Development of a novel magnetic resonance imaging acquisition and analysis workflow for the quantification of shock wave lithotripsy-induced renal hemorrhagic injury

Abstract

Introduction 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.

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“…Handa et al developed a method to quantify the hemorrhagic injury to kidneys post ESWL using a Multi-Spectral Neural Network (MSNN) classifier for segmentation and classification of MRI images. The model achieved a high accuracy (79%) and the prediction values correlated very well (R = 0.96) with the morphology [29]. [30,31].…”

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“…When compared with standard morphometric analysis, there was strong correlation between predicted and actual lesion volumes (0.97). This demonstrates the potential of a semi-automated approach to quantify renal injury after SWL [46]. Notably, the use of ex vivo kidneys for this study greatly improves the signal-to-noise ratio that can make machine learning challenging in this context.…”

Percutaneous nephrolithotomy in new versus those patients with previous history of Extracorporeal Shock Wave Lithotripsy on ipsilateral side

“…Apart from this, interstitial fibrosis occurs with segmental shrinkage of renal cortex. [11][12][13] In addition to these changes, pressure waves (when ESWL procedures are applied frequently), can thrust the stone fragments in the neighborhood of renal mucosa. Yuruk et al 10 noted, shrank calyceal infundibulum and submerged stone chips underneath the pelvicalyceal mucosal layer in patients who earlier undertook sessions of failed SWL.…”