Handa, Rajash K.Territo, Paul R.Blomgren, Philip M.Persohn, Scott A.Lin, ChenJohnson, Cynthia D.Jiang, LeiConnors, Bret A.Hutchins, Gary D.2018-06-082018-06-082017-10Handa, R. K., Territo, P. R., Blomgren, P. M., Persohn, S. A., Lin, C., Johnson, C. D., … Hutchins, G. D. (2017). Development of a Novel Magnetic Resonance Imaging Acquisition and Analysis Workflow for the Quantification of Shock Wave Lithotripsy-Induced Renal Hemorrhagic Injury. Urolithiasis, 45(5), 507–513. https://doi.org/10.1007/s00240-016-0959-52194-7228https://hdl.handle.net/1805/16435Introduction The current accepted standard for quantifying shock wave lithotripsy (SWL)-induced tissue damage is based on morphometric detection of renal hemorrhage in serial tissue sections from fixed kidneys. This methodology is time and labor intensive and is tissue destructive. We have developed a non-destructive magnetic resonance imaging (MRI) method that permits rapid assessment of SWL-induced hemorrhagic lesion volumes in post-mortem kidneys using native tissue contrast to reduce cycle time. Methods Kidneys of anesthetized pigs were targeted with shock waves using the Dornier Compact S lithotripter. Harvested kidneys were then prepared for tissue injury quantification. T1 weighted (T1W) and T2 weighted (T2W) images were acquired on a Siemens 3T Tim Trio MRI scanner. Images were co-registered, normalized, difference (T1W–T2W) images generated, and volumes classified and segmented using a Multi-Spectral Neural Network (MSNN) classifier. Kidneys were then subjected to standard morphometric analysis for measurement of lesion volumes. Results Classifications of T1W, T2W and difference image volumes were correlated with morphometric measurements of whole kidney and parenchymal lesion volumes. From these relationships, a mathematical model was developed that allowed predictions of the morphological parenchymal lesion volume from MRI whole kidney lesion volumes. Predictions and morphology were highly correlated (R=0.9691, n=20) and described by the relationship y=0.84x+0.09, and highly accurate with a sum of squares difference error of 0.79%. Conclusions MRI and the MSNN classifier provide a semi-automated segmentation approach, which provide a rapid and reliable means to quantify renal injury lesion volumes due to SWL.en-USPublisher PolicyKidneyMagnetic resonance imagingShock wave lithotripsyTissue injuryArticle