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Application of a full model-based iterative reconstruction (MBIR) in 80 kVp ultra-low-dose paranasal sinus CT imaging of pediatric patients

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Abstract

Objective

To evaluate the clinical application of a full model-based iterative reconstruction (MBIR) algorithm in the ultra-low-dose paranasal sinus CT imaging of children.

Materials and methods

In the first phase, 16 low-dose CT dacryocystography (DCG) (80 kV/64 mAs) scans were reconstructed with MBIR and filtered back-projection (FBP) to demonstrate noise reduction capability of MBIR. MBIR images were also compared with the images of 21 standard-dose paranasal sinus patients reconstructed with adaptive statistical iterative reconstruction (ASIR) algorithm. In the second phase, 14 pediatric tumors patients (images with ASIR in the initial scan) who came for follow-up paranasal sinus CT scan were prospectively enrolled with reduced radiation and MBIR algorithm. In both study phases, image noise and the contrast noise ratio (CNR) of sphenoid was measured; and subjective image quality was evaluated. CTDIvol and DLP were recorded, and effective dose calculated.

Results

The CTDIvol value for the DCG group was 63.9% lower than the standard-dose sinus group (1.09 ± 0.01 mGy vs. 3.02 ± 0.35 mGy). Compared with the ASIR reconstruction in the standard-dose sinus patient group, images with MBIR in the ultra-low-dose DCG group had 39.9% lower noise (9.5 ± 0.8HU vs. 15.8 ± 3.3HU) and 63.6% higher CNR (14.4 ± 4.7 vs. 8.8 ± 2.2), with similar subjective image quality score. For the tumor patients, 65.5% dose reduction was achieved. Subjective quality scores were similar between the initial and follow-up scans. Objective noise was significantly lower for the follow-up group.

Conclusion

MBIR provided equal or better image quality with significantly reduced radiation dose in paranasal sinus CT imaging of pediatric patients compared with standard-dose CT with ASIR algorithm.

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Acknowledgements

We are grateful to all subjects and their family members for their cooperation in providing clinical information for the study. We would like to express our sincere thanks to Dr. Jianying Li and Dr. Ning Guo for their technical support in understanding the model-based iterative reconstruction algorithm and in editing the manuscript.

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Authors and Affiliations

  1. Imaging Center, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing, 100045, China

    Jihang Sun, Qifeng Zhang, Xiaomin Duan, Yong Liu & Yun Peng

  2. Department of Ophthalmology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing, 100045, China

    Chengyue Zhang

  3. Department of Otolaryngology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing, 100045, China

    Pengpeng Wang

  4. Department of Hematology, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, No. 56, Nanlishi Road, Xicheng District, Beijing, 100045, China

    Chenguang Jia

Authors
  1. Jihang Sun
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  2. Qifeng Zhang
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  3. Xiaomin Duan
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  4. Chengyue Zhang
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  5. Pengpeng Wang
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  6. Chenguang Jia
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  7. Yong Liu
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  8. Yun Peng
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Corresponding author

Correspondence to Yun Peng.

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Funding

This study was supported by the Beijing Children’s Hospital Young Investigator Program (Grant Numbers BCH-YIPB-2016-06) and Clinical Technology Innovation Project of Beijing Municipal Commission (Grant Numbers xmlx201407).

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The present study was approved by the Ethics Committee of Beijing Children’s Hospital. The legal guardian of all the children signed written informed consents.

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Sun, J., Zhang, Q., Duan, X. et al. Application of a full model-based iterative reconstruction (MBIR) in 80 kVp ultra-low-dose paranasal sinus CT imaging of pediatric patients. Radiol med 123, 117–124 (2018). https://doi.org/10.1007/s11547-017-0812-0

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  • DOI: https://doi.org/10.1007/s11547-017-0812-0

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