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
    Number of pages12
    JournalAcademic Radiology
    Volume12
    Issue number10
    DOIs
    Publication statusPublished - 2005
    MoE publication typeA1 Journal article-refereed

    Fingerprint

    Artifacts
    Magnetic Resonance Spectroscopy
    Respiration

    Keywords

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

    Cite this

    Lötjönen, J., Pollari, M., Kivistö, S., & Lauerma, K. (2005). Correction of motion artifacts from cardiac cine magnetic resonance images. Academic Radiology, 12(10), 1273 - 1284. https://doi.org/10.1016/j.acra.2005.07.002
    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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    title = "Correction of motion artifacts from cardiac cine magnetic resonance images",
    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.",
    keywords = "magnetic resonance imaging, cardiac images, cine images, image registration, motion correction",
    author = "Jyrki L{\"o}tj{\"o}nen and Mika Pollari and Sari Kivist{\"o} and Kirsi Lauerma",
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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

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    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.

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    KW - cardiac images

    KW - cine images

    KW - image registration

    KW - motion correction

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    EP - 1284

    JO - Academic Radiology

    JF - Academic Radiology

    SN - 1076-6332

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    ER -