Source-linked AI summary
Diffusion Deformable Model for 4D Temporal Medical Image Generation
Boah Kim, Jong Chul Ye
TL;DR
Temporal medical image generation, including 4D cardiac volumes, remains limited, while generative approaches may produce artificial features and registration-based generators have focused on templates. The paper proposes DDM, which combines diffusion-estimated latent codes with deformation to generate intermediate volumes; cardiac MR experiments report realistic continuous deformations and better performance than existing registration-based models.
Problem
Temporal medical image generation approaches are limited, and existing generative approaches may produce artificial features while registration-based generators focus on templates rather than temporal volume frames.
Source: §1 · pp. 1–2Method
DDM uses a diffusion module to estimate a spatially informative latent code and a deformation module to generate intermediate frames by interpolating the code.
Source: §1 · p. 2, §2.3 · p. 5Results
Takeaways & Limitations
The model provides intermediate frames between cardiac volumes for analyzing anatomical changes, while preserving source topology along the continuous trajectory.
Source: §2.3 · p. 5, §4 · p. 9
Abstract
from arXiv · showhide
Temporal volume images with 3D+t (4D) information are often used in medical imaging to statistically analyze temporal dynamics or capture disease progression. Although deep-learning-based generative models for natural images have been extensively studied, approaches for temporal medical image generation such as 4D cardiac volume data are limited. In this work, we present a novel deep learning model that generates intermediate temporal volumes between source and target volumes. Specifically, we propose a diffusion deformable model (DDM) by adapting the denoising diffusion probabilistic model that has recently been widely investigated for realistic image generation. Our proposed DDM is composed of the diffusion and the deformation modules so that DDM can learn spatial deformation information between the source and target volumes and provide a latent code for generating intermediate frames along a geodesic path. Once our model is trained, the latent code estimated from the diffusion module is simply interpolated and fed into the deformation module, which enables DDM to generate temporal frames along the continuous trajectory while preserving the topology of the source image. We demonstrate the proposed method with the 4D cardiac MR image generation between the diastolic and systolic phases for each subject. Compared to the existing deformation methods, our DDM achieves high performance on temporal volume generation.
1 Introduction
The paper addresses limited generation of temporal medical volumes, proposing a diffusion deformable model that generates intermediate frames between source and target images. Tests on cardiac MR report realistic continuous deformations and performance above existing registration-based approaches.
1 Introduction
- Four-dimensional cardiac imaging can track 3D volume changes over time, but MRI requires relatively long scans to obtain 4D images.The passage frames temporal imaging as relevant to analyzing anatomical progression for diagnosis and therapy planning.Source: §1 · p. 1
- Existing registration-based generators are described as producing templates rather than temporal 3D volume frames along a continuous trajectory.Learning-based deformable registration can provide smooth fields that preserve topology.Source: §1 · p. 2
- DDM combines a diffusion module that estimates a spatially informative latent code with a deformation module that generates temporal frames.The code represents information about deformation from source toward target.Source: §1 · p. 2
- Interpolating the latent code yields continuous deformations from source to target while preserving source-image topology.The deformation module uses the scaled code to estimate deformation fields.Source: §1 · p. 2
- On 4D cardiac MR generation, DDM produces dynamic deformations between end-diastolic and systolic volumes and outperforms existing registration-based models.The experiments use the ACDC dataset and compare against methods that adjust registration fields.Source: §1 · p. 2
2 Proposed Method
The proposed DDM combines diffusion and deformation modules to estimate spatial information between source and target volumes and generate deformed images. After training, scaling the estimated latent code produces continuous intermediate frames from source to target.
2 Proposed Method
- DDM pairs a diffusion module that estimates latent code c with a deformation module that uses c and source S to generate a warped image.The deformation module estimates fields φ and applies them through a spatial transformation layer.Source: §2 · p. 2, Figure 1 · p. 3
2.1 Diffusion Deformable Model
- The diffusion module treats target T as reference data, perturbs it, and uses source S, target T, and the perturbed target to estimate latent code c.DDPM learns parameterized Gaussian transformations for reverse diffusion; its sampling step uses a learned mean and fixed variance.Source: Diffusion Module · p. 3
2.2 Model Training
- End-to-end unsupervised training combines diffusion and deformation losses, with the latter matching the warped source to the target and regularizing deformation-field smoothness.The deformation loss uses local normalized cross-correlation and a smoothness term; λR is set to 1 in the experiments.Source: §2.2 · p. 4
2.3 Intermediate Frames for Temporal Volume Generation
- At inference, the trained model estimates c and scales it by γ ∈ [0, 1] to generate temporally continuous deformed images from source to target.The intermediate frame is produced by warping source S with fields generated from the scaled code.Source: §2.3 · p. 5
3 Experiments
The experiments evaluate DDM on end-diastolic-to-end-systolic cardiac MRI generation and compare it with learning-based registration methods. DDM achieves stronger image-quality and segmentation results than VM and VM-diff, with faster test time than VM-diff.
Dataset and Metric
- Experiments used 100 ACDC cardiac MRI scans, with a 90/10 training-test split for end-diastolic-to-end-systolic volume generation.Scans were resampled and cropped to 128 × 128 × 32 voxels; training used λ = 20 for 800 epochs.Source: Dataset and Metric · pp. 5–6, Implementation Details · p. 6
- PSNR and NMSE compare generated deformed images with real temporal data, while Dice measures agreement between ground-truth and estimated deformed segmentation maps.The segmentation evaluation covers cardiac structures at the end-diastolic and end-systolic phases.Source: Dataset and Metric · p. 6
Results and Discussion
- DDM outperformed VM and VM-diff with higher PSNR, lower NMSE, and 80.2% average Dice (about 1% gain); its test time was 0.456 s versus VM-diff’s 2.902 s.Against VM, gains were significant on all metrics (p-values<0.05); against VM-diff, Dice was slightly higher without significant difference, while PSNR and NMSE gains were significant.Source: Table 1 · p. 7, Results and Discussion · p. 8
- VM’s scaled fields retain their relative spatial distributions, whereas VM-diff deforms little initially and sharply near the end; DDM’s fields change with position.DDM’s intermediate images show distinct changes from source to target; Fig. 3 places deformation fields on the left and generated frames on the right, with ground-truth references.Source: Figure 3 · p. 6, Results and Discussion · pp. 7–8
Loss-function analysis
- With deformation-only training, PSNR, NMSE, and Dice were 30.72, 0.473×10−8, and 0.799, respectively, below the joint model’s optimal results.As λ increases, PSNR initially rises and NMSE falls before converging above λ = 5, while Dice continues to improve.Source: Results and Discussion · p. 8, Figure 5 · p. 8
4 Conclusion
The conclusion presents DDM as a diffusion-adapted deformable-registration framework that estimates latent codes for continuous image deformations. In 4D cardiac MR generation, it produces dynamic deformations and outperforms existing registration-based models.
4 Conclusion
- DDM adapts the denoising diffusion probabilistic model to deformable registration and learns source-target distributions to estimate latent codes for continuous deformations.Source: §4 · pp. 8–9
- In 4D cardiac MR generation, DDM produces dynamic deformations from end-diastolic to systolic volumes and outperforms existing registration-based models.The authors describe intermediate frames as a potential real-time tool for analyzing anatomical changes in clinical applications.Source: §4 · p. 9