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Research ArticleNeurovascular/Stroke Imaging

DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning–Based Image Quality Improvement for Vessel Wall MRI

Mona Kharaji, Gador Canton, Yin Guo, Mohamad Hosaam Mosi, Zechen Zhou, Niranjan Balu and Mahmud Mossa-Basha
American Journal of Neuroradiology January 2025, 46 (1) 49-56; DOI: https://doi.org/10.3174/ajnr.A8424
Mona Kharaji
aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington
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Gador Canton
aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington
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  • ORCID record for Gador Canton
Yin Guo
bDepartment of Bioengineering (Y.G.), University of Washington, Seattle, Washington
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Mohamad Hosaam Mosi
aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington
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Zechen Zhou
cSubtle Medical Inc (Z.Z.), Menlo Park, California
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Niranjan Balu
aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington
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Mahmud Mossa-Basha
aFrom the Department of Radiology (M.K., G.C., M.H.M., N.B., M.M.-B.), University of Washington School of Medicine, Seattle, Washington
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Abstract

BACKGROUND AND PURPOSE: Accelerated and blood-suppressed postcontrast 3D intracranial vessel wall MRI (IVW) enables high-resolution rapid scanning but is associated with low SNR. We hypothesized that a deep-learning (DL) denoising algorithm applied to accelerated, blood-suppressed postcontrast IVW can yield high-quality images with reduced artifacts and higher SNR in shorter scan times.

MATERIALS AND METHODS: Sixty-four consecutive patients underwent IVW, including conventional postcontrast 3D T1-sampling perfection with application-optimized contrasts by using different flip angle evolution (SPACE) and delay alternating with nutation for tailored excitation (DANTE) blood-suppressed and CAIPIRINHIA-accelerated (CAIPI) 3D T1-weighted TSE postcontrast sequences (DANTE-CAIPI-SPACE). DANTE-CAIPI-SPACE acquisitions were then denoised by using an unrolled deep convolutional network (DANTE-CAIPI-SPACE+DL). SPACE, DANTE-CAIPI-SPACE, and DANTE-CAIPI-SPACE+DL images were compared for overall image quality, SNR, severity of artifacts, arterial and venous suppression, and lesion assessment by using 4-point or 5-point Likert scales. Quantitative evaluation of SNR and contrast-to-noise ratio (CNR) was performed.

RESULTS: DANTE-CAIPI-SPACE+DL showed significantly reduced arterial (1 [1–1.75] versus 3 [3–4], P < .001) and venous flow artifacts (1 [1–2] versus 3 [3–4], P < .001) compared with SPACE. There was no significant difference between DANTE-CAIPI-SPACE+DL and SPACE in terms of image quality, SNR, artifact ratings, and lesion assessment. For SNR ratings, DANTE-CAIPI-SPACE+DL was significantly better compared with DANTE-CAIPI-SPACE (2 [1–2], versus 3 [2–3], P < .001). No statistically significant differences were found between DANTE-CAIPI-SPACE and DANTE-CAIPI-SPACE+DL for image quality, artifact, arterial blood and venous blood flow artifacts, and lesion assessment. Quantitative vessel wall SNR and CNR median values were significantly higher for DANTE-CAIPI-SPACE+DL (SNR: 9.71, CNR: 4.24) compared with DANTE-CAIPI-SPACE (SNR: 5.50, CNR: 2.64) (P < .001 for each), but there was no significant difference between SPACE (SNR: 10.82, CNR: 5.21) and DANTE-CAIPI-SPACE+DL.

CONCLUSIONS: DL denoised postcontrast T1-weighted DANTE-CAIPI-SPACE accelerated and blood-suppressed IVW showed improved flow suppression with a shorter scan time and equivalent qualitative and quantitative SNR measures relative to conventional postcontrast IVW. It also improved SNR metrics relative to postcontrast DANTE-CAIPI-SPACE IVW. Implementing DL denoised DANTE-CAIPI-SPACE IVW has the potential to shorten protocol time while maintaining or improving the image quality of IVW.

ABBREVIATIONS:

CAIPI
controlled aliasing in parallel imaging
CNR
contrast-to-noise ratio
DANTE
delay alternating with nutation for tailored excitation
DL
deep learning
ICC
intraclass correlation coefficient
IVW
intracranial vessel wall MRI
SOC
standard of care
SPACE
sampling perfection with application-optimized contrasts by using different flip angle evolution
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American Journal of Neuroradiology: 46 (1)
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Cite this article
Mona Kharaji, Gador Canton, Yin Guo, Mohamad Hosaam Mosi, Zechen Zhou, Niranjan Balu, Mahmud Mossa-Basha
DANTE-CAIPI Accelerated Contrast-Enhanced 3D T1: Deep Learning–Based Image Quality Improvement for Vessel Wall MRI
American Journal of Neuroradiology Jan 2025, 46 (1) 49-56; DOI: 10.3174/ajnr.A8424

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DANTE-CAIPI deep learning vessel wall MR
Mona Kharaji, Gador Canton, Yin Guo, Mohamad Hosaam Mosi, Zechen Zhou, Niranjan Balu, Mahmud Mossa-Basha
American Journal of Neuroradiology Jan 2025, 46 (1) 49-56; DOI: 10.3174/ajnr.A8424
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