Computed tomography metal artifact reduction for radiotherapy using magnetic resonance imaging

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Abstract

When metal implants are scanned during medical x-ray computed tomography (CT), the conventional model that relates the image coefficients to the x-ray measurements (the sinogram) breaks down. The resulting streak and cupping artifacts affect the accuracy of external beam radiotherapy (RT) treatment plans that rely on CT images for electron density and particle relative stopping power estimates. Particularly in the highly sensitive proton RT this may lead to notable errors in the estimated beam penetration depth, and consequent dosimetric errors that may exclude some patients from proton treatment that would otherwise benefit from it. Metal artifact reduction (MAR) algorithms are therefore employed that however despite many years of development remain inadequate. This thesis investigates a promising way to increase the efficacy of MAR by using the superior anatomical information in artifact corrupted regions that is available in a co-registered Magnetic Resonance Image (MRI) that in RT may have been acquired for tumor delineation. This is challenging due to the inherently weak correlation between conventional-sequence MR intensities and CT values, as well as the potentially imperfect co-registration of the images. We develop a novel, Bayesian generative model of MRIs and CTs that is suitable to handle these challenges, and use it with promising results for MAR by image inpainting in the corrupted regions; as a prior for sinogram inpainting of metal-affected projections; and for prior modelling in model-based iterative reconstruction (MBIR). We finally validate our methods in the context of head-and-neck RT, finding dosimetric as well as image space improvements compared to a standard approaches in clinical use.
Original languageEnglish
PublisherTechnical University of Denmark
Number of pages156
Publication statusPublished - 2019

Cite this

@phdthesis{80216fdacc8345abaf51a2e455dedb4f,
title = "Computed tomography metal artifact reduction for radiotherapy using magnetic resonance imaging",
abstract = "When metal implants are scanned during medical x-ray computed tomography (CT), the conventional model that relates the image coefficients to the x-ray measurements (the sinogram) breaks down. The resulting streak and cupping artifacts affect the accuracy of external beam radiotherapy (RT) treatment plans that rely on CT images for electron density and particle relative stopping power estimates. Particularly in the highly sensitive proton RT this may lead to notable errors in the estimated beam penetration depth, and consequent dosimetric errors that may exclude some patients from proton treatment that would otherwise benefit from it. Metal artifact reduction (MAR) algorithms are therefore employed that however despite many years of development remain inadequate. This thesis investigates a promising way to increase the efficacy of MAR by using the superior anatomical information in artifact corrupted regions that is available in a co-registered Magnetic Resonance Image (MRI) that in RT may have been acquired for tumor delineation. This is challenging due to the inherently weak correlation between conventional-sequence MR intensities and CT values, as well as the potentially imperfect co-registration of the images. We develop a novel, Bayesian generative model of MRIs and CTs that is suitable to handle these challenges, and use it with promising results for MAR by image inpainting in the corrupted regions; as a prior for sinogram inpainting of metal-affected projections; and for prior modelling in model-based iterative reconstruction (MBIR). We finally validate our methods in the context of head-and-neck RT, finding dosimetric as well as image space improvements compared to a standard approaches in clinical use.",
author = "Nielsen, {Jonathan Scharff}",
year = "2019",
language = "English",
publisher = "Technical University of Denmark",

}

Computed tomography metal artifact reduction for radiotherapy using magnetic resonance imaging. / Nielsen, Jonathan Scharff.

Technical University of Denmark, 2019. 156 p.

Research output: Book/ReportPh.D. thesisResearch

TY - BOOK

T1 - Computed tomography metal artifact reduction for radiotherapy using magnetic resonance imaging

AU - Nielsen, Jonathan Scharff

PY - 2019

Y1 - 2019

N2 - When metal implants are scanned during medical x-ray computed tomography (CT), the conventional model that relates the image coefficients to the x-ray measurements (the sinogram) breaks down. The resulting streak and cupping artifacts affect the accuracy of external beam radiotherapy (RT) treatment plans that rely on CT images for electron density and particle relative stopping power estimates. Particularly in the highly sensitive proton RT this may lead to notable errors in the estimated beam penetration depth, and consequent dosimetric errors that may exclude some patients from proton treatment that would otherwise benefit from it. Metal artifact reduction (MAR) algorithms are therefore employed that however despite many years of development remain inadequate. This thesis investigates a promising way to increase the efficacy of MAR by using the superior anatomical information in artifact corrupted regions that is available in a co-registered Magnetic Resonance Image (MRI) that in RT may have been acquired for tumor delineation. This is challenging due to the inherently weak correlation between conventional-sequence MR intensities and CT values, as well as the potentially imperfect co-registration of the images. We develop a novel, Bayesian generative model of MRIs and CTs that is suitable to handle these challenges, and use it with promising results for MAR by image inpainting in the corrupted regions; as a prior for sinogram inpainting of metal-affected projections; and for prior modelling in model-based iterative reconstruction (MBIR). We finally validate our methods in the context of head-and-neck RT, finding dosimetric as well as image space improvements compared to a standard approaches in clinical use.

AB - When metal implants are scanned during medical x-ray computed tomography (CT), the conventional model that relates the image coefficients to the x-ray measurements (the sinogram) breaks down. The resulting streak and cupping artifacts affect the accuracy of external beam radiotherapy (RT) treatment plans that rely on CT images for electron density and particle relative stopping power estimates. Particularly in the highly sensitive proton RT this may lead to notable errors in the estimated beam penetration depth, and consequent dosimetric errors that may exclude some patients from proton treatment that would otherwise benefit from it. Metal artifact reduction (MAR) algorithms are therefore employed that however despite many years of development remain inadequate. This thesis investigates a promising way to increase the efficacy of MAR by using the superior anatomical information in artifact corrupted regions that is available in a co-registered Magnetic Resonance Image (MRI) that in RT may have been acquired for tumor delineation. This is challenging due to the inherently weak correlation between conventional-sequence MR intensities and CT values, as well as the potentially imperfect co-registration of the images. We develop a novel, Bayesian generative model of MRIs and CTs that is suitable to handle these challenges, and use it with promising results for MAR by image inpainting in the corrupted regions; as a prior for sinogram inpainting of metal-affected projections; and for prior modelling in model-based iterative reconstruction (MBIR). We finally validate our methods in the context of head-and-neck RT, finding dosimetric as well as image space improvements compared to a standard approaches in clinical use.

M3 - Ph.D. thesis

BT - Computed tomography metal artifact reduction for radiotherapy using magnetic resonance imaging

PB - Technical University of Denmark

ER -