Iterative reconstruction and deep learning algorithms for enabling low-dose computed tomography in midfacial trauma

Romke Rozema; Herbert T Kruitbosch; Baucke van Minnen; Bart Dorgelo; Joep Kraeima; Peter M A van Ooijen

doi:10.1016/j.oooo.2020.11.018

Iterative reconstruction and deep learning algorithms for enabling low-dose computed tomography in midfacial trauma

Oral Surg Oral Med Oral Pathol Oral Radiol. 2021 Aug;132(2):247-254. doi: 10.1016/j.oooo.2020.11.018. Epub 2020 Dec 8.

Authors

Romke Rozema¹, Herbert T Kruitbosch², Baucke van Minnen³, Bart Dorgelo⁴, Joep Kraeima³, Peter M A van Ooijen⁵

Affiliations

¹ Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. Electronic address: R.rozema01@umcg.nl.
² Center for Information Technology, University of Groningen, Groningen, The Netherlands.
³ Department of Oral and Maxillofacial Surgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
⁴ Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands; Department of Radiology, Martini Hospital, Groningen, The Netherlands.
⁵ Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.

PMID: 34034999
DOI: 10.1016/j.oooo.2020.11.018

Abstract

Objectives: The objective of this study was to quantitatively assess the image quality of Advanced Modeled Iterative Reconstruction (ADMIRE) and the PixelShine (PS) deep learning algorithm for the optimization of low-dose computed tomography protocols in midfacial trauma.

Study design: Six fresh frozen human cadaver head specimens were scanned by computed tomography using both standard and low-dose scan protocols. Three iterative reconstruction strengths were applied to reconstruct bone and soft tissue data sets and these were subsequently applied to the PS algorithm. Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) were calculated for each data set by using the image noise measurements of 10 consecutive image slices from a standardized region of interest template.

Results: The low-dose scan protocol resulted in a 61.7% decrease in the radiation dose. Radiation dose reduction significantly reduced, and iterative reconstruction and the deep learning algorithm significantly improved, the CNR for bone and soft tissue data sets. The algorithms improved image quality after substantial dose reduction. The greatest improvement in SNRs and CNRs was found using the iterative reconstruction algorithm.

Conclusion: Both the ADMIRE and PS algorithms significantly improved image quality after substantial radiation dose reduction.

MeSH terms

Algorithms
Deep Learning*
Humans
Radiation Dosage
Radiographic Image Interpretation, Computer-Assisted
Signal-To-Noise Ratio
Tomography, X-Ray Computed