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Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.ORCID iD: 0000-0002-0455-8904
Umeå University, Faculty of Science and Technology, Department of Chemistry.
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
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2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 4041Article in journal (Refereed) Published
Abstract [en]

In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.

Place, publisher, year, edition, pages
Nature Publishing Group, 2017. Vol. 7, article id 4041
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:umu:diva-134993DOI: 10.1038/s41598-017-04151-4ISI: 000403874900024PubMedID: 28642480OAI: oai:DiVA.org:umu-134993DiVA, id: diva2:1095678
Note

Originally included in thesis in manuscript form.

Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2018-06-09Bibliographically approved
In thesis
1. Applications of statistical methods in quantitative magnetic resonance imaging
Open this publication in new window or tab >>Applications of statistical methods in quantitative magnetic resonance imaging
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Magnetic resonance imaging, MRI, offers a vast range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow ups, or assessment of treatment response. In the past decade, there has been an increasing interest for quantitative imaging, which give repeatable measurements of the anatomy. Quantitative imaging allows for objective analysis of the images, which are grounded in physical properties of the underlying tissues. The aim of this thesis was to improve quantitative measurements of Dynamic contrast enhanced MRI (DCE-MRI), and the texture analysis of diffusion weighted MRI (DW-MRI).

DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The exam involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step is crucial when performing DCE-MRI analysis, the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and physiology of the patient. Paper I, the possibility of using two aspects of the MRI data, phase and magnitude, for improved CA quantification, is explored. We found that the combination of phase and magnitude information improved the CA quantification in regions with high CA concentration, and was more advantageous for high field strength scanners.

DW-MRI measures the diffusion of water in and between cells, which reflects the cell density and structure of the tissue. The structure of a tumor can give insights into the prognosis of the disease. Tumors are heterogeneous, both genetically and in the distribution of cells, and tumors with high intratumoral heterogeneity have poorer prognosis. This heterogeneity can be measured using texture analysis. In 1973, Haralick et al. presented a texture analysis method using a gray level co-occurrence matrix, GLCM, to gauge the spatial distribution of gray levels in the image. This method of assessing texture in images has been successfully applied in many areas of research, from satellite images to medical applications. Texture analysis in treatment outcome assessment is studied in Paper II, where we showed that texture can distinguish between groups of patients with different survival times, in images acquired prior to treatment start.

However, this type of texture analysis is not inherently quantitative in the way it is calculated today. This was studied in Paper III, where we investigated how texture features were affected by five parameters related to image acquisition and pre-processing. We found that the texture feature values were dependent on the choice of these imaging and preprocessing parameters. In Paper IV, a novel method for calculating Haralick texture features was presented, which makes the texture features asymptotically invariant to the size of the GLCM. This method allows for comparison of textures between images that have been analyzed in different ways.

In conclusion, the work in this thesis has been aimed at improving quantitative analysis of tumors using MRI and texture analysis.

Place, publisher, year, edition, pages
Umeå: Umeå universitet, 2017. p. 65
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1900
Keywords
Quantitative imaging, tumor imaging, dynamic contrast-enhanced MRI, diffusion weighted MRI, texture analysis
National Category
Radiology, Nuclear Medicine and Medical Imaging
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-134997 (URN)978-91-7601-729-6 (ISBN)
Public defence
2017-06-09, Bergasalen, byggnad 27, Norrlands universitetssjukhus, Umeå, 09:00 (English)
Opponent
Supervisors
Available from: 2017-05-19 Created: 2017-05-15 Last updated: 2018-06-09Bibliographically approved

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Brynolfsson, PatrikNilsson, DavidTorheim, TuridAsklund, ThomasThellenberg Karlsson, CamillaTrygg, JohanNyholm, TufveGarpebring, Anders

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