A model-based iterative reconstruction of the apparent diffusion coefficient (ADC) is introduced enabling the quantification of diffusion properties from undersampled DWI data of human knee joints. The approach uses an underlying model function to synthesize k-space data containing crucial phase information. A NLCG is implemented for ADC reconstruction comparing synthetic and measured k-space data. In vivo data of nine subjects show up to 3.5-fold acceleration of the DWI sequence without losing substantial accuracy of the ADC estimates.
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