Noncontact diffuse optical imaging of blood flow and oxygenation distributions in reconstructive rat skin flaps

Perioperative identification of ischemic-hypoxic tissue is essential for reducing the risk of mastectomy skin flap necrosis (MSFN), a major complication following mastectomy. Implant-based breast reconstruction is jeopardized in the presence of MSFN. However, existing technologies are limited by depth insensitivity, dye-related risks, the need for contact sensors, and an inability to simultaneously assess both blood flow and oxygenation. A multi-wavelength speckle contrast diffuse correlation tomography (MW-scDCT) system was developed for noncontact, dye-free, and depth-sensitive imaging of tissue hemodynamics in a rat model incorporating four distinct flap scenarios: sham (SH), implant (IM), half necrosis (HN), and full necrosis (FN), representing varying degrees of tissue viability. MW-scDCT measured longitudinal changes in relative blood flow index ( <b><i>rBFI</i></b> ) and oxy- and deoxy-hemoglobin concentrations ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Δ</mml:mi> </mml:mrow> <mml:mo>[</mml:mo> <mml:mrow> <mml:msub><mml:mrow><mml:mi>H</mml:mi> <mml:mi>b</mml:mi> <mml:mi>O</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> <mml:mo>]</mml:mo></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Δ</mml:mi> </mml:mrow> <mml:mo>[</mml:mo> <mml:mrow><mml:mrow><mml:mi>H</mml:mi> <mml:mi>b</mml:mi></mml:mrow> </mml:mrow> <mml:mo>]</mml:mo></mml:math> ) over seven days. Repeated measures analysis of variance and quadratic discriminant analysis were employed for statistical analysis. MW-scDCT enabled longitudinal imaging of tissue blood flow and oxygenation distributions, revealing significant differences across flap types and over time. In terms of discriminative power, integrating <b><i>rBFI</i></b> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Δ</mml:mi> </mml:mrow> <mml:mo>[</mml:mo> <mml:mrow> <mml:msub><mml:mrow><mml:mi>H</mml:mi> <mml:mi>b</mml:mi> <mml:mi>O</mml:mi></mml:mrow> <mml:mn>2</mml:mn></mml:msub> </mml:mrow> <mml:mo>]</mml:mo></mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>Δ</mml:mi> </mml:mrow> <mml:mo>[</mml:mo> <mml:mrow><mml:mrow><mml:mi>H</mml:mi> <mml:mi>b</mml:mi></mml:mrow> </mml:mrow> <mml:mo>]</mml:mo></mml:math> into a multivariable classification model substantially improved accuracy, achieving 80-95% compared to 40-70% using individual parameters. Binary classification of necrotic (FN + HN) versus non-necrotic (SH + IM) flaps showed up to 50% improvement in accuracy gains. MW-scDCT effectively distinguished necrotic from viable flaps and provided critical early postoperative insights into MSFN. These findings support its potential clinical utility as a perioperative monitoring tool to guide decision-making, reduce flap failure risk, and improve reconstructive outcomes.