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Research ArticleNeurovascular/Stroke Imaging

High-Resolution Head CTA: A Prospective Patient Study Comparing Image Quality of Photon-Counting Detector CT and Energy-Integrating Detector CT

Felix E. Diehn, Zhongxing Zhou, Jamison E. Thorne, Norbert G. Campeau, Alex A. Nagelschneider, Laurence J. Eckel, John C. Benson, Ajay A. Madhavan, Girish Bathla, Vance T. Lehman, Nathan R. Huber, Francis Baffour, Joel G. Fletcher, Cynthia H. McCollough and Lifeng Yu
American Journal of Neuroradiology October 2024, 45 (10) 1441-1449; DOI: https://doi.org/10.3174/ajnr.A8342
Felix E. Diehn
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Zhongxing Zhou
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Jamison E. Thorne
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Norbert G. Campeau
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Alex A. Nagelschneider
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Laurence J. Eckel
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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John C. Benson
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Ajay A. Madhavan
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Girish Bathla
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Vance T. Lehman
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Nathan R. Huber
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Francis Baffour
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Joel G. Fletcher
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Cynthia H. McCollough
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Lifeng Yu
aFrom the Department of Radiology, Mayo Clinic, Rochester, Minnesota
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Figures

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  • FIG 1.
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    FIG 1.

    Data preparation and training mechanism of a dedicated ultra-high resolution convolutional neural network (UHR-CNN) for denoising high resolution images from PCD-CT. The model used a modified 6-layer residual U-Net with 7 slices as input. The high-quality images were from iterative reconstruction (IR) images with a thick section thickness and the low-quality images were generated by adding noise patches from subtraction of filtered back-projection (FBP) and IR images followed by spatial decoupling.

  • FIG 2.
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    FIG 2.

    Reader evaluation, PCD-CT versus EID-CT. Horizontal percentage stacked bar chart plots demonstrate reader 1 and 2 (R1, R2) scores for PCD-SR versus EID-SR (A) and PCD-UHR-CNN versus EID-SR (B). Bars are split into segments and the length of each bar is 100%. Segments from left to right: significantly superior (+2), somewhat superior (+1), equal quality (0), somewhat inferior (−1), significantly inferior (−2).

  • FIG 3.
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    FIG 3.

    Data plot of diagnostic confidence of arterial findings. Grouped boxplot figure highlighting PCD-SR versus EID-SR and PCD-UHR-CNN versus EID-SR for diagnostic confidence scores of each reader. The bottom, top, and middle lines of each box indicate the 25th percentiles, 75th percentiles, and sample median of the data. Samples whose notches do not overlap are statistically significantly different at the 5% significance level. (Matlab 2021).

  • FIG 4.
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    FIG 4.

    Iodine contrast, noise, and CNR quantitative evaluation. Grouped boxplots for (A) iodine contrast, (B) noise, and (C) iodine CNR results measured in the bilateral distal ICA and midcervical ICA for EID-SR, PCD-SR, and PCD-UHR-CNN. The bottom, top, and middle lines of each box indicate the 25th percentiles, 75th percentiles, and sample median of the data. Samples whose notches do not overlap are significantly different at the 5% significance level. (Matlab 2021).

  • FIG 5.
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    FIG 5.

    Improved visualization of distal intracranial arterial branches on PCD-CT head CTA compared with EID-CT. Thin section oblique axial head CTA images highlighting the right M3 segment (arrows), by using EID-SR with 0.6-mm section thickness (A) and PCD-UHR-CNN CT with 0.2-mm section thickness (B) techniques. This distal intracranial arterial segment is best visualized by using PCD-CT rather than EID-CT, particularly with PCD-UHR-CNN–but also PCD-SR CT (the latter not shown). The right M3 segment is indeed difficult to visualize on EID-CT compared with PCD-CT. The insets demonstrate the improved visualization of the M3 segment (arrows) in the oblique sagittal plane.

  • FIG 6.
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    FIG 6.

    Improved confidence for diagnosing infundibula on PCD-CT head CTA compared with EID-CT. Thin section oblique axial head CTA images, by using EID-SR with 0.6-mm section thickness (A) and PCD-UHR-CNN CT with 0.2-mm section thickness (B) techniques. The diagnostic confidence for diagnosing this infundibulum rather than possible aneurysm improves with PCD-SR matched to EID technique (not shown) and even more so with PCD-UHR-CNN CT at the thinnest section thickness. An ∼2-mm left supraclinoid ICA outpouching (infundibulum or aneurysm) is seen in an oblique axial plane on EID-CT (solid arrow in A), but better delineated on PCD-CT (arrow in B). The anterior choroidal artery arising from the apex of the outpouching is much better visualized on PCD-CT (arrowhead in B) than EID-CT (dashed arrow in A), thereby confidently diagnosing an infundibulum rather than an aneurysm. The insets demonstrate the improved visualization of the anterior choroidal artery arising from the apex of the outpouching in the oblique sagittal plane.

  • FIG 7.
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    FIG 7.

    Improved characterization of arterial pathology on PCD-CT head CTA compared with EID-CT. Thin section oblique axial head CTA images by using EID-SR with 0.6-mm section thickness (A) and PCD-UHR-CNN CT with 0.2-mm section thickness (B) techniques. The diagnostic confidence for arterial pathology improves with PCD-SR matched to EID technique (not shown) and even more so with PCD-UHR-CNN CT at the thinnest section thickness. A right P2 segment stenosis has a relatively high-grade appearance on EID-CT (arrow in A). On PCD-CT, the stenosis is better demonstrated (arrow in B). The PCD-CT image depicts the stenosis as only mild to at most moderate in degree. The insets demonstrate the improved visualization of the right P2 stenosis in the oblique sagittal plane.

Tables

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    Table 1:

    PCD-CT versus EID-CT scan and reconstruction parameters

    ParameterEIDPCD
    Scanner modelForceAlpha
    kV100/Sn150 kV120 kV (manual kV)
    QRM/Effective mAs/IQ-levelHead/Neck CTA: 360/225 (AEC on), Head CTA: 280/175 (AEC off)230 (CAREkeV IQ Level)
    Collimation (mm)192 × 0.6120 × 0.2
    CTDIvol (mGy)37.1 ± 4.736.1 ± 4.0
    Section/Increment (mm)0.6/0.4 (mixing ratio 0.5)0.6/0.4; 0.2/0.2
    KernelQr54Qr56; Qr89
    Matrix10241024
    Iterative reconstruction and noise reductionADMIRE-3Quantum iterative reconstruction Strength 3; UHR-CNN
    Contrast injectionOminipaque 350, 100 mL at 4 mL/s, followed by 35 mL saline at 4 mL/sOminipaque 350, 100 mL at 4 mL/s, followed by 35 mL saline at 4 mL/s
    • Note:—IQ indicates image quality (a PCD setting); AEC, automatic exposure control.

    • View popup
    Table 2:

    Overall image quality and diagnostic confidence of arterial findings, PCD-CT versus EID-CT

    Session 1: PCD-SR vs EID-SRPCD-SR (1–5)EID-SR (1–5)P ValueB vs. A (−2 to 2)
    Reader 1
     Overall image quality4.0 ± 0.03.0 ± 0.01.0 ± 0.0
     Diagnostic confidence (0–100)90.4 ± 9.174.2 ± 15.9<.0010.8 ± 0.8
    Reader 2    
     Overall image quality4.9 ± 0.43.5 ± 0.5<.0011.0 ± 0.0
     Diagnostic confidence (0–100)86.4 ± 8.870.4 ± 7.9<.0010.5 ± 0.8
    Reader 1 & 2 combined
     Overall image quality4.5 ± 0.53.3 ± 0.4<.0011.0 ± 0.0
     Diagnostic confidence (0–100)88.4 ± 9.172.3 ± 12.6<.0010.7 ± 0.8
    Session 2: PCD-UHR-CNN vs EID-SRPCD-UHR-CNN (1–5)EID-SR (1–5)P valueB vs. A (−2 to 2)
    Reader 1
     Overall image quality4.0 ± 0.03.0 ± 0.01.0 ± 0.0
     Diagnostic confidence (0–100)93 ± 5.878.2 ± 9.3<.0010.6 ± 1.4
    Reader 2    
     Overall image quality4.9 ± 0.33.0 ± 0.0<.0011.9 ± 0.3
     Diagnostic confidence (0–100)88.6 ± 5.970.4 ± 5<.0011.0 ± 1.5
    Reader 1 & 2 combined    
     Overall image quality4.5 ± 0.53.0 ± 0.0<.0011.5 ± 0.5
     Diagnostic confidence (0–100)90.8 ± 6.274.3 ± 8.4<.0010.8 ± 1.4
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American Journal of Neuroradiology: 45 (10)
American Journal of Neuroradiology
Vol. 45, Issue 10
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Cite this article
Felix E. Diehn, Zhongxing Zhou, Jamison E. Thorne, Norbert G. Campeau, Alex A. Nagelschneider, Laurence J. Eckel, John C. Benson, Ajay A. Madhavan, Girish Bathla, Vance T. Lehman, Nathan R. Huber, Francis Baffour, Joel G. Fletcher, Cynthia H. McCollough, Lifeng Yu
High-Resolution Head CTA: A Prospective Patient Study Comparing Image Quality of Photon-Counting Detector CT and Energy-Integrating Detector CT
American Journal of Neuroradiology Oct 2024, 45 (10) 1441-1449; DOI: 10.3174/ajnr.A8342

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High-Res Head CTA: Photon vs. Energy CT
Felix E. Diehn, Zhongxing Zhou, Jamison E. Thorne, Norbert G. Campeau, Alex A. Nagelschneider, Laurence J. Eckel, John C. Benson, Ajay A. Madhavan, Girish Bathla, Vance T. Lehman, Nathan R. Huber, Francis Baffour, Joel G. Fletcher, Cynthia H. McCollough, Lifeng Yu
American Journal of Neuroradiology Oct 2024, 45 (10) 1441-1449; DOI: 10.3174/ajnr.A8342
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