CDFRegNet: A cross-domain fusion registration network for CT-to-CBCT image registration.

BACKGROUND AND OBJECTIVE: Computer tomography (CT) to cone-beam computed tomography (CBCT) image registration plays an important role in radiotherapy treatment placement, dose verification, and anatomic changes monitoring during radiotherapy. However, fast and accurate CT-to-CBCT image registration is still very challenging due to the intensity differences, the poor image quality of CBCT images, and inconsistent structure information. METHODS: To address these problems, a novel unsupervised network named cross-domain fusion registration network (CDFRegNet) is proposed. First, a novel edge-guided attention module (EGAM) is designed, aiming at capturing edge information based on the gradient prior images and guiding the network to model the spatial correspondence between two image domains. Moreover, a novel cross-domain attention module (CDAM) is proposed to improve the network's ability to guide the network to effectively map and fuse the domain-specific features. RESULTS: Extensive experiments on a real clinical dataset were carried out, and the experimental results verify that the proposed CDFRegNet can register CT to CBCT images effectively and obtain the best performance, while compared with other representative methods, with a mean DSC of 80.01±7.16%, a mean TRE of 2.27±0.62 mm, and a mean MHD of 1.50±0.32 mm. The ablation experiments also proved that our EGAM and CDAM can further improve the accuracy of the registration network and they can generalize well to other registration networks. CONCLUSION: This paper proposed a novel CT-to-CBCT registration method based on EGAM and CDAM, which has the potential to improve the accuracy of multi-domain image registration.

Incorporation of residual attention modules into two neural networks for low-dose CT denoising.

PURPOSE: The low-dose computed tomography (CT) imaging can reduce the damage caused by x-ray radiation to the human body. However, low-dose CT images have a different degree of artifacts than conventional CT images, and their resolution is lower than that of conventional CT images, which can affect disease diagnosis by clinicians. Therefore, methods for noise-level reduction and resolution improvement in low-dose CT images have inevitably become a research hotspot in the field of low-dose CT imaging. METHODS: In this paper, residual attention modules (RAMs) are incorporated into the residual encoder-decoder convolutional neural network (RED-CNN) and generative adversarial network with Wasserstein distance (WGAN) to learn features that are beneficial to improving the performances of denoising networks, and developed models are denoted as RED-CNN-RAM and WGAN-RAM, respectively. In detail, RAM is composed of a multi-scale convolution module and an attention module built on the residual network architecture, where the attention module consists of a channel attention module and a spatial attention module. The residual network architecture solves the problem of network degradation with increased network depth. The function of the attention module is to learn which features are beneficial to reduce the noise level of low-dose CT images to reduce the loss of detail in the final denoising images, which is also the key point of the proposed algorithms. RESULTS: To develop a robust network for low-dose CT image denoising, multidose-level torso phantom images provided by a cooperating equipment vendor are used to train the network, which can improve the network's adaptability to clinical application. In addition, a clinical dataset is used to test the network's migration capabilities and clinical applicability. The experimental results demonstrate that these proposed networks can effectively remove noise and artifacts from multidose CT scans. Subjective and objective analyses of multiple groups of comparison experiments show that the proposed networks achieve good noise suppression performance while preserving the image texture details. CONCLUSION: In this study, two deep learning network models are developed using multidose-level CT images acquired from a commercial spiral CT scanner. The two network models can reduce and even remove streaking artifacts, and noise from low-dose CT images confirms the effectiveness of the proposed algorithms.

Unsupervised learning for deformable registration of thoracic CT and cone-beam CT based on multiscale features matching with spatially adaptive weighting.

PURPOSE: Cone-beam computed tomography (CBCT) is a common on-treatment imaging widely used in image-guided radiotherapy. Fast and accurate registration between the on-treatment CBCT and planning CT is significant for and precise adaptive radiotherapy treatment (ART). However, existing CT-CBCT registration methods, which are mostly affine or time-consuming intensity- based deformation registration, still need further study due to the considerable CT-CBCT intensity discrepancy and the artifacts in low-quality CBCT images. In this paper, we propose a deep learning-based CT-CBCT registration model to promote rapid and accurate CT-CBCT registration for radiotherapy. METHODS: The proposed CT-CBCT registration model consists of a registration network and an innovative deep similarity metric network. The registration network is a novel fully convolution network adapted specially for patch-wise CT-CBCT registration. The metric network, going beyond intensity, automatically evaluates the high-dimensional attribute-based dissimilarity between the registered CT and CBCT images. In addition, considering the artifacts in low-quality CBCT images, we add spatial weighting (SW) block to adaptively attach more importance to those informative voxels while inhibit the interference of artifact regions. Such SW-based metric network is expected to extract the most meaningful and discriminative deep features, and form a more reliable CT-CBCT similarity measure to train the registration network. RESULTS: We evaluate the proposed method on clinical thoracic CBCT and CT dataset, and compare the registration results with some other common image similarity metrics and some state-of-the-art registration algorithms. The proposed method provides the highest Structural Similarity index (86.17 ± 5.09), minimum Target Registration Error of landmarks (2.37 ± 0.32 mm), and the best DSC coefficient (78.71 ± 10.95) of tumor volumes. Moreover, our model also obtains comparable distance error of lung surfaces (1.75 ± 0.35 mm). CONCLUSION: The proposed model shows both efficiency and efficacy for reliable thoracic CT-CBCT registration, and can generate the matched CT and CBCT images within few seconds, which is of great significance to clinical radiotherapy.

Locally Adaptive Total p-Variation Regularization for Non-Rigid Image Registration With Sliding Motion.

Due to the complicated thoracic movements which contain both sliding motion occurring at lung surfaces and smooth motion within individual organs, respiratory estimation is still an intrinsically challenging task. In this paper, we propose a novel regularization term called locally adaptive total p-variation (LaTpV) and embed it into a parametric registration framework to accurately recover lung motion. LaTpV originates from a modified Lp-norm constraint (1 < p < 2), where a prior distribution of p modeled by the Dirac-shaped function is constructed to specifically assign different values to voxels. LaTpV adaptively balances the smoothness and discontinuity of the displacement field to encourage an expected sliding interface. Additionally, we also analytically deduce the gradient of the cost function with respect to transformation parameters. To validate the performance of LaTpV, we not only test it on two mono-modal databases including synthetic images and pulmonary computed tomography (CT) images, but also on a more difficult thoracic CT and positron emission tomography (PET) dataset for the first time. For all experiments, both the quantitative and qualitative results indicate that LaTpV significantly surpasses some existing regularizers such as bending energy and parametric total variation. The proposed LaTpV based registration scheme might be more superior for sliding motion correction and more potential for clinical applications such as the diagnosis of pleural mesothelioma and the adjustment of radiotherapy plans.

OCTRexpert:A Feature-based 3D Registration Method for Retinal OCT Images.

Medical image registration can be used for studying longitudinal and cross-sectional data, quantitatively monitoring disease progression and guiding computer assisted diagnosis and treatments. However, deformable registration which enables more precise and quantitative comparison has not been well developed for retinal optical coherence tomography (OCT) images. This paper proposes a new 3D registration approach for retinal OCT data called OCTRexpert. To the best of our knowledge, the proposed algorithm is the first full 3D registration approach for retinal OCT images which can be applied to longitudinal OCT images for both normal and serious pathological subjects. In this approach, a pre-processing method is first performed to remove eye motion artifact and then a novel design-detection-deformation strategy is applied for the registration. In the design step, a couple of features are designed for each voxel in the image. In the detection step, active voxels are selected and the point-to-point correspondences between the subject and template images are established. In the deformation step, the image is hierarchically deformed according to the detected correspondences in multi-resolution. The proposed method is evaluated on a dataset with longitudinal OCT images from 20 healthy subjects and 4 subjects diagnosed with serious Choroidal Neovascularization (CNV). Experimental results show that the proposed registration algorithm consistently yields statistically significant improvements in both Dice similarity coefficient and the average unsigned surface error compared with the other registration methods.

Nonrigid Image Registration Using Spatially Region-Weighted Correlation Ratio and GPU-Acceleration.

OBJECTIVE: Nonrigid image registration with high accuracy and efficiency remains a challenging task for medical image analysis. In this paper, we present the spatially region-weighted correlation ratio (SRWCR) as a novel similarity measure to improve the registration performance. METHODS: SRWCR is rigorously deduced from a three-dimension joint probability density function combining the intensity channels with an extra spatial information channel. SRWCR estimates the optimal functional dependence between the intensities for each spatial bin, in which the spatial distribution modeled by a cubic B-spline function is used to differentiate the contribution of voxels. We also analytically derive the gradient of SRWCR with respect to the transformation parameters and optimize it using a quasi-Newton approach. Furthermore, we propose a GPU-based parallel mechanism to accelerate the computation of SRWCR and its derivatives. RESULTS: The experiments on synthetic images, public four-dimensional thoracic computed tomography (CT) dataset, retinal optical coherence tomography data, and clinical CT and positron emission tomography images confirm that SRWCR significantly outperforms some state-of-the-art techniques such as spatially encoded mutual information and Robust PaTch-based cOrrelation Ration. CONCLUSION: This study demonstrates the advantages of SRWCR in tackling the practical difficulties due to distinct intensity changes, serious speckle noise, or different imaging modalities. SIGNIFICANCE: The proposed registration framework might be more reliable to correct the nonrigid deformations and more potential for clinical applications.