Correction of motion artifacts from cardiac cine magnetic resonance images

Jyrki Lötjönen (Corresponding Author), Mika Pollari, Sari Kivistö, Kirsi Lauerma

Research output: Contribution to journalArticleScientificpeer-review

13 Citations (Scopus)

Abstract

Rationale and Objectives

An image registration method was developed to automatically correct motion artifacts, mostly from breathing, from cardiac cine magnetic resonance (MR) images.

Materials and Methods

The location of each slice in an image stack was optimized by maximizing a similarity measure of the slice with another image slice stack. The optimization was performed iteratively and both image stacks were corrected simultaneously. Two procedures to optimize the similarity were tested: standard gradient optimization and stochastic optimization in which one slice is chosen randomly from the image stacks and its location is optimized. In this work, cine short- and long-axis images were used. In addition to visual inspection results from real data, the performance of the algorithm was evaluated quantitatively by simulating the movements in four real MR data sets. The mean error and standard deviation were defined for 50 simulated movements as each slice was randomly displaced. The error rate, defined as the percentage of non-satisfactory registration results, was evaluated. The paired t-test was used to evaluate the statistical difference between the tested optimization methods.

Results

The algorithm developed was successfully applied to correct motion artifacts from real and simulated data. The results, where typical motion artifacts were simulated, indicated an error rate of about 3%. Subvoxel registration accuracy was also achieved. When different optimization methods were compared, the registration accuracy of the stochastic approach proved to be superior to the standard gradient technique (P < 10−9).

Conclusions

The novel method was capable of robustly and accurately correcting motion artifacts from cardiac cine MR images.

Original languageEnglish
Pages (from-to)1273-1284
JournalAcademic Radiology
Volume12
Issue number10
DOIs
Publication statusPublished - 2005
MoE publication typeA1 Journal article-refereed

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Artifacts
Magnetic Resonance Spectroscopy
Respiration

Keywords

  • magnetic resonance imaging
  • cardiac images
  • cine images
  • image registration
  • motion correction

Cite this

Lötjönen, Jyrki ; Pollari, Mika ; Kivistö, Sari ; Lauerma, Kirsi. / Correction of motion artifacts from cardiac cine magnetic resonance images. In: Academic Radiology. 2005 ; Vol. 12, No. 10. pp. 1273-1284.
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abstract = "Rationale and ObjectivesAn image registration method was developed to automatically correct motion artifacts, mostly from breathing, from cardiac cine magnetic resonance (MR) images.Materials and MethodsThe location of each slice in an image stack was optimized by maximizing a similarity measure of the slice with another image slice stack. The optimization was performed iteratively and both image stacks were corrected simultaneously. Two procedures to optimize the similarity were tested: standard gradient optimization and stochastic optimization in which one slice is chosen randomly from the image stacks and its location is optimized. In this work, cine short- and long-axis images were used. In addition to visual inspection results from real data, the performance of the algorithm was evaluated quantitatively by simulating the movements in four real MR data sets. The mean error and standard deviation were defined for 50 simulated movements as each slice was randomly displaced. The error rate, defined as the percentage of non-satisfactory registration results, was evaluated. The paired t-test was used to evaluate the statistical difference between the tested optimization methods.ResultsThe algorithm developed was successfully applied to correct motion artifacts from real and simulated data. The results, where typical motion artifacts were simulated, indicated an error rate of about 3{\%}. Subvoxel registration accuracy was also achieved. When different optimization methods were compared, the registration accuracy of the stochastic approach proved to be superior to the standard gradient technique (P < 10−9).ConclusionsThe novel method was capable of robustly and accurately correcting motion artifacts from cardiac cine MR images.",
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Lötjönen, J, Pollari, M, Kivistö, S & Lauerma, K 2005, 'Correction of motion artifacts from cardiac cine magnetic resonance images', Academic Radiology, vol. 12, no. 10, pp. 1273-1284. https://doi.org/10.1016/j.acra.2005.07.002

Correction of motion artifacts from cardiac cine magnetic resonance images. / Lötjönen, Jyrki (Corresponding Author); Pollari, Mika; Kivistö, Sari; Lauerma, Kirsi.

In: Academic Radiology, Vol. 12, No. 10, 2005, p. 1273-1284.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Correction of motion artifacts from cardiac cine magnetic resonance images

AU - Lötjönen, Jyrki

AU - Pollari, Mika

AU - Kivistö, Sari

AU - Lauerma, Kirsi

PY - 2005

Y1 - 2005

N2 - Rationale and ObjectivesAn image registration method was developed to automatically correct motion artifacts, mostly from breathing, from cardiac cine magnetic resonance (MR) images.Materials and MethodsThe location of each slice in an image stack was optimized by maximizing a similarity measure of the slice with another image slice stack. The optimization was performed iteratively and both image stacks were corrected simultaneously. Two procedures to optimize the similarity were tested: standard gradient optimization and stochastic optimization in which one slice is chosen randomly from the image stacks and its location is optimized. In this work, cine short- and long-axis images were used. In addition to visual inspection results from real data, the performance of the algorithm was evaluated quantitatively by simulating the movements in four real MR data sets. The mean error and standard deviation were defined for 50 simulated movements as each slice was randomly displaced. The error rate, defined as the percentage of non-satisfactory registration results, was evaluated. The paired t-test was used to evaluate the statistical difference between the tested optimization methods.ResultsThe algorithm developed was successfully applied to correct motion artifacts from real and simulated data. The results, where typical motion artifacts were simulated, indicated an error rate of about 3%. Subvoxel registration accuracy was also achieved. When different optimization methods were compared, the registration accuracy of the stochastic approach proved to be superior to the standard gradient technique (P < 10−9).ConclusionsThe novel method was capable of robustly and accurately correcting motion artifacts from cardiac cine MR images.

AB - Rationale and ObjectivesAn image registration method was developed to automatically correct motion artifacts, mostly from breathing, from cardiac cine magnetic resonance (MR) images.Materials and MethodsThe location of each slice in an image stack was optimized by maximizing a similarity measure of the slice with another image slice stack. The optimization was performed iteratively and both image stacks were corrected simultaneously. Two procedures to optimize the similarity were tested: standard gradient optimization and stochastic optimization in which one slice is chosen randomly from the image stacks and its location is optimized. In this work, cine short- and long-axis images were used. In addition to visual inspection results from real data, the performance of the algorithm was evaluated quantitatively by simulating the movements in four real MR data sets. The mean error and standard deviation were defined for 50 simulated movements as each slice was randomly displaced. The error rate, defined as the percentage of non-satisfactory registration results, was evaluated. The paired t-test was used to evaluate the statistical difference between the tested optimization methods.ResultsThe algorithm developed was successfully applied to correct motion artifacts from real and simulated data. The results, where typical motion artifacts were simulated, indicated an error rate of about 3%. Subvoxel registration accuracy was also achieved. When different optimization methods were compared, the registration accuracy of the stochastic approach proved to be superior to the standard gradient technique (P < 10−9).ConclusionsThe novel method was capable of robustly and accurately correcting motion artifacts from cardiac cine MR images.

KW - magnetic resonance imaging

KW - cardiac images

KW - cine images

KW - image registration

KW - motion correction

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M3 - Article

VL - 12

SP - 1273

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JO - Academic Radiology

JF - Academic Radiology

SN - 1076-6332

IS - 10

ER -