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Exploring the impact of super-resolution deep learning on MR angiography image quality

  • Advanced Neuroimaging
  • Published:
Neuroradiology Aims and scope Submit manuscript

Abstract

Purpose

The aim of this study is to assess the effect of super-resolution deep learning-based reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T.

Methods

This retrospective study involved 35 patients who underwent intracranial TOF-MRA using a 3-T MRI system with SR-DLR based on k-space properties in October and November 2022. We reconstructed MRA with SR-DLR (matrix = 1008 × 1008) and MRA without SR-DLR (matrix = 336 × 336). We measured the signal-to-noise ratio (SNR), contrast, and contrast-to-noise ratio (CNR) in the basilar artery (BA) and the anterior cerebral artery (ACA) and the sharpness of the posterior cerebral artery (PCA) using the slope of the signal intensity profile curve at the half-peak points. Two radiologists evaluated image noise, artifacts, contrast, sharpness, and overall image quality of the two image types using a 4-point scale. We compared quantitative and qualitative scores between images with and without SR-DLR using the Wilcoxon signed-rank test.

Results

The SNRs, contrasts, and CNRs were all significantly higher in images with SR-DLR than those without SR-DLR (p < 0.001). The slope was significantly greater in images with SR-DLR than those without SR-DLR (p < 0.001). The qualitative scores in MRAs with SR-DLR were all significantly higher than MRAs without SR-DLR (p < 0.001).

Conclusion

SR-DLR with k-space properties can offer the benefits of increased spatial resolution without the associated drawbacks of longer scan times and reduced SNR and CNR in intracranial MRA.

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Data sharing statement

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Abbreviations

ACA:

Anterior cerebral artery

BA:

Basilar artery

CNR:

Contrast-to-noise ratio

DLR:

Deep learning-based reconstruction

FWHM:

Full width at half maximum

PCA:

Posterior cerebral artery

SNR:

Signal-to-noise ratio

SR-DLR:

Super-resolution deep learning reconstruction

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Acknowledgment

We thank Ms. Tae Hamakawa from Department of Diagnostic Radiology, Kumamoto University, Japan, for her help with the measuring in the quantitative analysis. We thank Mr. Takumi Saito from Canon Medical systems for the adjustment of SR-DLR reconstruction parameters.

Funding

No funding.

Author information

Authors and Affiliations

  1. Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan

    Masamichi Hokamura, Hiroyuki Uetani, Takeshi Nakaura, Yasunori Nagayama, Masafumi Kidoh & Toshinori Hirai

  2. Department of Central Radiology, Kumamoto University Hospital, Honjo 1-1-1, Kumamoto, 860-8556, Japan

    Kensei Matsuo & Kosuke Morita

  3. Canon Medical Systems Corporation, 70-1, Yanagi-cho, Saiwai-ku, Kawasaki-shi, Kanagawa, 212-0015, Japan

    Yuichi Yamashita

  4. Department of Neurology, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan

    Mitsuharu Ueda

  5. Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan

    Akitake Mukasa

Authors
  1. Masamichi Hokamura
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  2. Hiroyuki Uetani
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  3. Takeshi Nakaura
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Corresponding author

Correspondence to Takeshi Nakaura.

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Conflict of interest statement

Toshinori Hirai has received research support from Canon Medical Systems. Yuichi Yamahita is an employee of Canon Medical Systems. Canon Medical Systems had no control over the interpretation, writing, or publication of this work.

Ethics approval

This retrospective study was approved by the institutional review board (Kumamoto University).

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Informed consent for this retrospective study was waived by the institutional ethics committee.

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Hokamura, M., Uetani, H., Nakaura, T. et al. Exploring the impact of super-resolution deep learning on MR angiography image quality. Neuroradiology 66, 217–226 (2024). https://doi.org/10.1007/s00234-023-03271-1

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  • DOI: https://doi.org/10.1007/s00234-023-03271-1

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