HCS-Net: Multi-level deformation strategy combined with quadruple attention for image registration

BACKGROUND AND OBJECTIVE

Non-rigid image registration plays a significant role in computer-aided diagnosis and surgical navigation for brain diseases. Registration methods that utilize convolutional neural networks (CNNs) have shown excellent accuracy when applied to brain magnetic resonance images (MRI). However, CNNs have limitations in understanding long-range spatial relationships in images, which makes it challenging to incorporate contextual information. And in intricate image registration tasks, it is difficult to achieve a satisfactory dense prediction field, resulting in poor registration performance.

METHODS

This paper proposes a multi-level deformable unsupervised registration model that combines Transformer and CNN to achieve non-rigid registration of brain MRI. Firstly, utilizing a dual encoder structure to establish the dependency relationship between the global features of two images and to merge features of varying scales, as well as to preserve the relative spatial position information of feature maps at different scales. Then the proposed multi-level deformation strategy utilizes different deformable fields of varying resolutions generated by the decoding structure to progressively deform the moving image. Ultimately, the proposed quadruple attention module is incorporated into the decoding structure to merge feature information from various directions and emphasize the spatial features in the dominant channels.

RESULTS

The experimental results on multiple brain MR datasets demonstrate that the promising network could provide accurate registration and is comparable to state-of-the-art methods.

CONCLUSION

The proposed registration model can generate superior deformable fields and achieve more precise registration effects, enhancing the auxiliary role of medical image registration in various fields and advancing the development of computer-aided diagnosis, surgical navigation, and related domains.

[Non-rigid registration for medical images based on deformable convolution and multi-scale feature focusing modules]

Non-rigid registration plays an important role in medical image analysis. U-Net has been proven to be a hot research topic in medical image analysis and is widely used in medical image registration. However, existing registration models based on U-Net and its variants lack sufficient learning ability when dealing with complex deformations, and do not fully utilize multi-scale contextual information, resulting insufficient registration accuracy. To address this issue, a non-rigid registration algorithm for X-ray images based on deformable convolution and multi-scale feature focusing module was proposed. First, it used residual deformable convolution to replace the standard convolution of the original U-Net to enhance the expression ability of registration network for image geometric deformations. Then, stride convolution was used to replace the pooling operation of the downsampling operation to alleviate feature loss caused by continuous pooling. In addition, a multi-scale feature focusing module was introduced to the bridging layer in the encoding and decoding structure to improve the network model's ability of integrating global contextual information. Theoretical analysis and experimental results both showed that the proposed registration algorithm could focus on multi-scale contextual information, handle medical images with complex deformations, and improve the registration accuracy. It is suitable for non-rigid registration of chest X-ray images.

Learning feature descriptors for pre- and intra-operative point cloud matching for laparoscopic liver registration

PURPOSE

In laparoscopic liver surgery, preoperative information can be overlaid onto the intra-operative scene by registering a 3D preoperative model to the intra-operative partial surface reconstructed from the laparoscopic video. To assist with this task, we explore the use of learning-based feature descriptors, which, to our best knowledge, have not been explored for use in laparoscopic liver registration. Furthermore, a dataset to train and evaluate the use of learning-based descriptors does not exist.

METHODS

We present the LiverMatch dataset consisting of 16 preoperative models and their simulated intra-operative 3D surfaces. We also propose the LiverMatch network designed for this task, which outputs per-point feature descriptors, visibility scores, and matched points.

RESULTS

We compare the proposed LiverMatch network with a network closest to LiverMatch and a histogram-based 3D descriptor on the testing split of the LiverMatch dataset, which includes two unseen preoperative models and 1400 intra-operative surfaces. Results suggest that our LiverMatch network can predict more accurate and dense matches than the other two methods and can be seamlessly integrated with a RANSAC-ICP-based registration algorithm to achieve an accurate initial alignment.

CONCLUSION

The use of learning-based feature descriptors in laparoscopic liver registration (LLR) is promising, as it can help achieve an accurate initial rigid alignment, which, in turn, serves as an initialization for subsequent non-rigid registration.

Non-rigid registration based on hierarchical deformation of coronary arteries in CCTA images

In this paper, we propose an accurate and rapid non-rigid registration method between blood vessels in temporal 3D cardiac computed tomography angiography images of the same patient. This method provides auxiliary information that can be utilized in the diagnosis and treatment of coronary artery diseases. The proposed method consists of the following four steps. First, global registration is conducted through rigid registration between the 3D vessel centerlines obtained from temporal 3D cardiac CT angiography images. Second, point matching between the 3D vessel centerlines in the rigid registration results is performed, and the corresponding points are defined. Third, the outliers in the matched corresponding points are removed by using various information such as thickness and gradient of the vessels. Finally, non-rigid registration is conducted for hierarchical local transformation using an energy function. The experiment results show that the average registration error of the proposed method is 0.987 mm, and the average execution time is 2.137 s, indicating that the registration is accurate and rapid. The proposed method that enables rapid and accurate registration by using the information on blood vessel characteristics in temporal CTA images of the same patient.

Augmented reality navigation with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors

PURPOSE

We present a novel augmented reality (AR) surgical navigation method with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors. A preliminary study is carried out to verify its feasibility.

METHODS

Two three-dimensional (3D) point clouds of the liver surface are derived from the preoperative images and intraoperative tracked US images, respectively. To compensate for the soft tissue deformation, the point cloud registration between the preoperative images and the liver is performed using the non-rigid iterative closest point (ICP) algorithm. A 3D AR device based on integral videography technology is designed to accurately display naked-eye 3D images for surgical navigation. Based on the above registration, naked-eye 3D images of the liver surface, planning path, entry points, and tumor can be overlaid in situ through our 3D AR device. Finally, the AR-guided targeting accuracy is evaluated through entry point positioning.

RESULTS

Experiments on both the liver phantom and in vitro pork liver were conducted. Several entry points on the liver surface were used to evaluate the targeting accuracy. The preliminary validation on the liver phantom showed average entry-point errors (EPEs) of 2.34 ± 0.45 mm, 2.25 ± 0.72 mm, 2.71 ± 0.82 mm, and 2.50 ± 1.11 mm at distinct US point cloud coverage rates of 100%, 75%, 50%, and 25%, respectively. The average EPEs of the deformed pork liver were 4.49 ± 1.88 mm and 5.02 ± 2.03 mm at the coverage rates of 100% and 75%, and the average covered-entry-point errors (CEPEs) were 4.96 ± 2.05 mm and 2.97 ± 1.37 mm at 50% and 25%, respectively.

CONCLUSION

Experimental outcomes demonstrate that the proposed AR navigation method based on US-assisted point cloud registration has achieved an acceptable targeting accuracy on the liver surface even in the case of liver deformation.

Non-rigid registration of medical images based on [Formula: see text] non-tensor product B-spline

In this study, a non-tensor product B-spline algorithm is applied to the search space of the registration process, and a new method of image non-rigid registration is proposed. The tensor product B-spline is a function defined in the two directions of x and y, while the non-tensor product B-spline [Formula: see text] is defined in four directions on the 2-type triangulation. For certain problems, using non-tensor product B-splines to describe the non-rigid deformation of an image can more accurately extract the four-directional information of the image, thereby describing the global or local non-rigid deformation of the image in more directions. Indeed, it provides a method to solve the problem of image deformation in multiple directions. In addition, the region of interest of medical images is irregular, and usually no value exists on the boundary triangle. The value of the basis function of the non-tensor product B-spline on the boundary triangle is only 0. The algorithm process is optimized. The algorithm performs completely automatic non-rigid registration of computed tomography and magnetic resonance imaging images of patients. In particular, this study compares the performance of the proposed algorithm with the tensor product B-spline registration algorithm. The results elucidate that the proposed algorithm clearly improves the accuracy.

Virtual histology of an entire mouse brain from formalin fixation to paraffin embedding. Part 2: Volumetric strain fields and local contrast changes

BACKGROUND

Fixation and embedding of post mortem brain tissue is a pre-requisite for both gold-standard conventional histology and X-ray virtual histology. This process alters the morphology and density of the brain microanatomy.

NEW METHOD

To quantify these changes, we employed synchrotron radiation-based hard X-ray tomography with 3 μm voxel length to visualize the same mouse brain after fixation in 4% formalin, immersion in ethanol solutions (50%, 70%, 80%, 90%, and 100%), xylene, and finally after embedding in a paraffin block. The volumetric data were non-rigidly registered to the initial formalin-fixed state to align the microanatomy within the entire mouse brain.

RESULTS

Volumetric strain fields were used to characterize local shrinkage, which was found to depend on the anatomical region and distance to external surface. X-ray contrast was altered and enhanced by preparation-induced inter-tissue density changes. The preparation step can be selected to highlight specific anatomical features. For example, fiber tract contrast is amplified in 100% ethanol.

COMPARISON WITH EXISTING METHODS

Our method provides volumetric strain fields, unlike approaches based on feature-to-feature or volume measurements. Volumetric strain fields are produced by non-rigid registration, which is less labor-intensive and observer-dependent than volume change measurements based on manual segmentations. X-ray microtomography provides spatial resolution at least an order of magnitude higher than magnetic resonance microscopy, allowing for analysis of morphology and density changes within the brain's microanatomy.

CONCLUSION

Our approach belongs to three-dimensional virtual histology with isotropic micrometer spatial resolution and therefore complements atlases based on a combination of magnetic resonance microscopy and optical micrographs of serial histological sections.

Research on registration and navigation technology of augmented reality for ex-vivo hepatectomy

PURPOSE

The application of augmented reality technology to the partial hepatectomy procedure has high practical significance, but the existing augmented reality navigation system has major drawbacks in the display and registration methods, which result in low precision. The augmented reality surgical navigation system proposed in this study has been improved in the above two aspects, which can significantly improve the surgical accuracy.

METHODS

The use of optical see-through head-mounted displays for imaging displays can prevent doctors from reconstructing the patient's two-dimensional image information in their minds and reduce the psychological burden of doctors. In the registration process, the biomechanical properties of the liver are introduced, and a non-rigid registration method based on biomechanics is proposed and realized by a meshless algorithm. In addition, this study uses the moving grid algorithm to carry out clinical experiments on ex-vivo pig liver for experimental verification.

RESULTS

The mark-based interactive registration error is 4.21 ± 1.6 mm, and the registration error is reduced after taking the biomechanical properties of the liver into account, which is - 0.153 ± 0.398 mm. The cutting error of the liver model is 0.159 ± 0.292 mm. In addition, with the aid of the navigation system proposed in this paper, the experiment of ex-vivo pig liver cutting was completed with an error of - 1.164 ± 0.576 mm.

CONCLUSIONS

As a proof-of-concept study, the augmented reality navigation system proposed in this study improves the traditional image-guided surgery in terms of display and registration methods, and the feasibility of the system is verified by ex-vivo pig liver experiments. Therefore, the navigation system has a certain guiding significance for clinical surgery.

Atlas-based Segmentation of Intracochlear Anatomy in Metal Artifact Affected CT Images of the Ear with Co-trained Deep Neural Networks

We propose an atlas-based method to segment the intracochlear anatomy (ICA) in the post-implantation CT (Post-CT) images of cochlear implant (CI) recipients that preserves the point-to-point correspondence between the meshes in the atlas and the segmented volumes. To solve this problem, which is challenging because of the strong artifacts produced by the implant, we use a pair of co-trained deep networks that generate dense deformation fields (DDFs) in opposite directions. One network is tasked with registering an atlas image to the Post-CT images and the other network is tasked with registering the Post-CT images to the atlas image. The networks are trained using loss functions based on voxel-wise labels, image content, fiducial registration error, and cycle-consistency constraint. The segmentation of the ICA in the Post-CT images is subsequently obtained by transferring the predefined segmentation meshes of the ICA in the atlas image to the Post-CT images using the corresponding DDFs generated by the trained registration networks. Our model can learn the underlying geometric features of the ICA even though they are obscured by the metal artifacts. We show that our end-to-end network produces results that are comparable to the current state of the art (SOTA) that relies on a two-steps approach that first uses conditional generative adversarial networks to synthesize artifact-free images from the Post-CT images and then uses an active shape model-based method to segment the ICA in the synthetic images. Our method requires a fraction of the time needed by the SOTA, which is important for end-user acceptance.

Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

BACKGROUND AND OBJECTIVE

Non-rigid image registration is a well-established method for estimating cardiac motion on 3D echocardiographic images. However, such images have relatively poor spatio-temporal resolution making registration challenging. Some of the main challenges are extracting features relevant to the registration problem and defining a suitable geometrical transformation to be applied. The latter can be tackled using a fuzzy inference system considering its potential in transformation modeling. From this point of view, feature-based image registration can be considered an identification problem in which the transformation parameters are computed through an optimization process. This study, thus, aims to estimate cardiac motion on 3D echocardiographic images based on feature-based non-rigid image registration through sets of modified fuzzy rules.

METHODS

The 3D volume features were extracted with the popular scale-invariant feature transform (SIFT) descriptors in 3D space. Sets of fuzzy rules were generated according to the extracted features to register every two consecutive frames. Finally, some supplementary rules modified the registration rule for estimating cardiac motion.

RESULTS

Applying the fuzzy feature-based inference system on the STRAUS synthetic database showed the proposed method to be competitive with other well-established registration algorithms in terms of tracking error and accuracy of strain estimates. The proposed algorithm yielded a tracking error of 1 mm and a relative circumferential strain error of 0.82±4.69%. In addition, the potential of the proposed algorithm for clinical applications was confirmed by evaluating its performance on an in-vivo database called CETUS.

CONCLUSION

This paper proposes a novel registration method based on fuzzy logic which was shown to enable tracking complex cardiac deformations in 3D echocardiographic images with high accuracy.

Augmented Reality during Open Liver Surgery Using a Markerless Non-rigid Registration System

INTRODUCTION

Intraoperative navigation during liver resection remains difficult and requires high radiologic skills because liver anatomy is complex and patient-specific. Augmented reality (AR) during open liver surgery could be helpful to guide hepatectomies and optimize resection margins but faces many challenges when large parenchymal deformations take place. We aimed to experiment a new vision-based AR to assess its clinical feasibility and anatomical accuracy.

PATIENTS AND METHODS

Based on preoperative CT scan 3-D segmentations, we applied a non-rigid registration method, integrating a physics-based elastic model of the liver, computed in real time using an efficient finite element method. To fit the actual deformations, the model was driven by data provided by a single RGB-D camera. Five livers were considered in this experiment. In vivo AR was performed during hepatectomy (n = 4), with manual handling of the livers resulting in large realistic deformations. Ex vivo experiment (n = 1) consisted in repeated CT scans of explanted whole organ carrying internal metallic landmarks, in fixed deformations, and allowed us to analyze our estimated deformations and quantify spatial errors.

RESULTS

In vivo AR tests were successfully achieved in all patients with a fast and agile setup installation (< 10 min) and real-time overlay of the virtual anatomy onto the surgical field displayed on an external screen. In addition, an ex vivo quantification demonstrated a 7.9 mm root mean square error for the registration of internal landmarks.

CONCLUSION

These first experiments of a markerless AR provided promising results, requiring very little equipment and setup time, yet providing real-time AR with satisfactory 3D accuracy. These results must be confirmed in a larger prospective study to definitively assess the impact of such minimally invasive technology on pathological margins and oncological outcomes.

Fast 4D elastic group-wise image registration. Convolutional interpolation revisited

BACKGROUND AND OBJECTIVE

This paper proposes a new and highly efficient implementation of 3D+t groupwise registration based on the free-form deformation paradigm.

METHODS

Deformation is posed as a cascade of 1D convolutions, achieving great reduction in execution time for evaluation of transformations and gradients.

RESULTS

The proposed method has been applied to 4D cardiac MRI and 4D thoracic CT monomodal datasets. Results show an average runtime reduction above 90%, both in CPU and GPU executions, compared with the classical tensor product formulation.

CONCLUSIONS

Our implementation, although fully developed for the metric sum of squared differences, can be extended to other metrics and its adaptation to multiresolution strategies is straightforward. Therefore, it can be extremely useful to speed up image registration procedures in different applications where high dimensional data are involved.

Topological recovery for non-rigid 2D/3D registration of coronary artery models

BACKGROUND AND OBJECTIVE

Intra-operative X-ray angiography, the current standard method for visualizing and diagnosing cardiovascular disease, is limited in its ability to provide essential 3D information. These limitations are disadvantages in treating patients. For example, it is a cause of lowering the success rate of interventional procedures. Here, we propose a novel 2D-3D non-rigid registration method to understand vascular geometry during percutaneous coronary intervention.

METHODS

The proposed method uses the local bijection pair distance as a cost function to minimize the effect of inconsistencies from center-line extraction. Moreover, novel cage-based 3D deformation and multi-threaded particle swarm optimization are utilized to implement real-time registration. We evaluated the proposed method for 154 examinations from 10 anonymous patients by coverage percentage, comparing the average distance of the 2D extracted center-line with that of the registered 3D center-line.

RESULTS

The proposed 2D-3D non-rigid registration method achieved an average distance of 1.98 mm with a 0.54 s computation time. Additionally, in aiming to reduce the uncertainty of XA images, we used the proposed method to retrospectively visualize the connections between 2D vascular segments and the distal part of occlusions.

CONCLUSIONS

Ultimately, the proposed 2D/3D non-rigid registration method can successfully register the 3D center-line of coronary arteries with corresponding 2D XA images, and is computationally sufficient for online usage. Therefore, this method can improve the success rate of such procedures as a percutaneous coronary intervention and provide the information necessary to diagnose cardiovascular diseases better.

Performance-aware programming for intraoperative intensity-based image registration on graphics processing units

Purpose

Intensity-based image registration has been proven essential in many applications accredited to its unparalleled ability to resolve image misalignments. However, long registration time for image realignment prohibits its use in intra-operative navigation systems. There has been much work on accelerating the registration process by improving the algorithm’s robustness, but the innate computation required by the registration algorithm has been unresolved.

Methods

Intensity-based registration methods involve operations with high arithmetic load and memory access demand, which supposes to be reduced by graphics processing units (GPUs). Although GPUs are widespread and affordable, there is a lack of open-source GPU implementations optimized for non-rigid image registration. This paper demonstrates performance-aware programming techniques, which involves systematic exploitation of GPU features, by implementing the diffeomorphic log-demons algorithm.

Results

By resolving the pinpointed computation bottlenecks on GPU, our implementation of diffeomorphic log-demons on Nvidia GTX Titan X GPU has achieved ~ 95 times speed-up compared to the CPU and registered a 1.3-M voxel image in 286 ms. Even for large 37-M voxel images, our implementation is able to register in 8.56 s, which attained ~ 258 times speed-up. Our solution involves effective employment of GPU computation units, memory, and data bandwidth to resolve computation bottlenecks.

Conclusion

The computation bottlenecks in diffeomorphic log-demons are pinpointed, analyzed, and resolved using various GPU performance-aware programming techniques. The proposed fast computation on basic image operations not only enhances the computation of diffeomorphic log-demons, but is also potentially extended to speed up many other intensity-based approaches. Our implementation is open-source on GitHub at https://bit.ly/2PYZxQz.

Balanced multi-image demons for non-rigid registration of magnetic resonance images

A new approach is introduced for non-rigid registration of a pair of magnetic resonance images (MRI). It is a generalization of the demons algorithm with low computational cost, based on local information augmentation (by integrating multiple images) and balanced implementation. Specifically, a single deformation that best registers more pairs of images is estimated. All these images are extracted by applying different operators to the two original ones, processing local neighbors of each pixel. The following five images were found to be appropriate for MRI registration: the raw image and those obtained by contrast-limited adaptive histogram equalization, local median, local entropy and phase symmetry. Thus, each local point in the images is supplemented by augmented information coming by processing its neighbor. Moreover, image pairs are processed in alternation for each iteration of the algorithm (in a balanced way), computing both a forward and a backward registration. The new method (called balanced multi-image demons) is tested on sagittal MRIs from 10 patients, both in simulated and experimental conditions, improving the performances over the classical demons approach with minimal increase of the computational cost (processing time around twice that of standard demons). Specifically, a simulated deformation was applied to the MRIs (either original or corrupted by additive Gaussian or speckle noises). In all tested cases, the new algorithm improved the estimation of the simulated deformation (squared estimation error decreased by about 65% in the average). Moreover, statistically significant improvements were obtained in experimental tests, in which different brain regions (i.e., brain, posterior fossa and cerebellum) were identified by the atlas approach and compared to those manually delineated (in the average, Dice coefficient increased of about 6%). The conclusion is that a balanced method applied to multiple information extracted from neighboring pixels is a low cost approach to improve registration of MRIs.

A general framework in single and multi-modality registration for lung imaging analysis using statistical prior shapes

BACKGROUND AND OBJECTIVE

A fusion of multi-slice computed tomography (MSCT) and single photon emission computed tomography (SPECT) represents a powerful tool for chronic obstructive pulmonary disease (COPD) analysis. In this paper, a novel and high-performance MSCT/SPECT non-rigid registration algorithm is proposed to accurately map the lung lobe information onto the functional imaging. Such a fusion can then be used to guide lung volume reduction surgery.

METHODS

The multi-modality fusion method proposed here is developed by a multi-channel technique which performs registration from MSCT scan to ventilation and perfusion SPECT scans simultaneously. Furthermore, a novel function with less parameters is also proposed to avoid the adjustment of the weighting parameter and to achieve a better performance in comparison with the exisitng methods in the literature.

RESULTS

A lung imaging dataset from a hospital and a synthetic dataset created by software are employed to validate single- and multi-modality registration results. Our method is demonstrated to achieve the improvements in terms of registration accuracy and stability by up to 23% and 54% respectively. Our multi-channel technique proposed here is also proved to obtain improved registration accuracy in comparison with single-channel method.

CONCLUSIONS

The fusion of lung lobes onto SPECT imaging is achievable by accurate MSCT/SPECT alignment. It can also be used to perform lobar lung activity analysis for COPD diagnosis and treatment.

Efficacy of non-rigid registration technique for misregistration in 3D-CTA fusion imaging

PURPOSE

To assess whether fusion 3D-CTA images can be corrected using non-rigid registration (NRR) for gastroenterology imaging.

METHODS

This study included 55 patients before gastroenterology surgery who underwent preoperative 3D-CTA prior to gastroenterological surgery. We recorded the coordinate of measurement points on the arterial vessels (X, Y, and Z) in each portal phase, original image of the arterial phase, and arterial phase with NRR. The distance of misregistration between the two points was calculated with the coordinate of the original image with NRR and that of the portal phase as true value.

RESULTS

The distance of misregistration between the two points in the original arterial and portal phase images was significantly higher than that in the arterial phase image with NRR on all directions (p < 0.01).

CONCLUSIONS

This study showed that NRR may correct misregistration on fusion 3D-CTA imaging. Hence, it can visualize correctly the anatomy of the vessel.

Alignment of Cortical Vessels viewed through the Surgical Microscope with Preoperative Imaging to Compensate for Brain Shift

Brain shift is a non-rigid deformation of brain tissue that is affected by loss of cerebrospinal fluid, tissue manipulation and gravity among other phenomena. This deformation can negatively influence the outcome of a surgical procedure since surgical planning based on pre-operative image becomes less valid. We present a novel method to compensate for brain shift that maps preoperative image data to the deformed brain during intra-operative neurosurgical procedures and thus increases the likelihood of achieving a gross total resection while decreasing the risk to healthy tissue surrounding the tumor. Through a 3D/2D non-rigid registration process, a 3D articulated model derived from pre-operative imaging is aligned onto 2D images of the vessels viewed through the surgical miscroscopic intra-operatively. The articulated 3D vessels constrain a volumetric biomechanical model of the brain to propagate cortical vessel deformation to the parenchyma and in turn to the tumor. The 3D/2D non-rigid registration is performed using an energy minimization approach that satisfies both projective and physical constraints. Our method is evaluated on real and synthetic data of human brain showing both quantitative and qualitative results and exhibiting its particular suitability for real-time surgical guidance.

Deformable Registration for Longitudinal Breast MRI Screening

MRI screening of high-risk patients for breast cancer provides very high sensitivity, but with a high recall rate and negative biopsies. Comparing the current exam to prior exams reduces the number of follow-up procedures requested by radiologists. Such comparison, however, can be challenging due to the highly deformable nature of breast tissues. Automated co-registration of multiple scans has the potential to aid diagnosis by providing 3D images for side-by-side comparison and also for use in CAD systems. Although many deformable registration techniques exist, they generally have a large number of parameters that need to be optimized and validated for each new application. Here, we propose a framework for such optimization and also identify the optimal input parameter set for registration of 3D T₁-weighted MRI of breast using Elastix, a widely used and freely available registration tool. A numerical simulation study was first conducted to model the breast tissue and its deformation through finite element (FE) modeling. This model generated the ground truth for evaluating the registration accuracy by providing the deformation of each voxel in the breast volume. An exhaustive search was performed over various values of 7 registration parameters (4050 different combinations of parameters were assessed) and the optimum parameter set was determined. This study showed that there was a large variation in the registration accuracy of different parameter sets ranging from 0.29 mm to 2.50 mm in median registration error and 3.71 mm to 8.90 mm in 95 percentile of the registration error. Mean registration errors of 0.32 mm, 0.29 mm, and 0.30 mm and 95 percentile errors of 3.71 mm, 5.02 mm, and 4.70 mm were obtained by the three best parameter sets. The optimal parameter set was applied to consecutive breast MRI scans of 13 patients. A radiologist identified 113 landmark pairs (~ 11 per patient) which were used to assess registration accuracy. The results demonstrated that using the optimal registration parameter set, a registration accuracy (in mm) of 3.4 [1.8 6.8] was achieved.

Registration of vascular structures using a hybrid mixture model

PURPOSE

Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions.

METHODS

A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student's t-distributions to model the former and Watson distributions for the latter.

RESULTS

The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than [Formula: see text] of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student's t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores.

CONCLUSION

The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.

Registration-based image enhancement improves multi-atlas segmentation of the thalamic nuclei and hippocampal subfields

Magnetic resonance imaging (MRI) is an important tool for analysis of deep brain grey matter structures. However, analysis of these structures is limited due to low intensity contrast typically found in whole brain imaging protocols. Herein, we propose a big data registration-enhancement (BDRE) technique to augment the contrast of deep brain structures using an efficient large-scale non-rigid registration strategy. Direct validation is problematic given a lack of ground truth data. Rather, we validate the usefulness and impact of BDRE for multi-atlas (MA) segmentation on two sets of structures of clinical interest: the thalamic nuclei and hippocampal subfields. The experimental design compares algorithms using T1-weighted 3 T MRI for both structures (and additional 7 T MRI for the thalamic nuclei) with an algorithm using BDRE. As baseline comparisons, a recent denoising (DN) technique and a super-resolution (SR) method are used to preprocess the original 3 T MRI. The performance of each MA segmentation is evaluated by the Dice similarity coefficient (DSC). BDRE significantly improves mean segmentation accuracy over all methods tested for both thalamic nuclei (3 T imaging: 9.1%; 7 T imaging: 15.6%; DN: 6.9%; SR: 16.2%) and hippocampal subfields (3 T T1 only: 8.7%; DN: 8.4%; SR: 8.6%). We also present DSC performance for each thalamic nucleus and hippocampal subfield and show that BDRE can help MA segmentation for individual thalamic nuclei and hippocampal subfields. This work will enable large-scale analysis of clinically relevant deep brain structures from commonly acquired T1 images.

Point-cloud registration using adaptive radial basis functions

Non-rigid registration is a common part of bioengineering model-generation workflows. Compared to common mesh-based methods, radial basis functions can provide more flexible deformation fields due to their meshless nature. We introduce an implementation of RBF non-rigid registration with iterative knot-placement to adaptively reduce registration error. The implementation is validated on surface meshes of the femur, hemi-pelvis, mandible, and lumbar spine. Mean registration surface errors ranged from 0.37 to 0.99 mm, Hausdorff distance from 1.84 to 2.47 mm, and DICE coefficients from 0.97 to 0.99. The implementation is available for use in the free and open-source GIAS2 library.

QUADRATIC: Quality of Dice in Registration Circuits

Image registration involves identification of a transformation to fit a target image to a reference image space. The success of the registration process is vital for correct interpretation of the results of many medical image-processing applications, including multi-atlas segmentation. While there are several validation metrics employed in rigid registration to examine the accuracy of the method, non-rigid registrations (NRR) are validated subjectively in most cases, validated in offline cases, or based on image similarity metrics, all of which have been shown to poorly correlate with true registration quality. In this paper, we model the error for each target scan by expanding on the idea of Assessing Quality Using Image Registration Circuits (AQUIRC), which created a model for error "quality" associated with NRR. In this paper, we model the Dice similarity coefficient (DSC) error in the network, for a more interpretable measure. We test four functional models using a leave-one-out strategy to evaluate the relationship between edge DSC and circuit DSC: linear, quadratic, third order, or multiplicative models. We found that the quadratic model most accurately learns the NRR-DSC, with a median correlation coefficient of 0.58 with the true NRR-DSC, we call this the QUADRATIC (QUAlity of Dice in RegistrATIon Circuits) model. The QUADRATIC model is used for multi-atlas segmentation based on majority vote. Choosing the four best atlases predicted from the QUDRATIC model resulted in a 7% increase in the DSC between segmented image and true labels.

A NEW REGISTRATION APPROACH FOR DYNAMIC ANALYSIS OF CALCIUM SIGNALS IN ORGANS

Wing disc pouches of fruit flies are a powerful genetic model for studying physiological intercellular calcium (Ca 2+) signals for dynamic analysis of cell signaling in organ development and disease studies. A key to analyzing spatial-temporal patterns of Ca 2+ signal waves is to accurately align the pouches across image sequences. However, pouches in different image frames may exhibit extensive intensity oscillations due to Ca 2+ signaling dynamics, and commonly used multimodal non-rigid registration methods may fail to achieve satisfactory results. In this paper, we develop a new two-phase non-rigid registration approach to register pouches in image sequences. First, we conduct segmentation of the region of interest. (i.e., pouches) using a deep neural network model. Second, we use a B-spline based registration to obtain an optimal transformation and align pouches across the image sequences. Evaluated using both synthetic data and real pouch data, our method considerably outperforms the state-of-the-art non-rigid registration methods.

A GPU-based symmetric non-rigid image registration method in human lung

Quantitative computed tomography (QCT) of the lungs plays an increasing role in identifying sub-phenotypes of pathologies previously lumped into broad categories such as chronic obstructive pulmonary disease and asthma. Methods for image matching and linking multiple lung volumes have proven useful in linking structure to function and in the identification of regional longitudinal changes. Here, we seek to improve the accuracy of image matching via the use of a symmetric multi-level non-rigid registration employing an inverse consistent (IC) transformation whereby images are registered both in the forward and reverse directions. To develop the symmetric method, two similarity measures, the sum of squared intensity difference (SSD) and the sum of squared tissue volume difference (SSTVD), were used. The method is based on a novel generic mathematical framework to include forward and backward transformations, simultaneously, eliminating the need to compute the inverse transformation. Two implementations were used to assess the proposed method: a two-dimensional (2-D) implementation using synthetic examples with SSD, and a multi-core CPU and graphics processing unit (GPU) implementation with SSTVD for three-dimensional (3-D) human lung datasets (six normal adults studied at total lung capacity (TLC) and functional residual capacity (FRC)). Success was evaluated in terms of the IC transformation consistency serving to link TLC to FRC. 2-D registration on synthetic images, using both symmetric and non-symmetric SSD methods, and comparison of displacement fields showed that the symmetric method gave a symmetrical grid shape and reduced IC errors, with the mean values of IC errors decreased by 37%. Results for both symmetric and non-symmetric transformations of human datasets showed that the symmetric method gave better results for IC errors in all cases, with mean values of IC errors for the symmetric method lower than the non-symmetric methods using both SSD and SSTVD. The GPU version demonstrated an average of 43 times speedup and ~5.2 times speedup over the single-threaded and 12-threaded CPU versions, respectively. Run times with the GPU were as fast as 2 min. The symmetric method improved the inverse consistency, aiding the use of image registration in the QCT-based evaluation of the lung.

A Robust and Accurate Non-rigid Medical Image Registration Algorithm Based on Multi-level Deformable Model

BACKGROUND

Compared to the rigid image registration task, the non-rigid image registration task faces much more challenges due to its high degree of freedom and inherent requirement of smoothness in the deformation field. The purpose was to propose an efficient coarse-to-fine non-rigid medical image registration algorithm based on a multilevel deformable model.

METHODS

In this paper, a robust and efficient coarse-to-fine non-rigid medical image registration algorithm is proposed. It contains three level deformation models, i.e., the global homography model, the local mesh-level homography model, and the local B-spline FFD (Free-Form Deformation) model. The coarse registration is achieved by the first two level models. In the global homography model, a robust algorithm for simultaneous outliers (error matched feature points) removal and model estimation is applied. In the local mesh-level homography model, a new similarity measure is proposed to improve the robustness and accuracy of local mesh based registration. In the fine registration, a local B-spline FFD model with normalized mutual information gradient is employed.

RESULTS

We verified the effectiveness of each stage of the proposed registration algorithm with many non-rigid transformation image pairs, and quantitatively compared our proposed registration algorithm with the HBFFD method which is based on the control points of multi-resolution. The experimental results show that our algorithm is more accurate than the hierarchical local B-spline FFD method.

CONCLUSION

Our algorithm can achieve high precision registration by coarse-to-fine process based on multi-level deformable model, which ourperforms the state-of-the-art methods.

Dual-core steered non-rigid registration for multi-modal images via bi-directional image synthesis

In prostate cancer radiotherapy, computed tomography (CT) is widely used for dose planning purposes. However, because CT has low soft tissue contrast, it makes manual contouring difficult for major pelvic organs. In contrast, magnetic resonance imaging (MRI) provides high soft tissue contrast, which makes it ideal for accurate manual contouring. Therefore, the contouring accuracy on CT can be significantly improved if the contours in MRI can be mapped to CT domain by registering MRI with CT of the same subject, which would eventually lead to high treatment efficacy. In this paper, we propose a bi-directional image synthesis based approach for MRI-to-CT pelvic image registration. First, we use patch-wise random forest with auto-context model to learn the appearance mapping from CT to MRI domain, and then vice versa. Consequently, we can synthesize a pseudo-MRI whose anatomical structures are exactly same with CT but with MRI-like appearance, and a pseudo-CT as well. Then, our MRI-to-CT registration can be steered in a dual manner, by simultaneously estimating two deformation pathways: 1) one from the pseudo-CT to the actual CT and 2) another from actual MRI to the pseudo-MRI. Next, a dual-core deformation fusion framework is developed to iteratively and effectively combine these two registration pathways by using complementary information from both modalities. Experiments on a dataset with real pelvic CT and MRI have shown improved registration performance of the proposed method by comparing it to the conventional registration methods, thus indicating its high potential of translation to the routine radiation therapy.

Learning Non-rigid Deformations for Robust, Constrained Point-based Registration in Image-Guided MR-TRUS Prostate Intervention

Accurate and robust non-rigid registration of pre-procedure magnetic resonance (MR) imaging to intra-procedure trans-rectal ultrasound (TRUS) is critical for image-guided biopsies of prostate cancer. Prostate cancer is one of the most prevalent forms of cancer and the second leading cause of cancer-related death in men in the United States. TRUS-guided biopsy is the current clinical standard for prostate cancer diagnosis and assessment. State-of-the-art, clinical MR-TRUS image fusion relies upon semi-automated segmentations of the prostate in both the MR and the TRUS images to perform non-rigid surface-based registration of the gland. Segmentation of the prostate in TRUS imaging is itself a challenging task and prone to high variability. These segmentation errors can lead to poor registration and subsequently poor localization of biopsy targets, which may result in false-negative cancer detection. In this paper, we present a non-rigid surface registration approach to MR-TRUS fusion based on a statistical deformation model (SDM) of intra-procedural deformations derived from clinical training data. Synthetic validation experiments quantifying registration volume of interest overlaps of the PI-RADS parcellation standard and tests using clinical landmark data demonstrate that our use of an SDM for registration, with median target registration error of 2.98 mm, is significantly more accurate than the current clinical method. Furthermore, we show that the low-dimensional SDM registration results are robust to segmentation errors that are not uncommon in clinical TRUS data.

Atomap: a new software tool for the automated analysis of atomic resolution images using two-dimensional Gaussian fitting

Scanning transmission electron microscopy (STEM) data with atomic resolution can contain a large amount of information about the structure of a crystalline material. Often, this information is hard to extract, due to the large number of atomic columns and large differences in intensity from sublattices consisting of different elements. In this work, we present a free and open source software tool for analysing both the position and shapes of atomic columns in STEM-images, using 2-D elliptical Gaussian distributions. The software is tested on variants of the perovskite oxide structure. By first fitting the most intense atomic columns and then subtracting them, information on all the projected sublattices can be obtained. From this, we can extract changes in the lattice parameters and shape of A-cation columns from annular dark field images of perovskite oxide heterostructures. Using annular bright field images, shifts in oxygen column positions are also quantified in the same heterostructure. The precision of determining the position of atomic columns is compared between STEM data acquired using standard acquisition, and STEM-images obtained as an image stack averaged after using non-rigid registration.

Non-rigid MR-TRUS image registration for image-guided prostate biopsy using correlation ratio-based mutual information

BACKGROUND

To improve the accuracy of ultrasound-guided biopsy of the prostate, the non-rigid registration of magnetic resonance (MR) images onto transrectal ultrasound (TRUS) images has gained increasing attention. Mutual information (MI) is a widely used similarity criterion in MR-TRUS image registration. However, the use of MI has been challenged because of intensity distortion, noise and down-sampling. Hence, we need to improve the MI measure to get better registration effect.

METHODS

We present a novel two-dimensional non-rigid MR-TRUS registration algorithm that uses correlation ratio-based mutual information (CRMI) as the similarity criterion. CRMI includes a functional mapping of intensity values on the basis of a generalized version of intensity class correspondence. We also analytically acquire the derivative of CRMI with respect to deformation parameters. Furthermore, we propose an improved stochastic gradient descent (ISGD) optimization method based on the Metropolis acceptance criteria to improve the global optimization ability and decrease the registration time.

RESULTS

The performance of the proposed method is tested on synthetic images and 12 pairs of clinical prostate TRUS and MR images. By comparing label map registration frame (LMRF) and conditional mutual information (CMI), the proposed algorithm has a significant improvement in the average values of Hausdorff distance and target registration error. Although the average Dice Similarity coefficient is not significantly better than CMI, it still has a crucial increase over LMRF. The average computation time consumed by the proposed method is similar to LMRF, which is 16 times less than CMI.

CONCLUSION

With more accurate matching performance and lower sensitivity to noise and down-sampling, the proposed algorithm of minimizing CRMI by ISGD is more robust and has the potential for use in aligning TRUS and MR images for needle biopsy.

Estimation of myocardial strain from non-rigid registration and highly accelerated cine CMR

Sparsely sampled cardiac cine accelerated acquisitions show promise for faster evaluation of left-ventricular function. Myocardial strain estimation using image feature tracking methods is also becoming widespread. However, it is not known whether highly accelerated acquisitions also provide reliable feature tracking strain estimates. Twenty patients and twenty healthy volunteers were imaged with conventional 14-beat/slice cine acquisition (STD), 4× accelerated 4-beat/slice acquisition with iterative reconstruction (R4), and a 9.2× accelerated 2-beat/slice real-time acquisition with sparse sampling and iterative reconstruction (R9.2). Radial and circumferential strains were calculated using non-rigid registration in the mid-ventricle short-axis slice and inter-observer errors were evaluated. Consistency was assessed using intra-class correlation coefficients (ICC) and bias with Bland-Altman analysis. Peak circumferential strain magnitude was highly consistent between STD and R4 and R9.2 (ICC = 0.876 and 0.884, respectively). Average bias was -1.7 ± 2.0 %, p < 0.001, for R4 and -2.7 ± 1.9 %, p < 0.001 for R9.2. Peak radial strain was also highly consistent (ICC = 0.829 and 0.785, respectively), with average bias -11.2 ± 18.4 %, p < 0.001, for R4 and -15.0 ± 21.2 %, p < 0.001 for R9.2. STD circumferential strain could be predicted by linear regression from R9.2 with an R² of 0.82 and a root mean squared error of 1.8 %. Similarly, radial strain could be predicted with an R² of 0.67 and a root mean squared error of 21.3 %. Inter-observer errors were not significantly different between methods, except for peak circumferential strain R9.2 (1.1 ± 1.9 %) versus STD (0.3 ± 1.0 %), p = 0.011. Although small systematic differences were observed in strain, these were highly consistent with standard acquisitions, suggesting that accelerated myocardial strain is feasible and reliable in patients who require short acquisition durations.

Marker-less tracking of brain surface deformations by non-rigid registration integrating surface and vessel/sulci features

PURPOSE

To compensate for brain shift in image-guided neurosurgery, we propose a new non-rigid registration method that integrates surface and vessel/sulci feature to noninvasively track the brain surface.

METHOD

Textured brain surfaces were acquired using phase-shift three-dimensional (3D) shape measurement, which offers 2D image pixels and their corresponding 3D points directly. Measured brain surfaces were noninvasively tracked using the proposed method by minimizing a new energy function, which is a weighted combination of 3D point corresponding estimation and surface deformation constraints. Initially, the measured surfaces were divided into featured and non-featured parts using a Frangi filter. The corresponding feature/non-feature points between intraoperative brain surfaces were estimated using the closest point algorithm. Subsequently, smoothness and rigidity constraints were introduced in the energy function for a smooth surface deformation and local surface detail conservation, respectively. Our 3D shape measurement accuracy was evaluated using 20 spheres for bias and precision errors. In addition, the proposed method was evaluated based on root mean square error (RMSE) and target registration error (TRE) with five porcine brains for which deformations were produced by gravity and pushing with different displacements in both the vertical and horizontal directions.

RESULTS

The minimum and maximum bias errors were 0.32 and 0.61 mm, respectively. The minimum and maximum precision errors were 0.025 and 0.30 mm, respectively. Quantitative validation with porcine brains showed that the average RMSE and TRE were 0.1 and 0.9 mm, respectively.

CONCLUSION

The proposed method appeared to be advantageous in integrating vessels/sulci feature, robust to changes in deformation magnitude and integrated feature numbers, and feasible in compensating for brain shift deformation in surgeries.

Learning intervention-induced deformations for non-rigid MR-CT registration and electrode localization in epilepsy patients

This paper describes a framework for learning a statistical model of non-rigid deformations induced by interventional procedures. We make use of this learned model to perform constrained non-rigid registration of pre-procedural and post-procedural imaging. We demonstrate results applying this framework to non-rigidly register post-surgical computed tomography (CT) brain images to pre-surgical magnetic resonance images (MRIs) of epilepsy patients who had intra-cranial electroencephalography electrodes surgically implanted. Deformations caused by this surgical procedure, imaging artifacts caused by the electrodes, and the use of multi-modal imaging data make non-rigid registration challenging. Our results show that the use of our proposed framework to constrain the non-rigid registration process results in significantly improved and more robust registration performance compared to using standard rigid and non-rigid registration methods.

Topology preserving non-rigid image registration using time-varying elasticity model for MRI brain volumes

In this paper, we present a new non-rigid image registration method that imposes a topology preservation constraint on the deformation. We propose to incorporate the time varying elasticity model into the deformable image matching procedure and constrain the Jacobian determinant of the transformation over the entire image domain. The motion of elastic bodies is governed by a hyperbolic partial differential equation, generally termed as elastodynamics wave equation, which we propose to use as a deformation model. We carried out clinical image registration experiments on 3D magnetic resonance brain scans from IBSR database. The results of the proposed registration approach in terms of Kappa index and relative overlap computed over the subcortical structures were compared against the existing topology preserving non-rigid image registration methods and non topology preserving variant of our proposed registration scheme. The Jacobian determinant maps obtained with our proposed registration method were qualitatively and quantitatively analyzed. The results demonstrated that the proposed scheme provides good registration accuracy with smooth transformations, thereby guaranteeing the preservation of topology.

A hybrid registration-based method for whole-body micro-CT mice images

The widespread use of whole-body small animal in vivo imaging in preclinical research has proposed the new demands on imaging processing and analysis. Micro-CT provides detailed anatomical structural information for continuous detection and different individual comparison, but the body deformation happened during different data acquisition needs sophisticated registration. In this paper, we propose a hybrid method for registering micro-CT mice images, which combines the strengths of point-based and intensity-based registration methods. Point-based non-rigid method using thin-plate spline robust point matching algorithm is utilized to acquire a coarse registration. And then intensity-based non-rigid method using normalized mutual information, Halton sampling and adaptive stochastic gradient descent optimization is used to acquire precise registration. Two accuracy metrics, Dice coefficient and average surface distance are used to do the quantitative evaluation. With the intra- and intersubject micro-CT mice images registration assessment, the hybrid method has been proven capable of excellent performance on micro-CT mice images registration.

Towards measuring neuroimage misalignment

To enhance neuro-navigation, high quality pre-operative images must be registered onto intra-operative configuration of the brain. Therefore evaluation of the degree to which structures may remain misaligned after registration is critically important. We consider two Hausdorff Distance (HD)-based evaluation approaches: the edge-based HD (EBHD) metric and the Robust HD (RHD) metric as well as various commonly used intensity-based similarity metrics such as Mutual Information (MI), Normalised Mutual Information (NMI), Entropy Correlation Coefficient (ECC), Kullback-Leibler Distance (KLD) and Correlation Ratio (CR). We conducted the evaluation by applying known deformations to simple sample images and real cases of brain shift. We conclude that the intensity-based similarity metrics such as MI, NMI, ECC, KLD and CR do not correlate well with actual alignment errors, and hence are not useful for assessing misalignment. On the contrary, the EBHD and the RHD metrics correlated well with actual alignment errors; however, they have been found to underestimate the actual misalignment. We also note that it is beneficial to present HD results as a percentile-HD curve rather than a single number such as the 95-percentile HD. Percentile-HD curves present the full range of alignment errors and also facilitate the comparison of results obtained using different approaches. Furthermore, the qualities that should be possessed by an ideal evaluation metric were highlighted. Future studies could focus on developing such an evaluation metric.

Near real-time robust non-rigid registration of volumetric ultrasound images for neurosurgery

Ultrasound images are acquired before and after the resection of brain tumors to help the surgeon to localize the tumor and its extent and to minimize the amount of residual tumor after the resection. Because the brain undergoes large deformation between these two acquisitions, deformable image-based registration of these data sets is of substantial clinical importance. In this work, we present an algorithm for non-rigid registration of ultrasound images (RESOUND) that models the deformation with free-form cubic B-splines. We formulate a regularized cost function that uses normalized cross-correlation as the similarity metric. To optimize the cost function, we calculate its analytic derivative and use the stochastic gradient descent technique to achieve near real-time performance. We further propose a robust technique to minimize the effect of non-corresponding regions such as the resected tumor and possible hemorrhage in the post-resection image. Using manually labeled corresponding landmarks in the pre- and post-resection ultrasound volumes, we illustrate that our registration algorithm reduces the mean target registration error from an initial value of 3.7 to 1.5 mm. We also compare RESOUND with the previous work of Mercier et al. (2013) and illustrate that it has three important advantages: (i) it is fully automatic and does not require a manual segmentation of the tumor, (ii) it produces smaller registration errors and (iii) it is about 30 times faster. The clinical data set is available online on the BITE database website.

Augment low-field intra-operative MRI with preoperative MRI using a hybrid non-rigid registration method

BACKGROUND

Preoperatively acquired diffusion tensor image (DTI) and blood oxygen level dependent (BOLD) have been proved to be effective in providing more anatomical and functional information; however, the brain deformation induced by brain shift and tumor resection severely impairs the correspondence between the image space and the patient space in image-guided neurosurgery.

METHOD

To address the brain deformation, we developed a hybrid non-rigid registration method to register high-field preoperative MRI with low-field intra-operative MRI in order to recover the deformation induced by brain shift and tumor resection. The registered DTI and BOLD are fused with low-field intra-operative MRI for image-guided neurosurgery.

RESULTS

The proposed hybrid registration method was evaluated by comparing the landmarks predicted by the hybrid registration method with the landmarks identified in the low-field intra-operative MRI for 10 patients. The prediction error of the hybrid method is 1.92±0.54 mm, and the compensation accuracy is 74.3±5.0%. Compared to the landmarks far from the resection region, those near the resection region demonstrated a higher compensation accuracy (P-value=.003) although these landmarks had larger initial displacements.

CONCLUSIONS

The proposed hybrid registration method is able to bring preoperatively acquired BOLD and DTI into the operating room and compensate for the deformation to augment low-field intra-operative MRI with rich anatomical and functional information.

Adaptive registration of magnetic resonance images based on a viscous fluid model

This paper develops a new viscous fluid registration algorithm that makes use of a closed incompressible viscous fluid model associated with mutual information. In our approach, we treat the image pixels as the fluid elements of a viscous fluid governed by the nonlinear Navier-Stokes partial differential equation (PDE) that varies in both temporal and spatial domains. We replace the pressure term with an image-based body force to guide the transformation that is weighted by the mutual information between the template and reference images. A computationally efficient algorithm with staggered grids is introduced to obtain stable solutions of this modified PDE for transformation. The registration process of updating the body force, the velocity and deformation fields is repeated until the mutual information reaches a prescribed threshold. We have evaluated this new algorithm in a number of synthetic and medical images. As consistent with the theory of the viscous fluid model, we found that our method faithfully transformed the template images into the reference images based on the intensity flow. Experimental results indicated that the proposed scheme achieved stable registrations and accurate transformations, which is of potential in large-scale medical image deformation applications.

Non-rigid registration of serial dedicated breast CT, longitudinal dedicated breast CT and PET/CT images using the diffeomorphic demons method

RATIONALE AND OBJECTIVES

Dedicated breast CT and PET/CT scanners provide detailed 3D anatomical and functional imaging data sets and are currently being investigated for applications in breast cancer management such as diagnosis, monitoring response to therapy and radiation therapy planning. Our objective was to evaluate the performance of the diffeomorphic demons (DD) non-rigid image registration method to spatially align 3D serial (pre- and post-contrast) dedicated breast computed tomography (CT), and longitudinally-acquired dedicated 3D breast CT and positron emission tomography (PET)/CT images.

METHODS

The algorithmic parameters of the DD method were optimized for the alignment of dedicated breast CT images using training data and fixed. The performance of the method for image alignment was quantitatively evaluated using three separate data sets; (1) serial breast CT pre- and post-contrast images of 20 women, (2) breast CT images of 20 women acquired before and after repositioning the subject on the scanner, and (3) dedicated breast PET/CT images of 7 women undergoing neo-adjuvant chemotherapy acquired pre-treatment and after 1 cycle of therapy.

RESULTS

The DD registration method outperformed no registration (p < 0.001) and conventional affine registration (p ≤ 0.002) for serial and longitudinal breast CT and PET/CT image alignment. In spite of the large size of the imaging data, the computational cost of the DD method was found to be reasonable (3-5 min).

CONCLUSIONS

Co-registration of dedicated breast CT and PET/CT images can be performed rapidly and reliably using the DD method. This is the first study evaluating the DD registration method for the alignment of dedicated breast CT and PET/CT images.

Relation between brain architecture and mathematical ability in children: a DBM study

Population-based studies indicate that between 5 and 9 percent of US children exhibit significant deficits in mathematical reasoning, yet little is understood about the brain morphological features related to mathematical performances. In this work, deformation-based morphometry (DBM) analyses have been performed on magnetic resonance images of the brains of 79 third graders to investigate whether there is a correlation between brain morphological features and mathematical proficiency. Group comparison was also performed between Math Difficulties (MD-worst math performers) and Normal Controls (NC), where each subgroup consists of 20 age and gender matched subjects. DBM analysis is based on the analysis of the deformation fields generated by non-rigid registration algorithms, which warp the individual volumes to a common space. To evaluate the effect of registration algorithms on DBM results, five nonrigid registration algorithms have been used: (1) the Adaptive Bases Algorithm (ABA); (2) the Image Registration Toolkit (IRTK); (3) the FSL Nonlinear Image Registration Tool; (4) the Automatic Registration Tool (ART); and (5) the normalization algorithm available in SPM8. The deformation field magnitude (DFM) was used to measure the displacement at each voxel, and the Jacobian determinant (JAC) was used to quantify local volumetric changes. Results show there are no statistically significant volumetric differences between the NC and the MD groups using JAC. However, DBM analysis using DFM found statistically significant anatomical variations between the two groups around the left occipital-temporal cortex, left orbital-frontal cortex, and right insular cortex. Regions of agreement between at least two algorithms based on voxel-wise analysis were used to define Regions of Interest (ROIs) to perform an ROI-based correlation analysis on all 79 volumes. Correlations between average DFM values and standard mathematical scores over these regions were found to be significant. We also found that the choice of registration algorithm has an impact on DBM-based results, so we recommend using more than one algorithm when conducting DBM studies. To the best of our knowledge, this is the first study that uses DBM to investigate brain anatomical features related to mathematical performance in a relatively large population of children.

On the estimation and correction of bias in local atrophy estimations using example atrophy simulations

Brain atrophy is considered an important marker of disease progression in many chronic neuro-degenerative diseases such as multiple sclerosis (MS). A great deal of attention is being paid toward developing tools that manipulate magnetic resonance (MR) images for obtaining an accurate estimate of atrophy. Nevertheless, artifacts in MR images, inaccuracies of intermediate steps and inadequacies of the mathematical model representing the physical brain volume change, make it rather difficult to obtain a precise and unbiased estimate. This work revolves around the nature and magnitude of bias in atrophy estimations as well as a potential way of correcting them. First, we demonstrate that for different atrophy estimation methods, bias estimates exhibit varying relations to the expected atrophy and these bias estimates are of the order of the expected atrophies for standard algorithms, stressing the need for bias correction procedures. Next, a framework for estimating uncertainty in longitudinal brain atrophy by means of constructing confidence intervals is developed. Errors arising from MRI artifacts and bias in estimations are learned from example atrophy simulations and anatomies. Results are discussed for three popular non-rigid registration approaches with the help of simulated localized brain atrophy in real MR images.

Developmental changes in hippocampal shape among preadolescent children

It is known that the largest developmental changes in the hippocampus take place during the prenatal period and during the first two years of postnatal life. Few studies have been conducted to address the normal developmental trajectory of the hippocampus during childhood. In this study shape analysis was applied to study the normal developing hippocampus in a group of 103 typically developing 6- to 10-year-old preadolescent children. The individual brain was normalized to a template, and then the hippocampus was manually segmented and further divided into the head, body, and tail sub-regions. Three different methods were applied for hippocampal shape analysis: radial distance mapping, surface-based template registration using the robust point matching (RPM) algorithm, and volume-based template registration using the Demons algorithm. All three methods show that the older children have bilateral expanded head segments compared to the younger children. The results analyzed based on radial distance to the centerline were consistent with those analyzed using template-based registration methods. In analyses stratified by sex, it was found that the age-associated anatomical changes were similar in boys and girls, but the age-association was strongest in girls. Total hippocampal volume and sub-regional volumes analyzed using manual segmentation did not show a significant age-association. Our results suggest that shape analysis is sensitive to detect sub-regional differences that are not revealed in volumetric analysis. The three methods presented in this study may be applied in future studies to investigate the normal developmental trajectory of the hippocampus in children. They may be further applied to detect early deviations from the normal developmental trajectory in young children for evaluating susceptibility for psychopathological disorders involving hippocampus.