160 research outputs found
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
Machine learning in Magnetic Resonance Imaging: Image reconstruction.
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20Â years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction. A wide range of approaches have been proposed, which can be applied in k-space and/or image-space. Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation. In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends
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