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

IF 1.1 4区医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Current Medical Imaging Reviews 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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引用次数: 0

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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来源期刊

Current Medical Imaging Reviews 医学-核医学

CiteScore

2.60

自引率

0.00%

发文量

246

审稿时长

1 months

期刊介绍： Current Medical Imaging Reviews publishes frontier review articles, original research articles, drug clinical trial studies and guest edited thematic issues on all the latest advances on medical imaging dedicated to clinical research. All relevant areas are covered by the journal, including advances in the diagnosis, instrumentation and therapeutic applications related to all modern medical imaging techniques. The journal is essential reading for all clinicians and researchers involved in medical imaging and diagnosis.