Real-time continuous image guidance for endoscopic retrograde cholangiopancreatography based on 3D/2D registration and respiratory compensation

BACKGROUND It has been confirmed that three-dimensional(3D)imaging allows easier identification of bile duct anatomy and intraoperative guidance of endoscopic retrograde cholangiopancreatography(ERCP),which reduces the radiation dose and procedure time with improved safety.However,current 3D biliary imaging does not have good real-time fusion with intraoperative imaging,a process meant to overcome the influence of intraoperative respiratory motion and guide navigation.The present study explored the feasibility of real-time continuous image-guided ERCP.AIM To explore the feasibility of real-time continuous image-guided ERCP.METHODS We selected 23D-printed abdominal biliary tract models with different structures to simulate different patients.The ERCP environment was simulated for the biliary phantom experiment to create a navigation system,which was further tested in patients.In addition,based on the estimation of the patient’s respiratory motion,preoperative 3D biliary imaging from computed tomography of 18 patients with cholelithiasis was registered and fused in real-time with 2D fluoroscopic sequence generated by the C-arm unit during ERCP.RESULTS Continuous image-guided ERCP was applied in the biliary phantom with a registration error of 0.46 mm±0.13 mm and a tracking error of 0.64 mm±0.24mm.After estimating the respiratory motion,3D/2D registration accurately transformed preoperative 3D biliary images to each image in the X-ray image sequence in real-time in 18 patients,with an average fusion rate of 88%.CONCLUSION Continuous image-guided ERCP may be an effective approach to assist the operator and reduce the use of X-ray and contrast agents.

Free form deformation and symmetry constraint‐based multimodal brain image registration using generative adversarial nets

Multi‐modal brain image registration has been widely applied to functional localisation,neurosurgery and computational anatomy.The existing registration methods based on the dense deformation fields involve too many parameters,which is not conducive to the exploration of correct spatial correspondence between the float and reference images.Meanwhile,the unidirectional registration may involve the deformation folding,which will result in the change of topology during registration.To address these issues,this work has presented an unsupervised image registration method using the free form deformation(FFD)and the symmetry constraint‐based generative adversarial networks(FSGAN).The FSGAN utilises the principle component analysis network‐based structural representations of the reference and float images as the inputs and uses the generator to learn the FFD model parameters,thereby producing two deformation fields.Meanwhile,the FSGAN uses two discriminators to decide whether the bilateral registration have been realised simultaneously.Besides,the symmetry constraint is utilised to construct the loss function,thereby avoiding the deformation folding.Experiments on BrainWeb,high grade gliomas,IXI and LPBA40 show that compared with state‐of‐the‐art methods,the FSGAN provides superior performance in terms of visual comparisons and such quantitative indexes as dice value,target registration error and computational efficiency.

Application of Opening and Closing Morphology in Deep Learning-Based Brain Image Registration

In order to improve the registration accuracy of brain magnetic resonance images(MRI),some deep learning registration methods use segmentation images for training model.How-ever,the segmentation values are constant for each label,which leads to the gradient variation con-centrating on the boundary.Thus,the dense deformation field(DDF)is gathered on the boundary and there even appears folding phenomenon.In order to fully leverage the label information,the morphological opening and closing information maps are introduced to enlarge the non-zero gradi-ent regions and improve the accuracy of DDF estimation.The opening information maps supervise the registration model to focus on smaller,narrow brain regions.The closing information maps supervise the registration model to pay more attention to the complex boundary region.Then,opening and closing morphology networks(OC_Net)are designed to automatically generate open-ing and closing information maps to realize the end-to-end training process.Finally,a new registra-tion architecture,VM_(seg+oc),is proposed by combining OC_Net and VoxelMorph.Experimental results show that the registration accuracy of VM_(seg+oc) is significantly improved on LPBA40 and OASIS1 datasets.Especially,VM_(seg+oc) can well improve registration accuracy in smaller brain regions and narrow regions.

Research on Detection Technology of Micro-Components on Circuit Board Based on Digital Image Processing

Aiming at the stability of the circuit board image in the acquisition process,this paper realizes the accurate registration of the image to be registered and the standard image based on the SIFT feature operator and RANSAC algorithm.The device detection model and data set are established based on Faster RCNN.Finally,the number of training was continuously optimized,and when the loss function of Faster RCNN converged,the identification result of the device was obtained.

A Review of Point Feature Based Medical Image Registration

Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms(PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However,to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of pointfeaturebased methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks.This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art alternatives.The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/Super Fusion.

Using shape contexts method for registration of contra lateral breasts in thermal images

AIM: To achieve symmetric boundaries for left and right breasts boundaries in thermal images by registration. METHODS: The proposed method for registration consists of two steps. In the first step, shape context, an approach as presented by Belongie and Malik was applied for registration of two breast boundaries. The shape context is an approach to measure shape similarity. Two sets of finite sample points from shape contours of two breasts are then presented. Consequently, the correspondences between the two shapes are found. By finding correspondences, the sample point which has the most similar shape context is obtained. RESULTS: In this study, a line up transformation which maps one shape onto the other has been estimated in order to complete shape. The used of a thin plate spline permitted good estimation of a plane transformation which has capability to map unselective points from one shape onto the other. The obtained aligningtransformation of boundaries points has been applied successfully to map the two breasts interior points. Some of advantages for using shape context method in this work are as follows:(1) no special land marks or key points are needed;(2) it is tolerant to all common shape deformation; and(3) although it is uncomplicated and straightforward to use, it gives remarkably powerful descriptor for point sets significantly upgrading point set registration. Results are very promising. The proposed algorithm was implemented for 32 cases. Boundary registration is done perfectly for 28 cases.CONCLUSION: We used shape contexts method that is simple and easy to implement to achieve symmetric boundaries for left and right breasts boundaries in thermal images.

Incoherent digital holographic spectral imaging with high accuracy of image pixel registration

Fresnel incoherent correlation holography(FINCH) is a unique three-dimensional(3D) imaging technique which has the advantages of scanning-free,high resolution,and easy matching with existing mature optical systems.In this article,an incoherent digital holographic spectral imaging method with high accuracy of spectral reconstruction based on liquid crystal tunable filter(LCTF) and FINCH is proposed.Using the programmable characteristics of spatial light modulator(SLM),a series of phase masks,none of whose focal lengths changes with wavelength,is designed and made.For each wavelength of LCTF output,SLM calls three phase masks with different phase constants at the corresponding wavelength,and CCD records three holograms.The spectral images obtained by this method have a constant magnification,which can achieve pixel-level image registration,restrain image registration errors,and improve spectral reconstruction accuracy.The results show that this method can not only obtain the 3D spatial information and spectral information of the object simultaneously,but also have high accuracy of spectral reconstruction and excellent color reproducibility.

THE STUDY ON AUTOMATIC REGISTRATION OF INSAR IMAGES

Interferometric Synthetic Aperture Radar (InSAR) allows production of high resolution DEM and detection of small earth motions using multiple pass SAR data sets obtained by remote sensing satellite. But the whole process has not yet reached sufficient robustness to warrant automated DEM production as commonly produced by stereovision with optical images. The automatic algorithm for precision registration is one of the bottlenecks in InSAR data processing. In this paper, an automatic approach with multi-step image matching algorithm is presented. All procedures are automatically implemented. The experiment is carried out successfully with SIR-C/L-band InSAR data. The triangular piecewise rectification is also advanced in reducing local distortion between the images and processing the large scene image. The primary result has prospect in the precision registration for the repeat-track InSAR data and reveals the potential of the presented automatic strategy.

Image registration of the human accommodating eye demonstrates equivalent increases in lens equatorial radius and central thickness

AIM: To compare the results of in vivo human high resolution image registration studies of the eye during accommodation to the predictions of mathematical and finite element models of accommodation. METHODS: Data from published high quality image registration studies of pilocarpine induced accommodative changes of equatorial lens radius(ELR) and central lens thickness(CLT) were statistically analyzed. RESULTS: The mean changes in ELR and CLT were 6.76 μm/diopter and 6.51 μm/diopter, respectively. The linear regressions, reflecting the association between ELR and accommodative amplitude(AAELR) was: slope=6.58 μm/diopter, r^2=0.98, P<0.0001 and between CLT and AACLT was: slope=6.75 μm/diopter, r^2=0.83, P<0.001. On the basis of these relationships, the CLT slope and the AAELR were used to predict the measured change in ELR(ELRpredicted). There was no statistical difference between ELRpredicted and the measured ELR as demonstrated by a Student's paired t-test: P=0.96 and linear regression analysis: slope=0.97, r^2=0.98, P<0.00001.CONCLUSION: Image registration with invariant positional references demonstrates that ELR and CLT equivalently minimally increase ~7.0 μm/diopter during accommodation. The small equivalent increases in ELRand CLT are associated with a large accommodative amplitude. These findings are consistent with the predictions of mathematical and finite element models that specified the stiffness of the lens nucleus is the same or greater than the lens cortex and that accommodation involves a small force(<5 g).

Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration

This paper presents a novel medical image registration algorithm named total variation constrained graphregularization for non-negative matrix factorization(TV-GNMF).The method utilizes non-negative matrix factorization by total variation constraint and graph regularization.The main contributions of our work are the following.First,total variation is incorporated into NMF to control the diffusion speed.The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information.Second,we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power.Third,the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.

High-Dimensional Spatial Standardization Algorithm for Diffusion Tensor Image Registration

Three high dimensional spatial standardization algorithms are used for diffusion tensor image(DTI)registration,and seven kinds of methods are used to evaluate their performances.Firstly,the template used in this paper was obtained by spatial transformation of 16 subjects by means of tensor-based standardization.Then,high dimensional standardization algorithms for diffusion tensor images,including fractional anisotropy(FA)based diffeomorphic registration algorithm,FA based elastic registration algorithm and tensor-based registration algorithm,were performed.Finally,7 kinds of evaluation methods,including normalized standard deviation,dyadic coherence,diffusion cross-correlation,overlap of eigenvalue-eigenvector pairs,Euclidean distance of diffusion tensor,and Euclidean distance of the deviatoric tensor and deviatoric of tensors,were used to qualitatively compare and summarize the above standardization algorithms.Experimental results revealed that the high-dimensional tensor-based standardization algorithms perform well and can maintain the consistency of anatomical structures.

Optical and SAR image registration based on improved nonsubsampled wavelet transform for sea islands

Homologous feature point extraction is a key problem in the optical and synthetic aperture radar （SAR） image registration for islands. A new feature point extraction method using a threshold shrink operator and non-subsampled wavelet transform （TSO-NSWT） for optical and SAR image registration was proposed. Moreover, the matching for this proposed feature was different from the traditional feature matching strategies and was performed using a similarity measure computed from neighborhood circles in low-frequency bands. Then, a number of reliably matched couples with even distributions were obtained, which assured the accuracy of the registration. Application of the proposed algorithm to SPOT-5 （multi-spectral） and YG-1 （SAR） images showed that a large number of accurately matched couples could be identified. Additionally, both of the root mean square error （RMSE） patterns of the registration parameters computed based on the TSO-NSWT algorithm and traditional NSWT algorithm were analyzed and compared, which further demonstrated the effectiveness of the proposed algorithm. The algorithm can supply the crucial step for island image registration and island recognition.

A Mosaic Technique Based on Image Registration

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Feasibility Study on Deformable Image Registration for Lung SBRT Patients for Dose-Driven Adaptive Therapy

The purpose of the study was to evaluate a treatment dose using planning computed tomography (pCT) that was deformed to pre-treatment cone beam computed tomography (CBCT) for lung stereotactic body radiation therapy (SBRT) treatment. Five lung SBRT patients were retrospectively selected, and their daily CBCTs were employed in this study. Dosimetric comparison was performed between the original and recalculated plans from the deformed pCT (dose per fraction) by comparing a target coverage and organs at risk. Dose summation of five fractions was computed and compared to the original plan. A phantom study was conducted to evaluate the dosimetric accuracy for the dose per fraction. In the phantom study, the difference between the mean Hounsfield Unit (HU) values of the original and deformed pCTs is less than 0.5%. In patient study, the mean HU deviation of the five deformed pCTs compared to that of the original pCT was within ±5%, which is dosimetrically insignificant. While the internal target volume (ITV) shrank by 17% on average among the five patients, mean lung dose (MLD) increased by up to 7%, and D95% of PTV decreased slightly but stayed within 5%. Results showed that MLD might be a better indicative metric of normal lung dose than V20Gy as the ITV volume decreases. This study showed a feasibility to use a deformed pCT for evaluation of the dose per fraction and for a possible plan adaptation in lung SBRT cases. Readers should be cautious in selecting patients before clinical application due to the image quality of CBCT.

Accuracy Analysis on the Automatic Registration of Multi-Source Remote Sensing Images Based on the Software of ERDAS Imagine

The automatic registration of multi-source remote sensing images (RSI) is a research hotspot of remote sensing image preprocessing currently. A special automatic image registration module named the Image Autosync has been embedded into the ERDAS IMAGINE software of version 9.0 and above. The registration accuracies of the module verified for the remote sensing images obtained from different platforms or their different spatial resolution. Four tested registration experiments are discussed in this article to analyze the accuracy differences based on the remote sensing data which have different spatial resolution. The impact factors inducing the differences of registration accuracy are also analyzed.

A new hybrid particle swarm optimization for multimodal brain image registration

Image registration is an important issue in medical analysis. In this process the spatial transformation that aligns the reference image and the floating image is estimated by optimizing a similarity metric. Mutual information (MI), a popular similarity metric, is a reliable criterion for medical image registration. In this paper, we present an improved method for multimodal image registration based on maximization of a new form of normalized MI incorporating particle swarm optimization, PSO, as a searching strategy. Also a new hybrid PSO algorithm is applied to approach more precise and robust results with better performance.

Point Reg Net: Invariant Features for Point Cloud Registration Using in Image-Guided Radiation Therapy

In image-guided radiation therapy, extracting features from medical point cloud is the key technique for multimodality registration. This novel framework, denoted Control Point Net (CPN), provides an alternative to the common applications of manually designed keypoint descriptors for coarse point cloud registration. The CPN directly consumes a point cloud, divides it into equally spaced 3D voxels and transforms the points within each voxel into a unified feature representation through voxel feature encoding (VFE) layer. Then all volumetric representations are aggregated by Weighted Extraction Layer which selectively extracts features and synthesize into global descriptors and coordinates of control points. Utilizing global descriptors instead of local features allows the available geometrical data to be better exploited to improve the robustness and precision. Specifically, CPN unifies feature extraction and clustering into a single network, omitting time-consuming feature matching procedure. The algorithm is tested on point cloud datasets generated from CT images. Experiments and comparisons with the state-of-the-art descriptors demonstrate that CPN is highly discriminative, efficient, and robust to noise and density changes.

Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Presence of higher breast density(BD)and persistence over time are risk factors for breast cancer.A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable.In this study,we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures.Three datasets of volunteers from two clinical trials were included.Breast MR images were acquired on 3T Siemens Biograph mMR,Prisma,and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique.Two whole-breast segmentation strategies,utiliz-ing image registration and 3D U-Net,were developed.Manual segmentation was performed.A task-based analysis was performed:a previously developed MR-based BD measure,MagDensity,was calculated and assessed using automated and manual segmentation.The mean squared error(MSE)and intraclass correlation coefficient(ICC)between MagDensity were evaluated using the manual segmentation as a reference.The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures(Δ_(2-1)),MSE,and ICC.The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation,with ICCs of 0.986(95%CI:0.974-0.993)and 0.983(95%CI:0.961-0.992),respectively.For test-retest analysis,MagDensity derived using the regis-tration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993(95%CI:0.982-0.997)when compared to other segmentation methods.In conclusion,the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD.Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment,with the registration exhibiting superior performance for highly reproducible BD measurements.

Multi-Scale PIIFD for Registration of Multi-Source Remote Sensing Images

This paper aims at providing multi-source remote sensing images registered in geometric space for image fusion.Focusing on the characteristics and differences of multi-source remote sensing images,a feature-based registration algorithm is implemented.The key technologies include image scale-space for implementing multi-scale properties,Harris corner detection for keypoints extraction,and partial intensity invariant feature descriptor(PIIFD)for keypoints description.Eventually,a multi-scale Harris-PIIFD image registration algorithm framework is proposed.The experimental results of fifteen sets of representative real data show that the algorithm has excellent,stable performance in multi-source remote sensing image registration,and can achieve accurate spatial alignment,which has strong practical application value and certain generalization ability.