AortaDiff: Automated Aortic Imaging Frameworks

• AortaDiff is a family of computational frameworks for automated aortic imaging that integrates diffusion-based synthesis, segmentation, and 3D reconstruction.
• The methods employ conditional diffusion models and U-Net architectures, achieving high Dice scores and robust quantification of aortic anatomy.
• They reduce manual processing while preserving anatomical accuracy, enabling CFD-compatible mesh generation and physiomarker derivation for clinical applications.

Searching arXiv for AortaDiff and closely related aortic imaging papers. AortaDiff denotes a set of aorta-focused computational frameworks centered on automated image analysis, geometry reconstruction, and diffusion-based modeling. In the available arXiv literature, the name is used explicitly for a unified multitask diffusion framework for contrast-free abdominal aortic aneurysm imaging (Ou et al., 1 Oct 2025) and for volume-guided conditional diffusion models for multi-branch aortic surface generation (An et al., 17 Jul 2025); in related summaries, it is also used for fully automated aortic segmentation and distensibility assessment from 2D phase-contrast cine MRI (Jani et al., 2021). The literature therefore suggests that AortaDiff is not a single canonical architecture, but a family of methods aimed at reducing manual aortic image processing while preserving anatomical fidelity, quantitative utility, and downstream clinical relevance.

1. Terminological scope and conceptual unification

The label “AortaDiff” is applied to distinct but adjacent technical programs: automated segmentation and distensibility quantification, conditional diffusion for synthetic contrast generation, and diffusion-guided 3D surface construction. Some papers use the term explicitly in the title, whereas others are described as “AortaDiff-style” or “AortaDiff-related” in summaries rather than in the original title itself (Ou et al., 1 Oct 2025).

Usage in the literature	Imaging/problem setting	Core mechanism
AortaDiff	NCCT-to-synthetic CECT for AAA	Conditional diffusion with joint lumen segmentation
AortaDiff	CT/MRI-to-3D aortic surface	Volume-guided diffusion centerline generation, contour extraction, NURBS fitting
AortaDiff (described in summary)	2D PC-cine MRI distensibility workflow	U-Net segmentation and downstream ArtFun quantification
AortaDiff-style	Aortic morphology classification	Topographical Pair Correlation Function
Not explicitly AortaDiff	MRI shape-analysis infrastructure	Segmentation, reconstruction, registration, PCA

Across these usages, the common objective is anatomically precise, scalable aortic analysis. The differences lie in target modality, target anatomy, and the mathematical object being predicted: 2D masks and distensibility measures in the MESA workflow, synthetic contrast and signed distance functions in AAA imaging, or centerlines and contour-derived surfaces in multi-branch reconstruction (Jani et al., 2021, An et al., 17 Jul 2025).

A plausible implication is that AortaDiff functions less as a single branded algorithm than as a research direction organized around anatomy-aware automation. In that direction, the clinical endpoint is not merely segmentation accuracy or visual realism, but reliable derivation of quantities such as aortic area, distensibility, lumen diameter, thrombus area, and CFD-ready geometry (Ou et al., 1 Oct 2025, An et al., 17 Jul 2025).

2. Automated aortic area and distensibility assessment from PC-cine MRI

In one formulation, AortaDiff was presented as a deep-learning pipeline for fully automated segmentation of the ascending and descending aorta on 2D phase-contrast cine MRI and downstream quantification of aortic area and distensibility (Jani et al., 2021). The clinical motivation was explicit: aortic distensibility is a marker of arterial stiffness, and increased stiffness is linked to aging, stroke, ischemic heart disease, heart failure, and future cardiovascular events. Because distensibility requires repeated measurements across the cardiac cycle, the method was framed as a way to replace or reduce manual work while preserving reproducibility and enabling large-scale study and clinical use.

The development cohort was MESA, the Multi-Ethnic Study of Atherosclerosis. Of 5,005 participants with CMR, 1,872 had suitable 2D phase-contrast cine aortic MRI at the main pulmonary artery bifurcation and corresponding segmentations. These were split into 1,123 training studies with 24,282 images, 374 validation studies with 8,067 images, and 375 internal test studies with 8,069 images. External clinical generalization was evaluated on 37 thoracic aortic aneurysm studies with 3,224 images. That cohort differed substantially from MESA: mean age $60 \pm 15$ years, 19 of 37 with bicuspid aortic valve, 18 tricuspid-valve patients with TAA, and no dissections (Jani et al., 2021).

The model was a U-Net-based convolutional neural network trained in Python with Keras/TensorFlow using categorical cross-entropy, Adam optimization, learning rate $1 \times 10^{-5}$, batch size 64, and 75 epochs. Inputs were 2D magnitude images resized to $256 \times 256$ with zero-padding if needed. Both binary and multi-class segmentation were evaluated, with the multi-class formulation distinguishing ascending and descending aorta as separate classes. Training was performed with and without data augmentation using random zoom, rotation, crop, and horizontal or vertical shifts. The CNN treated each image independently, so time-series information was not used by the network (Jani et al., 2021).

Ground-truth segmentation maps were generated with ArtFun and manually adjusted. For downstream quantification, the deep-learning contours were fed into ArtFun and superimposed on the corresponding velocity images from PC-cine MRI to derive geometric parameters. The strain and distensibility computations were defined as

$$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$

and

$$\text{Distensibility} = \frac{\text{strain}}{\text{pulse pressure}}.$$

Distensibility was reported in units of $\times 10^{-3}\ \text{mmHg}^{-1}$. The paper also noted that maximum and minimum areas were determined from geometry rather than by strict matching of systolic and diastolic phases, which affected distensibility more than area (Jani et al., 2021).

Segmentation was assessed with Dice coefficient; agreement of derived geometric parameters was assessed with concordance correlation coefficient and Bland–Altman analysis. The abstract reported Dice coefficients of 97.6% on the internal test set and 93.6% on the external TAA test set. Supplemental Table 1 further reported 97.32% for binary segmentation, 97.60% for binary segmentation with augmentation, 96.67% for multi-class ascending-aorta segmentation, 97.64% for multi-class descending-aorta segmentation, and 94.41% and 95.86% for augmented multi-class ascending and descending segmentation, respectively. The non-augmented multi-class model was selected for further evaluation because it performed best and could separate the two vessels (Jani et al., 2021).

Agreement for aortic area was strong. On the internal MESA test set, CCC values were 0.97 and 0.95 for ascending maximum and minimum area, and 0.96 and 0.98 for descending maximum and minimum area. On the external TAA test set, CCC values were 0.99 and 0.99 for ascending maximum and minimum area, and 0.93 and 0.93 for descending maximum and minimum area. Distensibility agreement was only moderate: 0.63 for ascending and 0.63 for descending aorta internally, and 0.62 for ascending and 0.47 for descending aorta externally (Jani et al., 2021).

The main technical lesson was asymmetry between geometric and derived physiological targets. The CNN-derived contours showed excellent agreement for aortic areas, including in a TAA cohort with no TAA cases in training, but distensibility was harder because it is a ratio, so small errors in either maximum or minimum area propagated strongly. Additional reported limitations were dependence on magnitude images only rather than phase images, lack of temporal modeling, difficulty with ascending-aortic filling defects and abnormal descending-aorta shapes in TAA, uncertainty in younger congenital populations, and the inability of deep-learning segmentations to capture sub-pixel areas (Jani et al., 2021).

3. Unified multitask diffusion for contrast-free AAA imaging

In its most explicit form, AortaDiff is a unified diffusion-based framework for contrast-free abdominal aortic aneurysm assessment from non-contrast CT (Ou et al., 1 Oct 2025). The problem setting is NCCT-to-synthetic CECT generation with simultaneous segmentation of the aortic lumen and thrombus-relevant anatomy. The paper’s central claim was that synthetic CECT should be anatomically faithful and directly useful for downstream quantification, rather than merely visually plausible.

The clinical rationale was tied to the risks of iodinated contrast: contrast-induced nephrotoxicity, allergic reactions, discomfort from IV administration, and environmental burden from iodine waste. In AAA, CECT is used to visualize the lumen and infer thrombus, but on NCCT blood and thrombus are nearly indistinguishable. The method therefore sought to solve two coupled tasks jointly: translate NCCT into synthetic CECT and segment the lumen so thrombus can be recovered as the remaining non-lumen region inside the aortic wall (Ou et al., 1 Oct 2025).

Architecturally, AortaDiff is a conditional diffusion model with a shared U-Net denoiser and two parallel output heads branching from the final decoder features. One head predicts the image noise for CECT synthesis, and the other predicts the signed distance function of the lumen mask. The SDF formulation was introduced to encourage smoother, more geometrically meaningful boundary prediction than direct hard-mask prediction. During inference, the model starts from pure Gaussian noise and iteratively denoises conditioned only on the NCCT scan; unlike some prior multitask diffusion methods, it does not require a coarse segmentation mask or initial prediction (Ou et al., 1 Oct 2025).

The mathematical formulation reported three principal components. First, an ROI perceptual reconstruction loss restricted to the aorta-enclosing bounding box $\mathbb{B}$: $$\mathcal{L}_{\text{recon}} = \mathcal{L}_p\left(\mathbf{x}_0[\mathbb{B}], \hat{\mathbf{x}}_0[\mathbb{B}]\right).$$ Second, an SDF-based segmentation loss: $$\mathcal{L}_{\text{seg}} = \left\| \hat{\mathbf{s}}_0 - \mathbf{s}_0 \right\|_1.$$ Third, Kendall-style uncertainty weighting for multitask balance: $$\mathcal{L}(W,\sigma_{1},\sigma_{2}) = \frac{1}{2\sigma_{1}^{2}} \mathcal{L}_{\text{seg}}(W) + \frac{1}{2\sigma_{2}^{2}} \mathcal{L}_{\text{recon}}(W) + \log\!\bigl(\sigma_{1}\sigma_{2}\bigr).$$ The denoising trajectory used a linear schedule with 1,000 diffusion timesteps (Ou et al., 1 Oct 2025).

AortaDiff also incorporated an aorta-focused generation strategy. Because NCCT and CECT were not perfectly aligned even after registration, the paper did not attempt naive whole-image synthesis. Instead, training and reconstruction loss were restricted to the aortic ROI, and inference used a RePaint-style inpainting or fusion strategy in which the aorta region was synthesized while the known background was retained from the NCCT after forward diffusion or noising at each timestep (Ou et al., 1 Oct 2025).

The dataset was the Oxford Abdominal Aortic Aneurysm cohort. It contained 264 patients, of whom 65 had both NCCT-CECT pairs and lumen masks and 199 had NCCT-CECT pairs without lumen masks. From aligned slice extraction, the authors obtained 4,370 paired triplets containing NCCT, aligned CECT, and lumen mask, split into 2,823 training triplets and 1,547 testing triplets. A further 7,366 NCCT-CECT slice pairs lacked lumen annotations and were used in a semi-supervised regime. Preprocessing clipped Hounsfield units to $1 \times 10^{-5}$0, applied per-slice min-max normalization to $1 \times 10^{-5}$1, and used input resolution $1 \times 10^{-5}$2. Training used AdamW, learning rate $1 \times 10^{-5}$3, batch size 16, no weight decay, and 1,000,000 iterations (Ou et al., 1 Oct 2025).

The semi-supervised strategy was simple but central. The model was first trained for one epoch on the 65 fully labeled cases, then trained with mixed batches containing labeled and unlabeled data in a 1:1 ratio. For labeled cases, both reconstruction and segmentation losses were used; for unlabeled cases, segmentation loss was omitted and optimization proceeded with the reconstruction loss alone (Ou et al., 1 Oct 2025).

Quantitative evaluation covered synthesis quality, segmentation, and clinical measurement accuracy. Within the aortic ROI, AortaDiff-F achieved PSNR 25.61 dB, SSIM 0.8385, and LPIPS 0.0671; AortaDiff-P achieved PSNR 25.48 dB, SSIM 0.8296, and LPIPS 0.0606. These results exceeded U-Net, whole-image CDM-W, and aorta-restricted CDM-A in the reported metrics. For lumen segmentation, AortaDiff-P achieved Dice 0.8933, compared with 0.8718 for nnU-Net and 0.7479 for MedSAM. For thrombus segmentation, AortaDiff-P achieved Dice 0.5326, compared with 0.4750 for nnU-Net and 0.4356 for MedSAM. On 10 random test cases, AortaDiff-P reduced lumen diameter MAE to 4.19 mm from 5.78 mm for nnU-Net and reduced thrombus area error to $1 \times 10^{-5}$4 from $1 \times 10^{-5}$5 (Ou et al., 1 Oct 2025).

The broader significance is that AortaDiff reframed contrast-free AAA imaging as a joint synthesis-and-quantification problem. The paper argued that prior multi-stage pipelines accumulate error and fail to exploit shared semantic and anatomical structure. Its results support that position quantitatively, particularly for thrombus, which remained the more difficult target even under the unified model (Ou et al., 1 Oct 2025).

4. Volume-guided diffusion for 3D aortic surface generation

A second explicit AortaDiff formulation addressed a different technical objective: generation of smooth, CFD-compatible 3D aortic surfaces directly from CT or MRI volumes (An et al., 17 Jul 2025). Here the main problem was not contrast synthesis or slice segmentation, but topologically consistent surface construction for diagnosis, preoperative planning, and computational fluid dynamics.

The paper proposed a three-stage geometry-aware pipeline: $1 \times 10^{-5}$6 Its central argument was that direct mesh extraction from image volumes often yields jagged boundaries, discontinuities, missing branches, and non-watertight geometry, all of which are problematic for CFD. AortaDiff addressed this by learning a plausible centerline first, then using that centerline to organize local contour extraction, and only then fitting a continuous surface (An et al., 17 Jul 2025).

Centerline generation used a volume-guided conditional diffusion model. The conditioning inputs were the image volume $1 \times 10^{-5}$7, a feature volume extracted from $1 \times 10^{-5}$8 using a ViT, the current noisy centerline sample $1 \times 10^{-5}$9, and timestep $256 \times 256$0. The forward corruption process was

$256 \times 256$1

and the reverse denoising step predicted noise $256 \times 256$2 before updating the centerline sample through the reverse diffusion equation given in the appendix. The supplied summary stated that the training setup matches the standard diffusion objective of predicting added noise from the noisy input and conditioning features (An et al., 17 Jul 2025).

Once the centerline $256 \times 256$3 was generated, each point $256 \times 256$4 defined a local orthogonal cross-sectional plane $256 \times 256$5 via the tangent vector $256 \times 256$6. A slice $256 \times 256$7 was extracted from the 3D volume, segmented using ScribblePrompt, converted into a 2D contour $256 \times 256$8, and mapped back into 3D as $256 \times 256$9. Adjacent contours were then aligned and fitted with NURBS to form a smooth parametric surface, later discretized into a continuous mesh. The same procedure was repeated for supra-aortic branches and integrated with the main aorta (An et al., 17 Jul 2025).

The NURBS fitting was described conceptually by the standard parametric form

$$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$0

with B-spline basis functions, weights, and control points. The significance of this choice was not merely smoothness in a visual sense, but smoothness compatible with numerically stable CFD meshes (An et al., 17 Jul 2025).

Evaluation used the VMR and AVT datasets and reported Chamfer Distance, Hausdorff Distance, and Earth Mover’s Distance. A parameter study found that 16 centerline points gave the best balance between geometric detail and stable NURBS fitting. For the overall setting on VMR, 16 points yielded $$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$1, $$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$2, and $$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$3. On AVT, 16 points yielded $$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$4, $$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$5, and $$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$6 (An et al., 17 Jul 2025).

Qualitative comparisons were made against SAM, MedSAM, TotalSeg, LoGB-Net, LoGB-Net plus marching cubes, EFDs plus marching cubes, and LoGB-Net plus surface nets. The appendix summary stated that AortaDiff gave smoother contours, better localization, fewer missing branches, more correct topology, and fewer aliasing artifacts than those alternatives. The test set included two aneurysm cases and one coarctation case, which the method handled successfully. The CFD simulations on generated meshes were reported to produce physiologically meaningful velocity, pressure, and wall shear stress fields, with coherent streamlines, smooth pressure gradients, and WSS hotspots in expected regions such as bifurcation points and inner curvature zones (An et al., 17 Jul 2025).

This suggests that, in this branch of the literature, AortaDiff is best understood as a geometry-first diffusion system. The predicted object is not the final mesh directly, but a structured scaffold—centerline, cross-sections, and fitted surface—from which a CFD-compatible mesh can be constructed (An et al., 17 Jul 2025).

5. Related morphology, dissection, and synthetic-data methods

Several adjacent papers do not define AortaDiff as a formal title, but they clarify the wider technical space in which the term is being used. One example is the Topographical Pair Correlation Function method for aortic shape classification, described as AortaDiff-style shape analysis in the supplied summary (Bruno et al., 17 Nov 2025). That work segmented the aorta from CT angiography, generated a triangular surface mesh, computed curvature-derived scalar fields at every vertex using the Rusinkiewicz algorithm, and defined a surface pair-correlation function

$$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$7

where $$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$8 is measured in mesh-edge steps rather than Euclidean distance. The use of edge count was deliberate so that classification would depend more on shape organization than absolute scale. When parameterized by shape index, the area under the TPCF curve contributed to a classification accuracy of 0.95 for disease presence and/or impending TEVAR success; Gaussian curvature and mean curvature gave 0.93, and bending ratio 0.92. The conceptual contribution was to encode not only how curved the aorta is, but how curvature abnormalities are spatially organized over the surface (Bruno et al., 17 Nov 2025).

A second adjacent line is automated Type B aortic dissection analysis from CTA, described as directly relevant to AortaDiff-style automated dissection analysis (Xu et al., 27 Jun 2025). That study used 100 retrospective CTA images from the ImageTBAD dataset, split into 80 training, 10 validation, and 10 testing cases, with 68 cases containing false lumen thrombosis. Four pipelines were compared: a single-step model, a sequential model, a sequential multi-task model, and an ensemble model using 3D U-Net and Swin-UnetR backbones. The headline testing Dice values were $$\text{Aortic strain} = \frac{A_{\max} - A_{\min}}{A_{\min}},$$9 for true lumen, $$\text{Distensibility} = \frac{\text{strain}}{\text{pulse pressure}}.$$0 for false lumen, and $$\text{Distensibility} = \frac{\text{strain}}{\text{pulse pressure}}.$$1 for false lumen thrombosis, outperforming the Yao et al. baseline. The sequential multi-task design produced the highest True FLT Dice of $$\text{Distensibility} = \frac{\text{strain}}{\text{pulse pressure}}.$$2, while the ensemble model gave the strongest balanced TL/FL performance. The paper emphasized that explicit specialization for FLT matters because FLT is small, sparse, and clinically important for surveillance and treatment planning (Xu et al., 27 Jun 2025).

Synthetic CTA generation for acute Type B aortic dissection provides another neighboring direction, described as AortaDiff-style synthetic generation (Abaid et al., 2024). That work fine-tuned a Stable Diffusion text-to-image model with DreamBooth and LoRA on a 100-case ImageTBAD dataset converted from 3D volumes into 2D slices. It used class-specific prompts for TL, FL, FLT, TL+FL, and a background class without TL/FL/FLT, along with negative prompts to suppress undesired features. Reported training settings were 100 epochs, batch size 2, learning rate $$\text{Distensibility} = \frac{\text{strain}}{\text{pulse pressure}}.$$3, 8-bit AdamW optimizer, DDS noise scheduler, fp16 checkpointing, and A6000 GPU training for about four days. Quantitative evaluation reported FID 77.82 and synthetic MS-SSIM values ranging from 0.35 to 0.39 across classes. A UNet trained on real data achieved DSC 0.83 for TL and 0.84 for FL, and detected FL in synthetic Class 2 and Class 4 samples, but failed to identify FLT reliably in synthetic Class 3, reinforcing the paper’s conclusion that medical-image-specific evaluation remains unresolved (Abaid et al., 2024).

A further infrastructural reference is the MRI-based pipeline for segmentation, reconstruction, mesh registration, and statistical shape analysis that explicitly does not mention AortaDiff in the supplied text, but is presented as conceptually close to AortaDiff-like methods (Shehata et al., 9 Sep 2025). On 744 cardiac MRI scans, this paper benchmarked nnU-Net, TotalSegmentator, MedSAM2, and fine-tuned TotalSegmentator variants, and found that nnU-Net trained from scratch achieved the best overall segmentation performance. It then reconstructed watertight 3D meshes, registered them with a PyTorch3D-based direct vertex-optimization method, and performed PCA on 599 healthy subjects. The proposed registration method outperformed rigid, CPD, and Deformetrica baselines with average distance 0.73, Hausdorff 2.28, RMSE 0.85, IoU 0.99, and Dice 0.99. Under rigid alignment, the first PCA mode explained 68.0% of variance and was dominated by overall aortic size; under similarity alignment, the first mode explained 22.9%, redistributing variance across more local shape changes (Shehata et al., 9 Sep 2025).

Finally, the broader context includes a non-training, mechanics-based aneurysm forecasting method described as AortaDiff-related in the supplied summary (Zhao et al., 2021). That work derived a flutter instability physiomarker

$$\text{Distensibility} = \frac{\text{strain}}{\text{pulse pressure}}.$$4

from 4D-flow MRI using patient-specific estimates of cross-sectional area, flow acceleration, pulse wave velocity, and related quantities. In a retrospective prognosis subcohort, the binary predictor $$\text{Distensibility} = \frac{\text{strain}}{\text{pulse pressure}}.$$5 yielded accuracy 0.986, sensitivity 0.962, specificity 1.000, and AUC 0.997 for abnormal growth and/or surgery at a growth threshold of 0.24 cm/year. Although this is not a diffusion model, it reinforces the point that aortic analysis is increasingly organized around downstream physiomarkers rather than segmentation alone (Zhao et al., 2021).

6. Limitations, misconceptions, and open directions

A central misconception is that AortaDiff refers to one stable, universally defined architecture. The literature suggests otherwise. The title is used explicitly for at least two substantially different diffusion systems—one for contrast-free AAA imaging and one for multi-branch surface generation—while several other works are only AortaDiff-style, AortaDiff-related, or conceptually proximal rather than formally named AortaDiff (Ou et al., 1 Oct 2025, An et al., 17 Jul 2025).

Another misconception is that strong segmentation or synthesis performance automatically implies strong physiological quantification. The PC-cine MRI workflow illustrates the limitation directly: aortic area agreement was excellent, but distensibility agreement was only moderate, especially in the descending aorta of the external TAA cohort where CCC fell to 0.47. The paper attributed this to the ratio structure of distensibility, magnitude-only input, lack of temporal information, abnormal anatomy in TAA, and the inability of deep-learning contours to capture sub-pixel areas (Jani et al., 2021).

A similar pattern appears in AAA and dissection tasks. In the unified AAA framework, lumen Dice reached 0.8933 and thrombus Dice 0.5326, but thrombus remained the more difficult target and the system still relied on aligned NCCT/CECT data and a pretrained nnU-Net for background or aorta mask handling during inference. The study was also confined to a single institution’s OxAAA cohort, so broader external validation remained necessary (Ou et al., 1 Oct 2025). In TBAD segmentation, FLT was likewise the hardest class despite substantial improvements over prior work, and in synthetic CTA generation, the authors explicitly warned about overfitting, language drift, non-medical-specific evaluation metrics, and the fact that 2D slice-based training ignores 3D continuity (Xu et al., 27 Jun 2025, Abaid et al., 2024).

The surface-generation branch raises a different set of limits. Although AortaDiff produced CFD-compatible meshes and showed robustness on AVT despite training on VMR, its reported success depended on a carefully engineered decomposition into centerline generation, prompt-guided contour extraction, and NURBS fitting. This suggests that end-to-end mesh prediction remains a difficult problem for tortuous, branching, and pathological vasculature. The parameter study also showed a narrow operating regime: too few centerline points lost anatomical detail, while too many caused contour interference and even NURBS fitting failures (An et al., 17 Jul 2025).

Open directions emerge consistently across the literature. The supplied summaries point toward more pathology-specific training data, higher-resolution imaging, better temporal modeling for phase-sensitive measures, stronger evaluation protocols for synthetic medical images, and broader use of registered shape atlases and morphology-aware descriptors. A plausible implication is that future AortaDiff systems will be judged less by isolated overlap metrics than by whether they improve clinically relevant measurements, hemodynamic simulation readiness, or patient-specific forecasting of disease progression (Jani et al., 2021, Shehata et al., 9 Sep 2025, Zhao et al., 2021).