The research, published in the Journal of Veterinary Internal Medicine, was conducted by Risa Yasuda, Kensuke Nakamura, Noboru Sasaki, and colleagues at the Hokkaido University Veterinary Teaching Hospital. The team enrolled forty-three client-owned dogs: fifteen healthy controls, twenty-one dogs with cardiac disease but without signs of right-sided heart failure, and seven dogs with right-sided heart failure evidenced by fluid accumulation in the abdomen. All of the affected dogs carried diagnoses of myxomatous mitral valve disease or tricuspid regurgitation, the most common acquired cardiac conditions in dogs, and many also had pulmonary hypertension. The work was designed as a prospective cross-sectional observational study, meaning every animal was examined under the same protocol at a single point in time.

Superb Microvascular Imaging, or SMI, is a novel ultrasonographic technique that can visualize blood flowing through very small vessels without the injection of contrast agents. Conventional Doppler ultrasound struggles with slow, low-velocity flow in tiny vessels because background tissue motion and low-frequency artifacts obscure the signal. SMI overcomes this by applying advanced clutter-suppression algorithms that strip away tissue noise while maintaining a high frame rate, allowing it to depict microvascular flow that Doppler methods miss. In this study, the researchers used monochrome SMI to image the arcuate arteries along the ventral surface of the left kidney in awake, unsedated dogs, capturing cine loops with a linear transducer at a frame rate of at least forty frames per second and a velocity range of plus or minus 2.3 centimeters per second.

To turn those images into numbers, the team took a quantitative approach borrowed from earlier contrast-enhanced ultrasound work. For each dog, three cine loops were recorded, and the frame with maximal blood flow signal was selected from each. A polygonal region of interest was drawn over the ventral renal cortex, covering at least a quarter of the ventral interlobular vessel region. The images were converted to 8-bit grayscale values ranging from zero to 255, and the mean grayscale intensity of all pixels within the region was calculated. Averaging three measurements produced the key variable, SMI signal intensity, which served as an index of renal microvascular perfusion: brighter images meant more blood flowing through the cortical microvasculature.

The results were striking. Healthy control dogs had a median SMI signal intensity of 41.3, with a 95 percent confidence interval of 39.1 to 43.8. Dogs with cardiac disease but no signs of right-sided heart failure showed a median of 21.6, and dogs with right-sided heart failure showed 18.6. Both cardiac groups were significantly lower than controls, with P values below .001, and notably, the signal was already diminished in dogs that had not yet developed overt heart failure. Representative images showed a clear, progressive thinning of microvascular signals across the cortex from control to non-RHF to RHF animals, the kind of visual demonstration that makes the physiological story instantly legible.

To understand what was driving the perfusion loss, the researchers measured two hemodynamic indices. The first, the left ventricular outflow tract velocity-time integral obtained by pulsed-wave Doppler echocardiography, served as a surrogate for cardiac output, essentially how much blood the heart ejects with each beat. The second, the short-to-long axis ratio of the caudal vena cava measured at its minimal inspiratory diameter, served as an indicator of venous congestion: a rounder, distended vena cava implies elevated venous pressure backing up from the right side of the heart. The vena cava ratios rose from 0.28 in controls to 0.48 in non-RHF dogs and 0.79 in RHF dogs, while the velocity-time integral fell from 13.0 centimeters in controls to roughly 9 in the cardiac groups.

Correlation analysis revealed that SMI signal intensity was negatively correlated with the vena cava ratio, with a Spearman coefficient of minus 0.59 and a P value below .001, and positively correlated with the velocity-time integral at a coefficient of 0.43 and P equal to .007. But the decisive result came from a full multivariable regression model that adjusted for cardiac output, body weight, age, and serum creatinine concentration. In that model, only the vena cava ratio remained independently associated with renal microvascular perfusion, with a regression coefficient of minus 21.82 and P equal to .001. The model explained a moderate share of the variance, with an adjusted R-squared of 0.38, and diagnostics confirmed no serious multicollinearity or assumption violations. In plain terms, congestion, not the pumping strength of the heart, was the variable that best explained the loss of renal microvascular signal.

The authors place this finding in the context of what is already known about the cardiorenal syndrome, the bidirectional dysfunction of heart and kidney. Elevated right atrial pressure transmits back through the caudal vena cava into the renal veins, and experimental work has shown that raised renal venous pressure causes medullary edema and compression of the peritubular capillaries, choking off renal blood flow. Traditional clinical tools capture this poorly. Glomerular filtration rate requires serial blood sampling over hours; serum creatinine and symmetric dimethylarginine rise only after substantial function has already been lost; central venous catheterization is invasive and reflects systemic rather than organ-level hemodynamics. Intrarenal venous flow Doppler, a pulsed-wave technique, has been used to assess renal congestion, but more than half of human heart failure patients show no alterations in those waveforms, and similar limitations are reported in dogs. SMI appears to detect changes earlier because it samples the smaller interlobular vessels distributed throughout the renal parenchyma rather than the larger interlobar veins.

The study also suggests practical advantages over its closest rival, contrast-enhanced ultrasonography, which has previously demonstrated reduced renal enhancement in dogs with preclinical mitral valve disease. CEUS requires injection of a contrast agent and continuous imaging of the same region over a relatively long period, whereas an SMI image can be acquired in a few seconds of viewing the kidney in SMI mode, and operators competent in routine abdominal ultrasound can learn to obtain it without extensive additional training. Doppler techniques, meanwhile, are angle-dependent and demand careful probe positioning. In the present study, a subset of dogs showed markedly low SMI signal even while both the vena cava ratio and the velocity-time integral remained within normal ranges, hinting that local factors, possibly neurohumoral mechanisms such as activation of the renin-angiotensin-aldosterone system, also shape renal microvascular flow.

The authors are careful to acknowledge the limitations inherent in a clinical study. Dogs with elevated creatinine were excluded to minimize confounding by chronic kidney disease, but that criterion may have excluded animals with established cardiorenal syndrome. Actual glomerular filtration rate and renal blood flow were not measured, central venous pressure and cardiac output were estimated echocardiographically rather than invasively, and factors such as age, sex, hydration, and medication could not be fully standardized. The SMI operator was not blinded to clinical status. Even so, the study is the first to demonstrate reduced renal microvascular perfusion in clinical canine cardiac patients using a noninvasive, contrast-free technique, and to tie that reduction independently to venous congestion. The researchers propose that SMI could become a simple, minimally invasive tool for detecting early renal hemodynamic compromise in dogs with heart disease, and future work incorporating glomerular filtration rate measurements and neurohumoral markers should clarify how early and how reliably that detection can occur.

Subject of Research: Assessment of renal microvascular perfusion in dogs with cardiac disease using Superb Microvascular Imaging

Article Title: Evaluation of renal microvascular perfusion in dogs with cardiac disease using Superb Microvascular Imaging

Article References: Yasuda, R., Nakamura, K., Sasaki, N., Yokoyama, N., Sasaoka, K., Sugawara-Suda, M., Kawamoto, S., Shiohara, N., Kawakami, Y., Sato, K., & Takiguchi, M. (2026). Evaluation of renal microvascular perfusion in dogs with cardiac disease using Superb Microvascular Imaging. Journal of Veterinary Internal Medicine, 40(5), Article aalag198. https://doi.org/10.1093/jvimsj/aalag198

Image Credits: AI Generated

DOI: 10.1093/jvimsj/aalag198

Keywords: dogs, cardiac disease, renal perfusion, Superb Microvascular Imaging, venous congestion, cardiorenal syndrome, ultrasonography, echocardiography, myxomatous mitral valve disease, pulmonary hypertension, right-sided heart failure, caudal vena cava

Axillary lymph nodes serve as pivotal anatomical structures in breast cancer staging and management, often dictating the therapeutic pathway and prognosis. Traditional ultrasound methodologies, while valuable, have faced limitations in accurately characterizing lymph node vascular supply. The advent of SMI addresses these challenges by enabling refined visualization of microvascular patterns without the drawbacks of contrast agents or invasive procedures. By capturing both semi-quantitative features, such as the number and distribution of vascular branches, and qualitative characteristics including the appearance of these vessels, researchers have gained unprecedented clarity in discerning pathological processes.

The technical prowess of SMI lies in its advanced Doppler algorithm, which isolates and depicts low-velocity blood flow within microvascular networks, overcoming noise interference seen in conventional power Doppler ultrasound (PDUS). This enhanced sensitivity facilitates a detailed evaluation of subtle vascular anomalies that correlate strongly with malignant transformation. In the study, 108 lymph nodes from 102 patients were meticulously analyzed, with post-imaging confirmation achieved through histopathology or rigorous clinical follow-up, thereby ensuring diagnostic accuracy and reliability in the findings.

A central aspect of the investigation was the diagnostic performance comparison of various vascular feature combinations observed through SMI. The synergy between distribution and appearance of vessels emerged as the most potent predictor of malignancy, achieving an area under the curve (AUC) of 0.776 with near 79% accuracy in receiver operating characteristic (ROC) analyses. This dual-parameter evaluation surpasses the capabilities of singular vascular descriptors alone and underscores the value of integrating multidimensional imaging data for clinical decision-making.

Moreover, the study illuminated the robustness of SMI through interobserver agreement assessments conducted by seasoned radiologists with varying experience levels. The substantial concordance observed in interpreting SMI images exempts A from concerns regarding operator dependency, assuring reproducibility and clinical applicability across different healthcare settings. This facet is especially critical when transitioning novel imaging technologies from research domains into widespread diagnostic practice.

Notably, the diagnostic prowess of SMI was accentuated among patients with confirmed primary breast cancer. Here, the accuracy soared to an impressive 92.76%, coupled with an elevated AUC of 0.855, signaling the method’s exceptional capability to correctly stratify lymph node pathology in this high-risk subgroup. This level of precision drastically benefits clinicians by minimizing unnecessary invasive procedures, thereby sparing patients undue morbidity while optimizing treatment planning.

SMI’s non-invasive nature and superior visualization not only elevate diagnostic confidence but also promise enhanced patient comfort and streamlined clinical workflows. Unlike contrast-enhanced imaging methods, SMI obviates the risks and costs associated with contrast administration, making it a patient-friendly alternative readily integrable into routine ultrasound examinations. This technological advancement aligns with contemporary trends emphasizing precision medicine and tailored therapeutic interventions.

Underlying the success of SMI is its ability to elucidate the intricate microvascular networks within lymph nodes, revealing heterogeneity indicative of pathological neovascularization. Malignant nodes characteristically exhibit chaotic, irregular vascular patterns, in stark contrast to the organized vasculature of benign nodes. By capturing these nuanced differences, SMI operates as a sensitive biomarker for tumor-induced angiogenesis, offering insights that extend beyond morphology into functional tissue characterization.

This pioneering research sets a precedent for future multicenter trials and integration of SMI with other imaging modalities such as elastography and contrast-enhanced ultrasound. Combining these complementary technologies could herald a new epoch in non-invasive lymph node assessment, further refining diagnostic algorithms and potentially enabling real-time intraoperative evaluations. The capacity to confidently differentiate lymph node status preoperatively has significant ramifications for surgical planning and patient prognosis.

The implications of adopting SMI in clinical breast cancer pathways are transformative. Enhanced diagnostic accuracy leads to better risk stratification, individualized treatment regimens, and potentially improved survival rates. By reducing false negatives and false positives, healthcare systems can allocate resources more effectively, decreasing the burden of overtreatment or delayed intervention. These systemic benefits reinforce the imperative to embrace cutting-edge imaging innovations that augment clinical precision.

Beyond breast cancer, the application of superb microvascular imaging extends to myriad pathological conditions where microvascular remodeling is a hallmark, including inflammatory diseases, vascular anomalies, and other malignancies. The successful demonstration of SMI in axillary lymph node evaluation thus paves the way for broader adoption across diverse medical disciplines, fostering interdisciplinary advances in diagnostic imaging.

The meticulous methodology employed in this research, featuring blinded independent reviews by experts and the largest cohort of suspicious ALNs studied using SMI to date, consolidates its scientific rigor. Such comprehensive analyses diminish biases and enhance the reproducibility of conclusions, thereby strengthening the evidence base supporting SMI as a next-generation diagnostic modality. This study embodies the confluence of technological innovation and clinical acumen driving progress in oncology diagnostics.

In summary, the adoption of superb microvascular imaging marks a significant milestone in breast cancer evaluation, enabling precise differentiation of suspicious axillary lymph nodes through detailed vascular characterization. The fusion of semi-quantitative and qualitative assessments propels diagnostic accuracy to unprecedented levels, fostering improved clinical outcomes while streamlining patient care pathways. As this technology continues to be validated and refined, it holds promise to revolutionize imaging protocols, exemplifying the future of non-invasive cancer diagnostics.

The research team’s contribution heralds a new era where vascular imaging details are integrated seamlessly into routine practice, empowering clinicians with actionable insights. By transcending the limitations of conventional ultrasound modalities, SMI stands poised to become a cornerstone technique in breast cancer diagnosis and beyond. Ongoing innovation and clinical evaluation will undoubtedly expand its capabilities, ultimately benefiting patients worldwide through earlier detection and personalized treatment strategies.

Subject of Research: Diagnostic evaluation of axillary lymph nodes in primary breast cancer using superb microvascular imaging (SMI).

Article Title: Diagnostic value of super microvascular imaging in differentiating axillary lymph nodes: semi-quantitative and qualitative approach in primary breast cancer and suspicious axillary nodes.

Article References:
Tokur, O., Aydin, S., Kilinc, F. et al. Diagnostic value of super microvascular imaging in differentiating axillary lymph nodes: semi-quantitative and qualitative approach in primary breast cancer and suspicious axillary nodes. BMC Cancer 25, 1705 (2025). https://doi.org/10.1186/s12885-025-14936-w

Image Credits: Scienmag.com

DOI: 10.1186/s12885-025-14936-w (Published 04 November 2025)