LatXGen: Towards Radiation-Free and Accurate Quantitative Analysis of Sagittal Spinal Alignment Via Cross-Modal Radiographic View Synthesis

• URL: http://arxiv.org/abs/2509.24165v1
• Date: Mon, 29 Sep 2025 01:29:53 GMT
• Title: LatXGen: Towards Radiation-Free and Accurate Quantitative Analysis of Sagittal Spinal Alignment Via Cross-Modal Radiographic View Synthesis
• Authors: Moxin Zhao, Nan Meng, Jason Pui Yin Cheung, Chris Yuk Kwan Tang, Chenxi Yu, Wenting Zhong, Pengyu Lu, Chang Shi, Yipeng Zhuang, Teng Zhang,
• Abstract summary: Adolescent Idiopathic Scoliosis (AIS) is a complex three-dimensional spinal deformity.<n>LatXGen is a novel generative framework that synthesizes realistic lateral spinal radiographs.<n>LatXGen produces accurate radiographs and outperforms existing GAN-based methods in both visual fidelity and quantitative metrics.
• Score: 5.000218104207936
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Adolescent Idiopathic Scoliosis (AIS) is a complex three-dimensional spinal deformity, and accurate morphological assessment requires evaluating both coronal and sagittal alignment. While previous research has made significant progress in developing radiation-free methods for coronal plane assessment, reliable and accurate evaluation of sagittal alignment without ionizing radiation remains largely underexplored. To address this gap, we propose LatXGen, a novel generative framework that synthesizes realistic lateral spinal radiographs from posterior Red-Green-Blue and Depth (RGBD) images of unclothed backs. This enables accurate, radiation-free estimation of sagittal spinal alignment. LatXGen tackles two core challenges: (1) inferring sagittal spinal morphology changes from a lateral perspective based on posteroanterior surface geometry, and (2) performing cross-modality translation from RGBD input to the radiographic domain. The framework adopts a dual-stage architecture that progressively estimates lateral spinal structure and synthesizes corresponding radiographs. To enhance anatomical consistency, we introduce an attention-based Fast Fourier Convolution (FFC) module for integrating anatomical features from RGBD images and 3D landmarks, and a Spatial Deformation Network (SDN) to model morphological variations in the lateral view. Additionally, we construct the first large-scale paired dataset for this task, comprising 3,264 RGBD and lateral radiograph pairs. Experimental results demonstrate that LatXGen produces anatomically accurate radiographs and outperforms existing GAN-based methods in both visual fidelity and quantitative metrics. This study offers a promising, radiation-free solution for sagittal spine assessment and advances comprehensive AIS evaluation.

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