Intensity-based Registration Framework Research Articles

A Coarse-to-Fine Registration on 3D Multi-Phase Abdominal CT Images

Registration is a technical support for the integration of nanomaterial imaging-aided diagnosis and treatment. In this paper, a coarse-to-fine three-dimensional (3D) multi-phase abdominal CT images registration method is proposed. Firstly, a linear model is used to coarsely register the paired multiphase images. Secondly, an intensity-based registration framework is proposed, which contains the data and spatial regularization terms and performs fine registration on the paired images obtained in the coarse registration step. The results illustrate that the proposed method is superior to some existing methods with the average MSE, PSNR, and SSIM values of 0.0082, 21.2695, and 0.8956, respectively. Therefore, the proposed method provides an efficient and robust framework for 3D multi-phase abdominal CT images registration.

4D in vivo quantification of ankle joint space width using dynamic MRI.

Spatio-temporal evolution of joint space width (JSW) during motion is of great importance to help with making early treatment plans for degenerative joint diseases like osteoarthritis (OA). These diseases can affect people of all ages leading to an acceleration of joint degeneration and to limitations in the activities of daily living. However, only a few studies have attempted to quantify the JSW from moving joints. In this paper, we present a generic pipeline to accurately determine the changes of the JSW during the joint motion cycle. The key idea is to combine spatial information of static MRI with temporal information of low-resolution (LR) dynamic MRI sequences via an intensity-based registration framework, leading to a high-resolution (HR) temporal reconstruction of the joint. This allows the temporal JSW to be measured in the HR domain using an Eulerian approach for solving partial differential equations (PDEs) inside a deforming inter-bone area where the HR reconstructed bone segmentations are considered as temporal Dirichlet boundaries. The proposed approach has been applied and evaluated on in vivo MRI data of five healthy children to non-invasively quantify the spatio-temporal evolution of the JSW of the ankle (tibiotalar joint) during the entire dorsi-plantar flexion motion cycle. Promising results were obtained, showing that this pipeline can be useful to perform large-scale studies containing subjects with OA for different joints like ankle and knee.

2D–3D registration for cranial radiation therapy using a 3D kV CBCT and a single limited field‐of‐view 2D kV radiograph

We present and evaluate a fully automated 2D-3D intensity-based registration framework using a single limited field-of-view (FOV) 2D kV radiograph and a 3D kV CBCT for 3D estimation of patient setup errors during brain radiotherapy. We evaluated two similarity measures, the Pearson correlation coefficient on image intensity values (ICC) and maximum likelihood measure with Gaussian noise (MLG), derived from the statistics of transmission images. Pose determination experiments were conducted on 2D kV radiographs in the anterior-posterior (AP) and left lateral (LL) views and 3D kV CBCTs of an anthropomorphic head phantom. In order to minimize radiation exposure and exclude nonrigid structures from the registration, limited FOV 2D kV radiographs were employed. A spatial frequency band useful for the 2D-3D registration was identified from the bone-to-no-bone spectral ratio (BNBSR) of digitally reconstructed radiographs (DRRs) computed from the 3D kV planning CT of the phantom. The images being registered were filtered accordingly prior to computation of the similarity measures. We evaluated the registration accuracy achievable with a single 2D kV radiograph and with the registration results from the AP and LL views combined. We also compared the performance of the 2D-3D registration solutions proposed to that of a commercial 3D-3D registration algorithm, which used the entire skull for the registration. The ground truth was determined from markers affixed to the phantom and visible in the CBCT images. The accuracy of the 2D-3D registration solutions, as quantified by the root mean squared value of the target registration error (TRE) calculated over a radius of 3 cm for all poses tested, was ICCAP : 0.56 mm, MLGAP : 0.74 mm, ICCLL : 0.57 mm, MLGLL : 0.54 mm, ICC (AP and LL combined): 0.19 mm, and MLG (AP and LL combined): 0.21 mm. The accuracy of the 3D-3D registration algorithm was 0.27 mm. There was no significant difference in mean TRE for the 2D-3D registration algorithms using a single 2D kV radiograph with similarity measure and image view point. There was no significant difference in mean TRE between ICCLL , MLGLL , ICC (AP and LL combined), MLG (AP and LL combined), and the 3D-3D registration algorithm despite the smaller FOV used for the 2D-3D registration. While submillimeter registration accuracy was obtained with both ICC and MLG using a single 2D kV radiograph, combining the results from the two projection views resulted in a significantly smaller (P≤0.05) mean TRE. Our results indicate that it is possible to achieve submillimeter registration accuracy with both ICC and MLG using either single or dual limited FOV 2D kV radiographs of the head in the AP and LL views. The registration accuracy suggests that the 2D-3D registration solutions presented are suitable for the estimation of patient setup errors not only during conventional brain radiation therapy, but also during stereotactic procedures and proton radiation therapy where tighter setup margins are required.

Semiautomated registration of pre- and intraoperative CT for image-guided percutaneous liver tumor ablation interventions.

In CT-guided liver tumor ablation interventions, registration of a preoperative contrast-enhanced CT image to the intraoperative CT image is hypothesized to improve guidance. This is a highly challenging registration task due to differences in patient poses and large deformations, and therefore high registration errors are expected. In this study, our objective is to develop a method that enables users to locally improve the registration where the registration fails, with minimal user interaction. The method is based on a conventional nonrigid intensity-based registration framework, extended with a novel point-to-surface penalty. The point-to-surface penalty serves to improve the alignment of the liver boundary, while requiring minimal user interaction during the intervention: annotating some points on the liver surface at those regions where the conventional registration seems inaccurate. The method is evaluated on 18 clinical datasets. It improves registration accuracy compared with the conventional nonrigid registration in terms of average surface distance (from 2.75 to 2.05mm) and target registration error (from 6.92 to 5.8mm). In this study, we introduce a semiautomated registration algorithm that improves the accuracy of image registration.

2D-3D registration for brain radiation therapy using a 3D CBCT and a single limited field-of-view 2D kV radiograph

We report results of an intensity-based 2D-3D rigid registration framework for patient positioning and monitoring during brain radiotherapy. We evaluated two intensity-based similarity measures, the Pearson Correlation Coefficient (ICC) and Maximum Likelihood with Gaussian noise (MLG) derived from the statistics of transmission images. A useful image frequency band was identified from the bone-to-no-bone ratio. Validation was performed on gold-standard data consisting of 3D kV CBCT scans and 2D kV radiographs of an anthropomorphic head phantom acquired at 23 different poses with parameter variations along six degrees of freedom. At each pose, a single limited field of view kV radiograph was registered to the reference CBCT. The ground truth was determined from markers affixed to the phantom and visible in the CBCT images. The mean (and standard deviation) of the absolute errors in recovering each of the six transformation parameters along the x, y and z axes for ICC were φx: 0.08(0.04)°, φy: 0.10(0.09)°, φz: 0.03(0.03)°, tx: 0.13(0.11) mm, ty: 0.08(0.06) mm and tz: 0.44(0.23) mm. For MLG, the corresponding results were φx: 0.10(0.04)°, φy: 0.10(0.09)°, φz: 0.05(0.07)°, tx: 0.11(0.13) mm, ty: 0.05(0.05) mm and tz: 0.44(0.31) mm. It is feasible to accurately estimate all six transformation parameters from a 3D CBCT of the head and a single 2D kV radiograph within an intensity-based registration framework that incorporates the physics of transmission images.

A frequency‐based approach to locate common structure for 2D‐3D intensity‐based registration of setup images in prostate radiotherapy

In many radiotherapy clinics, geometric uncertainties in the delivery of 3D conformal radiation therapy and intensity modulated radiation therapy of the prostate are reduced by aligning the patient's bony anatomy in the planning 3D CT to corresponding bony anatomy in 2D portal images acquired before every treatment fraction. In this paper, we seek to determine if there is a frequency band within the portal images and the digitally reconstructed radiographs (DRRs) of the planning CT in which bony anatomy predominates over non-bony anatomy such that portal images and DRRs can be suitably filtered to achieve high registration accuracy in an automated 2D-3D single portal intensity-based registration framework. Two similarity measures, mutual information and the Pearson correlation coefficient were tested on carefully collected gold-standard data consisting of a kilovoltage cone-beam CT (CBCT) and megavoltage portal images in the anterior-posterior (AP) view of an anthropomorphic phantom acquired under clinical conditions at known poses, and on patient data. It was found that filtering the portal images and DRRs during the registration considerably improved registration performance. Without filtering, the registration did not always converge while with filtering it always converged to an accurate solution. For the pose-determination experiments conducted on the anthropomorphic phantom with the correlation coefficient, the mean (and standard deviation) of the absolute errors in recovering each of the six transformation parameters were Theta(x):0.18(0.19) degrees, Theta(y):0.04(0.04) degrees, Theta(z):0.04(0.02) degrees, t(x):0.14(0.15) mm, t(y):0.09(0.05) mm, and t(z):0.49(0.40) mm. The mutual information-based registration with filtered images also resulted in similarly small errors. For the patient data, visual inspection of the superimposed registered images showed that they were correctly aligned in all instances. The results presented in this paper suggest that robust and accurate registration can be achieved with intensity-based methods by focusing on rigid bony structures in the images while diminishing the influence of artifacts with similar frequencies as soft tissue.