Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice

May Zaw Thin, Axel Behrens, Christopher Moore, Thomas Snoeks, Tammy Kalber, Julian Downward

Published: 2022-12-08 DOI: 10.1038/s41596-022-00769-5

Extended

Extended Data Fig. 1 Highlighting and binary thresholding of individual lung tumors using Analyze software.

a – e , Screenshots showing how to draw ROI around the tumour using the tool called ‘Draw wall’ ( a ) and adjust the binary threshold values to display the tumour area as white pixels with a black outline ( b ) as presented in 3D volume rendered ( c ), sagittal ( d ) and coronal images ( e ).

Extended Data Fig. 2 Isolation and extraction of individual lung tumours using Analyze software.

a – c , Images from 3D volume rendered ( a ), sagittal ( b ) and coronal panels ( c ) showing the tumour attached to the background. d – h , Screenshots showing how to use the tool called ‘Object Separator’ ( d ) and erase unwanted highlighted areas manually to isolate tumour nodule from the background ( e ) as presented in volume rendered ( f ), sagittal ( g ) and coronal images ( h ).

Extended Data Fig. 3 Binary thresholding and highlighting of trachea and heart using Analyze software.

a – d , Images from 3D volume rendered ( a ), sagittal ( b ) and coronal ( c ) panels showing part of binary thresholded trachea after adjusting the threshold values ( d ). e , Screenshot showing how to highlight two regions of the heart by marking and scrolling through the frames with the tool called ‘Draw’.

Extended Data Fig. 4 Calculating signal intensity of trachea, heart and air for automatic lung segmentation with Analyze software.

a , Calculation of the mean signal intensity of trachea and heart by setting parameters. b – d , Images showing step-by-step instructions to calculate signal intensity of air inside the lung starting with creating a calibration curve with results from the trachea and the heart ( b ), loading Image algebra from selected lung scan ( c ) and then dragging image into Input picture (arrow) followed by filling equation from the calibration curve for Output and naming output image with underscore (_) at the end ( d ). e , Image showing how to segment the lung from the background by setting threshold in region grow.

Extended Data Fig. 5 Troubleshooting of the 3D lung volume rendering using CTAn software.

a – e , Unrepresentative structures in 3D lung volume rendering ( a ) can be avoided by removing fat tissue (circle) ( b ), gas shadow from stomach (circle) ( c ), motion artifacts from the ribs (black arrow) ( d ) and the spine (black arrow) ( e ) from the ROI of lung.

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X-ray computed tomography is a reliable technique for the detection and longitudinal monitoring of pulmonary nodules. In preclinical stages of diagnostic or therapeutic development, the miniaturized versions of the clinical computed tomography scanners ar
Introduction
Materials
Procedure
Troubleshooting
Timing
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ACKNOWLEDGMENTS

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Micro-CT acquisition and image processing to track and characterize pulmonary nodules in mice

Extended

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