Diffusion reconstruction with validity masks and measurement consistency for low-frame structured illumination microscopy

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Reducing the raw-frame budget of structured illumination microscopy (SIM) lowers exposure burden but weakens orientation–phase diversity and increases sensitivity to noise and model mismatch. We present acquisition-aware physics-guided diffusion for SIM (APD-SIM), which combines a protocol-specific conditional diffusion prior with validity-masked Poisson–Gaussian refinement. Separate synthetic-data checkpoints were trained for controller-defined digital micromirror device (DMD) protocols with three-frame (DMD-3F), six-frame (DMD-6F), and nine-frame (DMD-9F) acquisition modes; no experimental frame was used for training or fine-tuning. On the same 30 held-out fields, the three-, six-, and nine-frame APD-SIM reconstructions (APD-SIM-3, APD-SIM-6, and APD-SIM-9) achieved peak signal-to-noise ratio (PSNR) values of 3 1 . 5 9 5 ± 3 . 8 0 1 , 4 1 . 0 7 8 ± 2 . 8 5 8 , and 4 3 . 4 9 1 ± 2 . 0 8 5 dB, respectively. Corresponding structural similarity index (SSIM) values were 0 . 7 8 5 2 5 ± 0 . 0 8 2 6 7 , 0 . 9 6 9 4 8 ± 0 . 0 2 3 1 6 , and 0 . 9 8 9 9 8 ± 0 . 0 0 6 9 9 , and ground-truth-referenced Fourier ring correlation (FRC) curve areas were 0 . 3 4 1 0 0 8 ± 0 . 0 7 3 2 4 1 , 0 . 3 9 5 6 0 9 ± 0 . 0 6 4 7 8 0 , and 0 . 4 0 7 9 6 3 ± 0 . 0 5 7 0 7 4 . DMD-6F used one-third fewer exposures than DMD-9F. Under byte-identical DMD-6F measurements, protocol-correct masking improved final PSNR by 2 . 9 4 2 dB and SSIM by 0 . 0 2 8 3 8 ; measurement-domain refinement improved the diffusion-only estimate by 1 0 . 9 6 dB and 0 . 3 9 8 , respectively. Relative to machine-learning structured illumination microscopy retrained for six inputs (ML-SIM-6R), APD-SIM-6 showed positive pooled image-domain effects, but their direction and magnitude depended on morphology and metric. Direct measurements from two commercial fixed-cell preparations were reconstructed by the complete two-stage pipeline using frozen protocol-specific checkpoints and fixed nominal hardware forward models. The hardware results provide qualitative evidence of zero-shot simulation-to-experiment transfer rather than a calibrated quantitative benchmark.

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