Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study.
Algorithms
Liver neoplasms
Phantoms, imaging
Reproducibility of results
Tomography, X-ray computed
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
01
09
2021
accepted:
24
01
2022
revised:
05
01
2022
pubmed:
18
2
2022
medline:
24
6
2022
entrez:
17
2
2022
Statut:
ppublish
Résumé
To compare image quality of deep learning reconstruction (AiCE) for radiomics feature extraction with filtered back projection (FBP), hybrid iterative reconstruction (AIDR 3D), and model-based iterative reconstruction (FIRST). Effects of image reconstruction on radiomics features were investigated using a phantom that realistically mimicked a 65-year-old patient's abdomen with hepatic metastases. The phantom was scanned at 18 doses from 0.2 to 4 mGy, with 20 repeated scans per dose. Images were reconstructed with FBP, AIDR 3D, FIRST, and AiCE. Ninety-three radiomics features were extracted from 24 regions of interest, which were evenly distributed across three tissue classes: normal liver, metastatic core, and metastatic rim. Features were analyzed in terms of their consistent characterization of tissues within the same image (intraclass correlation coefficient ≥ 0.75), discriminative power (Kruskal-Wallis test p value < 0.05), and repeatability (overall concordance correlation coefficient ≥ 0.75). The median fraction of consistent features across all doses was 6%, 8%, 6%, and 22% with FBP, AIDR 3D, FIRST, and AiCE, respectively. Adequate discriminative power was achieved by 48%, 82%, 84%, and 92% of features, and 52%, 20%, 17%, and 39% of features were repeatable, respectively. Only 5% of features combined consistency, discriminative power, and repeatability with FBP, AIDR 3D, and FIRST versus 13% with AiCE at doses above 1 mGy and 17% at doses ≥ 3 mGy. AiCE was the only reconstruction technique that enabled extraction of higher-order features. AiCE more than doubled the yield of radiomics features at doses typically used clinically. Inconsistent tissue characterization within CT images contributes significantly to the poor stability of radiomics features. • Image quality of CT images reconstructed with filtered back projection and iterative methods is inadequate for the majority of radiomics features due to inconsistent tissue characterization, low discriminative power, or low repeatability. • Deep learning reconstruction enhances image quality for radiomics and more than doubled the feature yield at doses that are typically used in clinical CT imaging. • Image reconstruction algorithms can optimize image quality for more reliable quantification of tissues in CT images.
Identifiants
pubmed: 35174400
doi: 10.1007/s00330-022-08592-y
pii: 10.1007/s00330-022-08592-y
pmc: PMC9213380
doi:
Types de publication
Case Reports
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4587-4595Subventions
Organisme : Bundesministerium für Wirtschaft und Energie
ID : 03EFHBE093
Informations de copyright
© 2022. The Author(s).
Références
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577
doi: 10.1148/radiol.2015151169
Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762
doi: 10.1038/nrclinonc.2017.141
Hagiwara A, Fujita S, Ohno Y, Aoki S (2020) Variability and standardization of quantitative imaging: monoparametric to multiparametric quantification, radiomics, and artificial intelligence. Invest Radiol 55:601–616
doi: 10.1097/RLI.0000000000000666
Meyer M, Ronald J, Vernuccio F et al (2019) Reproducibility of CT radiomic features within the same patient: influence of radiation dose and CT reconstruction settings. Radiology 293:583–591
doi: 10.1148/radiol.2019190928
Prezzi D, Owczarczyk K, Bassett P et al (2019) Adaptive statistical iterative reconstruction (ASIR) affects CT radiomics quantification in primary colorectal cancer. Eur Radiol 29:5227–5235
doi: 10.1007/s00330-019-06073-3
Midya A, Chakraborty J, Gonen M, Do RKG, Simpson AL (2018) Influence of CT acquisition and reconstruction parameters on radiomic feature reproducibility. J Med Imaging (Bellingham) 5:011020
Akagi M, Nakamura Y, Higaki T et al (2019) Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT. Eur Radiol 29:6163–6171
doi: 10.1007/s00330-019-06170-3
Racine D, Becce F, Viry A et al (2020) Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study. Phys Med 76:28–37
doi: 10.1016/j.ejmp.2020.06.004
Berenguer R, Pastor-Juan MDR, Canales-Vazquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288:407–415
doi: 10.1148/radiol.2018172361
Kim H, Park CM, Lee M et al (2016) Impact of reconstruction algorithms on CT radiomic features of pulmonary tumors: analysis of intra- and inter-reader variability and inter-reconstruction algorithm variability. PLoS One 11:e0164924
doi: 10.1371/journal.pone.0164924
Erdal BS, Demirer M, Little KJ et al (2020) Are quantitative features of lung nodules reproducible at different CT acquisition and reconstruction parameters? PLoS One 15:e0240184
doi: 10.1371/journal.pone.0240184
Jimenez-Del-Toro O, Aberle C, Bach M et al (2021) The discriminative power and stability of radiomics features with computed tomography variations: task-based analysis in an anthropomorphic 3D-printed CT phantom. Invest Radiol. https://doi.org/10.1097/RLI.0000000000000795
Muenzfeld H, Nowak C, Riedlberger S et al (2021) Intra-scanner repeatability of quantitative imaging features in a 3D printed semi-anthropomorphic CT phantom. Eur J Radiol 141:109818
doi: 10.1016/j.ejrad.2021.109818
Jahnke P, Limberg FR, Gerbl A et al (2017) Radiopaque three-dimensional printing: a method to create realistic CT phantoms. Radiology 282:569–575
doi: 10.1148/radiol.2016152710
Jahnke P, Schwarz S, Ziegert M, Schwarz FB, Hamm B, Scheel M (2019) Paper-based 3D printing of anthropomorphic CT phantoms: feasibility of two construction techniques. Eur Radiol 29:1384–1390
doi: 10.1007/s00330-018-5654-1
van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107
doi: 10.1158/0008-5472.CAN-17-0339
PyRadiomics documentation. Pyradiomics community https://pyradiomics.readthedocs.io/ . Accessed July 15, 2021
McGraw KO, Wong SP (1996) Forming inferences about some intraclass correlation coefficients. Psychol Methods 1:30–46
doi: 10.1037/1082-989X.1.1.30
Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86:420–428
doi: 10.1037/0033-2909.86.2.420
Barnhart HX, Haber M, Song J (2002) Overall concordance correlation coefficient for evaluating agreement among multiple observers. Biometrics 58:1020–1027
doi: 10.1111/j.0006-341X.2002.01020.x
Yamashita R, Perrin T, Chakraborty J et al (2020) Radiomic feature reproducibility in contrast-enhanced CT of the pancreas is affected by variabilities in scan parameters and manual segmentation. Eur Radiol 30:195–205
doi: 10.1007/s00330-019-06381-8
Lee SB, Cho YJ, Hong Y et al (2021) Deep learning-based image conversion improves the reproducibility of computed tomography radiomics features: a phantom study. Invest Radiol. https://doi.org/10.1097/RLI.0000000000000839
Choe J, Lee SM, Do KH et al (2019) Deep Learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology 292:365–373
doi: 10.1148/radiol.2019181960
Vaishnav JY, Jung WC, Popescu LM, Zeng R, Myers KJ (2014) Objective assessment of image quality and dose reduction in CT iterative reconstruction. Med Phys 41:071904
doi: 10.1118/1.4881148
Mileto A, Guimaraes LS, McCollough CH, Fletcher JG, Yu L (2019) State of the art in abdominal CT: the limits of iterative reconstruction algorithms. Radiology 293:491–503
doi: 10.1148/radiol.2019191422
Espinasse M, Pitre-Champagnat S, Charmettant B et al (2020) CT Texture analysis challenges: influence of acquisition and reconstruction parameters: a comprehensive review. Diagnostics (Basel) 10
Shafiq-Ul-Hassan M, Latifi K, Zhang G, Ullah G, Gillies R, Moros E (2018) Voxel size and gray level normalization of CT radiomic features in lung cancer. Sci Rep 8:10545
doi: 10.1038/s41598-018-28895-9