Literature DB >> 32086149

Effects of MRI image normalization techniques in prostate cancer radiomics.

Lars J Isaksson1, Sara Raimondi2, Francesca Botta3, Matteo Pepa4, Simone G Gugliandolo4, Simone P De Angelis2, Giulia Marvaso4, Giuseppe Petralia5, Ottavio De Cobelli6, Sara Gandini2, Marta Cremonesi7, Federica Cattani3, Paul Summers8, Barbara A Jereczek-Fossa9.   

Abstract

The variance in intensities of MRI scans is a fundamental impediment for quantitative MRI analysis. Intensity values are not only highly dependent on acquisition parameters, but also on the subject and body region being scanned. This warrants the need for image normalization techniques to ensure that intensity values are consistent within tissues across different subjects and visits. Many intensity normalization methods have been developed and proven successful for the analysis of brain pathologies, but evaluation of these methods for images of the prostate region is lagging. In this paper, we compare four different normalization methods on 49 T2-w scans of prostate cancer patients: 1) the well-established histogram normalization, 2) the generalized scale normalization, 3) an extension of generalized scale normalization called generalized ball-scale normalization, and 4) a custom normalization based on healthy prostate tissue intensities. The methods are compared qualitatively and quantitatively in terms of behaviors of intensity distributions as well as impact on radiomic features. Our findings suggest that normalization based on prior knowledge of the healthy prostate tissue intensities may be the most effective way of acquiring the desired properties of normalized images. In addition, the histogram normalization method outperform the generalized scale and generalized ball-scale methods which have proven superior for other body regions.
Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Image normalization; MRI; Prostate cancer; Radiomics

Mesh:

Substances:

Year:  2020        PMID: 32086149     DOI: 10.1016/j.ejmp.2020.02.007

Source DB:  PubMed          Journal:  Phys Med        ISSN: 1120-1797            Impact factor:   2.685


  13 in total

1.  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

Review 2.  Challenges in ensuring the generalizability of image quantitation methods for MRI.

Authors:  Kathryn E Keenan; Jana G Delfino; Kalina V Jordanova; Megan E Poorman; Prathyush Chirra; Akshay S Chaudhari; Bettina Baessler; Jessica Winfield; Satish E Viswanath; Nandita M deSouza
Journal:  Med Phys       Date:  2021-09-29       Impact factor: 4.506

3.  MR Denoising Increases Radiomic Biomarker Precision and Reproducibility in Oncologic Imaging.

Authors:  Matías Fernández Patón; Leonor Cerdá Alberich; Cinta Sangüesa Nebot; Blanca Martínez de Las Heras; Diana Veiga Canuto; Adela Cañete Nieto; Luis Martí-Bonmatí
Journal:  J Digit Imaging       Date:  2021-09-10       Impact factor: 4.903

4.  QdMRI: A system for comprehensive analysis of thoracic dynamics via dynamic MRI.

Authors:  Yubing Tong; Jayaram K Udupa; You Hao; Lipeng Xie; Joseph M McDonough; Caiyun Wu; Carina Lott; Abigail Clark; Jason B Anari; Drew A Torigian; Patrick J Cahill
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2022-04-04

5.  Integration of Deep Learning and Active Shape Models for More Accurate Prostate Segmentation in 3D MR Images.

Authors:  Massimo Salvi; Bruno De Santi; Bianca Pop; Martino Bosco; Valentina Giannini; Daniele Regge; Filippo Molinari; Kristen M Meiburger
Journal:  J Imaging       Date:  2022-05-11

6.  General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology.

Authors:  Yasmine Makhlouf; Manuel Salto-Tellez; Jacqueline James; Paul O'Reilly; Perry Maxwell
Journal:  Diagnostics (Basel)       Date:  2022-05-20

7.  Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.

Authors:  Laure Fournier; Lena Costaridou; Luc Bidaut; Nicolas Michoux; Frederic E Lecouvet; Lioe-Fee de Geus-Oei; Ronald Boellaard; Daniela E Oprea-Lager; Nancy A Obuchowski; Anna Caroli; Wolfgang G Kunz; Edwin H Oei; James P B O'Connor; Marius E Mayerhoefer; Manuela Franca; Angel Alberich-Bayarri; Christophe M Deroose; Christian Loewe; Rashindra Manniesing; Caroline Caramella; Egesta Lopci; Nathalie Lassau; Anders Persson; Rik Achten; Karen Rosendahl; Olivier Clement; Elmar Kotter; Xavier Golay; Marion Smits; Marc Dewey; Daniel C Sullivan; Aad van der Lugt; Nandita M deSouza
Journal:  Eur Radiol       Date:  2021-01-25       Impact factor: 5.315

8.  A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study.

Authors:  Rossana Castaldo; Nunzia Garbino; Carlo Cavaliere; Mariarosaria Incoronato; Luca Basso; Renato Cuocolo; Leonardo Pace; Marco Salvatore; Monica Franzese; Emanuele Nicolai
Journal:  Diagnostics (Basel)       Date:  2022-02-15

9.  Radiomics in pulmonary neuroendocrine tumours (NETs).

Authors:  Diletta Cozzi; Eleonora Bicci; Edoardo Cavigli; Ginevra Danti; Silvia Bettarini; Paolo Tortoli; Lorenzo Nicola Mazzoni; Simone Busoni; Silvia Pradella; Vittorio Miele
Journal:  Radiol Med       Date:  2022-05-10       Impact factor: 6.313

10.  Intensity harmonization techniques influence radiomics features and radiomics-based predictions in sarcoma patients.

Authors:  Amandine Crombé; Michèle Kind; David Fadli; François Le Loarer; Antoine Italiano; Xavier Buy; Olivier Saut
Journal:  Sci Rep       Date:  2020-09-23       Impact factor: 4.379

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