Image synthesis-based multi-modal image registration framework by using deep fully convolutional networks.

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.

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

The multiresolution approach is commonly used to speed up the mutual-information (MI) based registration process. Conventionally, a Gaussian pyramid is often used as a multiresolution representation. However, in multi-modal medical image registration, MI-based methods with Gaussian pyramid may suffer from the problem of short capture ranges especially at the lower resolution levels. This paper proposes a novel and straightforward multimodal image registration method based on wavelet representation, in which two matching criteria are used including sum of difference (SAD) for improving the registration robustness and MI for assuring the registration accuracy. Experimental results show that the proposed method obtains a longer capture range than the traditional MI-based Gaussian pyramid method meanwhile maintaining comparable accuracy.

Multi-modal image registration has been a challenging task in medical images because of the complex intensity relationship between images to be aligned. Registration methods often rely on the statistical intensity relationship between the images which suffers from problems such as statistical insufficiency. The proposed registration method works based on extracting structural features by utilizing the complex phase and gradient-based information. By employing structural relationships between different modalities instead of complex similarity measures, the multi-modal registration problem is converted into a mono-modal one. Therefore, conventional mono-modal similarity measures can be utilized to evaluate the registration results. This new registration paradigm has been tested on magnetic resonance (MR) brain images of different modes. The method has been evaluated based on target registration error (TRE) to determine alignment accuracy. Quantitative results demonstrate that the proposed method is capable of achieving comparable registration accuracy compared to the conventional mutual information.

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

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.

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

BackgroundMultimodal histology image registration is a process that transforms into a common coordinate system two or more images obtained from different microscopy modalities. The combination of information from various modalities can contribute to a comprehensive understanding of tissue specimens, aiding in more accurate diagnoses, and improved research insights. Multimodal image registration in histology samples presents a significant challenge due to the inherent differences in characteristics and the need for tailored optimization algorithms for each modality.ResultsWe developed MMIR a cloud-based system for multimodal histological image registration, which consists of three main modules: a project manager, an algorithm manager, and an image visualization system.ConclusionOur software solution aims to simplify image registration tasks with a user-friendly approach. It facilitates effective algorithm management, responsive web interfaces, supports multi-resolution images, and facilitates batch image registration. Moreover, its adaptable architecture allows for the integration of custom algorithms, ensuring that it aligns with the specific requirements of each modality combination. Beyond image registration, our software enables the conversion of segmented annotations from one modality to another.

The synthesis of pseudo-CT images is of particular interest in the development of hybrid PET-MRI devices and MRI-guided radiotherapy. These images can be used for attenuation correction during PET image reconstruction. Furthermore, using MRI-based radiotherapy planning would enable a more accurate dosimetry planning due to the superior soft tissue contrast of the scans. The previously proposed methods for pseudo-CT synthesis are characterised by mainly two drawbacks. First, most proposed methods are limited to the head and neck region and therefore not feasible in case of whole body applications. Second, the presence of aligned training pairs of both MRI and CT scans for a number of subjects is assumed. In this work, we present preliminary results for atlas-based approaches using multiple CT atlas scans (from different patients) to synthesise a pseudo-CT image for a new patient using only their MRI data. This application requires accurate and robust deformable multimodal registration. We employed a recent discrete optimisation registration framework together with a self-similarity-based metric to accurately match the CT atlases to the anatomy of the patient. The registered atlases are then jointly combined by means of local fusion strategies. We apply our method to different 3D whole body MRI scans and a total of 18 3D whole body CT atlases. In addition to intensity fusion, the proposed methods can also be used for label fusion. Since evaluation based directly on synthesised intensity values is problematic, we use the Dice overlap after the fusion of segmentation labels as a proxy measure. Our proposed new method, which uses MIND descriptors for multimodal label fusion shows overall the best results.

Joint segmentation and registration of images is a focused area of research nowadays. Jointly segmenting and registering noisy images and images having weak boundaries/intensity inhomogeneity is a challenging task. In medical image processing, joint segmentation and registration are essential methods that aid in distinguishing structures and aligning images for precise diagnosis and therapy. However, these methods encounter challenges, such as computational complexity and sensitivity to variations in image quality, which may reduce their effectiveness in real-world applications. Another major issue is still attaining effective joint segmentation and registration in the presence of artifacts or anatomical deformations. In this paper, a new nonparametric joint model is proposed for the segmentation and registration of multi-modality images having weak boundaries/noise. For segmentation purposes, the model will be utilizing local binary fitting data term and for registration, it is utilizing conditional mutual information. For regularization of the model, we are using linear curvature. The new proposed model is more efficient to segmenting and registering multi-modality images having intensity inhomogeneity, noise and/or weak boundaries. The proposed model is also tested on the images obtained from the freely available CHOAS dataset and compare the results of the proposed model with the other existing models using statistical measures such as the Jaccard similarity index, relative reduction, Dice similarity coefficient and Hausdorff distance. It can be seen that the proposed model outperforms the other existing models in terms of quantitatively and qualitatively.

In this research paper, authors propose multimodal brain image registration using discrete wavelet transform(DWT) followed by Gaussian pyramids. The reference and target images are decomposed into their LL, LH, HL and LL DWT coefficients and then are processed for image registration using Gaussian pyramids. The image registration is also done using Gaussian pyramids only and wavelets transforms only for comparison. The quality of registration is measured by comparing the maximum MI values used by the three methods and also by comparing their correlation coefficients. Our proposed technique proves to show better results when compared with the other two methods.

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

Medical image diagnosis and delineation of lesions in the human brain require information to combine from different imaging sensors. Image registration is considered to be an essential pre-processing technique of aligning images of different modalities. The brain is a naturally bilateral symmetrical organ, where the left half lobe resembles the right half lobe around the symmetrical axis. The identified symmetry axis in one MRI image can identify symmetry axes in multi-modal registered MRI images instantly. MRI sensors may induce different levels of noise and Intensity Non-Uniformity (INU) in images. These image degradations may cause difficulty in finding true transformation parameters for an optimization technique. We will be investigating the new variant of evolution strategy of genetic algorithm as an optimization technique that performs well even for the high level of noise and INU, compared to Nesterov, Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm (LBFGS), Simulated Annealing (SA), and Single-Stage Genetic Algorithm (SSGA). The proposed new multi-modal image registration technique based on a genetic algorithm with increasing precision levels and decreasing search spaces in successive stages is called the Multi-Stage Forward Path Regenerative Genetic Algorithm (MFRGA). Our proposed algorithm is better in terms of overall registration error as compared to the standard genetic algorithm. MFRGA results in a mean registration error of 0.492 in case of the same level of noise (1–9)% and INU (0–40)% in both reference and template image, and 0.317 in case of a noise-free template and reference with noise levels (1–9)% and INU (0–40)%. Accurate registration results in good segmentation, and we apply registration transformations to segment normal brain structures for evaluating registration accuracy. The brain segmentation via registration with our proposed algorithm is better even in cases of high levels of noise and INU as compared to GA and LBFGS. The mean dice similarity coefficient of brain structures CSF, GM, and WM is 0.701, 0.792, and 0.913, respectively.

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.