods under domain
shifts, and achieves superior or on par within-domain performance
- …