Abstract
Purpose:To apply a newly developed shape‐based registration scheme for motion compensation in MR urography and verify its efficacy in facilitating quantitative functional analysis.Methods:We have recently developed a shape‐based registration scheme that is robust w.r.t. intensity inconsistency. In this study, we utilized this robust registration tool to estimate kidney motion during the MRU scans and compensate for such motion to facilitate the quantitative functional analysis. To validate the efficacy of this scheme, MRU analysis was performed on dataset acquired from sedated subjects to obtain ground‐truth (motion‐free) functional estimates. Physiologically sound motion was then simulated to synthesize image sequences influenced by respiratory motion. Quantitative assessment and comparison were performed amongst ground‐truth, calculations without and with motion compensation for the following set of functional parameters: the contrast dynamic of the left and right cortex, medulla and aorta, the Patlak number and the globular filtration rate (GFR) based on the Patalk‐Rutland model, and the Patlak differential renal function (pDRF).Results:Without motion compensation, the generated relative enhancement curves contained large fluctuations and the estimated GFR values were underestimated by 26% (75.4 ml/min) and 30% (95.3 ml/min) compared with the ground truth of 101.9ml/min and 134.3ml/min for the left and right kidney respectively. Such large errors could result in misleading diagnosis if a typical threshold of 90ml/min were used to determine renal function abnormality. Upon proposed motion compensation, the relative enhancement curves were much smoother and the GFR estimation errors for the left and right kidneys reduced to 0.4% and 1.6% respectively, demonstrating the advantage of the introduced motion compensation method.Conclusion:The developed motion compensation method has demonstrated its ability to facilitate quantitative MRU functional analysis, with improved accuracy of pharmacokinetic modeling and quantitative parameter estimations. Future work will consider incorporating more complex and realistic pharmacokinetic models into the MRU analysis.
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