Deformable CT image registration via a dual feasible neural network

Lei, Yang; Fu, Yabo; Tian, Zhen; Wang, Tonghe; Dai, Xianjin; Roper, Justin; Yu, David S.; McDonald, Mark W.; Bradley, Jeffrey D.; Liu, Tian; Zhou, Jun; Yang, Xiaofeng

doi:10.1002/mp.15875

Cited by 8 publications

(4 citation statements)

References 28 publications

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“…We also used the Jacobian determinant for evaluation based on our previous study. 28 It was calculated for assessment of characteristics that affected fidelity of the predicted DVF, including topological preservation and minimization of physically unrealistic deformation. Performance was inspected through comparison of the original and deformed images, along with the subtracted images.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

Deformable lung 4DCT image registration via landmark‐driven cycle network

Matkovic,

Lei,

et al. 2023

Medical Physics

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BackgroundAn automated, accurate, and efficient lung four‐dimensional computed tomography (4DCT) image registration method is clinically important to quantify respiratory motion for optimal motion management.PurposeThe purpose of this work is to develop a weakly supervised deep learning method for 4DCT lung deformable image registration (DIR).MethodsThe landmark‐driven cycle network is proposed as a deep learning platform that performs DIR of individual phase datasets in a simulation 4DCT. This proposed network comprises a generator and a discriminator. The generator accepts moving and target CTs as input and outputs the deformation vector fields (DVFs) to match the two CTs. It is optimized during both forward and backward paths to enhance the bi‐directionality of DVF generation. Further, the landmarks are used to weakly supervise the generator network. Landmark‐driven loss is used to guide the generator's training. The discriminator then judges the realism of the deformed CT to provide extra DVF regularization.ResultsWe performed four‐fold cross‐validation on 10 4DCT datasets from the public DIR‐Lab dataset and a hold‐out test on our clinic dataset, which included 50 4DCT datasets. The DIR‐Lab dataset was used to evaluate the performance of the proposed method against other methods in the literature by calculating the DIR‐Lab Target Registration Error (TRE). The proposed method outperformed other deep learning‐based methods on the DIR‐Lab datasets in terms of TRE. Bi‐directional and landmark‐driven loss were shown to be effective for obtaining high registration accuracy. The mean and standard deviation of TRE for the DIR‐Lab datasets was 1.20 ± 0.72 mm and the mean absolute error (MAE) and structural similarity index (SSIM) for our datasets were 32.1 ± 11.6 HU and 0.979 ± 0.011, respectively.ConclusionThe landmark‐driven cycle network has been validated and tested for automatic deformable image registration of patients’ lung 4DCTs with results comparable to or better than competing methods.

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Section: Implementation and Evaluationmentioning

confidence: 99%

Deformable lung 4DCT image registration via landmark‐driven cycle network

Matkovic,

Lei,

et al. 2023

Medical Physics

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“…Medical image registration seeks to find an optimal spatial transformation that best aligns the underlying anatomical structures. Medical image registration is used in many clinical applications such as image guidance [4][5][6][7][8][9][10]. Fast and accurate image registration is necessary for effective quantitative analysis of tumor motion during the respiratory cycle, which allows for optimal gating window selection in phase-gated 4DCT treatments.…”

Section: Introductionmentioning

confidence: 99%

Deformable image registration of chest CT using deep cycle network

Luke

Lei

et al. 2023

Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

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This study aims to develop a deep learning method to effectively consider respiratory motion for the generation of realistic dose-volume histograms for more accurate and efficient propagation of organ and tumor contours from a target phase to all phases in lung 4DCT patient datasets. Our proposed method is a platform that performs Deformable Image Registration (DIR) of individual phase datasets in a simulation 4DCT and comprises a generator and discriminator. The generator accepts moving and target CTs as input and outputs the Deformation Vector Fields (DVFs) to match the two CTs. The generator is optimized during both forward and backward paths to enhance the bidirectionality of DVF generation. Further, the landmarks are used to weakly supervise the generator network, specifically through landmark-driven loss. The discriminator then judges the realism of the deformed CT to provide extra DVF regularization. A publicly available DIR-Lab dataset was used to evaluate the performance of the proposed method against other methods in the literature by calculating the DIR-Lab Target Registration Error (TRE). The proposed method outperformed other deep learning-based methods on the DIR-Lab datasets in terms of TRE. Bi-directional and landmark-driven loss were shown to be effective for obtaining high registration accuracy. The mean of TRE for the DIR-Lab datasets was 1.03±0.66 mm. These results demonstrated the feasibility and efficacy of our proposed method, which provides a potential method for realistic registration of phases in 4DCT lung datasets.

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“…The application of spatial transformer network (STN) in the field of registration 6 has allowed great progress in the application of deep learning in unsupervised image registration. [7][8][9][10] The unsupervised registration method based on deep learning is similar to the traditional method, which updates the network by calculating the loss of similarity measure between the fixed image and warped images predicted by the network and backpropagating it. The advantage is that the network is trained with a large amount of data to improve the network's ability for a specific task and obtain higher registration accuracy; only one forward calculation of the network is needed in the testing phase to produce prediction results, which greatly reduces the time spent on the calculation.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiscale feature fusion network for 3D head MRI image registration

Yang

Chen

et al. 2023

Medical Physics

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BackgroundImage registration technology has become an important medical image preprocessing step with the wide application of computer‐aided diagnosis technology in various medical image analysis tasks.PurposeWe propose a multiscale feature fusion registration based on deep learning to achieve the accurate registration and fusion of head magnetic resonance imaging (MRI) and solve the problem that general registration methods cannot handle the complex spatial information and position information of head MRI.MethodsOur proposed multiscale feature fusion registration network consists of three sequentially trained modules. The first is an affine registration module that implements affine transformation; the second is to realize non‐rigid transformation, a deformable registration module composed of top‐down and bottom‐up feature fusion subnetworks in parallel; and the third is a deformable registration module that also realizes non‐rigid transformation and is composed of two feature fusion subnetworks in series. The network decomposes the deformation field of large displacement into multiple deformation fields of small displacement by multiscale registration and registration, which reduces the difficulty of registration. Moreover, multiscale information in head MRI is learned in a targeted manner, which improves the registration accuracy, by connecting the two feature fusion subnetworks.ResultsWe used 29 3D head MRIs for training and seven volumes for testing and calculated the values of the registration evaluation metrics for the new algorithm to register anterior and posterior lateral pterygoid muscles. The Dice similarity coefficient was 0.745 ± 0.021, the Hausdorff distance was 3.441 ± 0.935 mm, the Average surface distance was 0.738 ± 0.098 mm, and the Standard deviation of the Jacobian matrix was 0.425 ± 0.043. Our new algorithm achieved a higher registration accuracy compared with state‐of‐the‐art registration methods.ConclusionsOur proposed multiscale feature fusion registration network can realize end‐to‐end deformable registration of 3D head MRI, which can effectively cope with the characteristics of large deformation displacement and the rich details of head images and provide reliable technical support for the diagnosis and analysis of head diseases.

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Deformable CT image registration via a dual feasible neural network

Cited by 8 publications

References 28 publications

Deformable lung 4DCT image registration via landmark‐driven cycle network

Deformable lung 4DCT image registration via landmark‐driven cycle network

Deformable image registration of chest CT using deep cycle network

Multiscale feature fusion network for 3D head MRI image registration

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