Metal artifact reduction from CT images using complementary MR images

Anderla, Andraš; Ćulibrk, Dubravko; Delso, Gaspar

doi:10.1109/telsks.2013.6704943

Cited by 4 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MATLAB development environment was utilized in this research due to the fact that solutions in the field of digital image processing, in general, require experimental work that includes simulation and testing with large sets of sample images (Gonzalez, Woods & Eddins, 2009). Different approaches which has been implemented in Matlab, used different image formats as an input in approaches accomplished with numerous techniques for eliminating certain parts of image that provide easier classification (Anderla, Culibrk & Delso, 2013). The image, in general, can be defined as a two-dimensional function f(x, y), where x and y represent the spatial coordinates, the pixel positions, and the amplitude f that is in the pair of coordinates (x, y) is called the intensity or grey level.…”

Section: Theoretical Foundationsmentioning

confidence: 99%

Proceedings - The Eleventh International Symposium GRID 2022

2022

View full text Add to dashboard Cite

It is my great pleasure to introduce You the research papers of the Eleventh Symposium on Graphic Engineering and Design.With this proceeding we continue the works of previous symposiums which have been held biennial since year 2002.We're delighted that this international symposium has once again gatered a great number of participants from many countries as it became a tradition over the past 20 years.The papers include the achievements of researches in the field of technology and scientific areas relevant to graphic technology and graphic design. Through the work of the symposium GRID we continued significant scientific cooperation with educational institutions all over the world. With them we are continuing good cooperation which is the driving force for the creation and display of new developments, both individual and common. I want to thank everyone who participated with their paper and presentation in the Symposium. Your contribution is significant for the improvement of the Symposium on Graphic Engineering and Design GRID 22. The research achievements presented here are also valuable to the scientific and professional community and are highly appreciated.

show abstract

Section: Theoretical Foundationsmentioning

confidence: 99%

Proceedings - The Eleventh International Symposium GRID 2022

2022

View full text Add to dashboard Cite

show abstract

“…Despite the obvious potential, to the best of our knowledge only a few prior attempts have been made to use MR for reducing CT metal artifacts, most notably the image inpainting algorithms described in Anderla et al (2013), Delso et al (2013), Park et al (2015). The principal difficulty faced by such MR-based approaches to CT MAR lies in the image contrast disparities between the two modalities, especially between bone and air, which are easily distinguishable in the CT but not in the MR scan unless dedicated sequences are used (Delso et al 2013).…”

Section: Contributions Of This Papermentioning

confidence: 99%

Magnetic resonance-based computed tomography metal artifact reduction using Bayesian modelling

2019

View full text Add to dashboard Cite

Metal artifact reduction (MAR) algorithms reduce the errors caused by metal implants in x-ray computed tomography (CT) images and are an important part of error management in radiotherapy. A promising MAR approach is to leverage the information in magnetic resonance (MR) images that can be acquired for organ or tumor delineation. This is however complicated by the ambiguous relationship between CT values and conventional-sequence MR intensities as well as potential co-registration issues. In order to address these issues, this paper proposes a self-tuning Bayesian model for MR-based MAR that combines knowledge of the MR image intensities in local spatial neighborhoods with the information in an initial, corrupted CT reconstructed using filtered back projection. We demonstrate the potential of the resulting model in three widely-used MAR scenarios: image inpainting, sinogram inpainting and model-based iterative reconstruction. Compared to conventional alternatives in a retrospective study on nine head-and-neck patients with CT and T1-weighted MR scans, we find improvements in terms of image quality and quantitative CT value accuracy within each scenario. We conclude that the proposed model provides a versatile way to use the anatomical information in a co-acquired MR scan to boost the performance of MAR algorithms.

show abstract

“…Using MR for MAR is a relatively new idea that has mainly been investigated with an image-based approach, e.g. by finding replacement CT values in local windows guided by MR voxel intensity differences, 12 or by creating a pseudo-CT (pCT) in which replacement CT values are assigned to discrete MR image segmentations. 13 Because these methods only use local MR intensity similarity to predict CT values, however, they typically produce errors in bone and air regions which both appear dark on MR images acquired with conventional sequences.…”

Section: -11mentioning

confidence: 99%

“…The few previous MR-based MARs in the literature that we are aware of 12,13 suffer from this disambiguation issue. The algorithm introduced by Anderla et al 12 works by looking in a 5x5x5 voxel window on the MR around each corrupted voxel (classified using Otsu's thresholding method), finding the voxel with smallest MR intensity difference to the window center and assigning its CT value to the voxel center on the CT.…”

Section: Comparison To Existing Mr-based Marsmentioning

confidence: 99%

“…The algorithm introduced by Anderla et al 12 works by looking in a 5x5x5 voxel window on the MR around each corrupted voxel (classified using Otsu's thresholding method), finding the voxel with smallest MR intensity difference to the window center and assigning its CT value to the voxel center on the CT. While this effectively constrains the search space to the very local 5x5x5 window and thus improves the bone/air disambiguation, it limits the potential accuracy of the algorithm and leads to failure in heavily corrupted regions.…”

Section: Comparison To Existing Mr-based Marsmentioning

confidence: 99%

See 1 more Smart Citation

CT metal artifact reduction using MR image patches

Nielsen

Edmund

Leemput

2018

Medical Imaging 2018: Physics of Medical Imaging

View full text Add to dashboard Cite

Metal implants give rise to metal artifacts in computed tomography (CT) images, which may lead to diagnostic errors and erroneous CT number estimates when the CT is used for radiation therapy planning. Methods for reducing metal artifacts by exploiting the anatomical information provided by coregistered magnetic resonance (MR) images are of great potential value, but remain technically challenging due to the poor contrast between bone and air on the MR image. In this paper, we present a novel MR-based algorithm for automatic CT metal artifact reduction (MAR), referred to as kerMAR. It combines kernel regression on known CT value/MR patch pairs in the uncorrupted patient volume with a forward model of the artifact corrupted values to estimate CT replacement values. In contrast to pseudo-CT generation that builds on multi-patient modelling, the algorithm requires no MR intensity normalisation or atlas registration. Image results for 7 head-and-neck radiation therapy patients with T1-weighted images acquired in the same fixation as the RT planning CT suggest a potential for more complete MAR close to the metal implants than the oMAR algorithm (Philips) used clinically. Our results further show improved performance in air and bone regions as compared to other MR-based MAR algorithms. In addition, we experimented with using kerMAR to define a prior for iterative reconstruction with the maximum likelihood transmission reconstruction algorithm, however with no apparent improvements

show abstract

Metal artifact reduction from CT images using complementary MR images

Cited by 4 publications

References 16 publications

Proceedings - The Eleventh International Symposium GRID 2022

Proceedings - The Eleventh International Symposium GRID 2022

Magnetic resonance-based computed tomography metal artifact reduction using Bayesian modelling

CT metal artifact reduction using MR image patches

Contact Info

Product

Resources

About