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Model-based Iterative Reconstruction in Low-radiation-dose Computed Tomography Colonography: Preoperative Assessment
Narumi Taguchi1, Seitaro Oda1, Masanori Imuta1
1Department of Diagnostic Radiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjyo, Chuo-ku, Kumamoto 860-8556, Japan.
Rationale And Objectives:
To assess the effect of model-based iterative reconstruction (MBIR) on image quality and diagnostic performance of low-radiation-dose computed tomography colonography (CTC) in the preoperative assessment of colorectal cancer.
Materials And Methods:
This study included 30 patients with colorectal cancer referred for surgical treatment. All patients underwent CTC with a standard dose (SD) protocol in the supine position and a low-dose (LD; radiation dose reduction of approximately 85%) protocol in the prone position. The SD protocol images were post-processed using filtered back projection (FBP), whereas the LD protocol images were post-processed using FBP and MBIR. Objective and subjective image quality parameters were compared among the three different methods. Preoperative evaluations, including site, length, and tumor and node staging were performed, and the findings were compared to the postsurgical findings.
Results:
The mean image noise of SD-FBP, LD-FBP, and LD-MBIR images was 17.3 ± 3.2, 40.5 ± 10.9, and 11.2 ± 2.0 Hounsfield units, respectively. There were significant differences for all comparison combinations among the three methods (P < .01). For image noise, the mean visual scores were significantly higher for SD-FBP and LD-MBIR than for LD-FBP, and the scores for SD-FBP and LD-MBIR were equivalent (3.9 ± 0.3 [SD-FBP], 2.0 ± 0.5 [LD-FBP], and 3.7 ± 0.3 [LD-MBIR]). Preoperative information was more accurate under SD-FBP and LD-MBIR than under LD-FBP, and the information was comparable between SD-FBP and LD-MBIR.
Conclusion:
MBIR can yield significantly improved image quality on low-radiation-dose CTC and provide preoperative information equivalent to that of standard-radiation-dose protocol.
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