用稀疏表面监督实现3D MRI-US图像精准配准，提升前列腺放疗实时导航精度。
Weakly Supervised Spatial Implicit Neural Representation Learning for 3D MRI-Ultrasound Deformable Image Registration in HDR Prostate Brachytherapy
- 基于隐式神经表示，用稀疏表面标签替代密集匹配，减少模态差异影响。
- 公开数据集前列腺配准Dice达0.93±0.05，平均表面距离0.87±0.10mm。
- 适合临床需快速、高精度图像配准的放射治疗场景，尤其适用标注稀缺情况。
目的：在高剂量率（HDR）前列腺近距离放疗中，准确的3D MRI-超声（US）可变形配准对实时引导至关重要。本文提出一种弱监督的空间隐式神经表示（SINR）方法，以应对模态差异和盆腔解剖挑战。该框架利用来自MRI/US分割的稀疏表面监督，而非密集强度匹配。SINR将形变建模为连续空间函数，通过患者特异性表面先验指导平稳速度场，实现生物合理形变。验证包含20例公开的Prostate-MRI-US-Biopsy病例和10例机构内HDR病例，评估指标包括骰子相似系数（DSC）、平均表面距离（MSD）和95%豪斯多夫距离（HD95）。结果：所提方法表现稳健。在公开数据集上，前列腺DSC为0.93±0.05，MSD为0.87±0.10 mm，HD95为1.58±0.37 mm；在机构数据集上，前列腺靶区（CTV）DSC为0.88±0.09，MSD为1.21±0.38 mm，HD95为2.09±1.48 mm。膀胱和直肠性能较低，因超声视野有限。视觉评估显示配准准确，差异微小。结论：本研究提出一种新型基于弱监督SINR的3D MRI-US可变形配准方法。通过利用稀疏表面监督与空间先验，实现高精度、鲁棒且计算高效配准，增强HDR前列腺近距离放疗中的实时图像引导，提升治疗精度。
原文摘要 · Abstract (English)
Purpose: Accurate 3D MRI-ultrasound (US) deformable registration is critical for real-time guidance in high-dose-rate (HDR) prostate brachytherapy. We present a weakly supervised spatial implicit neural representation (SINR) method to address modality differences and pelvic anatomy challenges. Methods: The framework uses sparse surface supervision from MRI/US segmentations instead of dense intensity matching. SINR models deformations as continuous spatial functions, with patient-specific surface priors guiding a stationary velocity field for biologically plausible deformations. Validation included 20 public Prostate-MRI-US-Biopsy cases and 10 institutional HDR cases, evaluated via Dice similarity coefficient (DSC), mean surface distance (MSD), and 95% Hausdorff distance (HD95). Results: The proposed method achieved robust registration. For the public dataset, prostate DSC was $0.93 \pm 0.05$, MSD $0.87 \pm 0.10$ mm, and HD95 $1.58 \pm 0.37$ mm. For the institutional dataset, prostate CTV achieved DSC $0.88 \pm 0.09$, MSD $1.21 \pm 0.38$ mm, and HD95 $2.09 \pm 1.48$ mm. Bladder and rectum performance was lower due to ultrasound's limited field of view. Visual assessments confirmed accurate alignment with minimal discrepancies. Conclusion: This study introduces a novel weakly supervised SINR-based approach for 3D MRI-US deformable registration. By leveraging sparse surface supervision and spatial priors, it achieves accurate, robust, and computationally efficient registration, enhancing real-time image guidance in HDR prostate brachytherapy and improving treatment precision.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。