Linear Time Invariant Model Based Motion Correction (LiMo-MoCo) of Dynamic Radial Contrast Enhanced MRI

Abstract

Early identification of kidney function deterioration is essential to determine which newborn patients with dilation of the renal pelvis (hydronephrosis) should undergo surgery. Kidney function can be measured by fitting a tracer kinetic (TK) model onto a series of Dynamic Contrast Enhanced (DCE) MR images and deriving the glomerular filtration rate (GFR) from the TK model. Unfortunately, heavy breathing and large bulk motion events create outliers and misalignments that introduce large errors in the TK estimates. Moreover, aligning the series of DCE images is not trivial due to the contrast differences between them and the undersampling artifacts due to fast imaging. We present a bulk motion detection and a linear time invariant (LTI) model-based motion correction approach for DCE-MRI alignment that leverages the temporal dynamics of the DCE data at each voxel. We evaluate our approach on 10 newborn patients that underwent DCE imaging without sedation. For each patient, we reconstructed the sequence of DCE images, detected and removed the volumes corrupted by motion using a self navigation approach, aligned the sequence using our approach and fitted the TK model to compute GFR. The results show that our approach correctly aligned all volumes and improved the TK model fit and, on average, reducing the normalized root-mean-squared error by 0.17.