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Small-bowel obstruction: coronal reformations from isotropic voxels at 16-section multi-detector row CT
Tracy A Jaffe1, Lucie C Martin, John Thomas
1Department of Radiology, Duke University Medical Center, Erwin Rd, Box 3808, Durham, NC 27710, USA. jaffe002@mc.duke.edu
Radiology
|November 19, 2005
Summary
Coronal reformations from 16-section multi-detector row computed tomography (CT) improve diagnostic confidence for small-bowel obstruction (SBO). This imaging technique enhances the accuracy of both diagnosing SBO and excluding it when necessary.
Area of Science:
- Radiology
- Medical Imaging
Background:
- Small-bowel obstruction (SBO) is a common surgical emergency.
- Accurate and timely diagnosis of SBO is crucial for patient management.
- Multi-detector row computed tomography (CT) is a primary imaging modality for evaluating SBO.
Purpose of the Study:
- To evaluate the added diagnostic value of coronal reformations from isotropic voxels using 16-section multi-detector row CT.
- To assess the impact of coronal reformations on the diagnosis of small-bowel obstruction (SBO).
Main Methods:
- Retrospective analysis of 100 patients with suspected SBO undergoing 16-section MDCT with coronal reformations.
- Three blinded readers interpreted CT scout, transverse scans alone, and transverse plus coronal scans.
- Diagnostic confidence for SBO and abnormal wall enhancement was scored on a 1-5 scale.
Main Results:
- Sensitivity and specificity for SBO diagnosis were similar across CT scout, transverse alone, and transverse plus coronal CT (87-88% sensitivity, 86-90% specificity).
- Transverse plus coronal CT significantly increased reader confidence in excluding SBO in patients without obstruction (P = .01).
- Transverse plus coronal CT significantly increased reader confidence in diagnosing SBO and identifying abnormal wall enhancement in patients with obstruction (P = .01).
Conclusions:
- Transverse 16-section multi-detector row CT datasets are highly effective for diagnosing SBO.
- The addition of coronal reformations from isotropic datasets enhances diagnostic confidence for both the presence and absence of SBO.
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