In vivo visualization of the microvasculature is feasible with super-resolution ultrasound imaging (SRI), but the method needs more affirmative data before clinical use. The kidneys have a rich vasculature, and microvascular dysfunction decreases the kidney function. Therefore, detection of subtle renal microvascular changes could benefit patients with renal disease. We hypothesized that our SRI setup can visualize the microvascular network of two healthy rat kidneys and subsequently demonstrate microvascular flow changes immediately after ischemiareperfusion. The left kidney of two male Sprague-Dawley rats was scanned during laparotomy using a customized BK5000 scanner, an X18L5s transducer, and interleaved contrast and B-mode sequences with focused beam transmission. Images were acquired over 10 min using SonoVue (1:10) as the contrast agent. After a baseline scan, one rat had the renal vein clamped, while the other rat had the renal artery clamped, both for 45 min. The kidneys were rescanned immediately after clamp release and after 60 min of reperfusion. Motion correction was applied before microbubble (MB) detection. The characteristic renal microvascular structure was visualized with anatomical distinction between the dense cortical vascular network and the straight vessels of the medulla. Immediately after vein clamp release, almost no MBs perfused the medullary vessels. After 60 min of reperfusion, MBs refilled the renal vascular bed, but with a slower velocity compared with the baseline scan. The results after artery clamping were subtler, with more MBs perfusing the medulla immediately after clamp release compared with the vein clamping. The results imply that this SRI setup can evaluate different stages of ischemic kidney disease in rats as it can visualize the entire renal vascular bed and differentiate the pattern of reperfusion in two types of ischemic injury.

Super Resolution (SR) imaging has the potential of visualizing the micro-vasculature down to the 10 µm level, but motion induced by breathing, heartbeats, and muscle contractions are often significantly above this level. The paper therefore introduces a method for estimating tissue motion and compensating for this. The processing pipeline is described and validated using Field II simulations of an artificial kidney. In vivo measurements were conducted using a modified bk5000 research scanner (BK Medical, Herlev, Denmark) with a BK 9009 linear array probe employing a pulse amplitude modulation scheme. The left kidney of ten Sprague-Dawley rats were scanned during open laparotomy. A 1:10 diluted SonoVue contrast agent (Bracco, Milan, Italy) was injected through a jugular vein catheter at 100 µl/min. Motion was estimated using speckle tracking and decomposed into contributions from the heartbeats, breathing and residual motion. The estimated peak motions and their precisions were:

Super-resolution ultrasound imaging (SRUS) enables in vivo microvascular imaging of deeper-lying tissues and organs, such as the kidneys or liver. The technique allows new insights into microvascular anatomy and physiology and the development of disease-related microvascular abnormalities. However, the microvascular anatomy is intricate and challenging to depict with the currently available imaging techniques, and validation of the microvascular structures of deeper-lying organs obtained with SRUS remains difficult. Our study aimed to directly compare the vascular anatomy in two in vivo 2D SRUS images of a Sprague–Dawley rat kidney with ex vivo μCT of the same kidney. Co-registering the SRUS images to the μCT volume revealed visually very similar vascular features of vessels ranging from ~ 100 to 1300 μm in diameter and illustrated a high level of vessel branching complexity captured in the 2D SRUS images. Additionally, it was shown that it is difficult to use μCT data of a whole rat kidney specimen to validate the super-resolution capability of our ultrasound scans, i.e., validating the actual microvasculature of the rat kidney. Lastly, by comparing the two imaging modalities, fundamental challenges for 2D SRUS were demonstrated, including the complexity of projecting a 3D vessel network into 2D. These challenges should be considered when interpreting clinical or preclinical SRUS data in future studies.

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