ReCo-Diff: residual-conditioned deterministic sampling for cold diffusion in sparse-view CT.
Authors
Affiliations (2)
Affiliations (2)
- National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Korea (the Republic of).
- Department of Computer Engineering, Korea National University of Transportation, 50, Daehak-ro, Daesowon-myeon, Chungju-si, 27469, Korea (the Republic of).
Abstract
Sparse-view computed tomography (CT) reduces radiation dose and
acquisition time but introduces structured streak artifacts that make reconstruction
severely ill-posed. Cold-diffusion approaches can model such deterministic degradation
processes, but existing sampling strategies may rely on scalar similarity criteria and
predefined thresholds to trigger sample-adaptive resets of the degradation-level trajectory.
We aim to replace this threshold-based sampling control with spatially resolved residual
conditioning. We propose ReCo-Diff, a residual-conditioned cold-diffusion
framework for sparse-view CT reconstruction. At each reverse step, a zero-residual
baseline prediction is re-degraded using the CT degradation operator, and the resulting
signed image-domain re-degradation discrepancy is used as a spatial conditioning map for
a second restoration pass. ReCo-Diff does not require an adaptive reset during
residual-conditioned sampling and instead follows a predetermined degradation schedule.
A one-time level transition is additionally used for highly sparse inputs. The method was
evaluated on the AAPM Low-Dose CT dataset under 18-, 36-, and 72-view settings using a
patient-wise split with one held-out test volume. ReCo-Diff achieved
PSNR values of 38.54, 42.57, and 46.98 dB at 18, 36, and 72 views, respectively. At 18
views, matched-computation analysis showed that ReCo-Diff achieved 38.54 dB compared
with 38.25 dB for a two-pass zero-residual control under the same 18-NFE budget. A
spatially shifted-residual control achieved 37.65 dB, indicating that effective residual
conditioning benefits from spatial correspondence with the current degraded state.
Significance. ReCo-Diff replaces adaptive trajectory control with step-wise spatial
residual conditioning in deterministic cold diffusion. Matched-computation analysis shows
that the reconstruction benefit depends on spatially aligned residual information rather
than on the additional network evaluation alone. The proposed strategy provides an
alternative approach to controlling error propagation under a predetermined degradation
schedule without relying on sample-adaptive reset decisions.