DVAP-Reg: Dual-view anatomical prior-driven cross-dimensional registration for spinal surgery navigation.

Purpose: This article proposed and investigated the application of bonebed-malleus short process registration in minimally invasive cochlear implantation, and assessed the value of the proposed strategy for image-guided otologic surgery.Materials and methods: Ten temporal bone specimens were marked with both shallow and deep targets in order to measure registration resolutions. Two registration methods were applied. Method A (a new registration method) consisted of bonebed-malleus short process registration, while method B (traditional registration method) comprised registration using four titanium screws attached at the mastoid surface. The target registration errors (TRE) were measured at both the shallow and deep fiducial targets. After registration achieved by method A, percutaneous cochlear drilling was conducted on the other eight temporal bone specimens in order to observe the deviation from the target point and entry point.Results: Using method A, the error observed at the shallow fiducial markers of specimens was less than 1 mm, while the error observed at the deep fiducial markers was approximately equal to 1 mm. No significant difference was observed when results were compared with the application of method B. In eight studied cases using method A, drilling operations were successfully conducted. The deviations from the target point and the entry point were 0.84 ± 0.30 and 0.66 ± 0.51 mm, respectively.Conclusions: Results of the present study indicated that the new registration method demonstrated identical accuracy when compared to traditional registration method, achieved less invasiveness. Thus, the proposed method might be a feasible registration method for image-guided otologic surgery due to its mild invasiveness and high accuracy.

PurposeCardiac motion tracking enables quantitative evaluation of myocardial strain, which is clinically interesting in cardiovascular disease research. However, motion tracking is difficult to perform manually. In this paper, we aim to develop and compare two fully automated motion tracking methods for the steady state free precession (SSFP) cine magnetic resonance imaging (MRI), and explore their use in real clinical scenario with different patient groups.MethodsWe proposed two automated cardiac motion tracking method: (a) a traditional registration‐based method, named full cardiac cycle registration, which simultaneously tracks all cine frames within a full cardiac cycle by joint registration of all frames; and (b) a modern convolutional neural network (CNN)‐based method, named Groupwise MotionNet, which enhances the temporal coherence by fusing motion along a continuous time scale. Both methods were evaluated on the healthy volunteer data from the MICCAI 2011 STACOM Challenge, as well as on patient data including hypertrophic cardiomyopathy (HCM) and myocardial infarction (MI).ResultsThe full cardiac cycle registration method achieved an average end‐point error (EPE) 2.89 ± 1.57 mm for cardiac motion tracking, with computation time of around 9 min per short‐axis cine MRI (size 128 × 128, 30 cardiac phases). In comparison, the Groupwise MotionNet achieved an average EPE of 0.94 ± 1.59 mm, taking < 1 s for a full cardiac phases. Further experiments showed that registration method had stable performance, independent of patient cohort and MRI machine, while the CNN‐based method relied on the training data to deliver consistently accurate results.ConclusionBoth registration‐based and CNN‐based method can track the cardiac motion from SSFP cine MRI in a fully automated manner, while taking temporal coherence into account. The registration method is generic, robust, but relatively slow; the CNN‐based method trained with heterogeneous data was able to achieve high tracking accuracy with real‐time performance.

Virtual-real registration in Outdoor Augmented Reality is committed to enhance user's spatial cognition by overlaying virtual geographical objects on real scene. According to analyze fiducial detection registration method in indoor AR, for the purpose of avoiding complex and tedious process of position tracking and camera calibration in traditional registration methods, it puts forward and practices a virtual-real spatial information visualization registration method using affine representations. Based on the observation from Koenderink and van Doorn, Ullman and Basri in 1991 which is given a set of four or more non-coplanar 3D points, the projection of all points in the set can be computed as a linear combination of the projection of just four of the points, it sets up global affine coordinate system in light of world coordinates, camera coordinates and virtual coordinates and extracts four feature points from scene image and calculates the global affine coordinates of key points of virtual objects. Then according to a linear homogeneous coordinates of the four feature point's projection, it calculates projection pixel coordinates of key points of virtual objects. In addition, it proposes an approach to obtain pixel relative depth for hidden surface removal. Finally, by a case study, it verifies the feasibility and efficiency of the registration methods. The method would not only explore a new research direction for Geographical Information Science, but also would provide location-based information and services for outdoor AR.

2D-3D registration is a crucial step in Image-Guided Intervention, such as spine surgery, total hip re-placement, and kinematic analysis. To find the information in common between pre-operative 3D CT images and intra-operative X-ray 2D images is vital to plan and navigate. In a nutshell, the goal is to find the movement and rotation of the 3D body’s volume to make them reorient with the patient body in the 2D image space. Due to the loss of dimensionality and different sources of images, efficient and fast registration is challenging. To this end, we propose a novel approach to incorporate a point set Neural Network to combine the information from different views, which enjoys the robustness of the traditional method and the geometrical information extraction ability. The pre-trained Deep BlindPnP captures the global information and local connectivity, and each implementation of view-independent Deep BlindPnP in different view pairs will select top-priority pairs candidates. The transformation of different viewpoints into the same coordinate will accumulate the correspondence. Finally, a POSEST-based module will output the final 6 DoF pose. Extensive experiments on a real-world clinical dataset show the effectiveness of the proposed framework compared to the single view. The accuracy and computation speed are improved by incorporating the point set neural network.

The image registration based on the features of the image is a major research direction.The points feature is used in traditional registration methods,but the lines and other basic features are ignored. So,an image auto registration method based on natural heterogeneous features is given out. First, the feature points are detected using Harris feature detector and the feature line segments are detected using Hough transfer, then the one to one matching points and lines could be obtained through rough match, then the points feature and the parameters of lines is used to match refinely, finally, the image registration could be achieved through the homography matrix calculated by matching corner points and the parameters of line segments.

Some similarity measures used in state-of-the-art multimodality image registration algorithms, (e.g., mutual information (MI)) have been shown to be suitable anatomical priors for maximum a posteriori reconstruction in emission tomography. Therefore, it is reasonable to assume that some originally designed anatomical priors may also be well suited for multimodality image registration. In this work, we evaluate the registration performance of three variants of an anatomical Markov prior, previously proposed by Bowsher et al. First, simulated data are used to verify whether the suggested registration criteria yield an optimum when an FDG positron emission tomography (PET) image and a T1-weighted magnetic resonance (MR) image of a human brain are perfectly aligned. Next, the registration accuracy of the proposed criteria is assessed for PET to MR and MR to PET registration of simulated human brain images, and compared to the accuracy reached by MI. Last, the new methods are applied to challenging measured rat and mouse brain data sets, consisting of low resolution FDG microPET images and high resolution microMR images with a strong bias field. It was shown that the anatomy-based Markov priors indeed yield a well-defined optimum for aligned PET-MR images and that similar registration accuracy can be achieved as with MI, especially for registration to MR images suffering from a bias field. Nevertheless, in contrast to MI, the new criteria usually require a good initial guess of the transformation parameters in order not to get stuck in a local optimum. The proposed methods are shown to be superior to MI for registering measured microMR brain images with a strong bias field to FDG microPET images if a good initialization is provided.

We propose a variational image registration method for a pair of images with different resolutions. Traditional image registration methods match images assuming that the resolutions of the reference and target images are homogeneous. For the registration of inhomogeneous-resolution image pairs, we first introduce a resolution-conversion method to harmonise the resolution of a pair of images using the rational-order pyramid transform.Then, we develop a variational method for image registration using this resolution-conversion method.

Spatial registration is an essential step in the analysis of fMRI data because it enables between-subject analyses of brain activity, measured either during task performance or in the resting state. In this study, we investigated how anatomical registration with MRTOOL affects the reliability of task-related fMRI activity. We used as a benchmark the results from two other spatial registration methods implemented in SPM12: the Unified Segmentation algorithm and the DARTEL toolbox. Structural alignment accuracy and the impact on functional activation maps were assessed with high-resolution T1-weighted images and a set of task-related functional volumes acquired in 10 healthy volunteers. Our findings confirmed that anatomical registration is a crucial step in fMRI data processing, contributing significantly to the total inter-subject variance of the activation maps. MRTOOL and DARTEL provided greater registration accuracy than Unified Segmentation. Although DARTEL had superior gray matter and white matter tissue alignment than MRTOOL, there were no significant differences between DARTEL and MRTOOL in test-retest reliability. Likewise, we found only limited differences in BOLD activation morphology between MRTOOL and DARTEL. The test-retest reliability of task-related responses was comparable between MRTOOL and DARTEL, and both proved superior to Unified Segmentation. We conclude that MRTOOL, which is suitable for single-subject processing of structural and functional MR images, is a valid alternative to other SPM12-based approaches that are intended for group analysis. MRTOOL now includes a normalization module for fMRI data and is freely available to the scientific community.

Translation of any novel and existing 3D-2D image registration methods into clinical image-guidance systems is limited due to lack of their objective validation on clinical image datasets. The main reason is that, besides the calibration of the 2D imaging system, a reference or ”gold standard” registration is very difficult to obtain on clinical image datasets. In the context of cerebral endovascular image-guided interventions (EIGIs), we present a calibration device in the form of a headband with integrated fiducial markers and, secondly, propose an automated pipeline comprising 3D and 2D image processing, analysis and annotation steps, the result of which is a retrospective calibration of the 2D imaging system and an optimal, i.e., “gold standard” registration of 3D and 2D images. The device and methods were used to create the ”gold standard” on 15 datasets of 3D and 2D cerebral angiograms, whereas each dataset was acquired on a patient undergoing EIGI for either aneurysm coiling or embolization of arteriovenous malformation. The use of the device integrated seamlessly in the clinical workflow of EIGI. While the automated pipeline eliminated all manual input or interactive image processing, analysis or annotation. In this way, the time to obtain the ”gold standard” was reduced from 30 to less than one minute and the “gold standard” of 3D-2D registration on all 15 datasets of cerebral angiograms was obtained with a sub-0.1 mm accuracy.

Changes in Organs at Risk Dose Estimates Due to Anatomic Changes and Registration Method During Head and Neck Radiotherapy and its Relationship to Percent Weight Loss

Objective: Complications after Endovascular Aneurysm Repair (EVAR) can be fatal. Patient follow-up for surveillance imaging is becoming more challenging as fewer patients are seen, particularly after the first year. The aim of this study was to develop an artificial intelligence model to predict the complication probability of individual patients to better identify those needing more intensive post-operative surveillance. Methods: Pre-operative CTA 3D reconstruction images of AAA from 273 patients who underwent EVAR from 2011-2020 were collected. Of these, 48 patients had post-operative complications including endoleak, AAA rupture, graft limb occlusion, renal artery occlusion, and neck dilation. A deep convolutional neural network model (VascAI©) was developed which utilized pre-operative 3D CT images to predict risk of complications after EVAR. The model was built with TensorFlow software and run on the Google Colab Platform. An initial training subset of 40 randomly selected patients with complications and 189 without were used to train the AI model while the remaining 8 positive and 36 negative cases tested its performance and prediction accuracy. Data down-sampling was used to alleviate data imbalance and data augmentation methodology to further boost model performance. Results: Successful training was completed on the 229 cases in the training set and then applied to predict the complication probability of each individual in the held-out performance testing cases. The model provided a complication sensitivity of 100% and identified all the patients who later developed complications after EVAR. Of 36 patients without complications, 16 (44%) were falsely predicted to develop complications. The results therefore demonstrated excellent sensitivity for identifying patients who would benefit from more stringent surveillance and decrease the frequency of surveillance in 56% of patients unlike to develop complications. Conclusion: AI models can be developed to predict the risk of post-operative complications with high accuracy. Compared to existing methods, the model developed in this study did not require any expert-annotated data but only the AAA CTA images as inputs. This model can play an assistive role in identifying patients at high risk for post-EVAR complications and the need for greater compliance in surveillance.

This paper presents a novel 2D/3D non-rigid registration method for lung lesions tracking in image-guided diagnoses and treatments. Preoperative 3D lung CT volumes were obtained at a series of respiratory phases and important anatomical points were extracted. A CT volume was selected as reference volume and others were considered floating volumes. Displacement vectors of anatomical points were calculated using coherent point drift (CPD) and diffeomorphic-demon methods. For each CT volume, 2D digitally reconstructed radiographs (DRR) were generated by ray projection to simulate intraoperative 2D X-ray, and grayscale-based similarity measures of DRR between reference and floating volumes were calculated. A pulmonary respiration model was constructed using cubic polynomial, which represents the non-linear correlation between displacement vectors and similarity measures. During operation, the pulmonary respiration model used X-ray to obtain displacement vectors of important anatomical points; deformation fields were calculated from the displacement vectors through B-spline transformations. Finally, simulated lung CT volumes corresponding to intraoperative X-ray were generated by applying deformation fields to reference volume. The proposed method was compared to a state-of-the-art 2D/3D non-rigid registration method named respiratory-phase matching. Experimental results on two datasets respectively from a lung cancer patient and an ex vivo pig lung, showed mean registration accuracy of the proposed method was 1.57 ± 0.92 mm and 2.65 ± 1.35 mm; an improvement of 23% and 9% over the matching method. Moreover, mean lung structure overlaps were 0.91 and 0.88, comparable to matching method. The proposed method has potential in aiding lung intervention diagnoses and real-time lesion tracking.

Registration between tactile and visual image for remote-control robot is a critical technology in these two image fusion. The thesis raised a new method of image registration on the basis of OCD-ICP. Making use of graphic fundamentals, the method advanced the four filter rules in image edge curve to get the optimal group progressively. Moreover, the most optical registration references could be gained on the above-obtained point group. Experiment proves the method could detect the correct curve point group, and the ICP with this points group is quite accurate and speedy in registration.

Spinal imaging serves as an invaluable tool in the non-invasive visualization and evaluation of spinal pathologies. A key basis for quantitative medical image analysis pertinent to clinical diagnosis and spinal surgery planning is the segmentation of vertebrae in computed tomography (CT) images. While fully convolutional networks in general dominate over medical image segmentation, with the U-Net being the architecture of choice, alternative methodologies may offer potential advancements. One promising approach is the deep implicit statistical shape model (DISSM), known for generating high-quality surfaces without discretization and for its robustness, underpinned by the use of rich and explicit anatomical priors, particularly for challenging cross-dataset clinical samples. This paper explores the utilization of DISSM for vertebra segmentation on two image datasets: a collection of 1005 CT spine images known as <i>CTSpine1K</i> for the shape decoder, and a set of 319 CT images known as <i>VerSe2020</i> for the pose estimation encoders (translation, rotation, scaling and principal component analysis). These images and their corresponding vertebra segmentations are used for the preparation, preprocessing, and training and testing of DISSM. The preprocessing and learning techniques are based on a DISSM software package (AshStuff/dissm) with our custom modifications. The obtained segmentation results yielded an overall mean Dice coefficient of 0.767, average symmetric surface distance of 1.93 mm, and 95^th percentile Hausdorff distance of 5.71 mm. We can therefore conclude that DISSM has the potential to further advance the field of vertebra segmentation.

Microwave ablation (MWA) is a minimally invasive procedure for the treatment of liver tumor. Accumulating clinical evidence has considered the minimal ablative margin (MAM) as a significant predictor of local tumor progression (LTP). In clinical practice, MAM assessment is typically carried out through image registration of pre- and post-MWA images. However, this process faces two main challenges: non-homologous match between tumor and coagulation with inconsistent image appearance, and tissue shrinkage caused by thermal dehydration. These challenges result in low precision when using traditional registration methods for MAM assessment. In this paper, we present a local contractive nonrigid registration method using a biomechanical model (LC-BM) to address these challenges and precisely assess the MAM. The LC-BM contains two consecutive parts: (1) local contractive decomposition (LC-part), which reduces the incorrect match between the tumor and coagulation and quantifies the shrinkage in the external coagulation region, and (2) biomechanical model constraint (BM-part), which compensates for the shrinkage in the internal coagulation region. After quantifying and compensating for tissue shrinkage, the warped tumor is overlaid on the coagulation, and then the MAM is assessed. We evaluated the method using prospectively collected data from 36 patients with 47 liver tumors, comparing LC-BM with 11 state-of-the-art methods. LTP was diagnosed through contrast-enhanced MR follow-up images, serving as the ground truth for tumor recurrence. LC-BM achieved the highest accuracy (97.9%) in predicting LTP, outperforming other methods. Therefore, our proposed method holds significant potential to improve MAM assessment in MWA surgeries.