Improved MRI reconstruction and denoising using SVD-based low-rank approximation

The reconstruction of multi-dimensional magnetic resonsance imaging (MRI) data can be a computationally demanding task. Signal-to-noise ratio is also a concern, specially in high-resolution imaging. Data compression may be useful not only for reducing reconstruction complexity and memory requirements, but also for reducing noise, as it is capable of eliminating spurious components. This work proposes the use of SVD-based low-rank approximation for the reconstruction and denoising of MRI data. The Akaike information criterion is used to estimate the appropriate model order. The model order is used to remove noisy components and to reduce the amount of data to be stored and processed. The proposed method is evaluated using in vivo MRI data. We present images reconstructed using less than 20% visual inspection. A quantitative evaluation is also presented.