diff --git a/ct2mri2024/index.html b/ct2mri2024/index.html index 00a0d7f..82007b2 100644 --- a/ct2mri2024/index.html +++ b/ct2mri2024/index.html @@ -975,13 +975,13 @@
Training and sampling scheme of the proposed methods. (a) During the multi-slice BBDM training, a target histogram-based style key is injected into the U-Net. (b) Target volume sampling proceeds in the manner of the Predictor-Corrector method. - During the co-prediction phase, multiple $\bm{\epsilon}^{i,k}_{\theta,t}$ are employed to establish connections among the predicted slices within $\bm{\bar{X}}_{t-1}$. + During the co-prediction phase, multiple ${\epsilon}^{i,k}_{\theta,t}$ are employed to establish connections among the predicted slices within ${\bar{X}}_{t-1}$. In the subsequent correction phase, the co-predicted volume is refined through a score-guided deterministic process.
Visualization of the latent space and algorithm for ISTA sampling. The trained U-Net produces inconsistent outputs for multi-slice inputs that include the $i^{th}$ slice. - The co-prediction unifies the direction of these independent inferences, while the correction aligns the co-predicted $\bar{\bm{x}}_{t}^i$ onto the manifold of $\bm{x}^i_{t}$. + The co-prediction unifies the direction of these independent inferences, while the correction aligns the co-predicted $\bar{{x}}_{t}^i$ onto the manifold of ${x}^i_{t}$.