Division of Radiation Biophysics, Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA 02114, United States of America.
Harvard Medical School, Boston, MA 02114, United States of America.
Phys Med Biol. 2022 Jul 22;67(15). doi: 10.1088/1361-6560/ac8045.
Soft-tissue sarcoma spreads preferentially along muscle fibers. We explore the utility of deriving muscle fiber orientations from diffusion tensor MRI (DT-MRI) for defining the boundary of the clinical target volume (CTV) in muscle tissue.We recruited eight healthy volunteers to acquire MR images of the left and right thigh. The imaging session consisted of (a) two MRI spin-echo-based scans, T1- and T2-weighted; (b) a diffusion weighted (DW) spin-echo-based scan using an echo planar acquisition with fat suppression. The thigh muscles were auto-segmented using the convolutional neural network. DT-MRI data were used as a geometry encoding input to solve the anisotropic Eikonal equation with the Hamiltonian Fast-Marching method. The isosurfaces of the solution modeled the CTV boundary.The auto-segmented muscles of the thigh agreed with manually delineated with the Dice score ranging from 0.8 to 0.94 for different muscles. To validate our method of deriving muscle fiber orientations, we compared anisotropy of the isosurfaces across muscles with different anatomical orientations within a thigh, between muscles in the left and right thighs of each subject, and between different subjects. The fiber orientations were identified reproducibly across all comparisons. We identified two controlling parameters, the distance from the gross tumor volume to the isosurface and the eigenvalues ratio, to tailor the proposed CTV to the satisfaction of the clinician.Our feasibility study with healthy volunteers shows the promise of using muscle fiber orientations derived from DW MRI data for automated generation of anisotropic CTV boundary in soft tissue sarcoma. Our contribution is significant as it serves as a proof of principle for combining DT-MRI information with tumor spread modeling, in contrast to using moderately informative 2D CT planes for the CTV delineation. Such improvements will positively impact the cancer centers with a small volume of sarcoma patients.
软组织肉瘤沿肌肉纤维优先扩散。我们探索从弥散张量磁共振成像（DT-MRI）中获取肌肉纤维方向来定义肌肉组织临床靶区（CTV）边界的效用。我们招募了 8 名健康志愿者，对左右大腿进行磁共振成像。成像过程包括：（a）两次基于磁共振自旋回波的扫描，T1 和 T2 加权；（b）使用带有脂肪抑制的平面回波扩散加权（DW）自旋回波扫描。大腿肌肉使用卷积神经网络自动分割。DT-MRI 数据被用作几何编码输入，使用哈密顿快速行进方法求解各向异性的 Eikonal 方程。解决方案的等位面模拟了 CTV 边界。大腿的自动分割肌肉与手动描绘的肌肉吻合良好，不同肌肉的 Dice 评分范围为 0.8 至 0.94。为了验证我们从肌肉纤维方向推导出肌肉纤维方向的方法，我们比较了不同解剖方向的大腿内肌肉之间、每个受试者的左右大腿之间以及不同受试者之间的各向异性。纤维方向在所有比较中都具有可重复性。我们确定了两个控制参数，即从大体肿瘤体积到等位面的距离和特征值比，以根据临床医生的满意度调整所提出的 CTV。我们对健康志愿者的可行性研究表明，使用 DW MRI 数据推导出的肌肉纤维方向可以为软组织肉瘤的自动生成各向异性 CTV 边界提供有希望的方法。我们的贡献是重大的，因为它证明了将 DT-MRI 信息与肿瘤扩散建模相结合的原理，而不是使用信息适度的 2D CT 平面进行 CTV 描绘。这些改进将对肉瘤患者数量较少的癌症中心产生积极影响。
