From the Departments of Radiology (J.M., J.N., T.H.K., L.R., M.J.G., N.H.), Surgery (J.S.H., J.C., M.G., J.G.A.), and Pathology (C.F., N.U., J.S.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; Department of Radiology, University of São Paulo, São Paulo, Brazil (J.M., A.N.A., N.H.); Department of Medicine, Division of Precision Medicine, NYU Grossman School of Medicine, New York, NY (R.S.V.); Department of Biomedical Engineering, Vanderbilt University, Nashville, Tenn (J.S.H.); Research and Education Institute, Hospital Sirio-Libanes, São Paulo, Brazil (A.N.A.); and Department of Radiology, Mayo Clinic, Rochester, Minn (J.M., N.H.).
Radiol Imaging Cancer. 2024 Nov;6(6):e240073. doi: 10.1148/rycan.240073.
Purpose To develop a radiology-pathology coregistration method for 1:1 automated spatial mapping between preoperative rectal MRI and ex vivo rectal whole-mount histology (WMH). Materials and Methods This retrospective study included consecutive patients with rectal adenocarcinoma who underwent total neoadjuvant therapy followed by total mesorectal excision with preoperative rectal MRI and WMH from January 2019 to January 2022. A gastrointestinal pathologist and a radiologist established three corresponding levels for each patient at rectal MRI and WMH, subsequently delineating external and internal rectal wall contours and the tumor bed at each level and defining eight point-based landmarks. An advanced deformable image coregistration model based on the linearized iterative boundary reconstruction (LIBR) approach was compared with rigid point-based registration (PBR) and state-of-the-art deformable intensity-based multiscale spectral embedding registration (MSERg). Dice similarity coefficient (DSC), modified Hausdorff distance (MHD), and target registration error (TRE) across patients were calculated to assess the coregistration accuracy of each method. Results Eighteen patients (mean age, 54 years ± 13 [SD]; nine female) were included. LIBR demonstrated higher DSC versus PBR for external and internal rectal wall contours and tumor bed (external: 0.95 ± 0.03 vs 0.86 ± 0.04, respectively, < .001; internal: 0.71 ± 0.21 vs 0.61 ± 0.21, < .001; tumor bed: 0.61 ± 0.17 vs 0.52 ± 0.17, = .001) and versus MSERg for internal rectal wall contours (0.71 ± 0.21 vs 0.63 ± 0.18, respectively; < .001). LIBR demonstrated lower MHD versus PBR for external and internal rectal wall contours and tumor bed (external: 0.56 ± 0.25 vs 1.68 ± 0.56, respectively, < .001; internal: 1.00 ± 0.35 vs 1.62 ± 0.59, < .001; tumor bed: 2.45 ± 0.99 vs 2.69 ± 1.05, = .03) and versus MSERg for internal rectal wall contours (1.00 ± 0.35 vs 1.62 ± 0.59, respectively; < .001). LIBR demonstrated lower TRE (1.54 ± 0.39) versus PBR (2.35 ± 1.19, = .003) and MSERg (2.36 ± 1.43, = .03). Computation time per WMH slice for LIBR was 35.1 seconds ± 12.1. Conclusion This study demonstrates feasibility of accurate MRI-WMH coregistration using the advanced LIBR method. MR Imaging, Abdomen/GI, Rectum, Oncology © RSNA, 2024.
目的 为术前直肠 MRI 与离体直肠全层组织学（WMH）之间的 1:1 自动空间映射开发一种放射科-病理学配准方法。
材料与方法 本回顾性研究纳入了 2019 年 1 月至 2022 年 1 月期间接受新辅助治疗后行全直肠系膜切除术的连续直肠腺癌患者，这些患者术前均行直肠 MRI 和 WMH。一名胃肠病学病理学家和放射科医生在每个患者的直肠 MRI 和 WMH 上确定了三个对应的水平，随后在每个水平上勾画外部和内部直肠壁轮廓以及肿瘤床，并定义了 8 个基于点的标志点。与刚性基于点的配准（PBR）和最先进的基于变形强度的多尺度谱嵌入配准（MSERg）相比，我们比较了基于线性迭代边界重建（LIBR）方法的高级变形图像配准模型。计算每个方法的患者间的 Dice 相似系数（DSC）、修改后的 Hausdorff 距离（MHD）和目标配准误差（TRE），以评估配准的准确性。
结果 纳入了 18 名患者（平均年龄，54 岁±13[标准差]；9 名女性）。与 PBR 相比，LIBR 在外、内直肠壁轮廓和肿瘤床上的 DSC 更高（外：0.95±0.03 比 0.86±0.04，均<0.001；内：0.71±0.21 比 0.61±0.21，均<0.001；肿瘤床：0.61±0.17 比 0.52±0.17， =.001），在内部直肠壁轮廓上与 MSERg 相比也更高（0.71±0.21 比 0.63±0.18，均<0.001）。与 PBR 相比，LIBR 在外、内直肠壁轮廓和肿瘤床上的 MHD 更低（外：0.56±0.25 比 1.68±0.56，均<0.001；内：1.00±0.35 比 1.62±0.59，均<0.001；肿瘤床：2.45±0.99 比 2.69±1.05， =.03），在内部直肠壁轮廓上与 MSERg 相比也更低（1.00±0.35 比 1.62±0.59，均<0.001）。LIBR 的 TRE（1.54±0.39）比 PBR（2.35±1.19， =.003）和 MSERg（2.36±1.43， =.03）更低。LIBR 每处理一个 WMH 切片的计算时间为 35.1 秒±12.1。
结论 本研究证明了使用先进的 LIBR 方法进行准确的 MRI-WMH 配准是可行的。
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