Assessment of lung deformation in patients with idiopathic pulmonary fibrosis with elastic registration technique on pulmonary three-dimensional ultrashort echo time MRI.

OBJECTIVE: To assess lung deformation in patients with idiopathic pulmonary fibrosis (IPF) using with elastic registration algorithm applied to three-dimensional ultrashort echo time (3D-UTE) MRI and analyze relationship of lung deformation with the severity of IPF. METHODS: Seventy-six patients with IPF (mean age: 62 ± 6 years) and 62 age- and gender-matched healthy controls (mean age: 58 ± 4 years) were prospectively enrolled. End-inspiration and end-expiration images acquired with a single breath-hold 3D-UTE sequence were registered using elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Jac-mean (absolute value of the log means of Jacobian determinants) and the Dice similarity coefficient (Dice) were compared between different groups. RESULTS: Compared with healthy controls, the Jac-mean of IPF patients significantly decreased (0.21 ± 0.08 vs. 0.27 ± 0. 07, p < 0.001). Furthermore, the Jac-mean and Dice correlated with the metrics of pulmonary function tests and the composite physiological index. The lung deformation in IPF patients with dyspnea Medical Research Council (MRC) ≥ 3 (Jac-mean: 0.16 ± 0.03; Dice: 0.06 ± 0.02) was significantly lower than MRC1 (Jac-mean: 0. 25 ± 0.03, p < 0.001; Dice: 0.10 ± 0.01, p < 0.001) and MRC 2 (Jac-mean: 0.22 ± 0.11, p = 0.001; Dice: 0.08 ± 0.03, p = 0.006). Meanwhile, Jac-mean and Dice correlated with health-related quality of life, 6 min-walk distance, and the extent of pulmonary fibrosis. Jac-mean correlated with pulmonary vascular-related indexes on high-resolution CT. CONCLUSION: The decreased lung deformation in IPF patients correlated with the clinical severity of IPF patients. Elastic registration of inspiratory-to-expiratory 3D UTE MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. CRITICAL RELEVANCE STATEMENT: This prospective study demonstrated that lung deformation decreased in idiopathic pulmonary fibrosis (IPF) patients and correlated with the severity of IPF. Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF. KEY POINTS: • Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI could evaluate lung deformation. • Lung deformation significantly decreased in idiopathic pulmonary fibrosis (IPF) patients, compared with the healthy controls. • Reduced lung deformation of IPF patients correlated with worsened pulmonary function and the composite physiological index (CPI).

Real-time correction of chromatic aberration in optical fluorescence microscopy.

Multi-color fluorescence imaging is a powerful tool for studying the spatial relationships and interactions among sub-cellular structures in biological specimens. However, if improperly corrected, geometrical distortions caused by mechanical drift, refractive index mismatch, or chromatic aberration can lead to lower image resolution. In this paper, we present an extension of the image processing framework of Scipion by integrating a protocol called OFM Corrector, which corrects geometrical distortions in real-time using a B-spline-based elastic continuous registration technique. Our proposal provides a simple strategy to overcome chromatic aberration by digitally re-aligning color channels in multi-color fluorescence microscopy images, even in 3D or time. Our method relies on a geometrical calibration, which we do with fluorescent beads excited by different wavelengths of light and subsequently registered to get the elastic warp as a reference to correct chromatic shift. Our software is freely available with a user-friendly GUI and can be broadly used for various biological imaging problems. The paper presents a valuable tool for researchers working in light microscopy facilities.

SpineRegNet: Spine Registration Network for volumetric MR and CT image by the joint estimation of an affine-elastic deformation field.

Spine registration for volumetric magnetic resonance (MR) and computed tomography (CT) images plays a significant role in surgical planning and surgical navigation system for the radiofrequency ablation of spine intervertebral discs. The affine transformation of each vertebra and elastic deformation of the intervertebral disc exist at the same time. This situation is a major challenge in spine registration. Existing spinal image registration methods failed to solve the optimal affine-elastic deformation field (AEDF) simultaneously, only consider the overall rigid or elastic alignment with the help of a manual spine mask, and encounter difficulty in meeting the accuracy requirements of clinical registration application. In this study, we propose a novel affine-elastic registration framework named SpineRegNet. The SpineRegNet consists of a Multiple Affine Matrices Estimation (MAME) Module for multiple vertebrae alignment, an Affine-Elastic Fusion (AEF) Module for joint estimation of the overall AEDF, and a Local Rigidity Constraint (LRC) Module for preserving the rigidity of each vertebra. Experiments on T2-weighted volumetric MR and CT images show that the proposed approach achieves impressive performance with mean Dice similarity coefficients of 91.36%, 81.60%, and 83.08% for the mask of the vertebrae on Datasets A-C, respectively. The proposed technique does not require a mask or manual participation during the tests and provides a useful tool for clinical spinal disease surgical planning and surgical navigation systems.

Deep learning for improving PET/CT attenuation correction by elastic registration of anatomical data.

BACKGROUND: For PET/CT, the CT transmission data are used to correct the PET emission data for attenuation. However, subject motion between the consecutive scans can cause problems for the PET reconstruction. A method to match the CT to the PET would reduce resulting artifacts in the reconstructed images. PURPOSE: This work presents a deep learning technique for inter-modality, elastic registration of PET/CT images for improving PET attenuation correction (AC). The feasibility of the technique is demonstrated for two applications: general whole-body (WB) imaging and cardiac myocardial perfusion imaging (MPI), with a specific focus on respiratory and gross voluntary motion. MATERIALS AND METHODS: A convolutional neural network (CNN) was developed and trained for the registration task, comprising two distinct modules: a feature extractor and a displacement vector field (DVF) regressor. It took as input a non-attenuation-corrected PET/CT image pair and returned the relative DVF between them-it was trained in a supervised fashion using simulated inter-image motion. The 3D motion fields produced by the network were used to resample the CT image volumes, elastically warping them to spatially match the corresponding PET distributions. Performance of the algorithm was evaluated in different independent sets of WB clinical subject data: for recovering deliberate misregistrations imposed in motion-free PET/CT pairs and for improving reconstruction artifacts in cases with actual subject motion. The efficacy of this technique is also demonstrated for improving PET AC in cardiac MPI applications. RESULTS: A single registration network was found to be capable of handling a variety of PET tracers. It demonstrated state-of-the-art performance in the PET/CT registration task and was able to significantly reduce the effects of simulated motion imposed in motion-free, clinical data. Registering the CT to the PET distribution was also found to reduce various types of AC artifacts in the reconstructed PET images of subjects with actual motion. In particular, liver uniformity was improved in the subjects with significant observable respiratory motion. For MPI, the proposed approach yielded advantages for correcting artifacts in myocardial activity quantification and potentially for reducing the rate of the associated diagnostic errors. CONCLUSION: This study demonstrated the feasibility of using deep learning for registering the anatomical image to improve AC in clinical PET/CT reconstruction. Most notably, this improved common respiratory artifacts occurring near the lung/liver border, misalignment artifacts due to gross voluntary motion, and quantification errors in cardiac PET imaging.

Comparison of Volumetric Quantitative PET Parameters Before and After a CT-Based Elastic Deformation on Dual-Time 18FDG-PET/CT Images: A Feasibility Study in a Perspective of Radiotherapy Planning in Head and Neck Cancer.

BACKGROUND: The use of 18FDG-PET/CT for delineating a gross tumor volume (GTV, also called MTV metabolic tumor volume) in radiotherapy (RT) planning of head neck squamous cell carcinomas (HNSCC) is not included in current recommendations, although its interest for the radiotherapist is of evidence. Because pre-RT PET scans are rarely done simultaneously with dosimetry CT, the validation of a robust image registration tool and of a reproducible MTV delineation method is still required. OBJECTIVE: Our objective was to study a CT-based elastic registration method on dual-time pre-RT 18FDG-PET/CT images to assess the feasibility of PET-based RT planning in patients with HNSCC. METHODS: Dual-time 18FDG-PET/CT [whole-body examination (wbPET) + 1 dedicated step (headPET)] were selected to simulate a 2-times scenario of pre-RT PET images deformation on dosimetry CT. ER-headPET and RR-headPET images were, respectively, reconstructed after CT-to-CT rigid (RR) and elastic (ER) registrations of the headPET on the wbPET. The MTVs delineation was performed using two methods (40%SUVmax, PET-Edge). The percentage variations of several PET parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG) were calculated between wbPET, ER-headPET, and RR-headPET. Correlation between MTV values was calculated (Deming linear regression). MTVs intersections were assessed by two indices (OF, DICE) and compared together (Wilcoxon test). Additional per-volume analysis was evaluated (Mann-Whitney test). Inter- and intra-observer reproducibilities were evaluated (ICC = intra-class coefficient). RESULTS: 36 patients (30M/6F; median age = 65 y) were retrospectively included. The changes in SUVmax, SUVmean and SUVpeak values between ER-headPET and RR-headPET images were <5%. The variations in MTV values between ER-headPET and wbPET images were -6 and -3% with 40%SUVmax and PET Edge, respectively. Their correlations were excellent whatever the delineation method (R2 > 0.99). The ER-headPET MTVs had significant higher mean OF and DICE with the wbPET MTVs, for both delineation methods (p ≤ 0.002); and also when lesions had a volume > 5cc (excellent OF = 0.80 with 40%SUVmax). The inter- and intra-observer reproducibilities for MTV delineation were excellent (ICC ≥ 0.8, close to 1 with PET-Edge). CONCLUSION: Our study demonstrated no significant changes in MTV after an elastic deformation of pre-RT 18FDG-PET/CT images acquired in dual-time mode. This opens possibilities for HNSCC radiotherapy planning improvement by transferring GTV-PET on dosimetry CT.

Novel atlas of fiber directions built from ex-vivo diffusion tensor images of porcine hearts.

Cardiac MR image-based predictive models integrating statistical atlases of heart anatomy and fiber orientations can aid in better diagnosis of cardiovascular disease, a major cause of death worldwide. Such atlases have been built from diffusion tensor (DT) images and can be used in anisotropic models for personalized computational electro-mechanical simulations when the fiber directions from DTI are not available. In this paper, we propose a framework for building the first statistical fiber atlas from high-resolution ex-vivo DT images of porcine hearts. A mean geometry that represents the average cardiac morphology of the dataset was first generated via groupwise registration. Then, the associated average cardiac fiber architecture was mapped out by computing the mean of the transformed DT fields of the subjects. To evaluate the stability of the atlas, we performed leave-one-out cross-validation. The resulting tensor statistics indicate that the fiber atlas could accurately describe the fiber architecture of a healthy pig heart.

[Radiotherapy treatment planning of prostate cancer using magnetic resonance imaging]. / Planification de la radiothérapie du cancer de la prostate par l'imagerie par résonance magnétique.

PURPOSE: Magnetic resonance imaging (MRI) plays an increasing role in radiotherapy dose planning. Indeed, MRI offers superior soft tissue contrast compared to computerized tomography (CT) and therefore could provide a better delineation of target volumes and organs at risk than CT for radiotherapy. Furthermore, an MRI-only radiotherapy workflow would suppress registration errors inherent to the registration of MRI with CT. However, the estimation of the electronic density of tissues using MRI images is still a challenging issue. The purpose of this work was to design and evaluate a pseudo-CT generation method for prostate cancer treatments. MATERIALS AND METHODS: A pseudo-CT was generated for ten prostate cancer patients using an elastic deformation based method. For each patient, dose delivered to the patient was calculated using both the planning CT and the pseudo-CT. Dose differences between CT and pseudo-CT were investigated. RESULTS: Mean dose relative difference in the planning target volume is 0.9% on average and ranges from 0.1% to 1.7%. In organs at risks, this value is 1.8%, 0.8%, 0.8% and 1% on average in the rectum, the right and left femoral heads, and the bladder respectively. CONCLUSION: The dose calculated using the pseudo-CT is very close to the dose calculated using the CT for both organs at risk and PTV. These results confirm that pseudo-CT images generated using the proposed method could be used to calculate radiotherapy treatment doses on MRI images.

Non-imaged based method for matching brains in a common anatomical space for cellular imagery.

BACKGROUND: Cellular imagery using histology sections is one of the most common techniques used in Neuroscience. However, this inescapable technique has severe limitations due to the need to delineate regions of interest on each brain, which is time consuming and variable across experimenters. NEW METHOD: We developed algorithms based on a vectors field elastic registration allowing fast, automatic realignment of experimental brain sections and associated labeling in a brain atlas with high accuracy and in a streamlined way. Thereby, brain areas of interest can be finely identified without outlining them and different experimental groups can be easily analyzed using conventional tools. This method directly readjusts labeling in the brain atlas without any intermediate manipulation of images. RESULTS: We mapped the expression of cFos, in the mouse brain (C57Bl/6J) after olfactory stimulation or a non-stimulated control condition and found an increased density of cFos-positive cells in the primary olfactory cortex but not in non-olfactory areas of the odor-stimulated animals compared to the controls. COMPARISON WITH EXISTING METHOD(S): Existing methods of matching are based on image registration which often requires expensive material (two-photon tomography mapping or imaging with iDISCO) or are less accurate since they are based on mutual information contained in the images. Our new method is non-imaged based and relies only on the positions of detected labeling and the external contours of sections. CONCLUSIONS: We thus provide a new method that permits automated matching of histology sections of experimental brains with a brain reference atlas.

Comparison on application of five kinds of elastic registration methods in the CT image registration of radiotherapy / 中国医学装备

Objective: To compare the applications of five kinds of routine elastic registration methods included of Horn-Schunck optical flow method, Demons algorithm, accelerated Demons algorithm, level set algorithm and fast free deformation algorithm in radiotherapy. Methods: 10 patients who underwent radiotherapy on pelvic cavity were enrolled in the research. And their CT images at the time of location and during radiotherapy were collected, and the five kinds of routine elastic registration methods were applied in the research, and through autonomic programming to implement elastic registration, and then the registered results were further compared. Results: The average minimum mean square error (MSE) of five kinds of routine elastic registration methods have been decreased 6.7%-26.0%, and the average correlation coefficient(CC) has been increased 2.6%-3.7%, and the average registration time was 107.5s-220.8s. Conclusions: All of the five kinds of elastic registration methods get better results, and it is recommended to use Horn-Schunck optical flow method for elastic registration of radiotherapy CT. And through using elastic registration model can more accurate simulate the movement of organ, and it is a direction of further study that monitor individual dosage of organ at risk.

Brain-shift compensation using intraoperative ultrasound and constraint-based biomechanical simulation.

PURPOSE: During brain tumor surgery, planning and guidance are based on preoperative images which do not account for brain-shift. However, this deformation is a major source of error in image-guided neurosurgery and affects the accuracy of the procedure. In this paper, we present a constraint-based biomechanical simulation method to compensate for craniotomy-induced brain-shift that integrates the deformations of the blood vessels and cortical surface, using a single intraoperative ultrasound acquisition. METHODS: Prior to surgery, a patient-specific biomechanical model is built from preoperative images, accounting for the vascular tree in the tumor region and brain soft tissues. Intraoperatively, a navigated ultrasound acquisition is performed directly in contact with the organ. Doppler and B-mode images are recorded simultaneously, enabling the extraction of the blood vessels and probe footprint, respectively. A constraint-based simulation is then executed to register the pre- and intraoperative vascular trees as well as the cortical surface with the probe footprint. Finally, preoperative images are updated to provide the surgeon with images corresponding to the current brain shape for navigation. RESULTS: The robustness of our method is first assessed using sparse and noisy synthetic data. In addition, quantitative results for five clinical cases are provided, first using landmarks set on blood vessels, then based on anatomical structures delineated in medical images. The average distances between paired vessels landmarks ranged from 3.51 to 7.32 (in mm) before compensation. With our method, on average 67% of the brain-shift is corrected (range [1.26; 2.33]) against 57% using one of the closest existing works (range [1.71; 2.84]). Finally, our method is proven to be fully compatible with a surgical workflow in terms of execution times and user interactions. CONCLUSION: In this paper, a new constraint-based biomechanical simulation method is proposed to compensate for craniotomy-induced brain-shift. While being efficient to correct this deformation, the method is fully integrable in a clinical process.

Handling topological changes during elastic registration : Application to augmented reality in laparoscopic surgery.

PURPOSE: Locating the internal structures of an organ is a critical aspect of many surgical procedures. Minimally invasive surgery, associated with augmented reality techniques, offers the potential to visualize inner structures, allowing for improved analysis, depth perception or for supporting planning and decision systems. METHODS: Most of the current methods dealing with rigid or non-rigid augmented reality make the assumption that the topology of the organ is not modified. As surgery relies essentially on cutting and dissection of anatomical structures, such methods are limited to the early stages of the surgery. We solve this shortcoming with the introduction of a method for physics-based elastic registration using a single view from a monocular camera. Singularities caused by topological changes are detected and propagated to the preoperative model. This significantly improves the coherence between the actual laparoscopic view and the model and provides added value in terms of navigation and decision-making, e.g., by overlaying the internal structures of an organ on the laparoscopic view. RESULTS: Our real-time augmentation method is assessed on several scenarios, using synthetic objects and real organs. In all cases, the impact of our approach is demonstrated, both qualitatively and quantitatively ( http://www.open-cas.org/?q=PaulusIJCARS16 ). CONCLUSION: The presented approach tackles the challenge of localizing internal structures throughout a complete surgical procedure, even after surgical cuts. This information is crucial for surgeons to improve the outcome for their surgical procedure and avoid complications.

Characterization of the Physiological Displacement of the Aortic Arch Using Non-Rigid Registration and MR Imaging.

OBJECTIVES: The aim of this work was to study physiological aortic arch three-dimensional displacement using non-rigid registration methods and magnetic resonance imaging (MRI). MATERIALS AND METHODS: Ten healthy volunteers underwent thoracic MRI. Prospective cardiac gating was performed with a 3D turbo field echo sequence to obtain end-systolic and end-diastolic MR images. The rigid and elastic behavior between these two cardiac phases was detected and compared using either an affine or an elastic registration method. To assess reproducibility, a second MRI acquisition was performed 14 days later. RESULTS: Affine registration between the end-systolic and end-diastolic MR images showed significant global translations of the aortic arch and the supra-aortic vessels in the x, y, and z directions (2.02 ± 1.6, -0.71 ± 1.1, and -1.21 ± 1.4 mm, respectively). Corresponding elastic registration indicated significant local displacement with a vector magnitude of 5.1 ± 0.89 mm for the brachiocephalic artery (BCA), of 4.26 ± 0.83 mm for the left common carotid artery (LCCA), and of 4.8 ± 0.86 mm for the left subclavian artery (LSCA). There was a difference in displacement between the supra-aortic trunks of the order of 2 mm. Vector displacement was not statistically different between the repeated acquisitions. CONCLUSIONS: The present results showed important deformations in the ostia of supra-aortic vessels during the cardiac cycle. It seems that aortic arch motions should be taken into account when designing and manufacturing fenestrated endografts. The elastic registration method provides more precise results, but is more complex and time-consuming than other methods.

Rigid and elastic registration for coronary artery IVUS images.

BACKGROUND: Intravascular ultrasound (IVUS) has been widely used in diagnosis and interventional treatment of cardiac vessel diseases. The coronary artery IVUS images are usually polluted by motion artifacts caused by cardiac motion, pulsatile blood and catheter twist during continuous pullback acquisition. OBJECTIVE: Strategies for rigid and elastic registration of coronary artery IVUS studies are developed to suppress the longitudinal motion and misalignment between successive frames. METHODS: Rigid registration is performed by searching for the optimal matching for each frame in other cycles based on the cyclic variation of gray-scale features. The image sequence is gated to properly identify the frames in each cardiac phase. Then, elastic registration between frames is achieved through an optimization algorithm based on thin plate spline (TPS) to correct the misalignment of successive slices. RESULTS: Experimental results with in vivo image data shows that the rigid registration performs better than the offline ECG gating. The elastic mapping relation between lumen contours in successive frames is smooth and continuous. CONCLUSION: The serrated vessel wall borders in longitudinal cuts are smoothed after rigid registration while image segmentation and feature extraction are required. The point-to-point correspondence between lumen contours detected from two matched frames is obtained with elastic registration.

Local analysis of strains and rotations for macromolecular electron microscopy maps.

Macromolecular complexes perform their physiological functions by local rearrangements of their constituents and biochemically interacting with their reaction partners. These rearrangements may involve local rotations and the induction of local strains causing different mechanical efforts and stretches at the different areas of the protein. The analysis of these local deformations may reveal important insight into the way proteins perform their tasks. In this paper we introduce a method to perform this kind of local analysis using Electron Microscopy volumes in a fully objective and automatic manner. For doing so, we exploit the continuous nature of the result of an elastic image registration using B-splines as its basis functions. We show that the results obtained by the new automatic method are consistent with previous observations on these macromolecules.

3D surface-based registration of ultrasound and histology in prostate cancer imaging.

Several transrectal ultrasound (TRUS)-based techniques aiming at accurate localization of prostate cancer are emerging to improve diagnostics or to assist with focal therapy. However, precise validation prior to introduction into clinical practice is required. Histopathology after radical prostatectomy provides an excellent ground truth, but needs accurate registration with imaging. In this work, a 3D, surface-based, elastic registration method was developed to fuse TRUS images with histopathologic results. To maximize the applicability in clinical practice, no auxiliary sensors or dedicated hardware were used for the registration. The mean registration errors, measured in vitro and in vivo, were 1.5±0.2 and 2.1±0.5mm, respectively.

Level set motion-based elastic registration of 3D CT images in radiotherapy / 中国医学装备

Objective: To apply level set motion-based elastic registration method to radiotherapy CT image, then to provide technical support for the accurate evaluation of tumor and changing process of patients with endanger organ and its accumulative dose. Methods: Based on Vemuri’s level set motion method, we wrote the algorithm program using Matlab software and applied on two sets of 3D CT images from patients with cervical cancer and NPC for fully automatic elastic registration. Results: Comparing CT images before and after registration, for the cervical cancer patient, the minimum mean square error (MSE) decreased by 55.1%and correlation coefficient (CC) increased by 5.3%. For the NPC patient, MSE decreased by 32.1%and CC increased by 4.6%. Conclusion: From the image difference and evaluation parameters, the efficacy of level set motion-based elastic registration method was preliminarily demonstrated. In order to apply this method to clinical radiotherapy, dit needs to find a more accurate mathematical algorithm further in order to compute human anatomy deformation through image motion.

Registration of histological whole slide images guided by vessel structures.

INTRODUCTION: The registration of histological whole slide images is an important prerequisite for modern histological image analysis. A partial reconstruction of the original volume allows e.g. colocalization analysis of tissue parameters or high-detail reconstructions of anatomical structures in 3D. METHODS: In this paper, we present an automatic staining-invariant registration method, and as part of that, introduce a novel vessel-based rigid registration algorithm using a custom similarity measure. The method is based on an iterative best-fit matching of prominent vessel structures. RESULTS: We evaluated our method on a sophisticated synthetic dataset as well as on real histological whole slide images. Based on labeled vessel structures we compared the relative differences for corresponding structures. The average positional error was close to 0, the median for the size change factor was 1, and the median overlap was 0.77. CONCLUSION: The results show that our approach is very robust and creates high quality reconstructions. The key element for the resulting quality is our novel rigid registration algorithm.

Elastic image registration to quantify 3-D regional myocardial deformation from volumetric ultrasound: experimental validation in an animal model.

Although real-time 3-D echocardiography has the potential to allow more accurate assessment of global and regional ventricular dynamics compared with more traditional 2-D ultrasound examinations, it still requires rigorous testing and validation should it break through as a standard examination in routine clinical practice. However, only a limited number of studies have validated 3-D strain algorithms in an in vivo experimental setting. The aim of the present study, therefore, was to validate a registration-based strain estimation methodology in an animal model. Volumetric images were acquired in 14 open-chest sheep instrumented with ultrasonic microcrystals. Radial strain (ɛRR), longitudinal strain (ɛLL) and circumferential strain (ɛCC) were estimated during different stages: at rest, during reduced and increased cardiac inotropy induced by esmolol and dobutamine infusion, respectively, and during acute ischemia. Agreement between image-based and microcrystal-based strain estimates was evaluated by their linear correlation, indicating that all strain components could be estimated with acceptable accuracy (r = 0.69 for ɛRR, r = 0.64 for ɛLL and r = 0.62 for ɛCC). These findings are comparable to the performance of the current state-of-the-art commercial 3-D speckle tracking methods. Furthermore, shape of the strain curves, timing of peak values and location of dysfunctional regions were identified well. Whether 3-D elastic registration performs better than 3-D block matching-based methodologies still remains to be proven.

Registro elástico de imágenes médicas multimodales basado en medidas de variabilidad local / Non-rigid Multimodal Medical Image Registration Based on Local Variability

En este artículo se propone un enfoque no paramétrico para el registro elástico de imágenes médicas multimodales, cuya idea principal radica en el uso de medidas de variabilidad local, basadas en la entropía, la varianza o una combination de ambas. La metodología empleada consiste en encontrar el campo vectorial de los desplazamientos entre los pixeles de las imágenes candidata y patrón empleando una tecnica compuesta por tres pasos: primero, se obtiene una aproximación del campo vectorial por medio de un registro paramétrico entre ambas imágenes; segundo, se mapean las imágenes registradas paramétricamente a un espacio de intensidades donde pueden ser comparadas; tercero, se obtiene el flujo óptico entre las imágenes en el espacio al que fueron mapeadas. El algoritmo propuesto se evalúo usando un conjunto de imágenes de resonancia magnética y tomografía computarizada adquiridas desde diferentes vistas, las cuales fueron deformadas sintéticamente. Los resultados obtenidos en la estimación del campo de desplazamientos con las cuatro medidas de variabilidad local propuestas muestran un error medio menor que 1.4 mm, y en el caso de la entropía menor a 1 mm. Además, se demuestra la convergencia del algoritmo con ayuda de la entropía conjunta. Asó, la metodología descrita representa una nueva alternativa para el registro elástico multimodal de imágenes médicas.

In this work, we present a novel approach for multimodal elastic registration of medical images, where the key idea is to use local variability measures based on entropy, variance or a combination of these metrics. The proposed methodology relies on finding the displacements vector field between pixels of a source image and a target one, using the following three steps: first, an initial approximation of the vector field is achieved by using a parametric registration based on particle filtering between the images to align; second, the images previously registered are mapped to a common space where their intensities can be compared; and third, we obtain the optical flow between the images in this new space. To evaluate the proposed algorithm, a set of computed tomography and magnetic resonance images obtained in different views, were modified with synthetic deformation fields. The results obtained with the four proposed local variability measures show an average error of less than 1.4 mm, and in the case of the entropy less than 1 mm. In addition, the convergence of the algorithm is highlighted by the joint entropy. Therefore, the described methodology could be considered as a new alternative for multimodal elastic registration of medical images.

New Mixing Rigid-elastic Multiresolution Algorithm for Medical Image / 医疗卫生装备

Objective To present a new algorithm for multidimensional medical image registration from global registration to local registration in sequence. Methods Firstly, the global registration was achieved by the method of affine transformation composed of B-splines,whose knots were the four vertexes of the medical image. Then the knots of the B-splines were increased, and the transformation function was more complex and elastic than ever,which completed the elastic aligning for the detail of the medical image. Results The whole registration algorithm represented the principle aligning from global registration to local registration. Conclusion It is proved by experiments that the presented algorithm can decrease the time of calculation and increase the robustness of registration.