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Applications of statistical methods in quantitative magnetic resonance imaging
Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.ORCID iD: 0000-0002-0455-8904
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. , 65 p.
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 1900
Keyword [en]
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: urn:nbn:se:umu:diva-134997ISBN: 978-91-7601-729-6 (print)OAI: oai:DiVA.org:umu-134997DiVA: diva2:1095719
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: 2017-05-18Bibliographically approved
List of papers
1. Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling
Open this publication in new window or tab >>Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling
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2015 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 74, no 4, 1156-1164 p.Article in journal (Refereed) Published
Abstract [en]

Purpose: The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE-MRI.

Methods: We developed a maximum likelihood estimator that combines the phase signal in the DCE-MRI image series with an additional CA estimate, e.g. the estimate obtained from magnitude data. A number of simulations were performed to investigate the ability of the estimator to reduce bias and noise in CA estimates. Noise levels ranging from that of a body coil to that of a dedicated head coil were investigated at both 1.5T and 3T.

Results: Using the proposed method, the root mean squared error in the bolus peak was reduced from 2.24 to 0.11 mM in the vessels and 0.16 to 0.08 mM in the tumor rim for a noise level equivalent of a 12-channel head coil at 3T. No improvements were seen for tissues with small CA uptake, such as white matter.

Conclusion: Phase information reduces errors in the estimated CA concentrations. A larger phase response from higher field strengths or higher CA concentrations yielded better results. Issues such as background phase drift need to be addressed before this method can be applied in vivo.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2015
Keyword
dynamic contrast-enhanced MRI, contrast agent quantification, phase, inverse problem
National Category
Medical Image Processing Probability Theory and Statistics Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-95051 (URN)10.1002/mrm.25490 (DOI)000364215200028 ()25324043 (PubMedID)
Funder
Swedish Research Council, 340-2013- 5342; 13514Swedish Cancer Society, CAN 2010/381
Available from: 2014-10-21 Created: 2014-10-21 Last updated: 2017-05-15Bibliographically approved
2. ADC texture-An imaging biomarker for high-grade glioma?
Open this publication in new window or tab >>ADC texture-An imaging biomarker for high-grade glioma?
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2014 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 10, 101903- p.Article in journal (Refereed) Published
Abstract [en]

Purpose:

Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers.

Methods:

Twenty-three consecutive high-grade glioma patients were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression.

Results:

The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001.

Conclusions:

By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort. (C) 2014 Author(s).

Keyword
texture analysis, glioma, multivariate image analysis, ADC
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:umu:diva-96625 (URN)10.1118/1.4894812 (DOI)000343032400019 ()
Available from: 2014-11-27 Created: 2014-11-24 Last updated: 2017-05-15Bibliographically approved
3. Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
Open this publication in new window or tab >>Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
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(English)Manuscript (preprint) (Other academic)
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:umu:diva-134993 (URN)
Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2017-05-16
4. Gray-level invariant Haralick texture features
Open this publication in new window or tab >>Gray-level invariant Haralick texture features
(English)Manuscript (preprint) (Other academic)
National Category
Medical Biotechnology
Identifiers
urn:nbn:se:umu:diva-134995 (URN)
Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2017-05-17

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