Impact of model-based iterative reconstruction on low-contrast lesion detection and image quality in abdominal CT: a 12-reader-based comparative phantom study with filtered back projection at different tube voltages
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To evaluate the impact of model-based iterative reconstruction (MBIR) on image quality and low-contrast lesion detection compared with filtered back projection (FBP) in abdominal computed tomography (CT) of simulated medium and large patients at different tube voltages.
A phantom with 45 hypoattenuating lesions was placed in two water containers and scanned at 70, 80, 100, and 120 kVp. The 120-kVp protocol served as reference, and the volume CT dose index (CTDIvol) was kept constant for all protocols. The datasets were reconstructed with MBIR and FBP. Image noise and contrast-to-noise-ratio (CNR) were assessed. Low-contrast lesion detectability was evaluated by 12 radiologists.
MBIR decreased the image noise by 24% and 27%, and increased the CNR by 30% and 29% for the medium and large phantoms, respectively. Lower tube voltages increased the CNR by 58%, 46%, and 16% at 70, 80, and 100 kVp, respectively, compared with 120 kVp in the medium phantom and by 9%, 18% and 12% in the large phantom. No significant difference in lesion detection rate was observed (medium: 79-82%; large: 57-65%; P > 0.37).
Although MBIR improved quantitative image quality compared with FBP, it did not result in increased low-contrast lesion detection in abdominal CT at different tube voltages in simulated medium and large patients.
• MBIR improved quantitative image quality but not lesion detection compared with FBP.
• Increased CNR by low tube voltages did not improve lesion detection.
• Changes in image noise and CNR do not directly influence diagnostic accuracy.
KeywordsMultidetector computed tomography Radiological phantom Model-based iterative reconstruction Filtered back projection Low-contrast detection
Compliance with ethical standards
The scientific guarantor of this publication is Sebastian T. Schindera.
Conflict of interest
Sebastian T. Schindera received a research grant by Siemens Healthcare and Bayer Healthcare
The authors state that this work has not received any funding.
Statistics and biometry
No complex statistical methods were necessary for this paper.
Institutional Review Board approval was not required because of the design as a phantom study.
• performed at one institution
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