Image Quality Improvement of Low-dose Abdominal CT using Deep Learning Image Reconstruction Compared with the Second Generation Iterative Reconstruction

Pub Date : 2024-01-01 DOI:10.2174/1573405620666230525104809

Hyo-Jin Kang, Jeong Min Lee, Sae Jin Park, Sang Min Lee, Ijin Joo, Jeong Hee Yoon

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Abstract

Background: Whether deep learning-based CT reconstruction could improve lesion conspicuity on abdominal CT when the radiation dose is reduced is controversial.

Objectives: To determine whether DLIR can provide better image quality and reduce radiation dose in contrast-enhanced abdominal CT compared with the second generation of adaptive statistical iterative reconstruction (ASiR-V).

Aims: This study aims to determine whether deep-learning image reconstruction (DLIR) can improve image quality.

Method: In this retrospective study, a total of 102 patients were included, who underwent abdominal CT using a DLIR-equipped 256-row scanner and routine CT of the same protocol on the same vendor's 64-row scanner within four months. The CT data from the 256-row scanner were reconstructed into ASiR-V with three blending levels (AV30, AV60, and AV100), and DLIR images with three strength levels (DLIR-L, DLIR-M, and DLIR-H). The routine CT data were reconstructed into AV30, AV60, and AV100. The contrast-to-noise ratio (CNR) of the liver, overall image quality, subjective noise, lesion conspicuity, and plasticity in the portal venous phase (PVP) of ASiR-V from both scanners and DLIR were compared.

Results: The mean effective radiation dose of PVP of the 256-row scanner was significantly lower than that of the routine CT (6.3±2.0 mSv vs. 2.4±0.6 mSv; p< 0.001). The mean CNR, image quality, subjective noise, and lesion conspicuity of ASiR-V images of the 256-row scanner were significantly lower than those of ASiR-V images at the same blending factor of routine CT, but significantly improved with DLIR algorithms. DLIR-H showed higher CNR, better image quality, and subjective noise than AV30 from routine CT, whereas plasticity was significantly better for AV30.

Conclusion: DLIR can be used for improving image quality and reducing radiation dose in abdominal CT, compared with ASIR-V.

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基于深度学习图像重建的低剂量腹部CT图像质量改善与第二代迭代重建的比较

背景:当辐射剂量降低时，基于深度学习的CT重建能否改善腹部CT病变的显著性存在争议。目的:对比第二代自适应统计迭代重建(ASiR-V)，确定DLIR在增强腹部CT中是否能提供更好的图像质量和降低辐射剂量。目的:本研究旨在确定深度学习图像重建(DLIR)是否可以提高图像质量。方法:在本回顾性研究中，共纳入102例患者，他们在4个月内使用配备dlir的256排扫描仪进行腹部CT检查，并在同一供应商的64排扫描仪上进行相同方案的常规CT检查。将256排CT数据重建为具有3个混合级别(AV30、AV60、AV100)的ASiR-V和具有3个强度级别(DLIR- l、DLIR- m、DLIR- h)的DLIR图像。将常规CT数据重建为AV30、AV60、AV100。比较两种扫描仪和DLIR对ASiR-V肝脏的噪比(CNR)、整体图像质量、主观噪声、病变显著性和门静脉期(PVP)的可塑性。结果:256排CT PVP平均有效辐射剂量明显低于常规CT(6.3±2.0 mSv vs. 2.4±0.6 mSv;p < 0.001)。256排扫描仪的ASiR-V图像的平均CNR、图像质量、主观噪声和病变显著性均显著低于常规CT在相同混合系数下的ASiR-V图像，但DLIR算法显著提高了ASiR-V图像。与常规CT相比，DLIR-H在CNR、图像质量和主观噪声方面均优于AV30，而在可塑性方面则明显优于AV30。结论:与ASIR-V相比，DLIR可改善腹部CT图像质量，降低辐射剂量。

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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