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. 2024 Aug 17;10(16):e36515.
doi: 10.1016/j.heliyon.2024.e36515. eCollection 2024 Aug 30.

Ultrasound super-resolution imaging for the assessment of renal allograft dysfunction: A pilot study

Yugang Hu  1 , Yumeng Lei  2 , Meihui Yu  2 , Yao Zhang  1 , Xingyue Huang  1 , Ge Zhang  2 , Qing Deng  1
Affiliations

Ultrasound super-resolution imaging for the assessment of renal allograft dysfunction: A pilot study

Yugang Hu et al. Heliyon. .

Abstract

Background: The purpose of this study was to examine the feasibility and practical application of ultrasound (US) super-resolution imaging (SRI) in evaluating microvasculature and measuring renal allograft function.

Methods: Sixteen consecutive patients who received kidney transplants were prospectively enrolled. The patients were assigned as: normal allograft function (n = 6), and allograft malfunction (n = 10). Localizing each potential contrast signal resulted in super-resolution images (SRI). SRI was utilized to assess micro-vessel density (MVD) and microvascular flow rate, whereas contrast-enhanced (CE) US images were statistically processed to get the time to peak (TTP) and peak intensity. Logistic regression was utilized to evaluate their relationship.

Results: US SRI may be utilized effectively on allografts to show microvasculature with significantly higher resolution than typical color Doppler flow and CEUS pictures. In the multivariate analysis, MVD and TTP were significant US markers of renal allograft failure (p = 0.031 and p = 0.045). The combination of MVD and TTP produced an AUC of 0.783 (p < 0.05) for allograft dysfunction.

Conclusions: SRI can accurately portray the microvasculature of renal allografts, while MVD and TTP are appropriate US markers for assessing renal allograft failure.

Keywords: Kidney transplantation; Renal allograft dysfunction; Super-resolution imaging; Ultrasound.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Ultrasound images of allograft dysfunction and normal allograft respectively. The color-doppler flow image (A), super-resolution image (B), super-resolved velocity image (C), and the zoomed-in sections indicated as the white box of the color-doppler flow image (D), super-resolution image (E), super-resolved velocity image (F) for allograft dysfunction, respectively. The color-doppler flow image (G), super-resolution image (H), super-resolved velocity image (I), and the zoomed-in sections indicated as the white box of the color-doppler flow image (J), super-resolution image (K), super-resolved velocity image (L) for normal allograft, respectively.
Fig. 2
Fig. 2
Representative images of an allograft dysfunction patient. B-mode image (A), contrast-enhanced ultrasound image (B), super-resolution image (C), super-resolved velocity image (D), and the zoomed-in regions indicated as the white box of the B-mode image (E), contrast-enhanced ultrasound image (F), super-resolution image (G), super-resolved velocity image (H). Yellow lines highlight the resolution improvement among the images.
Fig. 3
Fig. 3
Representative images of a normal allograft patient. B-mode image (A), contrast-enhanced ultrasound image (B), super-resolution image (C), super-resolved velocity image (D), and the zoomed-in regions indicated as the red box of the B-mode image (E), contrast-enhanced ultrasound image (F), super-resolution image (G), super-resolved velocity image (H). Yellow lines highlight the resolution improvement among the images.
Fig. 4
Fig. 4
Graph shows the receiver operating characteristic curve of the combination of micro-vessel density (MVD) and time to peak (TTP) for allograft dysfunction.
See this image and copyright information in PMC

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