In vivo depiction of cortical bone vascularization with ultra-high resolution-CT and deep learning algorithm reconstruction using osteoid osteoma as a model

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

Diagnostic and Interventional Imaging Pub Date : 2024-01-01 DOI:10.1016/j.diii.2023.07.001

Fatma Boubaker , Pedro Augusto Gondim Teixeira , Gabriela Hossu , Nicolas Douis , Pierre Gillet , Alain Blum , Romain Gillet

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引用次数: 0

Abstract

Purpose

The purpose of this study was to evaluate the ability to depict in vivo bone vascularization using ultra-high-resolution (UHR) computed tomography (CT) with deep learning reconstruction (DLR) and hybrid iterative reconstruction algorithm, compared to simulated conventional CT, using osteoid osteoma as a model.

Materials and methods

Patients with histopathologically proven cortical osteoid osteoma who underwent UHR-CT between October 2019 and October 2022 were retrospectively included. Images were acquired with a 1024 × 1024 matrix and reconstructed with DLR and hybrid iterative reconstruction algorithm. To simulate conventional CT, images with a 512 × 512 matrix were also reconstructed. Two radiologists (R1, R2) independently evaluated the number of blood vessels entering the nidus and crossing the bone cortex, as well as vessel identification and image quality with a 5-point scale. Standard deviation (SD) of attenuation in the adjacent muscle and that of air were used as image noise and recorded.

Results

Thirteen patients with 13 osteoid osteomas were included. There were 11 men and two women with a mean age of 21.8 ± 9.1 (SD) years. For both readers, UHR-CT with DLR depicted more nidus vessels (11.5 ± 4.3 [SD] (R1) and 11.9 ± 4.6 [SD] (R2)) and cortical vessels (4 ± 3.8 [SD] and 4.3 ± 4.1 [SD], respectively) than UHR-CT with hybrid iterative reconstruction (10.5 ± 4.3 [SD] and 10.4 ± 4.6 [SD], and 4.1 ± 3.8 [SD] and 4.3 ± 3.8 [SD], respectively) and simulated conventional CT (5.3 ± 2.2 [SD] and 6.4 ± 2.5 [SD], 2 ± 1.2 [SD] and 2.4 ± 1.6 [SD], respectively) (P < 0.05). UHR-CT with DLR provided less image noise than simulated conventional CT and UHR-CT with hybrid iterative reconstruction (P < 0.05). UHR-CT with DLR received the greatest score and simulated conventional CT the lowest score for vessel identification and image quality.

Conclusion

UHR-CT with DLR shows less noise than UHR-CT with hybrid iterative reconstruction and significantly improves cortical bone vascularization depiction compared to simulated conventional CT.

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以骨样骨瘤为模型，利用超高分辨率计算机断层扫描和深度学习算法重建对皮质骨血管的活体描述

材料和方法回顾性纳入了在2019年10月至2022年10月期间接受超高分辨率计算机断层扫描（UHR-CT）的经组织病理学证实的皮质类骨瘤患者。图像采用 1024 × 1024 矩阵采集，并使用 DLR 和混合迭代重建算法进行重建。为模拟传统 CT，还重建了矩阵为 512 × 512 的图像。两名放射科医生（R1、R2）分别独立评估进入蝶窦和穿过骨皮质的血管数量，以及血管识别和图像质量，评分标准为 5 分。邻近肌肉衰减的标准差（SD）和空气衰减的标准差（SD）被用作图像噪声并记录在案。其中男性 11 人，女性 2 人，平均年龄为 21.8 ± 9.1 (SD) 岁。与混合迭代重建的 UHR-CT 相比，两位读者的 DLR UHR-CT 显示了更多的瘤灶血管（11.5 ± 4.3 [SD] (R1)和 11.9 ± 4.6 [SD] (R2)）和皮质血管（分别为 4 ± 3.8 [SD] 和 4.3 ± 4.1 [SD] (10.5±4.3[SD])）。5 ± 4.3 [SD] 和 10.4 ± 4.6 [SD] ，以及分别为 4.1 ± 3.8 [SD] 和 4.3 ± 3.8 [SD] ）和模拟传统 CT（分别为 5.3 ± 2.2 [SD] 和 6.4 ± 2.5 [SD] ，2 ± 1.2 [SD] 和 2.4 ± 1.6 [SD] ）（P <0.05）。与模拟传统 CT 和混合迭代重建 UHR-CT 相比，使用 DLR 的 UHR-CT 图像噪声更小（P < 0.05）。在血管识别和图像质量方面，使用 DLR 的 UHR-CT 得分最高，而模拟传统 CT 得分最低。

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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.