Ultra-high-resolution CT of the temporal bone: Comparison between deep learning reconstruction and hybrid and model-based iterative reconstruction

IF 4.9 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Diagnostic and Interventional Imaging Pub Date : 2024-06-01 DOI:10.1016/j.diii.2024.02.001

Achille Beysang , Nicolas Villani , Fatma Boubaker , Ulysse Puel , Michael Eliezer , Gabriela Hossu , Karim Haioun , Alain Blum , Pedro Augusto Gondim Teixeira , Cécile Parietti-Winkler , Romain Gillet

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引用次数: 0

Abstract

Purpose

The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction algorithm compared to simulated conventional CT.

Materials and methods

CT acquisitions were performed with a Mercury 4.0 phantom. Images were acquired with a 1024 × 1024 matrix and a 0.25 mm slice thickness and reconstructed using DLR, model-based, and hybrid iterative reconstruction algorithms. To simulate conventional CT, images were also reconstructed with a 512 × 512 matrix and a 0.5 mm slice thickness. Spatial resolution, noise power spectrum, and objective high-contrast detectability were compared. Three radiologists evaluated the clinical acceptability of these algorithms by assessing the thickness and image quality of the stapes footplate and superstructure elements, as well as the image quality of the chorda tympani nerve bony and tympanic segments using a 5-point confidence scale on 13 temporal bone CT examinations reconstructed with the four algorithms.

Results

UHR-CT provided higher spatial resolution than simulated conventional CT at the penalty of higher noise. DLR and model-based iterative reconstruction provided better noise reduction than hybrid iterative reconstruction, and DLR had the highest detectability index, regardless of the dose level. All stapedial structure thicknesses were thinner using UHR-CT by comparison with conventional simulated CT (P < 0.009). DLR showed the best visualization scores compared to the other reconstruction algorithms (P < 0.032).

Conclusion

UHR-CT with DLR results in less noise than UHR-CT with hybrid iterative reconstruction and significantly improves stapes and tympanic chorda tympani nerve depiction compared to simulated conventional CT and UHR-CT with iterative reconstruction.

查看原文本刊更多论文

颞骨的超高分辨率 CT：深度学习重建与混合重建和基于模型的迭代重建的比较。

目的：本研究旨在评估超高分辨率计算机断层扫描（UHR-CT）与模拟传统 CT 相比，使用深度学习（DLR）、基于模型和混合迭代重建算法评估镫骨和鼓室神经解剖结构的能力：使用 Mercury 4.0 模型进行 CT 采集。采用 1024 × 1024 矩阵和 0.25 毫米切片厚度采集图像，并使用 DLR、基于模型和混合迭代重建算法进行重建。为模拟传统 CT，还使用 512 × 512 矩阵和 0.5 毫米切片厚度重建图像。对空间分辨率、噪声功率谱和客观高对比度可探测性进行了比较。三位放射科医生通过评估镫骨脚板和上部结构元素的厚度和图像质量，以及用四种算法重建的 13 个颞骨 CT 检查的鼓室神经骨性和鼓室节段的图像质量，采用 5 点置信度来评估这些算法的临床可接受性：与模拟传统 CT 相比，UHR-CT 的空间分辨率更高，但噪声也更高。DLR 和基于模型的迭代重建比混合迭代重建的降噪效果更好，而且无论剂量水平如何，DLR 的可探测性指数最高。与传统模拟 CT 相比，UHR-CT 的所有镫骨结构厚度都更薄（P < 0.009）。与其他重建算法相比，DLR 的可视化评分最高（P < 0.032）：结论：采用 DLR 的 UHR-CT 比采用混合迭代重建的 UHR-CT 噪声更小，与模拟传统 CT 和采用迭代重建的 UHR-CT 相比，能显著改善镫骨和鼓室脊神经的描绘。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Diagnostic and Interventional Imaging Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

8.50

自引率

29.10%

发文量

126

审稿时长

11 days

期刊介绍： Diagnostic and Interventional Imaging accepts publications originating from any part of the world based only on their scientific merit. The Journal focuses on illustrated articles with great iconographic topics and aims at aiding sharpening clinical decision-making skills as well as following high research topics. All articles are published in English. Diagnostic and Interventional Imaging publishes editorials, technical notes, letters, original and review articles on abdominal, breast, cancer, cardiac, emergency, forensic medicine, head and neck, musculoskeletal, gastrointestinal, genitourinary, interventional, obstetric, pediatric, thoracic and vascular imaging, neuroradiology, nuclear medicine, as well as contrast material, computer developments, health policies and practice, and medical physics relevant to imaging.