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

被引:0
|
作者
Rajash K. Handa
Paul R. Territo
Philip M. Blomgren
Scott A. Persohn
Chen Lin
Cynthia D. Johnson
Lei Jiang
Bret A. Connors
Gary D. Hutchins
机构
[1] Indiana University School of Medicine,Department of Anatomy and Cell Biology
[2] Indiana University School of Medicine,Department of Radiology and Imaging Sciences
来源
Urolithiasis | 2017年 / 45卷
关键词
Magnetic resonance imaging; Kidney; Shock wave lithotripsy; Tissue injury;
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学科分类号
摘要
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. 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 the measurement of lesion volumes. 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%. 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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页码:507 / 513
页数:6
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