2019,
Medical Physics.
DOI:
10.1002/mp.13976
Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge
Elizabeth M. McKenzie, Anand Santhanam, Dan Ruan, Daniel O'Connor, Minsong Cao, Ke Sheng
Abstract:
Purpose To develop and demonstrate the efficacy of a novel head‐and‐neck multimodality image registration technique using deep‐learning‐based cross‐modality synthesis. Methods and Materials Twenty‐five head‐and‐neck patients received magnetic resonance (MR) and computed tomography (CT) (CTaligned ) scans on the same day with the same immobilization. Fivefold cross validation was used with all of the MR‐CT pairs to train a neural network to generate synthetic CTs from MR images. Twenty‐four of 25 patients also had a separate CT without immobilization (CTnon‐aligned ) and were used for testing. CTnon‐aligned 's were deformed to the synthetic CT, and compared to CTnon‐aligned registered to MR. The same registrations were performed from MR to CTnon‐aligned and from synthetic CT to CTnon‐aligned . All registrations used B‐splines for modeling the deformation, and mutual information for the objective. Results were evaluated using the 95% Hausdorff distance among spinal cord contours, landmark error, inverse consistency, and Jacobian determinant of the estimated deformation fields. Results When large initial rigid misalignment is present, registering CT to MRI‐derived synthetic CT aligns the cord better than a direct registration. The average landmark error decreased from 9.8 ± 3.1 mm in MR→CTnon‐aligned to 6.0 ± 2.1 mm in CTsynth →CTnon‐aligned deformable registrations. In the CT to MR direction, the landmark error decreased from 10.0 ± 4.3 mm in CTnon‐aligned →MR deformable registrations to 6.6 ± 2.0 mm in CTnon‐aligned →CTsynth deformable registrations. The Jacobian determinant had an average value of 0.98. The proposed method also demonstrated improved inverse consistency over the direct method. Conclusions We showed that using a deep learning‐derived synthetic CT in lieu of an MR for MR→CT and CT→MR deformable registration offers superior results to direct multimodal registration.
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2024-02-28 10:57:00
前进:
#paper Mckenzie E M , Santhanam A , Ruan D ,et al.Multimodality image registration in the head‐and‐neck using a deep learning‐derived synthetic CT as a bridge[J].Medical Physics, 2020, 47(3).DOI:10.1002/mp.13976.
本文提出并验证一种利用深度学习驱动的跨模态综合技术的头颈多模式图像配准方法。
采用CycleGAN将MRI 转化为合成CT(sCT),将头颈部的MRI-CT多模态配准转化为sCT-CT的单模态配准。配准方法采用传统的B-spline方法。实验结果表明sCT→CT 配准精度好于MRI→CT。平均配准误差从9.8mm下降到6.0mm