Low-radiation dose scan protocol for preoperative imaging for dental implant surgery using deep learning-based reconstruction in multidetector CT

Purpose Maxillofacial trauma predominantly affects young adults between 20 and 40 years of age. Although radioprotection is a legal requirement, the significant potential of dose reduction in computed tomography (CT) is still underused in the clinical routine. The objective of this study was to evaluate whether maxillofacial fractures can be reliably detected and classified using ultra-low-dose CT. Materials and Methods CT images of 123 clinical cases with maxillofacial fractures were classified by two readers using the AOCOIAC software and compared with the corresponding results from post-treatment images. In group 1, consisting of 97 patients with isolated facial trauma, pre-treatment CT images at different dose levels (volumetric computed tomography dose index: ultra-low dose, 2.6 mGy; low dose, <10 mGy; and regular dose, <20 mGy) were compared with post-treatment cone-beam computed tomography (CBCT). In group 2, consisting of 31 patients with complex midface fractures, pre-treatment shock room CT images were compared with post-treatment CT at different dose levels or CBCT. All images were presented in random order and classified by 2 readers blinded to the clinical results. All cases with an unequal classification were re-evaluated. Results In both groups, ultra-low-dose CT had no clinically relevant effect on fracture classification. Fourteen cases in group 2 showed minor differences in the classification code, which were no longer obvious after comparing the images directly to each other. Conclusion Ultra-low-dose CT images allowed the correct diagnosis and classification of maxillofacial fractures. These results might lead to a substantial reconsideration of current reference dose levels.

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“… 16 The most recent deep-learning-based reconstructions could enable further optimization of ultra-low-dose image quality. 17 …”

Section: Discussionmentioning

confidence: 99%

Can ultra-low-dose computed tomography reliably diagnose and classify maxillofacial fractures in the clinical routine?

et al. 2023

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Impact of a new deep-learning-based reconstruction algorithm on image quality in ultra-high-resolution CT: clinical observational and phantom studies

Sakai

Hida

Matsuura

et al. 2022

The British Journal of Radiology

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Objectives: To demonstrate the effect of an improved deep learning-based reconstruction (DLR) algorithm on Ultra-High-Resolution Computed Tomography (U-HRCT) scanners. Methods: Clinical and phantom studies were conducted. Thirty patients who underwent contrast-enhanced CT examination during the follow-up period were enrolled. Images were reconstructed using improved DLR [termed, New DLR, i.e., Advanced Intelligent Clear-IQ Engine (AiCE) Body Sharp] and conventional DLR (Conv DLR, AiCE Body) algorithms. Two radiologists assessed the overall image quality using a 5-point scale (5 = excellent; 1 = unacceptable). The noise power spectra (NPSs) were calculated to assess the frequency characteristics of the image noise, and the square root of area under the curve (√AUC NPS) between 0.05 and 0.50 cycle/mm was calculated as an indicator of the image noise. Dunnett’s test was used for statistical analysis of the visual evaluation score, with statistical significance set at p < 0.05. Results: The overall image quality of New DLR was better than that of the Conv DLR (4.2 ± 0.4 and 3.3 ± 0.4, respectively; p < 0.0001). All New DLR images had an overall image quality score above the average or excellent. The √AUCNPS value of New DLR was lower than that of Conv DLR (13.8 and 14.2, respectively). The median values of reconstruction time required with New DLR and Conv DLR were 5.0 and 7.8 min, respectively. Conclusions: The new DLR algorithm improved the image quality within a practical reconstruction time. Advances in knowledge: The new DLR enables us to choose whether to improve image quality or reduce the dose.

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Low-radiation dose scan protocol for preoperative imaging for dental implant surgery using deep learning-based reconstruction in multidetector CT

Cited by 2 publications

References 20 publications

Can ultra-low-dose computed tomography reliably diagnose and classify maxillofacial fractures in the clinical routine?

Can ultra-low-dose computed tomography reliably diagnose and classify maxillofacial fractures in the clinical routine?

Impact of a new deep-learning-based reconstruction algorithm on image quality in ultra-high-resolution CT: clinical observational and phantom studies

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