Ajay Manicka

Intravascular optical coherence tomography (OCT) images entangle tissue attenuation with the imaging system's own physics, so attenuation has been estimated with simplified closed-form inversions or with learned inverses tied to a specific scanner. DiffOCT writes the Beer-Lambert attenuation model as a differentiable forward model and runs gradient descent on a single B-scan to recover attenuation per pixel, with no training data or segmentation. Across 5,120 inversions on five lipid phantoms, recovered values rose monotonically with scatterer concentration and matched curve-fitting slopes; the same procedure ran unmodified on 29,269 clinical pullback frames from 128 patients, though the clinical data show geometric transfer only, not calibrated absolute values.

Intravascular optical coherence tomography (OCT) images entangle tissue attenuation with the imaging system's own physics, so attenuation has been estimated with simplified closed-form inversions or with learned inverses tied to a specific scanner. DiffOCT writes the Beer-Lambert attenuation model as a differentiable forward model and runs gradient descent on a single B-scan to recover attenuation per pixel, with no training data or segmentation. Across 5,120 inversions on five lipid phantoms, recovered values rose monotonically with scatterer concentration and matched curve-fitting slopes; the same procedure ran unmodified on 29,269 clinical pullback frames from 128 patients, though the clinical data show geometric transfer only, not calibrated absolute values.