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Han R, Jones CK, Lee J, Wu P, Vagdargi P, Uneri A, Helm PA, Luciano M, Anderson WS, Siewerdsen JH. Deformable MR-CT image registration using an unsupervised, dual-channel network for neurosurgical guidance. Med Image Anal 2022; 75:102292. [PMID: 34784539 PMCID: PMC10229200 DOI: 10.1016/j.media.2021.102292] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 10/22/2021] [Accepted: 10/25/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE The accuracy of minimally invasive, intracranial neurosurgery can be challenged by deformation of brain tissue - e.g., up to 10 mm due to egress of cerebrospinal fluid during neuroendoscopic approach. We report an unsupervised, deep learning-based registration framework to resolve such deformations between preoperative MR and intraoperative CT with fast runtime for neurosurgical guidance. METHOD The framework incorporates subnetworks for MR and CT image synthesis with a dual-channel registration subnetwork (with synthesis uncertainty providing spatially varying weights on the dual-channel loss) to estimate a diffeomorphic deformation field from both the MR and CT channels. An end-to-end training is proposed that jointly optimizes both the synthesis and registration subnetworks. The proposed framework was investigated using three datasets: (1) paired MR/CT with simulated deformations; (2) paired MR/CT with real deformations; and (3) a neurosurgery dataset with real deformation. Two state-of-the-art methods (Symmetric Normalization and VoxelMorph) were implemented as a basis of comparison, and variations in the proposed dual-channel network were investigated, including single-channel registration, fusion without uncertainty weighting, and conventional sequential training of the synthesis and registration subnetworks. RESULTS The proposed method achieved: (1) Dice coefficient = 0.82±0.07 and TRE = 1.2 ± 0.6 mm on paired MR/CT with simulated deformations; (2) Dice coefficient = 0.83 ± 0.07 and TRE = 1.4 ± 0.7 mm on paired MR/CT with real deformations; and (3) Dice = 0.79 ± 0.13 and TRE = 1.6 ± 1.0 mm on the neurosurgery dataset with real deformations. The dual-channel registration with uncertainty weighting demonstrated superior performance (e.g., TRE = 1.2 ± 0.6 mm) compared to single-channel registration (TRE = 1.6 ± 1.0 mm, p < 0.05 for CT channel and TRE = 1.3 ± 0.7 mm for MR channel) and dual-channel registration without uncertainty weighting (TRE = 1.4 ± 0.8 mm, p < 0.05). End-to-end training of the synthesis and registration subnetworks also improved performance compared to the conventional sequential training strategy (TRE = 1.3 ± 0.6 mm). Registration runtime with the proposed network was ∼3 s. CONCLUSION The deformable registration framework based on dual-channel MR/CT registration with spatially varying weights and end-to-end training achieved geometric accuracy and runtime that was superior to state-of-the-art baseline methods and various ablations of the proposed network. The accuracy and runtime of the method may be compatible with the requirements of high-precision neurosurgery.
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Affiliation(s)
- R Han
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - C K Jones
- The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States
| | - J Lee
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD, United States
| | - P Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - P Vagdargi
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States
| | - A Uneri
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - P A Helm
- Medtronic Inc., Littleton, MA, United States
| | - M Luciano
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States
| | - W S Anderson
- Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States
| | - J H Siewerdsen
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States; The Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States; Department of Computer Science, Johns Hopkins University, Baltimore, MD, United States; Department of Neurosurgery, Johns Hopkins Hospital, Baltimore, MD, United States.
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Casamitjana A, Mancini M, Iglesias JE. Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images. Simul Synth Med Imaging 2021; 12965:44-54. [PMID: 34778892 PMCID: PMC8582976 DOI: 10.1007/978-3-030-87592-3_5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Nonlinear inter-modality registration is often challenging due to the lack of objective functions that are good proxies for alignment. Here we propose a synthesis-by-registration method to convert this problem into an easier intra-modality task. We introduce a registration loss for weakly supervised image translation between domains that does not require perfectly aligned training data. This loss capitalises on a registration U-Net with frozen weights, to drive a synthesis CNN towards the desired translation. We complement this loss with a structure preserving constraint based on contrastive learning, which prevents blurring and content shifts due to overfitting. We apply this method to the registration of histological sections to MRI slices, a key step in 3D histology reconstruction. Results on two public datasets show improvements over registration based on mutual information (13% reduction in landmark error) and synthesis-based algorithms such as CycleGAN (11% reduction), and are comparable to registration with label supervision. Code and data are publicly available at https://github.com/acasamitjana/SynthByReg.
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Affiliation(s)
| | - Matteo Mancini
- Department of Neuroscience, University of Sussex, Brighton, UK
- NeuroPoly Lab, Polytechnique Montreal, Canada
- CUBRIC, Cardiff University, UK
| | - Juan Eugenio Iglesias
- Center for Medical Image Computing, University College London, UK
- Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, USA
- Computer Science and AI Laboratory, Massachusetts Institute of Technology, USA
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