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Research ArticlePhysics and Instrumentation: SPECT and SPECT/CT

Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study

Jaewon Yang, Luyao Shi, Rui Wang, Edward J. Miller, Albert J. Sinusas, Chi Liu, Grant T. Gullberg and Youngho Seo
Journal of Nuclear Medicine November 2021, 62 (11) 1645-1652; DOI: https://doi.org/10.2967/jnumed.120.256396
Jaewon Yang
1Physics Research Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California;
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Luyao Shi
2Biomedical Engineering, Yale University, New Haven, Connecticut;
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Rui Wang
3Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut;
4Department of Engineering Physics, Tsinghua University, Beijing, China; and
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Edward J. Miller
3Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut;
5Internal Medicine (Cardiology), Yale University, New Haven, Connecticut
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Albert J. Sinusas
2Biomedical Engineering, Yale University, New Haven, Connecticut;
3Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut;
5Internal Medicine (Cardiology), Yale University, New Haven, Connecticut
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Chi Liu
2Biomedical Engineering, Yale University, New Haven, Connecticut;
3Radiology and Biomedical Imaging, Yale University, New Haven, Connecticut;
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Grant T. Gullberg
1Physics Research Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California;
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Youngho Seo
1Physics Research Laboratory, Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California;
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  • FIGURE 1.
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    FIGURE 1.

    Schematic of proposed DCNN-based AC performed in image space (left), compared with conventional AC performed through system matrix during SPECT image reconstruction (right). BN = batch normalization; conv = convolution; recon = reconstruction; ReLU = rectified linear unit.

  • FIGURE 2.
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    FIGURE 2.

    Joint histogram of voxels in the myocardium: SPECTNC vs. SPECTCTAC (A) and SPECTDL vs. SPECTCTAC (B). Counts were log-scaled (i.e., log10(counts)) to visualize small counts in joint histograms.

  • FIGURE 3.
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    FIGURE 3.

    Bland–Altman plots for percentage segmental errors across all subjects, male and female subjects, and subjects with HLU and LLU: SPECTNC (A) and SPECTDL (B).

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    FIGURE 4.

    Polar map (right) and box plots (left) for percentage segmental errors across all subjects.

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    FIGURE 5.

    Examples of 3 subjects (A, male; B, male; C, male) with smallest absolute mean segmental error in short-axis (SA) and vertical long-axis (VLA) views and polar maps.

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    FIGURE 6.

    Examples of 3 subjects with 25th (A, male), 50th (B, male), and 75th (C, female) percentiles of absolute mean segmental errors in short-axis (SA) and vertical long-axis (VLA) views and polar maps.

  • FIGURE 7.
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    FIGURE 7.

    Examples of 2 subjects with most overestimated (A, male) and most underestimated (B, female) mean segmental errors in short-axis (SA) and vertical long-axis (VLA) views and polar maps.

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    TABLE 1

    Average Error, Average Absolute Error, and Correlation Coefficients of All Segments Across All Subjects

    CohortSPECT typeError (%)|Error| (%)Pearson’s correlation coefficient (%)P
    All (n = 100)NC−6.11 ± 8.067.96 ± 6.2388.05<0.001
    DL0.49 ± 4.353.31 ± 2.8796.17<0.001
    Male (n = 58)NC−6.96 ± 8.628.80 ± 6.7286.66<0.001
    DL0.22 ± 4.283.19 ± 2.8596.220.10
    Female (n = 42)NC−4.95 ± 7.056.80 ± 5.2990.36<0.001
    DL0.85 ± 4.443.46 ± 2.9096.12<0.001
    HLU (n = 44)NC−7.30 ± 8.288.71 ± 6.7986.72<0.001
    DL−0.21 ± 4.993.75 ± 3.3094.750.25
    LLU (n = 56)NC−5.18 ± 7.757.38 ± 5.7089.36<0.001
    DL1.03 ± 3.692.96 ± 2.4397.33<0.001
    • Data are mean ± SD.

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    TABLE 2

    Selected Subjects for Figures 5–7 and Their Average Errors Across 17 Segments for SPECTDL and SPECTNC

    Subject no.Figure no.DL error (%)NC error (%)Criteria for DL selection
    66 (male, HLU)5A0.10 ± 4.08−12.61 ± 10.04Smallest |error|
    54 (male, HLU)5B0.10 ± 2.26−8.73 ± 8.44Smallest |error|
    93 (male, LLU)5C−0.10 ± 2.99−6.32 ± 6.69Smallest |error|
    9 (male, LLU)6A1.10 ± 2.46−9.04 ± 7.4925th percentile |error|
    89 (male, HLL)6B1.99 ± 3.05−4.59 ± 6.8550th percentile |error|
    77 (female, HLL)6C3.15 ± 4.071.17 ± 5.2575th percentile |error|
    7 (male, LLU)7A9.20 ± 4.552.65 ± 2.63Most overestimated error
    57 (female, HLU)7B−7.21 ± 4.85−12.25 ± 7.25Most underestimated error
    • Supplemental Figure 1 provides average segmental errors of all subjects through box plots.

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Journal of Nuclear Medicine: 62 (11)
Journal of Nuclear Medicine
Vol. 62, Issue 11
November 1, 2021
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Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study
Jaewon Yang, Luyao Shi, Rui Wang, Edward J. Miller, Albert J. Sinusas, Chi Liu, Grant T. Gullberg, Youngho Seo
Journal of Nuclear Medicine Nov 2021, 62 (11) 1645-1652; DOI: 10.2967/jnumed.120.256396

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Direct Attenuation Correction Using Deep Learning for Cardiac SPECT: A Feasibility Study
Jaewon Yang, Luyao Shi, Rui Wang, Edward J. Miller, Albert J. Sinusas, Chi Liu, Grant T. Gullberg, Youngho Seo
Journal of Nuclear Medicine Nov 2021, 62 (11) 1645-1652; DOI: 10.2967/jnumed.120.256396
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Keywords

  • cardiac SPECT
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  • attenuation correction
  • deep learning
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