Affiliations: [a] Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung City, Taiwan, ROC
| [b] Department of Medical Imaging and Radiology, Shu-Zen Junior College of Medicine and Management, Kaohsiung City, Taiwan, ROC
| [c] GE Healthcare, New Taipei, Taiwan, ROC
Correspondence:
[*]
Corresponding author: Chia-Chi Hsiao, Department of Radiology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC. Tel.: +886 7 342 2121. E-mail: [email protected].
Abstract: PURPOSE: This study aims to introduce a novel low-dose abdominal computed tomography (CT) protocol adapted with model-based iterative reconstruction (MBIR), To validate the adaptability of this protocol, objective image quality and subjective clinical scores of low-dose MBIR images are compared with the normal-dose images. METHODS: Normal-dose abdominal CT images of 58 patients and low-dose abdominal CT images of 52 patients are reconstructed using both conventional filtered back projection (FBP) and MBIR methods with and without smooth applying. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) are used to compare image quality between the normal-dose and low-dose CT scans. CT dose indices (CTDI) of normal-dose and low-dose abdominal CT images on post-contrast venous phase are also compared. RESULTS: The SNR, CNR and clinical score of low-dose MBIR images all show significant higher values (Bonferroni p < 0.05) than those of normal-dose images with conventional FBP method. A total of around 40% radiation dose reduction (CTDI: 5.3 vs 8.7 mGy) could be achieved via our novel abdominal CT protocol. CONCLUSIONS: With the higher SNR/CNR and clinical scores, the low-dose CT abdominal imaging protocol with MBIR could effectively reduce the radiation for patients and provide equal or even higher image quality and also its adaptability in clinical abdominal CT image diagnosis.
Keywords: Low-dose computed tomography (low-dose CT), model-based iterative reconstruction (MBIR), filtered back projection (FBP), Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR)
