Literature DB >> 21978050

The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms.

Shelley A Waugh1, Richard A Lerski, Luc Bidaut, Alastair M Thompson.   

Abstract

PURPOSE: Texture analysis (TA) has proved to be useful to distinguish different tissues and disease states using magnetic resonance imaging (MRI). TA has been successfully applied clinically to improve identification of abnormalities in the brain, liver, and bone and, more recently, has been used to enhance the specificity of breast MRI. This preclinical study used a custom-made phantom containing different grades of reticulated foam embedded in agarose gel to assess the capability of TA to distinguish between different texture objects, under different imaging conditions. The aim was to assess whether TA could be used reliably with clinical protocols that were not optimized for texture analysis and also to investigate the effect that changing imaging sequence parameters would have on the outcome of TA.
METHODS: Clinical fast gradient echo sequences and two different breast RF coils were used in order to reflect standard clinical practice. Three protocols were used: (1) a high spatial resolution protocol run on a 1.5 Tesla (T) MRI scanner, (2) a parameter matched sequence run on a 3.0 T magnet, and (3) a high temporal resolution protocol also run on a 3.0 T magnet.For each protocol, three sequence parameters (repetition time, bandwidth/echo time, and flip angle) were altered from the baseline values to assess the impact of changes in acquisition parameters on the outcome of TA.
RESULTS: TA was performed using MAZDA software and clearly differentiated four foam phantoms when using the wavelet transform method (WAV), also moderately so with the co-occurrence matrix method (COM). The outcome was generally improved for imaging protocols acquired on the 3.0 T scanner, particularly for the high spatial resolution protocol where changes to the acquisition parameters influenced the TA, especially changes to the bandwidth/echo time. For the other protocols, TA outcome was less affected by changes to the imaging parameters.
CONCLUSIONS: This phantom study shows that acquisition parameters and protocols that are typically used for clinical breast imaging can result in good TA. Our findings suggest that changes to sequence parameters may not greatly influence the outcome of texture analysis, but rather that spatial resolution may be the most important factor to consider.

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Year:  2011        PMID: 21978050     DOI: 10.1118/1.3622605

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  29 in total

1.  Classification of suspicious lesions on prostate multiparametric MRI using machine learning.

Authors:  Deukwoo Kwon; Isildinha M Reis; Adrian L Breto; Yohann Tschudi; Nicole Gautney; Olmo Zavala-Romero; Christopher Lopez; John C Ford; Sanoj Punnen; Alan Pollack; Radka Stoyanova
Journal:  J Med Imaging (Bellingham)       Date:  2018-09-06

2.  Magnetic resonance imaging texture analysis classification of primary breast cancer.

Authors:  S A Waugh; C A Purdie; L B Jordan; S Vinnicombe; R A Lerski; P Martin; A M Thompson
Journal:  Eur Radiol       Date:  2015-06-12       Impact factor: 5.315

3.  Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.

Authors:  David Molina; Julián Pérez-Beteta; Belén Luque; Elena Arregui; Manuel Calvo; José M Borrás; Carlos López; Juan Martino; Carlos Velasquez; Beatriz Asenjo; Manuel Benavides; Ismael Herruzo; Alicia Martínez-González; Luis Pérez-Romasanta; Estanislao Arana; Víctor M Pérez-García
Journal:  Br J Radiol       Date:  2016-06-20       Impact factor: 3.039

4.  A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study.

Authors:  Marie-Judith Saint Martin; Fanny Orlhac; Pia Akl; Fahad Khalid; Christophe Nioche; Irène Buvat; Caroline Malhaire; Frédérique Frouin
Journal:  MAGMA       Date:  2020-11-12       Impact factor: 2.310

5.  Bone texture analysis using CT-simulation scans to individuate risk parameters for radiation-induced insufficiency fractures.

Authors:  V Nardone; P Tini; S F Carbone; A Grassi; M Biondi; L Sebaste; T Carfagno; E Vanzi; G De Otto; G Battaglia; G Rubino; P Pastina; G Belmonte; L N Mazzoni; F Banci Buonamici; M A Mazzei; L Pirtoli
Journal:  Osteoporos Int       Date:  2017-02-27       Impact factor: 4.507

Review 6.  Machine Learning-Based Radiomics in Neuro-Oncology.

Authors:  Felix Ehret; David Kaul; Hans Clusmann; Daniel Delev; Julius M Kernbach
Journal:  Acta Neurochir Suppl       Date:  2022

7.  Differentiation of paediatric posterior fossa tumours by the multiregional and multiparametric MRI radiomics approach: a study on the selection of optimal multiple sequences and multiregions.

Authors:  Jie Dong; Suxiao Li; Lei Li; Shengxiang Liang; Bin Zhang; Yun Meng; Xiaofang Zhang; Yong Zhang; Shujun Zhao
Journal:  Br J Radiol       Date:  2021-11-19       Impact factor: 3.039

8.  Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.

Authors:  Rafael Ortiz-Ramón; Andrés Larroza; Silvia Ruiz-España; Estanislao Arana; David Moratal
Journal:  Eur Radiol       Date:  2018-05-14       Impact factor: 5.315

9.  Impact of rescanning and repositioning on radiomic features employing a multi-object phantom in magnetic resonance imaging.

Authors:  Simon Bernatz; Yauheniya Zhdanovich; Jörg Ackermann; Ina Koch; Peter J Wild; Daniel Pinto Dos Santos; Thomas J Vogl; Benjamin Kaltenbach; Nicolas Rosbach
Journal:  Sci Rep       Date:  2021-07-09       Impact factor: 4.379

10.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice?

Authors:  Fergus Davnall; Connie S P Yip; Gunnar Ljungqvist; Mariyah Selmi; Francesca Ng; Bal Sanghera; Balaji Ganeshan; Kenneth A Miles; Gary J Cook; Vicky Goh
Journal:  Insights Imaging       Date:  2012-10-24
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