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Photoacoustic imaging of kidney fibrosis for assessing pretransplant organ quality
Eno Hysi, Xiaolin He, Muhannad N. Fadhel, Tianzhou Zhang, Adriana Krizova, Michael Ordon, Monica Farcas, Kenneth T. Pace, Victoria Mintsopoulos, Warren L. Lee, Michael C. Kolios, Darren A. Yuen
Eno Hysi, Xiaolin He, Muhannad N. Fadhel, Tianzhou Zhang, Adriana Krizova, Michael Ordon, Monica Farcas, Kenneth T. Pace, Victoria Mintsopoulos, Warren L. Lee, Michael C. Kolios, Darren A. Yuen
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Resource and Technical Advance Nephrology

Photoacoustic imaging of kidney fibrosis for assessing pretransplant organ quality

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Abstract

Roughly 10% of the world’s population has chronic kidney disease (CKD). In its advanced stages, CKD greatly increases the risk of hospitalization and death. Although kidney transplantation has revolutionized the care of advanced CKD, clinicians have limited ways of assessing donor kidney quality. Thus, optimal donor kidney–recipient matching cannot be performed, meaning that some patients receive damaged kidneys that function poorly. Fibrosis is a form of chronic damage often present in donor kidneys, and it is an important predictor of future renal function. Currently, no safe, easy-to-perform technique exists that accurately quantifies renal fibrosis. We describe a potentially novel photoacoustic (PA) imaging technique that directly images collagen, the principal component of fibrotic tissue. PA imaging noninvasively quantifies whole kidney fibrotic burden in mice, and cortical fibrosis in pig and human kidneys, with outstanding accuracy and speed. Remarkably, 3-dimensional PA imaging exhibited sufficiently high resolution to capture intrarenal variations in collagen content. We further show that PA imaging can be performed in a setting that mimics human kidney transplantation, suggesting the potential for rapid clinical translation. Taken together, our data suggest that PA collagen imaging is a major advance in fibrosis quantification that could have widespread preclinical and clinical impact.

Authors

Eno Hysi, Xiaolin He, Muhannad N. Fadhel, Tianzhou Zhang, Adriana Krizova, Michael Ordon, Monica Farcas, Kenneth T. Pace, Victoria Mintsopoulos, Warren L. Lee, Michael C. Kolios, Darren A. Yuen

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Figure 2

Development and validation of a spectral unmixing algorithm to identify collagen content using photoacoustic (PA) imaging.

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Development and validation of a spectral unmixing algorithm to identify ...
(A) Photoacoustic (PA) imaging setup for blood-collagen phantom gels. (B) Representative coregistered ultrasound (US) and PA images (at 850 nm) of a blood-collagen phantom. Scale bar: 10 mm (applies to both images). (C) Validation of our spectral unmixing algorithm to quantify collagen. Data represent mean ± SD, with n = 60 measurements per phantom (n = 4). The r2 denotes the goodness of the linear fit, derived from univariate linear regression. dB, decibels.

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