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Experience With Iterative Reconstruction Techniques for Abdominopelvic Computed Tomography in Morbidly and Super
Khalid W Shaqdan1, Avinash R Kambadakone, Peter Hahn
1From the Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Objective:
The aim of this study was to investigate the diagnostic performance of abdominopelvic computed tomography (CT) images reconstructed using filtered back projection (FBP) and iterative reconstruction (IR) algorithms in morbidly and super obese patients.
Materials And Methods:
One hundred eighty-seven abdominopelvic CT examinations in portal venous phase were performed between February 2015 and February 2016 in 182 patients (mean age = 52 years, mean body mass index = 45.5). One hundred fourteen of 187 examinations were reconstructed using IR and 73 examinations were processed using FBP. Patients were further stratified based on body mass index. Sixty CT scans were reviewed by a single reader for image quality, image noise, and artifacts. Objective noise and attenuation were also determined. Size-specific dose estimate and CT dose index volume were compared and statistically analyzed.
Results:
A diagnostic interpretation was rendered for all 187 examinations. A single-reader review of 60 cases showed greater diagnostic acceptability for IR when compared with FBP (image quality = 4.2 and 3.8 [P = 0.035], noise = 1.5 and 1.6 [P = 0.692], artifact = 1.4 and 1.5 [P = 0.759], respectively). For all examinations, the IR group had lower objective image noise (IR = 9.3 and FBP = 14.3; P < 0.001) and higher contrast-to-noise ratio (IR = 17.2 and FBP = 11.7; P < 0.001) without increase in radiation dose (size-specific dose estimate [IR = 15.1, FBP = 16.5 mGy; P = 0.045] and CT dose index volume [IR = 17.6, FBP = 18 mGy; P = 0.62]).
Conclusions:
In morbidly and super obese patients, diagnostic quality images could be reliably generated with minimal artifacts and noise using newer generation scanners integrated with IR without increasing radiation dose.
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