DTI quality control assessment via error estimation from Monte Carlo simulations

Mahshid Farzinfar, Yin Li, Audrey R. Verde, Ipek Oguz, Guido Gerig, Martin A. Styner

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing the microscopic tissue structure of white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC) schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo (MC) simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus, determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient directions before and after QC.

    Original languageEnglish (US)
    Title of host publicationMedical Imaging 2013: Image Processing
    Volume8669
    DOIs
    StatePublished - 2013
    EventMedical Imaging 2013: Image Processing - Lake Buena Vista, FL, United States
    Duration: Feb 10 2013Feb 12 2013

    Other

    OtherMedical Imaging 2013: Image Processing
    CountryUnited States
    CityLake Buena Vista, FL
    Period2/10/132/12/13

    Fingerprint

    Diffusion tensor imaging
    Diffusion Tensor Imaging
    quality control
    Quality Control
    Error analysis
    Quality control
    tensors
    Imaging techniques
    simulation
    Artifacts
    Tensors
    artifacts
    Anisotropy
    Brain Diseases
    Monte Carlo simulation
    Image quality
    Cluster Analysis
    Noise
    gradients
    Brain

    Keywords

    • Diffusion tensor imaging
    • Quality control and Monte Carlo simulation

    ASJC Scopus subject areas

    • Atomic and Molecular Physics, and Optics
    • Electronic, Optical and Magnetic Materials
    • Biomaterials
    • Radiology Nuclear Medicine and imaging

    Cite this

    Farzinfar, M., Li, Y., Verde, A. R., Oguz, I., Gerig, G., & Styner, M. A. (2013). DTI quality control assessment via error estimation from Monte Carlo simulations. In Medical Imaging 2013: Image Processing (Vol. 8669). [86692C] https://doi.org/10.1117/12.2006925

    DTI quality control assessment via error estimation from Monte Carlo simulations. / Farzinfar, Mahshid; Li, Yin; Verde, Audrey R.; Oguz, Ipek; Gerig, Guido; Styner, Martin A.

    Medical Imaging 2013: Image Processing. Vol. 8669 2013. 86692C.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Farzinfar, M, Li, Y, Verde, AR, Oguz, I, Gerig, G & Styner, MA 2013, DTI quality control assessment via error estimation from Monte Carlo simulations. in Medical Imaging 2013: Image Processing. vol. 8669, 86692C, Medical Imaging 2013: Image Processing, Lake Buena Vista, FL, United States, 2/10/13. https://doi.org/10.1117/12.2006925
    Farzinfar M, Li Y, Verde AR, Oguz I, Gerig G, Styner MA. DTI quality control assessment via error estimation from Monte Carlo simulations. In Medical Imaging 2013: Image Processing. Vol. 8669. 2013. 86692C https://doi.org/10.1117/12.2006925
    Farzinfar, Mahshid ; Li, Yin ; Verde, Audrey R. ; Oguz, Ipek ; Gerig, Guido ; Styner, Martin A. / DTI quality control assessment via error estimation from Monte Carlo simulations. Medical Imaging 2013: Image Processing. Vol. 8669 2013.
    @inproceedings{c53d541facc44c0ea398b1420e9c6c7e,
    title = "DTI quality control assessment via error estimation from Monte Carlo simulations",
    abstract = "Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing the microscopic tissue structure of white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC) schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo (MC) simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus, determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient directions before and after QC.",
    keywords = "Diffusion tensor imaging, Quality control and Monte Carlo simulation",
    author = "Mahshid Farzinfar and Yin Li and Verde, {Audrey R.} and Ipek Oguz and Guido Gerig and Styner, {Martin A.}",
    year = "2013",
    doi = "10.1117/12.2006925",
    language = "English (US)",
    isbn = "9780819494436",
    volume = "8669",
    booktitle = "Medical Imaging 2013: Image Processing",

    }

    TY - GEN

    T1 - DTI quality control assessment via error estimation from Monte Carlo simulations

    AU - Farzinfar, Mahshid

    AU - Li, Yin

    AU - Verde, Audrey R.

    AU - Oguz, Ipek

    AU - Gerig, Guido

    AU - Styner, Martin A.

    PY - 2013

    Y1 - 2013

    N2 - Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing the microscopic tissue structure of white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC) schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo (MC) simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus, determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient directions before and after QC.

    AB - Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing the microscopic tissue structure of white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC) schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo (MC) simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus, determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient directions before and after QC.

    KW - Diffusion tensor imaging

    KW - Quality control and Monte Carlo simulation

    UR - http://www.scopus.com/inward/record.url?scp=84878273218&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84878273218&partnerID=8YFLogxK

    U2 - 10.1117/12.2006925

    DO - 10.1117/12.2006925

    M3 - Conference contribution

    SN - 9780819494436

    VL - 8669

    BT - Medical Imaging 2013: Image Processing

    ER -