A Novel Reconstruction Approach Using Model Consistency Condition for Accelerated Quantitative MRI (MOCCA)

Alexey Samsonov¹

In this work, we put forward a MOdel Consistency Condition for Accelerated imaging (MOCCA) derive a novel, practical method for improved reconstruction of parametric image series and quantitative maps from undersampled data. The novel method reconstructs parametric image series sampled below the Nyquist limit using prior knowledge of a signal evolution in the parametric dimension. The method is resilient to data errors such as noise and poor representation of signal evolution in the parametric dimension caused by imaging imperfections such as motion artifacts. The method was validated on T1/T1 relaxometry data.