Multi-modal image registration using structural features.

Multi-modal image registration has significant meanings in clinical diagnosis, treatment planning, and image-guided surgery. Since different modalities exhibit different characteristics, finding a fast and accurate correspondence between images of different modalities is still a challenge. In this paper, we propose an image synthesis-based multi-modal registration framework. Image synthesis is performed by a ten-layer fully convolutional network (FCN). The network is composed of 10 convolutional layers combined with batch normalization (BN) and rectified linear unit (ReLU), which can be trained to learn an end-to-end mapping from one modality to the other. After the cross-modality image synthesis, multi-modal registration can be transformed into mono-modal registration. The mono-modal registration can be solved by methods with lower computational complexity, such as sum of squared differences (SSD). We tested our method in T1-weighted vs T2-weighted, T1-weighted vs PD, and T2-weighted vs PD image registrations with BrainWeb phantom data and IXI real patients' data. The result shows that our framework can achieve higher registration accuracy than the state-of-the-art multi-modal image registration methods, such as local mutual information (LMI) and α-mutual information (α-MI). The average registration errors of our method in experiment with IXI real patients' data were 1.19, 2.23, and 1.57 compared to 1.53, 2.60, and 2.36 of LMI and 1.34, 2.39, and 1.76 of α-MI in T2-weighted vs PD, T1-weighted vs PD, and T1-weighted vs T2-weighted image registration, respectively. In this paper, we propose an image synthesis-based multi-modal image registration framework. A deep FCN model is developed to perform image synthesis for this framework, which can capture the complex nonlinear relationship between different modalities and discover complex structural representations automatically by a large number of trainable mapping and parameters and perform accurate image synthesis. The framework combined with the deep FCN model and mono-modal registration methods (SSD) can achieve fast and robust results in multi-modal medical image registration. Graphical abstract The workflow of proposed multi-modal image registration framework.

Image registration is an elementary task in medical image processing and analysis, which can be divided into monomodal and multimodal. Direct 3D multimodal registration in volumetric medical images can provide more insight into the interpretation of subsequent image processing applications than 2D methods. This paper is dedicated to the development of a 3D multimodal image registration algorithm based on a viscous fluid model associated with the Bhattacharyya distance. In our approach, a modified Navier-Stoke's equation is exploited as the foundation of the multimodal image registration framework. The hopscotch method is numerically implemented to solve the velocity field, whose values at the explicit locations are first computed and the values at the implicit positions are solved by transposition. The differential of the Bhattacharyya distance is incorporated into the body force function, which is the main driving force for deformation, to enable multimodal registration. A variety of simulated and real brain MR images were utilized to assess the proposed 3D multimodal image registration system. Preliminary experimental results indicated that our algorithm produced high registration accuracy in various registration scenarios and outperformed other competing methods in many multimodal image registration tasks.Clinical Relevance- This facilitates the disease diagnosis and treatment planning that requires accurate 3D multimodal image registration without massive image data and extensive training regardless of the imaging modality.

Over the past few decades, fast<strong>-</strong>growth has occurred in the area of medical image acquisition devices, and physicians now rely on the utilization of medical images for the diagnosis, treatment plans, and surgical guidance. Researchers have classified medical images according to two structures: anatomical and functional structures. Due to this classification, the data obtained from two or more images of the same object frequently provide complementary and more abundant information through a process known as multimodal medical model registration. Image registration is spatially mapping the coordinate system of the two images obtained from a different viewpoint and utilizing various sensors. Several automatic multimodal medical image registration algorithms have been introduced based on types of medical images and their applications to increase the reliability, robustness, and accuracy. Due to the diversity in imaging and the different demands<strong> </strong>for applications, there is no single registration algorithm that can do that. This paper introduces a novel method for developing a multimodal medical image registration system that can select the most accepted registration algorithm from a group of registration algorithms for a variety of input datasets. The method described here is based on a machine learning technique that selects the most promising candidate. Several experiments have been conducted, and the results reveal that the novel approach leads to considerably faster reliability, accuracy, and more robustness registration algorithm selection.

Deep learning based data-adaptive descriptor for non-rigid multi-modal medical image registration

CoCycleReg: Collaborative cycle-consistency method for multi-modal medical image registration

Multimodal medical image deformable registration is a critical foundation for liver tumor interventional therapy, assisting doctors in path planning and ablation efficacy evaluation. Research on this topic is still limited, especially regarding the challenges in liver multimodal deformable registration, such as significant image intensity differences and large organ deformations. In this paper, we propose DMCR--a novel multimodal multiscale image registration model based on a dual-stream Mamba with contrastive representation. During the encoding stage, images from different modalities are fed into distinct feature branches constructed using the Mamba architecture. In the registration stage, we propose a multiscale registration approach based on a decoding strategy, wherein the deformation field is progressively propagated and fused across multiple scales. Furthermore, we introduce a modality-invariant contrastive loss to guide the model in capturing intrinsic image features during the encoding stage, while disregarding modality-specific details, thus enhancing the effectiveness of subsequent registration. We trained and tested our model on multimodal liver ablation datasets from three different medical centers. The results demonstrate that our proposed model achieves superior registration performance compared to state-of-the-art registration methods and also exhibits better generalization capability.Clinical relevance- This model effectively performs multi-modal image deformable registration, assisting physicians in facilitating interventional procedures and evaluating multimodal therapeutic outcomes.

In this thesis, we focus on two important medical image processing and analysis problems: multimodal image registration and cerebrovascular segmentation. First, for the purpose of improving the robustness of image registration, we propose two novel registration objective functions and a new technique for better optimization performance. The first novel objective function is a combination of the multi-dimensional mutual information and an angle measure on the maximum distance-gradient (MDG) vector field. The MDG vector field is a new spatial feature field designed for registration tasks. It encodes both local edge information and globally defined spatial information related to the intensity difference, the distance, and the direction of a voxel to a certain object boundary point. This objective function then integrates both the magnitude and the orientation information of the MDG feature into the image registration process. The other novel objective function can be obtained if the statistical joint intensity mappings estimated from the pre-aligned training image pairs are available. This a priori knowledge can then be used as reference to guide the multi-resolution based registration process, by minimizing a new Kullback-Leibler distance (KLD) based similarity measure between the observed joint intensity distribution and the reference joint intensity distribution. To achieve better optimization, which evidently affects the robustness of a registration process, we propose a general technique based on random transformation disturbance to assist optimization procedures to avoid local optima, and hence to achieve robust optimization results. The technique is based on different properties of the local neighborhood of a global optimum and those of local optima. Second, this thesis presents a statistical segmentation technique for the extraction of vasculatures in three-dimensional rotational angiography (3D-RA). This method uses maximum intensity projections (MIP) to improve the accuracy and robustness of threshold estimation using the expectation maximization (EM) algorithm. It is fully automatic and computationally efficient. Finally, encouraging experimental results of the proposed approaches on both synthetic and clinical medical images are presented in the thesis.

Multi-modal registration is a fundamental step for many medical imaging procedures. In this paper, the sum of conditional variance (SCV) similarity measure is proposed for 3D multi-modal medical image registration. The SCV similarity measure is based on minimizing the sum of conditional variances that are calculated using the joint histogram of the two images to be registered. Standard Gauss-Newton optimization is used to automatically minimize this measure which allows fast computational time and high accuracy. Experimental results show that our proposed approach is robust, computationally efficient and also more accurate when compared with the standard mutual information (MI) based approach and also the recently proposed sum-of-squared-difference on entropy images (eSSD) approach.

Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions – A comparative study on generalizability

Real-time multi-modal rigid registration based on a novel symmetric-SIFT descriptor

Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR)-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration. In the proposed method, PCANet is firstly trained on numerous medical images to learn convolution kernels for this network. Then, a pair of input medical images to be registered is processed by the learned PCANet. The features extracted by various layers in the PCANet are fused to produce multilevel features. The structural representation images are constructed for two input images based on nonlinear transformation of these multilevel features. The Euclidean distance between structural representation images is calculated and used as the similarity metrics. The objective function defined by the similarity metrics is optimized by L-BFGS method to obtain parameters of the free-form deformation (FFD) model. Extensive experiments on simulated and real multimodal image datasets show that compared with the state-of-the-art registration methods, such as modality-independent neighborhood descriptor (MIND), normalized mutual information (NMI), Weber local descriptor (WLD), and the sum of squared differences on entropy images (ESSD), the proposed method provides better registration performance in terms of target registration error (TRE) and subjective human vision.

Brain tumor detection and medical image registration are critical tasks in neuroimaging, enabling accurate diagnosis, treatment planning, and follow-up monitoring. Magnetic Resonance Imaging (MRI) remains the preferred modality for visualizing tumor morphology, while image registration facilitates the alignment of multimodal scans for enhanced clinical interpretation. This survey reviews recent advances in deep learning-based methods for brain tumor classification and medical image registration, focusing on Convolutional Neural Networks (CNNs), transfer learning architectures (e.g., VGG-19, ResNet, DenseNet201), and modern registration frameworks including supervised, unsupervised, GAN-based, and reinforcement learning approaches. A comprehensive analysis of 50+ peer-reviewed studies highlights state-of-the-art performance, with brain tumor classification achieving up to 97.5% precision (CNN + FCM) and 96.97% accuracy (DWT+PCA+DNN), while multimodal MRI registration methods report Dice Similarity Coefficient (DSC) values exceeding 0.90 and reduced Target Registration Error (TRE) compared to traditional deformable registration techniques. Trends indicate a shift toward hybrid models combining segmentation and registration, domain adaptation for cross-dataset generalization, and lightweight frameworks for real-time clinical deployment. In conclusion, deep learning approaches have significantly advanced the accuracy, efficiency, and automation of brain tumor detection and medical image registration. However, challenges such as limited annotated multimodal datasets, robust similarity measures for heterogeneous modalities, and the integration of clinically interpretable AI models remain open for future research.

Adding magnetic resonance (MR)-derived information to standard transrectal ultrasound (TRUS) images for guiding prostate biopsy is of substantial clinical interest. A tumor visible on MR images can be projected on ultrasound (US) by using MR-US registration. A common approach is to use surface-based registration. The authors hypothesize that biomechanical modeling will better control deformation inside the prostate than a regular nonrigid surface-based registration method. The authors developed a novel method by extending a nonrigid surface-based registration algorithm with biomechanical finite element (FE) modeling to better predict internal deformations of the prostate. Data were collected from ten patients and the MR and TRUS images were rigidly registered to anatomically align prostate orientations. The prostate was manually segmented in both images and corresponding surface meshes were generated. Next, a tetrahedral volume mesh was generated from the MR image. Prostate deformations due to the TRUS probe were simulated using the surface displacements as the boundary condition. A three-dimensional thin-plate spline deformation field was calculated by registering the mesh vertices. The target registration errors (TREs) of 35 reference landmarks determined by surface and volume mesh registrations were compared. The median TRE of a surface-based registration with biomechanical regularization was 2.76 (0.81-7.96) mm. This was significantly different than the median TRE of 3.47 (1.05-7.80) mm for regular surface-based registration without biomechanical regularization. Biomechanical FE modeling has the potential to improve the accuracy of multimodal prostate registration when comparing it to a regular nonrigid surface-based registration algorithm and can help to improve the effectiveness of MR guided TRUS biopsy procedures.

In this paper, we introduce SAMmorph, a novel multimodal medical image registration framework addressing challenges in aligning cross‐modality images due to significant appearance differences and heavy reliance on expensive annotations. It comprises three key components: the VoxelSAM module for generating pseudo‐labels, the FusionLayer which takes moving/fixed images and their pseudo‐labels as input, integrates them via two optional schemes (stack: concatenating along the channel dimension to form a 4‐channel tensor; enhance: modulating image intensity with mask confidence to generate a 3‐channel tensor) to produce combined features for registration, and the deformable registration network which includes two architectures (joint and independent registration networks) to implement the bidirectional registration strategy. Experiments demonstrate that SAMmorph outperforms state‐of‐the‐art methods across various metrics on the ‐RegPro dataset, achieving a dice similarity coefficient (DSC) of 0.906, target registration error (TRE) of 6.06 mm, compared to those being 0.789 and 7.632 mm achieved by the strongest tested baseline method CorrMLP, with statistical significance confirmed by Wilcoxon signed‐rank tests (). This work presents a promising approach for improving medical image registration accuracy and efficiency, with potential applications in diverse medical imaging scenarios. The source codes have been released at: https://github.com/qilin‐world/SAMmorph .

With respect to the demands of adaptive and 4D-radiotherapy applications, an algorithm is proposed for a fully automatic, multimodality deformable registration that follows the concept of translational relocation of regularly distributed image subvolumes governed by local anatomical features. Thereby, the problem of global deformable registration is broken down to multiple independent local registration steps which allows for straightforward parallelization of the algorithm. In a subsequent step, possible local misregistrations are corrected for by minimization of the elastic energy of the displacement field under consideration of image information. The final displacement field results from interpolation of the subvolume shift vectors. The algorithm can employ as a similarity measure both the correlation coefficient and mutual information. The latter allows the application to intermodality deformable registration problems. The typical calculation time on a modern multiprocessor PC is well below 1 min, which facilitates almost-interactive, "online" usage. CT-to-MRI and CT-to-cone-beam-CT registrations of head-and-neck data sets are presented, as well as inhale-to-exhale registrations of lung CT data sets. For quantitative evaluation of registration accuracy, a virtual thorax phantom was developed; additionally, a landmark-based evaluation on four lung respiratory-correlated CT data sets was performed. This consistently resulted in average registration residuals on the order of the voxel size or less (3D-residuals approximately 1-2 mm). Summarizing, the presented algorithm allows an accurate multimodality deformable registration with calculation times well below 1 min, and thus bears promise as a versatile basic tool in adaptive and 4D-radiotherapy applications.