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

Abstract

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

Highlights

A radiation dose reduction from the reference 50 mAs to the reference 20 mAs protocol resulted in decreased Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) outcomes
SNR, signal-to-noise ratio; CNR, contrast-to-noise ratio; Computed tomography (CT), computed tomography; CTDIvol, computed tomography dose index; DLP, dose length product; ADMIRE, Advanced Modeled Iterative Reconstruction; HU, Hounsfield units; PS, PixelShine deep learning algorithm. This is the first study to assess the use of ADMIRE and PS algorithms to improve image quality after substantial radiation dose reduction for CT protocols to assess midfacial trauma
This study demonstrated that radiation dose reduction, increasing the iterative reconstruction (IR) strength, and the use of the PS algorithm were all significantly associated with SNR and CNR outcomes

Summary

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. Three iterative reconstruction strengths were applied to reconstruct bone and soft tissue data sets and these were subsequently applied to the PS algorithm. 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. Both the ADMIRE and PS algorithms significantly improved image quality after substantial radiation dose reduction. Both the ADMIRE and PS algorithms significantly improved image quality after substantial radiation dose reduction. (Oral Surg Oral Med Oral Pathol Oral Radiol 2021;132:247À254)

Methods

Results

Conclusion