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 multi-level DEFORMABLE model. Methods: In this paper, a robust and efficient coarse-to-fine non-rigid medical image REGISTRATION algorithm is pro-posed. It contains three level deformation models, i. e., the global homography model, the local mesh-level homogra-phy 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 trans-formation 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 de-formable model, which ourperforms the state-of-the-art methods.

Background: Medical image interpolation is recently introduced as a helpful tool to obtain further information via initial available images taken by tomography systems. To do this, DEFORMABLE image REGISTRATION algorithms are mainly utilized to perform image interpolation using tomography images. Materials and Methods: In this work, 4DCT thoracic images of five real patients provided by DIR-lab group were utilized. Four implemented REGISTRATION algorithms as 1) Original Horn-Schunck, 2) Inverse consistent Horn-Schunck, 3) Original Demons and 4) Fast Demons were implemented by means of DIRART software packages. Then, the calculated vector fields are processed to reconstruct 4DCT images at any desired time using optical flow based on interpolation method. As a comparative study, the accuracy of interpolated image obtained by each strategy is measured by calculating mean square error between the interpolated image and real middle image as ground truth dataset. Results: Final results represent the ability to accomplish image interpolation among given two-paired images. Among them, Inverse Consistent Horn-Schunck algorithm has the best performance to reconstruct interpolated image with the highest accuracy while Demons method had the worst performance. Conclusion: Since image interpolation is affected by increasing the distance between two given available images, the performance accuracy of four different REGISTRATION algorithms is investigated concerning this issue. As a result, Inverse Consistent Horn-Schunck does not essentially have the best performance especially in facing large displacements happened due to distance increment.

Introduction of a Simple Algorithm to Create Synthetic‑ computed Tomography of the Head from Magnetic Resonance ImagingBackground: Recently, magnetic resonance imaging (MRI)‑ based radiotherapy has become a favorite science field for treatment planning purposes. In this study, a simple algorithm was introduced to create synthetic computed tomography (sCT) of the head from MRI. Methods: A simple atlas‑ based method was proposed to create sCT images based on the paired T1/T2‑ weighted MRI and bone/brain window CT. Dataset included 10 patients with glioblastoma multiforme and 10 patients with other brain tumors. To generate a sCT image, first each MR from dataset was registered to the target‑ MR, the resulting transformation was applied to the corresponding CT to create the set of deformed CTs. Then, deformed‑ CTs were fused to generate a single sCT image. The sCT images were compared with the real CT images using geometric measures (mean absolute error [MAE] and dice similarity coefficient of bone [DSCbone]) and Hounsfield unit gamma‑ index (Г HU) with criteria 100 HU/2 mm. Results: The evaluations carried out by MAE, DSCbone, and Г HU showed a good agreement between the synthetic and real CT images. The results represented the range of 78– 93 HU and 0. 80– 0. 89 for MAE and DSCbone, respectively. The Г HU also showed that approximately 91%– 93% of pixels fulfilled the criteria 100 HU/2 mm for brain tumors. Conclusion: This method showed that MR sequence (T1w or T2w) should be selected depending on the type of tumor. In addition, the brain window synthetic CTs are in better agreement with real CT relative to bone window sCT images.

Background: Kilovoltage Cone Beam Computed Tomography (kVCBCT) is used for patient setup, monitoring the delivered dose, and adapting the treatment to changes in the patient’s condition. Radiation therapy has recently shifted from image guidance to dose guidance, resulting in accurately calculating the daily dose, calculated by re-simulating CT-based treatment planning, to increase the precision of the actual treatment dosage. The use of kVCBCT instead of re-simulated CT can simplify the patient pathway and reduce potential therapeutic errors by eliminating the need for additional simulation.Objective: The present study aimed to assess the dosimetric effects of anatomical changes on prostate tumors using Deformation Image REGISTRATION (DIR) and kVCBCT.Material and Methods: In this experimental study, eight patients with primary prostate cancer were treated with Volumetric Modulated Arc Therapy (VMAT), and kVCBCT images were obtained for each patient during the first treatment fraction. Both the planning CT (pCT) and kVCBCT images were imported into DIR software. The pCT was then deformed to the kVCBCT image and imported into a Treatment Planning System (TPS). A new contour was created on the deformed Computed Tomography (dCT) using Atlas-based Auto-segmentation (ABAS). Daily dCT plans were individually created based on the same planning principles using the new contours and also denoted dCTp1 through dCTp8. The outcomes of dose calculations were compared using Dose Volume Histograms (DVH), including mean Planning Target Volume (PTV) doses at the prescribed dose and dose volume limitations for the bladder and rectal wall.Results: The mean doses to the PTV in the eight dCT-based plans were the same as those in the pCT-based plans. However, the mean doses to organs at risk in the dCT plans were different from those in the pCT plans. The mean doses to the bladder were on average 4% lower than those in the pCT plans, while the mean doses to the rectum were on average 8% higher than those in the pCT plans. Conclusion: The use of VMAT based on kilovoltage kVCBCT and Deformtion Image REGISTRATION (DIR) can lead to re-decreasing the dose to the bladder while increasing that to the rectum, with the same PTV dose coverage.

Background: Retinal imaging employs various modalities, each providing distinct perspectives on ocular structures. However, the integration of information from these modalities, which often have differing resolutions, requires effective image REGISTRATION techniques. Existing retinal image REGISTRATION methods typically rely on rigid or affine transformations, which may not adequately address the complexities of multimodal retinal images. Method: This study introduces a nonrigid fuzzy image REGISTRATION approach designed to align optical coherence tomography (OCT) images with fundus images. The method employs a fuzzy inference system (FIS) that uses vessel locations as key features for REGISTRATION. The FIS applies specific rules to map points from the source image to the reference image, facilitating accurate alignment. Results: The proposed method achieved a mean absolute REGISTRATION error of 44. 57 ± 39. 38 µm in the superior–inferior orientation and 11. 46 ± 10. 06 µm in the nasal-temporal orientation. These results underscore the method’s precision in aligning multimodal retinal images. Conclusion: The nonrigid fuzzy image REGISTRATION approach demonstrates robust and versatile performance in integrating multimodal retinal imaging data. Despite its straightforward implementation, the method effectively addresses the challenges of multimodal retinal image REGISTRATION, providing a reliable tool for advanced ocular imaging analysis.

The presented DEFORMABLE field theory deals with electromagnetic local forces on the basis of field energy density. In this theory, any movement, rigid or deforming, distorts the electromagnetic field continuum. This leads to novel concepts of total and local forces explicitly related to the elastic deformation gradient rather than the classical gradient of the magnetic field. It is shown how the magnetic vector potential, as the magnetic invariant variable, is associated to this DEFORMABLE field continuum and is, meanwhile, reference-independent. Then, within an adiabatic virtual work, the local magnetic energy derivatives are analytically performed, converging to overall electromagnetic force and stress tensors, including Lorenz, inherent magnetization and strict magnetostriction forces.