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AJNR Awards, New Junior Editors, and more. Read the latest AJNR updates

Research ArticleAdult Brain

A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations

A. Sreekumari, D. Shanbhag, D. Yeo, T. Foo, J. Pilitsis, J. Polzin, U. Patil, A. Coblentz, A. Kapadia, J. Khinda, A. Boutet, J. Port and I. Hancu
American Journal of Neuroradiology February 2019, 40 (2) 217-223; DOI: https://doi.org/10.3174/ajnr.A5926
A. Sreekumari
aFrom the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India
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  • ORCID record for A. Sreekumari
D. Shanbhag
aFrom the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India
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D. Yeo
bGE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York
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T. Foo
bGE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York
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J. Pilitsis
cAlbany Medical College (J.Pilitsis), Albany, New York
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J. Polzin
dGE Healthcare (J.Polzin), Milwaukee, Wisconsin
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U. Patil
aFrom the GE Global Research Center (A.S., D.S., U.P.), Bangalore, India
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A. Coblentz
eUniversity Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada
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A. Kapadia
eUniversity Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada
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J. Khinda
eUniversity Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada
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A. Boutet
eUniversity Hospital Network (A.C., A.K., J.K., A.B.), Toronto, Ontario, Canada
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J. Port
fMayo Clinic (J.Port), Rochester, Minnesota.
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I. Hancu
bGE Global Research Center (D.Y., T.F., I.H.), Niskayuna, New York
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Figures

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    Fig 1.

    Workflow for image rating and usage.

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    Fig 2.

    CNN architecture used in the experiment. Here NF represents number of filters and FS represents filter size.

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    Fig 3.

    Representative filter responses from the fourth convolution layer of the CNN (Conv2D_4). Rows 1 and 2, Filter responses for motion-corrupted axial FLAIR/T2* input images, respectively. Rows 3 and 4, Filter responses from axial/sagittal T1 input images without motion, respectively. Filter responses are independent of image contrast and highlight the recognizable motion artifacts in the motion-corrupted images (arrows).

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    Fig 4.

    Examples of classification performance for 3 series. A few slices are displayed from each series (left), together with the slice ratings for the entire series (right). The numbers at the top left corner of each image represent the slice number.

Tables

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    Table 1:

    Results of the survey—rule out MS clinical scan indicationa

    Doctor IDNo. Series of Insufficient QualityTechnician ID
    T1 (n = 26)T2 (n = 31)T3 (n = 12)T4 (n = 13)
    Unneeded RescansUnneeded RecallsUnneeded RescansUnneeded RecallsUnneeded RescansUnneeded RecallsUnneeded RescansUnneeded Recalls
    D0247570214011
    D1131411914522
    D23541348023022
    D324118135213010
    D4287862116014
    D5304843119017
    Mean ± SD25.7 ± 7.47.8 ± 47.2 ± 48.8 ± 63.2 ± 2.91.7 ± 1.415 ± 6.10.3 ± 0.812.7 ± 6.8
    • Note:—ID indicates identification. The numbers in parenthesis next to the technician identification numbers represent the total numbers of insufficient quality series identified by each rater.

    • ↵a All numbers reported are from the 49 series of the survey. Each series was evaluated twice, assuming that the scan indication was MS and stroke.

    • View popup
    Table 2:

    Results of the survey—rule out stroke clinical scan indicationa

    Doctor IDNo. Series of Insufficient QualityTechnician ID
    T1 (n = 12)T2 (n = 28)T3 (n = 7)T4 (n = 13)
    Unneeded RescansUnneeded RecallsUnneeded RescansUnneeded RecallsUnneeded RescansUnneeded RecallsUnneeded RescansUnneeded Recalls
    D1210026050110
    D213231500622
    D38612213371
    D411431812604
    D57502103360
    Mean ± SD8.2 ± 4.25.4 ± 31.4 ± 0.720.4 ± 4.20.4 ± 0.22.6 ± 0.83.6 ± 1.16 ± 3.41 ± 0.4
    • Note:—ID indicates identification. The numbers in parenthesis next to the technician identification numbers represent the total numbers of insufficient quality series identified by each rater.

    • ↵a All numbers reported are from the 49 series of the survey. Each series was evaluated twice, assuming that the scan indication was multiple sclerosis and stroke.

    • View popup
    Table 3:

    Matrix documenting the number of unneeded rescans and recalls created by the DL approach with different thresholds, assuming that series were scanned to rule out MSa

    DL (T = 0.1)DL (T = 0.5)DL (T = 0.8)
    RescansRecallsRescansRecallsRescansRecalls
    D0286390
    D183151211
    D211831146
    D3510106144
    D42115571
    D51133663
    Mean ± SD3.2 ± 2.810.5 ± 2.17 ± 4.75.3 ± 1.410.2 ± 2.62.5 ± 0.9
    • ↵a All numbers are from the 49 test series. Here D0–D5 represent the same individuals as in Tables 1 and 2.

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    Table 4:

    Matrix documenting the number of unneeded rescans and recalls created by the DL approach with different thresholds, assuming that the series were scanned to rule out strokea

    DL (T = 0.5)DL (T = 0.1)DL (T = 5e–4)DL (T = 1e–6)
    RescansRecallsRescansRecallsRescansRecallsRescansRecalls
    D125016011040
    D2151833307
    D32001328234
    D41711036427
    D52011217123
    Mean ± SD19.4 ± 3.80.4 ± 0.211.8 ± 1.41.8 ± 1.37 ± 2.92 ± 1.62.2 ± 1.54.2 ± 3
    • ↵a All numbers are from the 49 test series. Here D1–D5 represent the same individuals as in Tables 1 and 2. Physician D0, whose ratings were used to train the DL algorithm, is now absent (as in Table 2) because no “stroke” ratings were available for this reader.

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American Journal of Neuroradiology: 40 (2)
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Cite this article
A. Sreekumari, D. Shanbhag, D. Yeo, T. Foo, J. Pilitsis, J. Polzin, U. Patil, A. Coblentz, A. Kapadia, J. Khinda, A. Boutet, J. Port, I. Hancu
A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations
American Journal of Neuroradiology Feb 2019, 40 (2) 217-223; DOI: 10.3174/ajnr.A5926

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A Deep Learning–Based Approach to Reduce Rescan and Recall Rates in Clinical MRI Examinations
A. Sreekumari, D. Shanbhag, D. Yeo, T. Foo, J. Pilitsis, J. Polzin, U. Patil, A. Coblentz, A. Kapadia, J. Khinda, A. Boutet, J. Port, I. Hancu
American Journal of Neuroradiology Feb 2019, 40 (2) 217-223; DOI: 10.3174/ajnr.A5926
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