Abstract
Dynamic magnetic resonance imaging (MRI) is used in multiple clinical applications, but can still benefit from higher spatial or temporal resolution. A dynamic MR image reconstruction method from partial (k, t)-space measurements is introduced that recovers and inherently separates the information in the dynamic scene. The reconstruction model is based on a low-rank plus sparse decomposition prior, which is related to robust principal component analysis. An algorithm is proposed to solve the convex optimization problem based on an alternating direction method of multipliers. The method is validated with numerical phantom simulations and cardiac MRI data against state of the art dynamic MRI reconstruction methods. Results suggest that using the proposed approach as a means of regularizing the inverse problem remains competitive with state of the art reconstruction techniques. Additionally, the decomposition induced by the reconstruction is shown to help in the context of motion estimation in dynamic contrast enhanced MRI.
Highlights
M AGNETIC resonance imaging (MRI) is a medical imaging technique that produces images of internal structures of the body
A dynamic magnetic resonance (MR) image reconstruction method from sub-Nyquist measurements based on an intrinsic separation between low-rank plus sparse components is introduced
Numerous methods have been developed to reduce acquisition time, but it can still benefit from higher acceleration rates and efficient reconstruction algorithms
Summary
M AGNETIC resonance imaging (MRI) is a medical imaging technique that produces images of internal structures of the body. Dynamic MRI, a magnetic resonance signal with both spatial and temporal information, is used in multiple clinical applications such as cardiovascular imaging or dynamic contrast enhanced MRI. MRI is inherently a slow process due to a combination of different constraints including nuclear relaxation times, peripheral nerve stimulation, power absorption and signal to noise. This can limit spatial and temporal MR resolutions, yet they are critical to monitor dynamic processes where events change on relatively small. The imaging equation in dynamic MRI can be written as (1). Where represents the measured ( )-space signal, is the desired image function and represents the noise.
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