The value of a deep learning image reconstruction algorithm for assessing vertebral compression fractures using dual-energy computed tomography

IF 3.2 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

European Journal of Radiology Pub Date : 2025-06-16 DOI:10.1016/j.ejrad.2025.112244

Jiayi Tang , Luyou Yan , Kun Zhang , Suping Chen , Ping Liu , Jinling Wang , Yewen He

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引用次数: 0

Abstract

Purpose

To evaluate the value of deep learning image reconstruction (DLIR) in improving image quality of virtual non-hydroxyapatite (VNHAP) and virtual monoenergetic images (VMIs), and radiologists’ performance in detecting acute vertebral compression fractures (VCFs).

Methods

VMIs at 70 keV and VNHAP images from 103 vertebrae (46 normal vertebra, 29 acute and 28 chronic VCFs) were reconstructed with four algorithms: adaptive statistical iterative reconstruction (AR50), DLIR-Low (DL), DLIR-Middle (DM), and DLIR-High (DH). Objective indexes including CT values for 70 keV VMIs and water density for VNHAP images, noise, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR] of all vertebral bodies, as well as intervertebral ratio [IVR] of acute and chronic VCFs. Subjective image quality scoring and VCFs diagnosis were independently performed by four radiologists. Employing MR examinations as the reference, the specificity, sensitivity, accuracy, and predictive metrics for detecting acute VCFs were assessed.

Results

DLIR reduced image noise and improved SNR and CNR for all vertebra on both 70 keV VMIs and VNHAP images. DH-reconstructed VNHAP images had the highest IVR for acute VCFs and outperformed for all comparisons. The four-reader subjective scores on image quality: DH > DM > DL > AR50. For the differentiation between acute and chronic VCFs, the four readers with VNHAP images showed enhanced performance than those with 70 keV VMIs, while DLIR further improved the efficiency, with DH performed the best.

Conclusion

DLIR improved the quality of VMIs and VNHAP images, elevated the contrast of acute VCFs, and enhanced radiologists’ performance in detecting acute VCFs. DH performed the best.

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深度学习图像重建算法在双能计算机断层成像评估椎体压缩性骨折中的价值

目的评价深度学习图像重建（DLIR）在提高虚拟非羟基磷灰石（VNHAP）和虚拟单能图像（VMIs）图像质量方面的价值，以及放射科医师对急性椎体压缩性骨折（vcf）的检测效果。方法采用自适应统计迭代重建（AR50）、DLIR-Low （DL）、DLIR-Middle （DM）和DLIR-High （DH）四种算法重建103个椎体（46个正常椎体、29个急性椎体和28个慢性椎体）的70 keV和VNHAP图像的svmi。客观指标包括70 keV VCFs的CT值、VNHAP图像的水密度、各椎体的噪声、信噪比（SNR）、噪比（CNR）、急、慢性VCFs的椎间比（IVR）。主观影像质量评分和VCFs诊断由4名放射科医师独立完成。以MR检查为参考，评估检测急性vcf的特异性、敏感性、准确性和预测指标。结果在70 keV vmi和VNHAP图像上，dlir降低了图像噪声，提高了所有椎体的信噪比和CNR。dh重建的VNHAP图像在急性vcf中具有最高的IVR，并且在所有比较中都优于IVR。四个读者对图像质量的主观评分：DH >；DM祝辞DL祝辞AR50。对于急性和慢性vcf的区分，4个具有VNHAP图像的读取器比70个keV vcf的读取器表现更好，而DLIR进一步提高了效率，其中DH表现最好。结论dlir提高了vmi和VNHAP图像质量，提高了急性vcf的对比度，提高了放射科医师对急性vcf的检测能力。DH表现最好。

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

European Journal of Radiology 医学-核医学

CiteScore

6.70

自引率

3.00%

发文量

398

审稿时长

42 days

期刊介绍： European Journal of Radiology is an international journal which aims to communicate to its readers, state-of-the-art information on imaging developments in the form of high quality original research articles and timely reviews on current developments in the field. Its audience includes clinicians at all levels of training including radiology trainees, newly qualified imaging specialists and the experienced radiologist. Its aim is to inform efficient, appropriate and evidence-based imaging practice to the benefit of patients worldwide.