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腹盆腔CT图像质量:使用深度学习重建技术对薄层(0.5毫米)图像的评估

Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction.

作者信息

Oostveen Luuk J, Smit Ewoud J, Dekker Helena M, Buckens Constantinus F, Pegge Sjoert A H, de Lange Frank, Sechopoulos Ioannis, Prokop Mathias

机构信息

Department of Medical Imaging, Radboud University Medical Center, PO Box 9101 (Rte 766), 6500 HB, Nijmegen, The Netherlands.

Multi-Modality Medical Imaging (M3I) Group, Technical Medical Center, University of Twente, Enschede, The Netherlands.

出版信息

AJR Am J Roentgenol. 2023 Mar;220(3):381-388. doi: 10.2214/AJR.22.28319. Epub 2022 Oct 19.

Abstract

Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep learning reconstruction (DLR) can yield thin-section images with low noise. The purpose of this study is to compare abdominopelvic CT image quality between thin-section DLR images and thin- and thick-section hybrid iterative reconstruction (HIR) images. This retrospective study included 50 patients (31 men and 19 women; median age, 64 years) who underwent abdominopelvic CT between June 15, 2020, and July 29, 2020. Images were reconstructed at 0.5-mm section using DLR and at 0.5-mm and 3.0-mm sections using HIR. Five radiologists independently performed pairwise comparisons (0.5-mm DLR and either 0.5-mm or 3.0-mm HIR) and recorded the preferred image for subjective image quality measures (scale, -2 to 2). The pooled scores of readers were compared with a score of 0 (denoting no preference). Image noise was quantified using the SD of ROIs on regions of homogeneous liver. For comparison of 0.5-mm DLR images and 0.5-mm HIR images, the median pooled score was 2 (indicating a definite preference for DLR) for noise and overall image quality and 1 (denoting a slight preference for DLR) for sharpness and natural appearance. For comparison of 0.5-mm DLR and 3.0-mm HIR, the median pooled score was 1 for the four previously mentioned measures. These assessments were all significantly different ( < .001) from 0. For artifacts, the median pooled score for both comparisons was 0, which was not significant for comparison with 3.0-mm HIR ( = .03) but was significant for comparison with 0.5-mm HIR ( < .001) due to imbalance in scores of 1 ( = 28) and -1 (slight preference for HIR, = 1). Noise for 0.5-mm DLR was lower by mean differences of 12.8 HU compared with 0.5-mm HIR and 4.4 HU compared with 3.0-mm HIR (both < .001). Thin-section DLR improves subjective image quality and reduces image noise compared with currently used thin- and thick-section HIR, without causing additional artifacts. Although further diagnostic performance studies are warranted, the findings suggest the possibility of replacing current use of both thin- and thick-section HIR with the use of thin-section DLR only during clinical interpretations.

摘要

由于厚层图像(通常为3 - 5毫米)具有较低的图像噪声,放射科医生通常使用它们进行临床解读,不过他们可能还会参考薄层图像(通常为0.5 - 0.625毫米)来解决问题。深度学习重建(DLR)可以生成低噪声的薄层图像。本研究的目的是比较薄层DLR图像与薄层和厚层混合迭代重建(HIR)图像之间的腹盆腔CT图像质量。这项回顾性研究纳入了50例患者(31名男性和19名女性;中位年龄64岁),这些患者在2020年6月15日至2020年7月29日期间接受了腹盆腔CT检查。使用DLR以0.5毫米层厚进行图像重建,使用HIR以0.5毫米和3.0毫米层厚进行图像重建。五名放射科医生独立进行成对比较(0.5毫米DLR与0.5毫米或3.0毫米HIR),并记录主观图像质量测量(范围为 - 2至2)中更偏好的图像。将读者的汇总分数与0分(表示无偏好)进行比较。使用均匀肝脏区域的感兴趣区(ROI)的标准差对图像噪声进行量化。对于0.5毫米DLR图像与0.5毫米HIR图像的比较,噪声和整体图像质量的汇总分数中位数为2(表明明确偏好DLR),锐度和自然外观的汇总分数中位数为1(表示略微偏好DLR)。对于0.5毫米DLR与3.0毫米HIR的比较,上述四项测量的汇总分数中位数均为1。这些评估与0相比均有显著差异(P <.001)。对于伪影,两种比较的汇总分数中位数均为0,与3.0毫米HIR比较时不显著(P =.03),但与0.5毫米HIR比较时显著(P <.001),这是由于分数1(n = 28)和 - 1(略微偏好HIR,n = 1)的不平衡所致。与0.5毫米HIR相比,0.5毫米DLR的噪声平均差异低12.8 HU,与3.0毫米HIR相比低4.4 HU(均P <.001)。与目前使用的薄层和厚层HIR相比,薄层DLR提高了主观图像质量并降低了图像噪声,且不会产生额外的伪影。尽管有必要进行进一步的诊断性能研究,但研究结果表明在临床解读期间仅使用薄层DLR替代目前同时使用的薄层和厚层HIR的可能性。

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