An anatomy‐aware spatio‐temporal generative extrapolation network for distal ascending aorta restoration in standard echocardiographic views
Xuanyu Chen, Hexiang Weng, Hanlin Cheng, Zhanru Qi, Zhongqing Shi, Guanjun Guo, Xiaoxian Wang, Hui Chen, Fen Chen, Aijuan Fang, Chunjie Shan, Yidi Chen, Shouhua Luo, Jing Yao, Sunnan QianAbstract
Background
In two‐dimensional transthoracic echocardiography (TTE), the standard parasternal long‐axis (PLAX) view enables reliable evaluation of the aortic valve and aortic root. Owing to the limited field of view of the ultrasound probe and rib‐window occlusion, the distal ascending aorta (AA) is frequently truncated, compromising the diagnosis of aortic aneurysm and dilation.
Purpose
We aim to address the incomplete visualization of the distal AA in standard PLAX views. By aggregating fragmented anatomical features intermittently visible in adjacent frames, we enhance the clarity of vessel wall edges and generate the distal AA structure, thereby completing the missing AA imagery in standard PLAX views.
Materials and methods
We retrospectively collected 1160 TTE videos from Nanjing Drum Tower Hospital (August 2022 – December 2024), all showing complete AA anatomy. After de‐identification, 1059 cases were randomly split into training/validation/test sets (6:2:2), and 101 cases were reserved for observer agreement experiments. An additional 152 videos from three different device models (EPIQ CVX, GE Voluson E10, Resona A20T) formed an external validation set. Two training data types were constructed: (i) dual‐view synchronous data – a standard PLAX sequence and an AO‐PLAX sequence acquired after a cranial probe shift, phase‐matched at end diastole (ED); and (ii) locally masked data – the distal AA region was masked, forcing the model to infer missing anatomy from left‐side context. Input sequences of five frames (ED 2) were extracted. The proposed Spatio‐Temporal Ascending‐Aorta generative adversarial network (STAA‐GAN) first aligns adjacent frames using an Edge Boosted Feature Alignment (EBFA) module with multi‐stage modulated deformable convolutions. A channel‐wise fusion strategy then aggregates temporally aligned features. The fused representation is fed into a Swin‐UNet generator enhanced with a Strip based Cross window Prediction (SCP) module to capture long‐range structural dependencies. Training employs a composite loss including pixel reconstruction, feature reconstruction, texture consistency, relativistic average least‐squares GAN (RaLSGAN), temporal coherence, and structure‐aware terms, with data‐dependent weighting. Statistical analyzes included Bland‐Altman, Passing‐Bablok regression, paired t ‐tests with Cohen's d, Bonferroni correction for multiple comparisons, and two one‐sided tests (TOST, ).
Results
STAA‐GAN achieved the best image‐quality values among the compared methods, with peak signal‐to‐noise ratio (PSNR) = 22.51 dB, structural similarity index (SSIM) = 0.7574, multi‐scale structural similarity index (MS‐SSIM) = 0.8598, and learned perceptual image patch similarity (LPIPS) = 0.1758. All 12 pairwise comparisons (SRN, IOH, and U‐Transformer versus STAA‐GAN, designated as the reference group) remained statistically significant after Bonferroni correction (), with absolute effect sizes ranging from 0.255 to 3.439. The per‐frame inference time was 203 ms. In clinical consistency validation, the mean difference between measurements on generated and original images was cm with 95% limits of agreement within cm; TOST with cm confirmed clinical equivalence for all comparisons (). The segmentation Dice coefficient on generated images averaged across five cross‐validation folds. External validation on three different ultrasound devices (Philips, GE, Mindray) maintained consistent equivalence (TOST , Cohen's ). These results demonstrate that STAA‐GAN produces clinically interchangeable distal ascending aorta images with high fidelity and real‐time feasibility.
Conclusions
STAA‐GAN enables extrapolation of the missing distal ascending aorta in standard PLAX views, achieving improved image quality and clinically equivalent diameter measurements on both internal and external validation datasets. These findings support its potential for assisting distal ascending aorta assessment in routine echocardiography.