Aleatoric uncertainty in accelerated prostate MRI reconstruction: echo-train dropout versus Gaussian noise Monte Carlo sampling.

PURPOSE: Although undersampling combined with deep learning (DL)-based reconstruction shortens MRI acquisition, it increases the chance of inaccuracies, highlighting the need for quantifiable uncertainty measures. Two inference-time perturbation strategies, echo-train dropout (ET-Drop) and Gaussian noise Monte Carlo sampling (GN-MC), were compared in terms of the correlation between their variance-based uncertainty maps and absolute reconstruction error in DL-accelerated T2w prostate MRI.

METHODS

This retrospective multi-center study used a publicly available dataset with 312 k-spaces from NYU for training and a dataset with 120 k-spaces from University Medical Center Groningen for external validation. Fully sampled 3 T data were retrospectively undersampled to acceleration factors R = 3 and R = 6 and reconstructed by a vSHARP model. Per slice, five GN-MC perturbations were reconstructed by adding complex noise at 2.5σ, and five ET-Drop perturbations, created by omitting non-central echo trains. Voxel-wise aleatoric uncertainty was defined as the variance (σ2) across these reconstructions and correlated with absolute reconstruction error over whole slices and within the prostate.

RESULTS

Both uncertainties yielded moderate slice-level correlations with absolute error. At R = 3, ET-Drop slightly outperformed GN-MC (median ρ = 0.39 vs 0.35; p < 0.001). At R = 6, the ranking reversed (0.44 vs 0.40; p < 0.001). Correlations within the prostate fell to 0.10-0.15. ET-Drop variance maps were dominated by coil sensitivities.

CONCLUSION

Both perturbation strategies yield variance-based uncertainty maps that correlate moderately with voxel-wise error. More importantly, they consistently highlighted acquisition-related fragility, supporting the role of uncertainty mapping as a useful quality-control tool in prostate MRI.