基于多椎体结构建模的CT-MRI脊柱多阶段配准方法

doi:10.11996/JG.j.2095-302X.2026051033

摘要/Abstract

摘要：

脊柱微创手术中，单一模态影像无法同时呈现骨性结构与软组织信息，术中空间定位仍存在较大困难。传统跨模态配准方法难以适配CT与MRI灰度差异、扫描范围不一致、成像伪影及脊柱多组织差异化运动特征，易出现配准失效、椎体定位偏移等问题。针对上述问题，提出一种基于多椎体结构建模的CT-MRI脊柱多阶段配准方法。在粗配准阶段，利用椎体分割结果构建解剖约束，通过提取2种模态共有椎体标签并选取代表性椎体作为锚点，实现跨模态影像的刚性与仿射变换初始化，从而减小大尺度位姿差异，避免非刚性配准陷入局部最优。在精配准阶段，构建融合多椎体仿射建模与微分同胚形变的深度神经网络，利用多椎体仿射分支描述不同椎体节段的近刚性运动，并通过速度场积分生成微分同胚形变场，以刻画椎间盘及周围软组织的非线性形变，实现高精度非刚性配准。实验在自建脊柱CT-MRI数据集上进行，在腰椎区域的Dice，HD₉₅和ASD分别达到91.43%，2.61 mm和1.16 mm，均优于对比方法。定量结果表明，该方法能够提高腰椎椎体结构的区域重叠程度和边界对齐精度；可视化结果进一步表明，其能够较好地协调椎体节段的局部近刚性运动与周围组织的连续形变，保持较好的稳定性和解剖合理性，可为术前多模态影像融合以及后续手术导航提供技术基础。

关键词: 脊柱, 多模态配准, CT-MRI, 深度学习, 微分同胚配准

Abstract:

In minimally invasive spinal surgery, a single imaging modality cannot simultaneously provide sufficient information on both osseous structures and soft tissues, making intraoperative spatial localization challenging. Conventional cross-modality registration methods often struggle to accommodate the intensity differences between CT and MRI, inconsistent scan coverage, imaging artifacts, and the distinct motion characteristics of multiple spinal tissues, which may lead to registration failure and vertebral localization errors. To address these issues, a multi-stage spinal CT-MRI registration method based on multi-vertebra structural modeling was proposed. In the coarse registration stage, anatomical constraints were constructed using vertebral segmentation results. Common vertebral labels were extracted from the two modalities, and a representative vertebra was selected as an anchor to initialize rigid and affine transformations, thereby reducing large-scale pose differences and preventing non-rigid registration from becoming trapped in local optima. In the fine registration stage, a deep neural network integrating multi-vertebra affine modeling and diffeomorphic deformation was developed. The multi-vertebra affine branch characterized the approximately rigid motion of different vertebral segments, while velocity-field integration was employed to generate a diffeomorphic deformation field for modeling the nonlinear deformation of intervertebral discs and surrounding soft tissues, thereby achieving high-precision non-rigid registration. Experiments were conducted on a self-built spinal CT-MRI dataset. The proposed method achieved Dice, HD₉₅, and ASD values of 91.43%, 2.61 mm, and 1.16 mm, respectively, in the lumbar region, outperforming all comparison methods. Quantitative results demonstrated that the proposed method improved the regional overlap and boundary alignment accuracy of lumbar vertebral structures. The visualization results further demonstrated that it could effectively coordinate the local near-rigid motion of vertebral segments with the continuous deformation of surrounding tissues, maintaining good stability and anatomical rationality. This provided a technical foundation for preoperative multimodal image fusion and subsequent surgical navigation.

Key words: spine, multimodal registration, CT-MRI, deep learning, diffeomorphic registration

中图分类号:

杨志永, 刘迪, 姜杉, 周泽洋, 韩军, 朱珊, 陈超. 基于多椎体结构建模的CT-MRI脊柱多阶段配准方法[J]. 图学学报, 2026, 47(5): 1033-1042.

YANG Zhiyong, LIU Di, JIANG Shan, ZHOU Zeyang, HAN Jun, ZHU Shan, CHEN Chao. A multi-stage CT-MRI registration method for spine based on multi-vertebra structural modeling[J]. Journal of Graphics, 2026, 47(5): 1033-1042.

图/表 10

图1 CT自动分割效果((a) 原始CT图像；(b)~(d) 不同矢状位分割结果；(e) 三维重建结果)

Fig. 1 Automatic segmentation results of CT ((a) Original CT image; (b)~(d) Segmentation results at different sagittal slices; (e) 3D reconstruction result)

图2 MRI自动分割结果((a) 原始MRI图像；(b)~(d) 不同矢状位分割结果；(e) 三维重建结果)

Fig. 2 Automatic segmentation results of MRI ((a) Original MRI image; (b)~(d) Segmentation results at different sagittal slices; (e) 3D reconstruction result)

图3 脊柱 CT-MRI 精配准深度神经网络结构

Fig. 3 Deep neural network architecture for spinal CT-MRI fine registration

表1 精配准网络主要结构参数

Table 1 Main structural parameters of the fine registration network

模块	尺度变化	主要操作	通道数
输入层	1	CT与MRI通道拼接	2
编码器	1→1/16	4个3D下采样卷积块，步长为2，InstanceNorm3D，LeakyReLU(0.2)	2→16→32→32→32
多椎体仿射分支	1/16	深层特征展平后分别输入K个全连接层，每层输出一个3×4仿射矩阵	K×12
多尺度仿射先验	1/16，1/8，1/4，1/2	将K个椎体仿射矩阵转换为多尺度稠密位移先验，并在通道维度拼接	3K
解码融合模块	1/16→1	4个组合单元逐层融合编码器特征、上采样特征和仿射先验	32
特征细化模块	1	3个3D卷积块进一步细化全分辨率特征	32→32→16→16
速度场预测层	1	3×3×3卷积输出三维速度场	16→3
形变场生成	1	scaling-and-squaring积分，积分步数为7	3

图4 CT-MRI 粗配准结果((a)~(c) 不同矢状位配准结果；(d) 三维融合结果)

Fig. 4 CT-MRI coarse registration results ((a)~(c) Registration results on different sagittal slices; (d) 3D fusion result)

图5 CT-MRI 精配准结果((a)~(c) 不同矢状位配准结果；(d) 三维融合结果)

Fig. 5 CT-MRI fine registration results ((a)~(c) Registration results on different sagittal slices; (d) 3D fusion result)

图6 不同配准方法生成的可视化结果

Fig. 6 Visualization results generated by different registration methods

表2 不同方法的CT-MRI配准定量结果对比

Table 2 Quantitative comparison of CT-MRI registration results among different methods

方法	Dice /%↑	HD₉₅ /mm↓	ASD /mm↓
Affine^[22]	79.64±6.95	7.58±2.31	3.64±0.81
ANTs-SyN^[10]	81.37±3.34	6.18±1.54	2.37±0.43
VoxelMorph^[13]	88.95±2.05	5.47±1.22	1.61±0.35
LapIRN^[23]	86.24±3.52	5.27±1.47	1.75±0.58
TransMorph^[15]	90.65±1.84	3.85±1.18	1.43±0.38
Ours	91.43±1.52	2.61±0.81	1.16±0.41

表3 精配准网络推理效率对比

Table 3 Comparison of inference efficiency among fine registration networks

方法	配准时间/s	参数量/M	GPU memory/MB
VoxelMorph^[13]	0.496±0.003	0.31	1 682
LapIRN^[23]	0.429±0.003	1.26	2 742
TransMorph^[15]	0.645±0.004	46.73	6 704
Ours	0.710±0.005	8.28	4 733

表4 消融实验结果对比

Table 4 Ablation study of the proposed method

方法	Dice /% ↑	HD₉₅ /mm ↓	ASD /mm ↓
B	76.53±4.83	6.24±1.46	2.18±0.52
B+Coarse	84.51±2.34	4.86±1.23	1.63±0.43
B+Coarse+MVA	86.28±1.78	4.56±1.06	1.48±0.44
B+Coarse+MVA+Diff	89.62±1.54	3.12±0.92	1.28±0.42
Ours	91.43±1.52	2.61±0.81	1.16±0.41

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