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

Research ArticleAdult Brain
Open Access

Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging

T. Yamamoto, C. Lacheret, H. Fukutomi, R.A. Kamraoui, L. Denat, B. Zhang, V. Prevost, L. Zhang, A. Ruet, B. Triaire, V. Dousset, P. Coupé and T. Tourdias
American Journal of Neuroradiology August 2022, 43 (8) 1099-1106; DOI: https://doi.org/10.3174/ajnr.A7589
T. Yamamoto
aFrom the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France
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C. Lacheret
bNeuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.)
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  • ORCID record for C. Lacheret
H. Fukutomi
aFrom the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France
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R.A. Kamraoui
dLaboratoire Bordelais de Recherche en Informatique (R.A.K., P.C.), University Bordeaux, Le Centre National de la Recherche Scientifique, Bordeaux Institut National Polytechnique, Talence, France
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  • ORCID record for R.A. Kamraoui
L. Denat
aFrom the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France
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B. Zhang
eCanon Medical Systems Europe (B.Z.), Zoetermeer, the Netherlands
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V. Prevost
fCanon Medical Systems (V.P., B.T.), Tochigi, Japan
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L. Zhang
gCanon Medical Systems China (L.Z.), Beijing, China
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A. Ruet
cService de Neurologie (A.R.), Centre Hospitalier Universitaire de Bordeaux, Bordeaux, France
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B. Triaire
fCanon Medical Systems (V.P., B.T.), Tochigi, Japan
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V. Dousset
aFrom the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France
bNeuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.)
hNeurocentreMagendie (V.D., T.T.), University of Bordeaux, L’Institut National de la Santé et de la Recherche Médicale, Bordeaux, France
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P. Coupé
dLaboratoire Bordelais de Recherche en Informatique (R.A.K., P.C.), University Bordeaux, Le Centre National de la Recherche Scientifique, Bordeaux Institut National Polytechnique, Talence, France
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T. Tourdias
aFrom the Institut de Bio-imagerie (T.Y., H.F., L.D., V.D., T.T.), University Bordeaux, Bordeaux, France
bNeuroimagerie Diagnostique et Thérapeutique (C.L., V.D., T.T.)
hNeurocentreMagendie (V.D., T.T.), University of Bordeaux, L’Institut National de la Santé et de la Recherche Médicale, Bordeaux, France
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Figures

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  • FIG 1.
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    FIG 1.

    A, Boxplots of image-quality evaluation. The median values are connected with dotted lines. dDLR generally improved the image quality. Even images of inferior quality were retrieved to moderate quality. B, Representative reconstructed axial slices of the different FLAIR acquisitions without (upper row) and with dDLR (lower row). The amount of noise and the progressive shadowing of lesions when the scan time decreased can be appreciated. One, 2, and 3 asterisks indicate P < .05, P < .01, and P < .001, respectively.

  • FIG 2.
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    FIG 2.

    Boxplots of quantitative metrics to evaluate the manual and automatic segmentation. The median values are connected with dotted lines. One, 2, and 3 asterisks indicate P < .05, P < .01, and P < .001, respectively, for post hoc paired comparisons with Wilcoxon signed-rank test after 2-way ANOVA with the aligned rank transform procedure. † indicates voxelwise metric; §, lesion-wise metric.

  • FIG 3.
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    FIG 3.

    Illustrative axial slices of ultrafast FLAIR without and with dDLR. Standard FLAIR without dDLR is also shown for reference. The red mask represents the criterion standard that comes from the delineation of standard FLAIR by 2 expert readers; the blue mask is the manual delineation from a third reader; and the green mask is the automatic segmentation from volBrain software. After we applied dDLR, some lesions showed contours closer to the criterion standard (black arrows). dDLR also retrieved lesions that were missed on the original image (white arrows and arrowhead).

Tables

  • Figures
  • Parameters of MR imaging acquisitions

    3D FLAIRMPRAGE
    StandardFastUltrafastShortest
    TR (ms)70005000400030006.3
    TE (ms)/effective TE (ms)445.5/145.0445.5/145.0445.5/145.0445.5/145.02.8
    TI (ms)207015801270910950
    BW (Hz)558558558558279
    Echo space (ms)4.54.54.54.5
    FOV (mm)230 × 230230 × 230230 × 230230 × 230230 × 230
    Matrix224 × 224224 × 224224 × 224224 × 224224 × 224
    Thickness (mm)11111
    Parallel imaging2.0 × 3.02.0 × 3.02.0 × 3.02.0 × 3.01.8 × 1.5
    Frequency oversampling1.41.21.21.21.2
    Half Fourier85%70%70%
    Flip angle90°90°90°90°9°
    Refocus angleVFAVFAVFAVFA
    Shot interval (ms)2500
    Recovery time (ms)744
    Scan time (minute : second)4:542:351:401:156:18
    • Note:—VFA indicates variable flip angle; BW, Bandwidth.

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American Journal of Neuroradiology: 43 (8)
American Journal of Neuroradiology
Vol. 43, Issue 8
1 Aug 2022
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Cite this article
T. Yamamoto, C. Lacheret, H. Fukutomi, R.A. Kamraoui, L. Denat, B. Zhang, V. Prevost, L. Zhang, A. Ruet, B. Triaire, V. Dousset, P. Coupé, T. Tourdias
Validation of a Denoising Method Using Deep Learning–Based Reconstruction to Quantify Multiple Sclerosis Lesion Load on Fast FLAIR Imaging
American Journal of Neuroradiology Aug 2022, 43 (8) 1099-1106; DOI: 10.3174/ajnr.A7589

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Deep Learning for MS Lesion Quantification
T. Yamamoto, C. Lacheret, H. Fukutomi, R.A. Kamraoui, L. Denat, B. Zhang, V. Prevost, L. Zhang, A. Ruet, B. Triaire, V. Dousset, P. Coupé, T. Tourdias
American Journal of Neuroradiology Aug 2022, 43 (8) 1099-1106; DOI: 10.3174/ajnr.A7589
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