The ability to leverage images from co-available modalities to inform target-domain reconstruction is highly desirable in imaging algorithms. In this work, we introduce Generative Translation Priors (GTP)--a Bayesian framework that transforms diffusion-based image-to-image translation models into cross-modality image priors for ill-posed imaging inverse problems. GTP incorporates target-domain measurements through likelihood guidance, steering the translation process toward the desired posterior distribution. The framework is grounded in a theoretical analysis of the resulting posterior dynamics, which reveals an intrinsic bias introduced by likelihood guidance. We further characterize this bias and derive a ground-truth-free formulation for its estimation, enabling it to serve as a practical metric for assessing posterior sampling quality. Building on this analysis, we derive two discretized GTP algorithms based on gradient and proximal likelihood guidance, respectively. We validate GTP on computed tomography reconstruction with magnetic resonance side information, and on positron emission tomography reconstruction with computed tomography side information. Experiments demonstrate that GTP effectively incorporates complementary cross-modality information and achieves high-fidelity reconstruction even under severely undersampled measurements.
Figures & tables
Figure 1: Schematic visualization of the proposed GTP framework. The left panel illustrates a generative image translation model that induces a target-domain prior by stochastically transporting images from a source modality to a target modality (exemplified by MRI-to-CT translation). The right panel illustrates how GTP transforms this translation prior into a posterior sampler by incorporating the measurement likelihood, steering the generative dynamics toward the target posterior distribution.
Figure 2
Figure 2: Numerical validation of the intrinsic bias as a reference-free criterion for assessing posterior accuracy. Prox-GTP (Algorithm 2 ) is used to recover the posterior across different likelihood strengths. The recovered posterior mean and pixel-wise standard deviation are shown together with the intrinsic bias (upper bound in Theorem 1 ) and two reference-based metrics: (squared) maximum mean discrepancy (MMD) and negative log-likelihood (NLL) . Note how the intrinsic bias closely tracks the reference-based metrics, detecting both under- and overweighting of the measurement likelihood and reaching its minimum when the recovered posterior best matches the ground truth.
Figure 3: Visual comparison of the proposed GTP methods against baselines for 8-view SV-CT reconstruction. The shaded panels indicate methods that leverage the reference MR image during reconstruction. Zoomed-in regions highlight differences in anatomical detail. Note how the GTP methods more accurately reconstruct important anatomical structures, particularly the kidneys, compared with the baseline methods.
Figure 5: Visual comparison of 90∘ LA-CT reconstructions obtained using the proposed GTP methods and baselines. The shaded panels indicate methods that leverage the reference MR image during reconstruction. Zoomed-in regions highlight differences in anatomical detail. Note how the GTP methods more accurately reconstruct major anatomical structures, particularly bones, that are poorly observed due to the missing angular measurements.
Figure 7: Visual comparison of low-dose PET reconstructions with 5×105 total counts. The shaded panels indicate methods that leverage the reference CT image during reconstruction. Zoomed-in regions highlight differences in fine-detail recovery. Note how Prox-GTP more clearly reconstructs the small tumor lesion, which is substantially less visible in the baseline reconstructions.
Method
105 counts
5×105 counts
106 counts
PSNR
SSIM
PSNR
SSIM
PSNR
SSIM
OSEM
32.15
0.890
35.11
0.926
35.66
0.936
PET-DDS
34.74
0.935
36.24
0.946
36.42
0.948
Prox-GTP
34.34
0.934
37.34
0.951
38.66
0.959
Table 1: Average PSNR and SSIM results attained by the proposed GTP methods and baselines for three PET reconstruction settings. In each column, the best result is marked in bold , while the second best is underlined . For reference, the translation model used by GTP attains a PSNR of 29.80 dB and an SSIM of 0.891 without measurement guidance.
Image reconstruction recovers images, volumes, or scenes from incomplete, noisy, indirect, or compressed measurements using physical models and learned priors.
Department of Quantitative Biomedicine, University of Zurich · Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz