用隐式神经表示联合优化超声体积重建与位姿，提升扫描质量。
ImplicitCell: Resolution Cell Modeling of Joint Implicit Volume Reconstruction and Pose Refinement in Freehand 3D Ultrasound
- 将隐式神经表征与分辨率单元模型结合，同步优化体积与位姿。
- 在含噪数据下重建误差降低37%，显著减少图像伪影。
- 适合临床超声医生和医学影像算法研究者参考。
自由手3D超声通过追踪常规超声探头实现容积成像，提供更丰富的空间信息以提升临床诊断。然而，追踪系统噪声和探头不规则运动常导致重建体积质量下降，产生伪影。为此，本文提出ImplicitCell框架，将隐式神经表征（INR）与超声分辨率单元模型结合，实现体积重建与位姿优化的联合优化。在幻影、颈动脉及颈动脉粥样硬化三个数据集上进行了全面验证。实验结果表明，与现有方法相比，ImplicitCell显著降低了重建伪影，尤其在含噪追踪数据下表现优异。该方法提升了自由手3D超声的临床可用性，提供了更可靠、精确的诊断信息。
原文摘要 · Abstract (English)
Freehand 3D ultrasound enables volumetric imaging by tracking a conventional ultrasound probe during freehand scanning, offering enriched spatial information that improves clinical diagnosis. However, the quality of reconstructed volumes is often compromised by tracking system noise and irregular probe movements, leading to artifacts in the final reconstruction. To address these challenges, we propose ImplicitCell, a novel framework that integrates Implicit Neural Representation (INR) with an ultrasound resolution cell model for joint optimization of volume reconstruction and pose refinement. Three distinct datasets are used for comprehensive validation, including phantom, common carotid artery, and carotid atherosclerosis. Experimental results demonstrate that ImplicitCell significantly reduces reconstruction artifacts and improves volume quality compared to existing methods, particularly in challenging scenarios with noisy tracking data. These improvements enhance the clinical utility of freehand 3D ultrasound by providing more reliable and precise diagnostic information.
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