This lecture explains the principles of model-based reconstruction methods and their linearization using dictionaries for MR parameter mapping.
In current clinical practice, diagnosis based on MRI primarily relies on the qualitative assessment of images, based on the contrast changes in tissues. Direct quantification of the parameters underlying various MRI contrast mechanisms provides more accurate and reproducible information, which can improve the diagnostic accuracy, particularly in early disease. Consequently, there is an increased interest in methods for quantitative MRI including measurements of T1, T2, T2* relaxation times, diffusion, etc.
A major challenge in the adoption of quantitative MRI protocols in clinical practice is the often long acquisition time required to obtain data for parameter estimation. Increasing the scan time in MRI examination is related with increased costs and reduced patient comfort. Moreover, long scans are prone to artifacts, in particular due to motion. In some areas of the body, affected by respiratory motion quantitative MRI is challenging because the acquisition is typically limited to a breath hold.
Quantitative MR typically requires the acquisition of multiple images with different acquisition parameters and subsequent pixel-wise fitting to an appropriate signal model. The above mentioned temporal constrains have led to the estimation of MR parameters from very few data points, which entails poor accuracy and does not give indication of multi-compartmental behavior, and restricting the spatial resolution of the scans, which may lead to missing pathology. Methods for fast acquisition are therefore necessary to allow the acquisition of higher number of measurements and improve the resolution.
This lecture presents methods for reducing the acquisition time in quantitative MRI based on constrained reconstruction. Besides the generic constraints that can be used for image series, the known signal model in quantitative MRI permits designing a model based constraint tailored to the specific application. This is a much stronger prior knowledge, which, provided that the model is accurate, enables even higher accelerations and improved image quality. As a special class of model-based reconstruction, dictionary based methods are considered, which replace the inversion of a non-linear signal model with a search in a so-called dictionary. The dictionary is usually generated in a way to provide a sparse representation of the measured signal evolution in the parametric direction and applied in a sparsity constrained reconstruction.
The main concepts are explained for the example of relaxation parameter mapping. A brief overview of additional methods for efficient quantitative MRI is given including applications such as diffusion, magnetization transfer, and fat fraction imaging. Finally, the relation to the novel approach for quantitative MRI coined MR Fingerprinting is explained.
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