Literature DB >> 33242256

A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions.

Le Cao1, Xiang Liu1, Jianying Li2, Tingting Qu1, Lihong Chen1, Yannan Cheng1, Jieliang Hu1, Jingtao Sun1, Jianxin Guo1.   

Abstract

OBJECTIVE: To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT.
METHODS: This was a prospective study. 40 patients with hepatic lesions underwent abdominal CT using routine dose (120kV, noise index (NI) setting of 11 with automatic tube current modulation) in the arterial-phase (AP) and portal-phase (PP), and low dose (NI = 24) in the delayed-phase (DP). All images were reconstructed at 1.25 mm thickness using ASIR-V at 50% strength. In addition, images in DP were reconstructed using DLIR in high setting (DLIR-H). The CT value and standard deviation (SD) of hepatic parenchyma, spleen, paraspinal muscle and lesion were measured. The overall image quality includes subjective noise, sharpness, artifacts and diagnostic confidence were assessed by two radiologists blindly using a 5-point scale (1, unacceptable and 5, excellent). Dose between AP and DP was compared, and image quality among different reconstructions were compared using SPSS20.0.
RESULTS: Compared to AP, DP significantly reduced radiation dose by 76% (0.76 ± 0.09 mSv vs 3.18 ± 0.48 mSv), DLIR-H DP images had lower image noise (14.08 ± 2.89 HU vs 16.67 ± 3.74 HU, p < 0.001) but similar overall image quality score as the ASIR-V50% AP images (3.88 ± 0.34 vs 4.05 ± 0.44, p > 0.05). For the DP images, DLIR-H significantly reduced image noise in hepatic parenchyma, spleen, muscle and lesion to (14.77 ± 2.61 HU, 14.26 ± 2.67 HU, 14.08 ± 2.89 HU and 16.25 ± 4.42 HU) from (24.95 ± 4.32 HU, 25.42 ± 4.99 HU, 23.99 ± 5.26 HU and 27.01 ± 7.11) with ASIR-V50%, respectively (all p < 0.001) and improved image quality score (3.88 ± 0.34 vs 2.87 ± 0.53; p < 0.05).
CONCLUSION: DLIR-H significantly reduces image noise and generates images with clinically acceptable quality and diagnostic confidence with 76% dose reduction. ADVANCES IN KNOWLEDGE: (1) DLIR-H yielded a significantly lower image noise, higher CNR and higher overall image quality score and diagnostic confidence than the ASIR-V50% under low signal conditions. (2) Our study demonstrated that at 76% lower radiation dose, the DLIR-H DP images had similar overall image quality to the routine-dose ASIR-V50% AP images.

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Year:  2020        PMID: 33242256      PMCID: PMC7934287          DOI: 10.1259/bjr.20201086

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  17 in total

1.  Impact of the adaptive statistical iterative reconstruction technique on image quality in ultra-low-dose CT.

Authors:  Yan Xu; Wen He; Hui Chen; Zhihai Hu; Juan Li; Tingting Zhang
Journal:  Clin Radiol       Date:  2013-05-21       Impact factor: 2.350

2.  CT dose reduction using Automatic Exposure Control and iterative reconstruction: A chest paediatric phantoms study.

Authors:  Joël Greffier; Fabricio Pereira; Francesco Macri; Jean-Paul Beregi; Ahmed Larbi
Journal:  Phys Med       Date:  2016-04-04       Impact factor: 2.685

3.  Assessment of the dose reduction potential of a model-based iterative reconstruction algorithm using a task-based performance metrology.

Authors:  Ehsan Samei; Samuel Richard
Journal:  Med Phys       Date:  2015-01       Impact factor: 4.071

4.  The Detection of Focal Liver Lesions Using Abdominal CT: A Comparison of Image Quality Between Adaptive Statistical Iterative Reconstruction V and Adaptive Statistical Iterative Reconstruction.

Authors:  Sangyun Lee; Heejin Kwon; Jihan Cho
Journal:  Acad Radiol       Date:  2016-10-10       Impact factor: 3.173

5.  Low-dose CT angiography using ASiR-V for potential living renal donors: a prospective analysis of image quality and diagnostic accuracy.

Authors:  Woong Kyu Han; Joon Chae Na; Sung Yoon Park
Journal:  Eur Radiol       Date:  2019-08-30       Impact factor: 5.315

6.  Low-Dose CT With the Adaptive Statistical Iterative Reconstruction V Technique in Abdominal Organ Injury: Comparison With Routine-Dose CT With Filtered Back Projection.

Authors:  Nam Kyung Lee; Suk Kim; Seung Baek Hong; Tae Un Kim; Hwaseong Ryu; Ji Won Lee; Jin You Kim
Journal:  AJR Am J Roentgenol       Date:  2019-04-30       Impact factor: 3.959

7.  Detection and characterization of focal liver lesions with ultra-low dose computed tomography in neoplastic patients.

Authors:  A Larbi; C Orliac; J Frandon; F Pereira; A Ruyer; J Goupil; F Macri; J P Beregi; J Greffier
Journal:  Diagn Interv Imaging       Date:  2018-02-01       Impact factor: 4.026

8.  Detection of Colorectal Hepatic Metastases Is Superior at Standard Radiation Dose CT versus Reduced Dose CT.

Authors:  Corey T Jensen; Nicolaus A Wagner-Bartak; Lan N Vu; Xinming Liu; Bharat Raval; David Martinez; Wei Wei; Yuan Cheng; Ehsan Samei; Shiva Gupta
Journal:  Radiology       Date:  2018-11-27       Impact factor: 11.105

Review 9.  Image quality in CT: From physical measurements to model observers.

Authors:  F R Verdun; D Racine; J G Ott; M J Tapiovaara; P Toroi; F O Bochud; W J H Veldkamp; A Schegerer; R W Bouwman; I Hernandez Giron; N W Marshall; S Edyvean
Journal:  Phys Med       Date:  2015-10-12       Impact factor: 2.685

10.  Clinical value of a new generation adaptive statistical iterative reconstruction (ASIR-V) in the diagnosis of pulmonary nodule in low-dose chest CT.

Authors:  Hui Tang; Zhentang Liu; Zhijun Hu; Taiping He; Dou Li; Nan Yu; Yongjun Jia; Hong Shi
Journal:  Br J Radiol       Date:  2019-09-06       Impact factor: 3.039

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  10 in total

Review 1.  Advanced CT techniques for assessing hepatocellular carcinoma.

Authors:  Yuko Nakamura; Toru Higaki; Yukiko Honda; Fuminari Tatsugami; Chihiro Tani; Wataru Fukumoto; Keigo Narita; Shota Kondo; Motonori Akagi; Kazuo Awai
Journal:  Radiol Med       Date:  2021-05-05       Impact factor: 3.469

2.  [A nonlocal spectral similarity-induced material decomposition method for noise reduction of dual-energy CT images].

Authors:  L Wang; Y Wang; Z Bian; J Ma; J Huang
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2022-05-20

3.  Accuracy of two deep learning-based reconstruction methods compared with an adaptive statistical iterative reconstruction method for solid and ground-glass nodule volumetry on low-dose and ultra-low-dose chest computed tomography: A phantom study.

Authors:  Cherry Kim; Thomas Kwack; Wooil Kim; Jaehyung Cha; Zepa Yang; Hwan Seok Yong
Journal:  PLoS One       Date:  2022-06-23       Impact factor: 3.752

4.  An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging.

Authors:  Nicolò Cardobi; Alessandro Dal Palù; Federica Pedrini; Alessandro Beleù; Riccardo Nocini; Riccardo De Robertis; Andrea Ruzzenente; Roberto Salvia; Stefania Montemezzi; Mirko D'Onofrio
Journal:  Cancers (Basel)       Date:  2021-04-30       Impact factor: 6.639

5.  Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms.

Authors:  Samjhana Thapaliya; Samuel L Brady; Elanchezhian Somasundaram; Christopher G Anton; Brian D Coley; Alexander J Towbin; Bin Zhang; Jonathan R Dillman; Andrew T Trout
Journal:  Abdom Radiol (NY)       Date:  2021-10-04

6.  Influence of deep learning image reconstruction and adaptive statistical iterative reconstruction-V on coronary artery calcium quantification.

Authors:  Yiran Wang; Hefeng Zhan; Jiameng Hou; Xueyan Ma; Wenjie Wu; Jie Liu; Jianbo Gao; Ying Guo; Yonggao Zhang
Journal:  Ann Transl Med       Date:  2021-12

7.  Image Quality and Lesion Detectability of Lower-Dose Abdominopelvic CT Obtained Using Deep Learning Image Reconstruction.

Authors:  June Park; Jaeseung Shin; In Kyung Min; Heejin Bae; Yeo-Eun Kim; Yong Eun Chung
Journal:  Korean J Radiol       Date:  2022-01-27       Impact factor: 3.500

8.  Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study.

Authors:  Yali Li; Yaojun Jiang; Xi Yu; Binbin Ren; Chunyu Wang; Sihui Chen; Duoshan Ma; Danyang Su; Huilong Liu; Xiangyang Ren; Xiaopeng Yang; Jianbo Gao; Yan Wu
Journal:  Front Endocrinol (Lausanne)       Date:  2022-08-11       Impact factor: 6.055

9.  Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.

Authors:  Joël Greffier; Salim Si-Mohamed; Julien Frandon; Maeliss Loisy; Fabien de Oliveira; Jean Paul Beregi; Djamel Dabli
Journal:  Med Phys       Date:  2022-06-24       Impact factor: 4.506

10.  Impact of novel deep learning image reconstruction algorithm on diagnosis of contrast-enhanced liver computed tomography imaging: Comparing to adaptive statistical iterative reconstruction algorithm.

Authors:  Shuo Yang; Yifan Bie; Guodong Pang; Xingchao Li; Kun Zhao; Changlei Zhang; Hai Zhong
Journal:  J Xray Sci Technol       Date:  2021       Impact factor: 1.535

  10 in total

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