Literature DB >> 33462642

MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study.

Joshua Shur1, Matthew Blackledge2, James D'Arcy2, David J Collins2, Maria Bali1, Martin O'Leach2, Dow-Mu Koh3,4.   

Abstract

PURPOSE: To evaluate robustness and repeatability of magnetic resonance imaging (MRI) texture features in water and tissue phantom test-retest study.
MATERIALS AND METHODS: Separate water and tissue phantoms were imaged twice with the same protocol in a test-retest experiment using a 1.5-T scanner. Protocols were acquired to favour signal-to-noise ratio and resolution. Forty-six features including first order statistics and second-order texture features were extracted, and repeatability was assessed by calculating the concordance correlation coefficient. Separately, base image noise and resolution were manipulated in an in silico experiment, and robustness of features was calculated by assessing percentage coefficient of variation and linear correlation of features with noise and resolution. These simulation data were compared with the acquired data. Features were classified by their degree (high, intermediate, or low) of robustness and repeatability.
RESULTS: Eighty percent of the MRI features were repeatable (concordance correlation coefficient > 0.9) in the phantom test-retest experiment. The majority (approximately 90%) demonstrated a strong or intermediate correlation with image acquisition parameter, and 19/46 (41%) and 13/46 (28%) of features were highly robust to noise and resolution, respectively (coefficient of variation < 5%). Agreement between the acquired and simulation data varied, with the range of agreement within feature classes between 11 and 92%.
CONCLUSION: Most MRI features were repeatable in a phantom test-retest study. This phantom data may serve as a lower limit of feature MRI repeatability. Robustness of features varies with acquisition parameter, and appropriate features can be selected for clinical validation studies.

Entities:  

Keywords:  Magnetic resonance imaging; Phantoms (imaging); Radiomics; Reproducibility of results; Texture analysis

Mesh:

Year:  2021        PMID: 33462642      PMCID: PMC7813908          DOI: 10.1186/s41747-020-00199-6

Source DB:  PubMed          Journal:  Eur Radiol Exp        ISSN: 2509-9280


  28 in total

1.  Measurement of signal-to-noise ratios in MR images: influence of multichannel coils, parallel imaging, and reconstruction filters.

Authors:  Olaf Dietrich; José G Raya; Scott B Reeder; Maximilian F Reiser; Stefan O Schoenberg
Journal:  J Magn Reson Imaging       Date:  2007-08       Impact factor: 4.813

2.  The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions.

Authors:  Larry G Kessler; Huiman X Barnhart; Andrew J Buckler; Kingshuk Roy Choudhury; Marina V Kondratovich; Alicia Toledano; Alexander R Guimaraes; Ross Filice; Zheng Zhang; Daniel C Sullivan
Journal:  Stat Methods Med Res       Date:  2014-06-11       Impact factor: 3.021

Review 3.  Texture Analysis in Cerebral Gliomas: A Review of the Literature.

Authors:  N Soni; S Priya; G Bathla
Journal:  AJNR Am J Neuroradiol       Date:  2019-05-23       Impact factor: 3.825

4.  Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters.

Authors:  Roberto Berenguer; María Del Rosario Pastor-Juan; Jesús Canales-Vázquez; Miguel Castro-García; María Victoria Villas; Francisco Mansilla Legorburo; Sebastià Sabater
Journal:  Radiology       Date:  2018-04-24       Impact factor: 11.105

5.  Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET.

Authors:  Florent Tixier; Mathieu Hatt; Catherine Cheze Le Rest; Adrien Le Pogam; Laurent Corcos; Dimitris Visvikis
Journal:  J Nucl Med       Date:  2012-03-27       Impact factor: 10.057

6.  Reproducibility and Prognosis of Quantitative Features Extracted from CT Images.

Authors:  Yoganand Balagurunathan; Yuhua Gu; Hua Wang; Virendra Kumar; Olya Grove; Sam Hawkins; Jongphil Kim; Dmitry B Goldgof; Lawrence O Hall; Robert A Gatenby; Robert J Gillies
Journal:  Transl Oncol       Date:  2014-02-01       Impact factor: 4.243

Review 7.  Applications and limitations of radiomics.

Authors:  Stephen S F Yip; Hugo J W L Aerts
Journal:  Phys Med Biol       Date:  2016-06-08       Impact factor: 3.609

8.  How can we combat multicenter variability in MR radiomics? Validation of a correction procedure.

Authors:  Fanny Orlhac; Augustin Lecler; Julien Savatovski; Jessica Goya-Outi; Christophe Nioche; Frédérique Charbonneau; Nicholas Ayache; Frédérique Frouin; Loïc Duron; Irène Buvat
Journal:  Eur Radiol       Date:  2020-09-25       Impact factor: 5.315

9.  Repeatability of Multiparametric Prostate MRI Radiomics Features.

Authors:  Michael Schwier; Joost van Griethuysen; Mark G Vangel; Steve Pieper; Sharon Peled; Clare Tempany; Hugo J W L Aerts; Ron Kikinis; Fiona M Fennessy; Andriy Fedorov
Journal:  Sci Rep       Date:  2019-07-01       Impact factor: 4.379

10.  Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.

Authors:  Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin
Journal:  Nat Commun       Date:  2014-06-03       Impact factor: 14.919

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  5 in total

1.  Tissue volume estimation and age prediction using rapid structural brain scans.

Authors:  Harriet Hobday; James H Cole; Ryan A Stanyard; Richard E Daws; Vincent Giampietro; Owen O'Daly; Robert Leech; František Váša
Journal:  Sci Rep       Date:  2022-07-14       Impact factor: 4.996

2.  Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features.

Authors:  Hsun-Ping Hsieh; Ding-You Wu; Kuo-Chuan Hung; Sher-Wei Lim; Tai-Yuan Chen; Yang Fan-Chiang; Ching-Chung Ko
Journal:  J Pers Med       Date:  2022-03-24

3.  Enhancing the stability of CT radiomics across different volume of interest sizes using parametric feature maps: a phantom study.

Authors:  Laura J Jensen; Damon Kim; Thomas Elgeti; Ingo G Steffen; Lars-Arne Schaafs; Bernd Hamm; Sebastian N Nagel
Journal:  Eur Radiol Exp       Date:  2022-09-15

4.  Test-Retest Data for the Assessment of Breast MRI Radiomic Feature Repeatability.

Authors:  R W Y Granzier; A Ibrahim; S Primakov; S A Keek; I Halilaj; A Zwanenburg; S M E Engelen; M B I Lobbes; P Lambin; H C Woodruff; M L Smidt
Journal:  J Magn Reson Imaging       Date:  2021-12-22       Impact factor: 5.119

5.  MRI-Based Radiomics Analysis for the Pretreatment Prediction of Pathologic Complete Tumor Response to Neoadjuvant Systemic Therapy in Breast Cancer Patients: A Multicenter Study.

Authors:  Renée W Y Granzier; Abdalla Ibrahim; Sergey P Primakov; Sanaz Samiei; Thiemo J A van Nijnatten; Maaike de Boer; Esther M Heuts; Frans-Jan Hulsmans; Avishek Chatterjee; Philippe Lambin; Marc B I Lobbes; Henry C Woodruff; Marjolein L Smidt
Journal:  Cancers (Basel)       Date:  2021-05-18       Impact factor: 6.639

  5 in total

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