Enhanced Accuracy in Registration of Cortex Functional Data via Large-Deformation Differomorphic Maps

PurposeKilovoltage cone beam computed tomography (kVCBCT)‐guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART.MethodsWe evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity‐based deformable registration, (2) hybrid deformable registration, and (3) a two‐step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062 M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD95) and Dice similarity coefficient (DSC), and each method was compared with the intensity‐based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal‐to‐noise ratio (SNR), uniformity, and contrast‐to‐noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal‐to‐noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose‐volume histogram (DVH) and 3.0%/0.3 mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference.ResultsThe dCT images generated by the hybrid (dCTH) and two‐step (dCTC) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCTH and dCTC methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCTH and dCTC methods, respectively. For the geometric evaluation, only the two‐step registration method improved registration accuracy. The dCTH method yielded an average HD95 of 12 mm and average DSC of 0.73, whereas dCTC yielded an average HD95 of 2.9 mm and average DSC of 0.902. The DVH showed that the dCTC‐based dose calculations differed by <2% from the expected results for treatment targets and volumes of organs at risk. Additionally, gamma indices for dCTC‐based treatment plans were >95% at all points, whereas they were <95% for kVCBCT‐based treatment plans.ConclusionThe two‐step registration method outperforms the intensity‐based and hybrid registration methods. While the hybrid and two‐step‐based methods improved the image quality of kVCBCT in a linear accelerator, only the two‐step method improved the registration accuracy of the corresponding structures among the pCT and kVCBCT datasets. A two‐step registration process is recommended for applying kVCBCT to ART, which achieves better registration accuracy for local and global image structures. This method appears to be beneficial for radiotherapy dose calculation in patients with pelvic cancer.

Accurate registration is a key step for precise and real-time measurement of appearance phenotype using the forward intersection method. For example, in order to reconstruct the details of the plant organs (leaf, fruit, stem, etc.), precisely matched image points from varied object points should be of high density in the image region of a target organ in each image. However, this task is not achieved stably with relevant methods in current literature. In this paper, the multiple basic parameters for affine registration are obtained by estimating the distance reference value, obtaining the camera attitude from the inertial module, calibrating the internal parameters of the camera, calibrating the structure parameters of the vision system, aberration modelling, and other methods. The basic parameters are used to express the vectors of object points to be measured, the optical vectors of the image points to be matched and the scale factors of the optical projection in varied camera coordinate systems. In the registration process, these vectors are affine operated to map the first target image point to each image via the strategy of mapping. The system error of the approximately matched pixel is eliminated in the micro-region of error centred around the approximately matched pixel. Research shows that this affine registration algorithm is able to obtain a large number of highdensity and accurate matched image points that correspond to different target points in the leaf region of each image. This is suitable for forward intersection measurement of the plant appearance phenotype details with a real-time vision system based on our results. The proposed registration method is highly robust, efficient and precise; and requires relatively little computation resources.

Image registration technology has been widely used in many parts of the computer vision system such as the automatic optical inspection system which is used to detect the printed circuit board (PCB) defects. The accuracy of the image registration will deeply influence the system's performance, so this study proposed an accurate image registration algorithm and applied it to the PCB defect detection. Good features to track feature detector and speeded up robust feature descriptor are combined to extract efficient features to achieve the first accurate image registration. Afterwards, cross‐correlation functions were used to compute the shift between the reference image and the first‐registered image for further accurate registration. Experimental results show that the authors’ algorithm performs a much better registration, with a lower root‐mean‐square error value between the reference image and transformed image. What is more, they applied it to detect the defects of PCB with a high accuracy.

PurposeA fast and accurate intraoperative registration method is important for Computer-Aided Orthopedic Surgery (CAOS). A-mode ultrasound (US) is able to acquire bone surface data in a non-invasive manner. To utilize A-mode US in CAOS, a suitable registration algorithm is necessary with a small number of registration points and the presence of measurement errors. Therefore, we investigated the effects of (1) the number of registration points and (2) the Ultrasound Point Localization Error (UPLE) on the overall registration accuracy.MethodsWe proposed a new registration method (ICP-PS), including the Iterative Closest Points (ICP) algorithm and a Perturbation Search algorithm. This method enables to avoid getting stuck in the local minimum of ICP iterations and to find the adjacent global minimum. This registration method was subsequently validated in a numerical simulation and a cadaveric experiment using a 3D-tracked A-mode US system.ResultsThe results showed that ICP-PS outperformed the standard ICP algorithm. The registration accuracy improved with the addition of ultrasound registration points. In the numerical simulation, for 25 sample points with zero UPLE, the averaged registration error of ICP-PS reached 0.25 mm, while 1.71 mm for ICP, decreasing by 85.38%. In the cadaver experiment, using 25 registration points, ICP-PS achieved an RMSE of 2.81 mm relative to 5.84 mm for the ICP, decreasing by 51.88%.ConclusionsThe simulation approach provided a well-defined framework for estimating the necessary number of ultrasound registration points and acceptable level of UPLE for a given required level of accuracy for intraoperative registration in CAOS. ICP-PS method is suitable for A-mode US based intraoperative registration. This study would facilitate the application of A-mode US probe in registering the point cloud to a known shape model, which also has the potential for accurately estimating bone position and orientation for skeletal motion tracking and surgical navigation.

PurposeThe purpose of this paper is to present a method for registration of 3D computed tomography to 2D single-plane fluoroscopy knee images to provide 3D motion information for knee joints. This 3D kinematic information has unique utility for examining joint kinematics in conditions such as ligament injury, osteoarthritis and after joint replacement.MethodsWe proposed a non-invasive rigid body image registration method which is based on two different multimodal similarity measures. This hybrid registration method helps to achieve a trade-off among different challenges including, time complexity and accuracy.ResultsWe performed a number of experiments to evaluate the performance of the proposed method. The experimental results show that the proposed method is as accurate as one of the most recent registration methods while it is several times faster than that method.ConclusionThe proposed method is a non-invasive, fast and accurate registration method, which can provide 3D information for knee joint kinematic measurements. This information can be very helpful in improving the accuracy of diagnosis and providing targeted treatment.

A method for accurate registration on point clouds is presented in the paper. Manual alignment or the use of landmarks is avoided in the process of multi-view point clouds. Firstly, the differential geometric information is extracted from the point clouds. The extended Gaussian sphere and combination features are used to define the corresponding points of crude alignment. Secondly, the optimal algorithm，the point-to-point Iterated Closest Point, is applied to the accurate registration on point clouds. Thus, the complete point cloud can be obtained in the method.

Deformable image registration (DIR) is increasingly used for target volume definition in radiotherapy. However, this method is challenging for postoperative breast cancer patients due to the large deformations and non-correspondence caused by tumor resection and clip insertion. In this study, an improved B-splines based DIR method was developed to address this issue for higher registration accuracy. The conventional B-splines based DIR method was improved with the introduction of point metric and masking technique. The point metric minimizes the distance between 2 point sets with known correspondence for regularization of intensity-based B-splines registration. The masking technique reduces the influence of non-corresponding regions in breast computed tomography (CT) images. Two sets of CT images before and after breast surgery were used for image registration. One set was the diagnostic CT image acquired before surgery, and another set was the planning CT image acquired after surgery for breast cancer radiotherapy. A total of 26 sets of CT images from 13 patients were collected retrospectively for the test. The improved DIR method's registration accuracy was evaluated by target registration error (TRE), the Jacobian determinant, and visual assessment. For soft tissue, the difference in the median TRE between the improved DIR method and the conventional DIR method was statistically significant (2.27 vs. 5.88, P<0.05). The Jacobian determinant of the deformation field was positive for all patients. For visual assessment, the improved DIR method with point metric achieved better matching for soft tissue. The improved DIR method's registration accuracy was higher than the conventional DIR method based on the preliminary results. With point metric and masking technique, the influence of large deformations and non-correspondence on registration between pre- and post-operative CT images can be effectively reduced. Therefore, this method provides a feasible way for target volume definition in postoperative breast cancer radiotherapy treatment planning.

In this study, we evaluated SPECT/MRI registration of ictal data, using similarity based registration methods. An absolute gold standard for registration evaluation was obtained by considering realistic normal and ictal SPECT simulations deduced from a high resolution T1-weighted MRI data set.Those simulations were also used to study the impact of photon attenuation and Compton scatter corrections on registration accuracy. Evaluation of registration was also performed using inconsistency measurements for six patients with temporo-mesial epilepsy. For these data, as no Gold Standard was available, registration accuracy was assessed using inconsistency measurements involving a registration loop between inter-ictal SPECT, ictal SPECT and MRI data. Five registration methods based on statistical similarity measurements were compared, namely: mutual information (MI), normalized mutual information (NMI), L1 and L2 norm-based correlation ratios (CR) and correlation coefficient (CC).It was found that the simulation context had more influence on registration accuracy than the choice of the similarity criterion. Ictal SPECT as well as correction for uniform attenuation clearly decreased SPECT/MRI registration accuracy.KeywordsMutual InformationAttenuation CorrectionScatter CorrectionRegistration MethodNormalize Mutual InformationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The quality of dosimetry in radiotherapy treatment requires the accurate delimitation of the gross tumor volume. This can be achieved by complementing the anatomical detail provided by CT images through fusion with other imaging modalities that provide additional metabolic and physiological information. Therefore, use of multiple imaging modalities for radiotherapy treatment planning requires an accurate image registration method. This work describes tests carried out on a Discovery LS positron emission/computed tomography (PET/CT) system by General Electric Medical Systems (GEMS), for its later use to obtain images to delimit the target in radiotherapy treatment. Several phantoms have been used to verify image correlation, in combination with fiducial markers, which were used as a system of external landmarks. We analyzed the geometrical accuracy of two different fusion methods with the images obtained with these phantoms. We first studied the fusion method used by the PET/CT system by GEMS (hardware fusion) on the basis that there is satisfactory coincidence between the reconstruction centers in CT and PET systems; and secondly the fiducial fusion, a registration method, by means of least-squares fitting algorithm of a landmark points system. The study concluded with the verification of the centroid position of some phantom components in both imaging modalities. Centroids were estimated through a calculation similar to center-of-mass, weighted by the value of the CT number and the uptake intensity in PET. The mean deviations found for the hardware fusion method were: deltax/ +/-sigma = 3.3 mm +/- 1.0 mm and /deltax/ +/-sigma = 3.6 mm +/- 1.0 mm. These values were substantially improved upon applying fiducial fusion based on external landmark points: /deltax/ +/-sigma = 0.7 mm +/- 0.8 mm and /deltax/ +/-sigma = 0.3 mm 1.7 mm. We also noted that differences found for each of the fusion methods were similar for both the axial and helical CT image acquisition protocols.

In this paper, we propose an accurate, robust and swift registration method of magnetic resonance (MR) and 3D ultrasound for percutaneous renal intervention. We refine the traditional ICP algorithm with a pre-registration Procrustes initial alignment to improve the accuracy and efficiency. We propose the orthogonal feature extraction principle to increase the robustness while reducing the human interaction required during the registration. The accuracy, robustness and efficiency performance of the proposed registration method is examined via experiment on real human data.

Image registration is a fundamental requirement for medical image analysis. Deep registration methods based on deep learning have been widely recognized for their capabilities to perform fast end-to-end registration. Many deep registration methods achieved state-of-the-art performance by performing coarse-to-fine registration, where multiple registration steps were iterated with cascaded networks. Recently, Non-Iterative Coarse-to-finE (NICE) registration methods have been proposed to perform coarse-to-fine registration in a single network and showed advantages in both registration accuracy and runtime. However, existing NICE registration methods mainly focus on deformable registration, while affine registration, a common prerequisite, is still reliant on time-consuming traditional optimization-based methods or extra affine registration networks. In addition, existing NICE registration methods are limited by the intrinsic locality of convolution operations. Transformers may address this limitation for their capabilities to capture long-range dependency, but the benefits of using transformers for NICE registration have not been explored. In this study, we propose a Non-Iterative Coarse-to-finE Transformer network (NICE-Trans) for image registration. Our NICE-Trans is the first deep registration method that (i) performs joint affine and deformable coarse-to-fine registration within a single network, and (ii) embeds transformers into a NICE registration framework to model long-range relevance between images. Extensive experiments with seven public datasets show that our NICE-Trans outperforms stateof-the-art registration methods on both registration accuracy and runtime.

Purpose. To develop an optimization scheme and semi-physical regularization method for accurate and efficient deformable image registration (DIR) between magnetic resonance imaging (MRI) and computed tomography (CT) for breast cancer radiation therapy planning. Methods and Materials. A sequentially-applied semi-physical model regularization method with bending energy limited diffeomorphism (BELD) was developed to regularize transformation in free-form (B-spline) DIR. Bony structures were first segmented to account for their rigidity during deformation. Feature points were identified and filtered to be used as a penalty term. Rigidity, BELD, and feature point distance penalty terms are applied sequentially during the optimization process based on mutual information metric. Sample MRI and CT images of breast cancer patients were used to demonstrate the registration. Agreements of feature point pairs and contour transfer between the two image modalities were measured to illustrate the accuracy and efficiency for the developed DIR method. Results. The newly developed tool can accurately register MRI to CT with minimum user intervention for situations with moderate to large deformations. For the sample cases tested, the average feature point pair distance was reduced from 6.7 to 3.2 mm by simple B-spline registration and to 1.8 mm by the newly-developed registration method. Compared to simultaneous optimization of multiple penalty terms, the computation time was shortened by 30% and the accuracy was improved by 15%. The bone area warping artifact in deformed MRI disappeared in the proposed method. Considering the resolution of MR images is 1.3 mm, the resulting feature point pair distance is satisfactory. Conclusion. The proposed DIR method of sequentially applying physical model regularization terms during the optimization process can reduce the pitfalls of local extremity, weighting factor adjustment and multi-resolution optimization stages, therefore improving the accuracy and efficiency of MRI to CT registration even with moderate to large deformations commonly seen in radiation treatment planning with multi-modality images.

Synchronous measurement of temperature distribution with infrared cameras has been increasingly used in the thermomechanical analysis of sheet material behavior. Measured temperatures are used, however, only qualitatively due to the absence of well-developed coordinate mapping methods applicable to thermographic measurements. The study introduces a primitive, easy to use, yet effective approach for accurate coordinate mapping from real to image coordinates, and inverse, applicable to even highly oblique thermographic and photographic measurements of planar deformations. The method is not limited to any camera model and precisely counteracts inhomogeneous image distortion. It can be used as a stand-alone optical measurement approach, but at the same time, it allows flawless correlation with available digital image correlation systems by enabling accurate mapping between thermographic and photographic measurements of planar deformation in real and image coordinates.KeywordsTemperatureSheet metalFull-fieldOptical

Electro-anatomical maps (EAM) and CT surface registration are widely used for catheter navigation in atrial fibrillation ablations. However, few studies have investigated the registration algorithm. Moreover, some of them are semiautomatic, so that physicians must be proficient; some are inaccurate for catheter navigation. A both automatic and accurate registration method is needed. A Hausdorff distance based approach (HD) was proposed for EAM/CT registration. First, using principal axes based registration, EAM/CT pairs were coarsely aligned. Then, using Hausdorff distance as the fine objective function, EAM/CT pairs were finely aligned. Six real EAM/CT pairs were collected from five patients and 38 simulated pairs were generated. Each pair was aligned using Carto-Merge, a stochastic approach (SA) and HD. Considering the balance of operability, accuracy and robustness, HD obtained the best EAM/CT registration results among the three approaches. Experiments validate that the proposed method registers EAM and CT surface both automatically and accurately.

Geometric correction is used to correct the registration errors in remotely sensed images. These images are often compared to ground control points (GCPs) either by using an accurate map (image to map) or using another geo-referenced image (image to image) and then resampled. Accordingly, the exact locations and the appropriate pixel values can be calculated in more accurate, time-wise and effortless manner. In the traditional methods, the GCPs are manually selected and then the transformation models are applied which yield time consuming and less accurate processes. The objective of this work is to develop an automatic approach for image registration based on another geo-referenced image using five feature extraction models. They are Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Discrete Wavelet Transforms (DWT), (SIFT & DWT), and (SURF & DWT). The GCPs were selected based on the least-squares adjustments as the basis for improving the spatial accuracy of all the linking points in both images. The obtained results showed that models have higher accuracy in image registration with Root Mean Square Error (RMSE) less than 0.5. The developed automated image registration method provides more accurate results and saves time, money and effort.