ArticleQuantitative imaging in medicine and surgery2026
Spatiotemporal characterization of placental hypoxia and microvascular dysfunction in a preeclampsia rat model using photoacoustic and contrast-enhanced ultrasound.
Article in Quantitative imaging in medicine and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Placental ischemia and hypoxia are central drivers of preeclampsia (PE) pathogenesis. However, conventional Doppler ultrasound primarily evaluates uterine and umbilical arterial resistance to indirectly infer placental impedance, lacking the sensitivity to directly quantify intraplacental tissue oxygenation and microvascular perfusion dynamics. This study aimed to explore the feasibility of using a multimodal imaging approach, combining photoacoustic imaging (PAI) and contrast-enhanced ultrasound (CEUS), to characterize the longitudinal spatiotemporal features of placental dysfunction in an Nω-nitro-L-arginine methyl ester (L-NAME)-induced PE rat model. Methods: Pregnant Sprague-Dawley rats were randomly assigned to control and PE groups (n=10 per group). PE was induced via daily L-NAME administration (250 mg/kg/day) from gestational day (GD) 7 to GD18. Longitudinal multimodal imaging was performed on the same cohort of rats at GD14, GD16, and GD18. PAI was utilized to quantitatively map hemoglobin oxygen saturation (sO Results: PAI revealed a physiological oxygenation gradient (labyrinth > basal) in normal placentas. In the PE group, a progressive hypoxic shift was observed, with mean sO Conclusions: This preliminary study demonstrates the feasibility of longitudinal multimodal imaging to characterize placental dysfunction. Specifically, PAI effectively captured the progressive hypoxic shift, complementing the CEUS-derived "volume-velocity dissociation", which suggests that microvascular rarefaction precedes flow velocity alterations. By integrating metabolic and perfusion metrics, this platform holds promise as a quantitative tool for preclinical monitoring and therapeutic evaluation.
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