Image Registration System Research Articles

Optimization and Application Analysis of Phase Correction Method Based on Improved Image Registration in Ultrasonic Image Detection

ABSTRACTIn order to prevent and detect a wide range of disorders, including those of the brain, thoracic, digestive, urogenital, and cardiovascular systems, ultrasound technology is essential for assessing physiological data and tissue morphology. Its capacity to deliver real‐time, high‐frequency scans makes it a handy and non‐invasive diagnostic tool. However, issues like patient movements and probe jitter from human error can provide a large amount of interference, resulting in inaccurate test findings. Techniques for image registration can assist in locating and eliminating unwanted interference while maintaining crucial data. Even though there has been research on improving these techniques in Matlab, there are no specialized systems for interference removal, and the procedure is still time‐consuming, particularly when working with huge quantities of ultrasound images. The phase correlation technique, which converts images into the frequency domain and makes noise suppression easier, is one of the most efficient algorithms now in use since it can tolerate noise with resilience. Nevertheless, little research has been done on using this technique to identify displacement in blood vessel wall ultrasound images. To address these gaps, this work presents an image registration system that uses the phase correlation algorithm. The system provides rotation, zoom registration, picture translation, and displacement detection of the vessel wall in addition to interference removal. Furthermore, batch processing is included to increase the effectiveness of registering multiple ultrasound pictures. Through efficient interference management and streamlined registration, this method offers a workable way to improve the precision and efficacy of ultrasonic diagnostics.

A Review of Medical Image Registration for Different Modalities.

Medical image registration has become pivotal in recent years with the integration of various imaging modalities like X-ray, ultrasound, MRI, and CT scans, enabling comprehensive analysis and diagnosis of biological structures. This paper provides a comprehensive review of registration techniques for medical images, with an in-depth focus on 2D-2D image registration methods. While 3D registration is briefly touched upon, the primary emphasis remains on 2D techniques and their applications. This review covers registration techniques for diverse modalities, including unimodal, multimodal, interpatient, and intra-patient. The paper explores the challenges encountered in medical image registration, including geometric distortion, differences in image properties, outliers, and optimization convergence, and discusses their impact on registration accuracy and reliability. Strategies for addressing these challenges are highlighted, emphasizing the need for continual innovation and refinement of techniques to enhance the accuracy and reliability of medical image registration systems. The paper concludes by emphasizing the importance of accurate medical image registration in improving diagnosis.

Performance Evaluation of Deformable Image Registration Systems - SmartAdapt® and Velocity™.

To commission and validate commercial deformable image registration (DIR) systems (SmartAdapt® and Velocity™) using task group 132 (TG-132) digital phantom datasets. Additionally, the study compares and verifies the DIR algorithms of the two systems. TG-132 digital phantoms were obtained from the American Association of Physicists in Medicine website and imported into SmartAdapt® and Velocity™ systems for commissioning and validation. The registration results were compared with known shifts using rigid registrations and deformable registrations. Virtual head and neck phantoms obtained online (DIR Evaluation Project) and some selected clinical data sets from the department were imported into the two DIR systems. For both of these datasets, DIR was carried out between the source and target images, and the contours were then propagated from the source to the target image data set. The dice similarity coefficient (DSC), mean distance to agreement (MDA), and Jacobian determinant measures were utilised to evaluate the registration results. The recommended criteria for commissioning and validation of DIR system from TG-132 was error <0.5*voxel dimension (vd). Translation only registration: Both systems met TG-132 recommendations except computed tomography (CT)-positron emission tomography registration in both systems (Velocity ~1.1*vd, SmartAdapt ~1.6*vd). Translational and rotational registration: Both systems failed the criteria for all modalities (For velocity, error ranged from 0.6*vd [CT-CT registration] to 3.4*vd [CT-cone-beam CT (CBCT) registration]. For SmartAdapt® the range was 0.6*vd [CT-CBCT] to 3.6*vd [CT-CT]). Mean ± standard deviation for DSC, MDA and Jacobian metrics were used to compare the DIR results between SmartAdapt® and Velocity™. The DIR algorithms of SmartAdapt® and Velocity™ were commissioned and their deformation results were compared. Both systems can be used for clinical purpose. While there were only minimal differences between the two systems, Velocity™ provided lower values for parotids, bladder, rectum, and prostate (soft tissue) compared to SmartAdapt. However, for mandible, spinal cord, and femoral heads (rigid structures), both systems showed nearly identical results.

MMIR: an open-source software for the registration of multimodal histological images

BackgroundMultimodal histology image registration is a process that transforms into a common coordinate system two or more images obtained from different microscopy modalities. The combination of information from various modalities can contribute to a comprehensive understanding of tissue specimens, aiding in more accurate diagnoses, and improved research insights. Multimodal image registration in histology samples presents a significant challenge due to the inherent differences in characteristics and the need for tailored optimization algorithms for each modality.ResultsWe developed MMIR a cloud-based system for multimodal histological image registration, which consists of three main modules: a project manager, an algorithm manager, and an image visualization system.ConclusionOur software solution aims to simplify image registration tasks with a user-friendly approach. It facilitates effective algorithm management, responsive web interfaces, supports multi-resolution images, and facilitates batch image registration. Moreover, its adaptable architecture allows for the integration of custom algorithms, ensuring that it aligns with the specific requirements of each modality combination. Beyond image registration, our software enables the conversion of segmented annotations from one modality to another.

СУБПІКСЕЛЬНЕ СКАНУВАННЯ ДЛЯ АНАЛІЗУ ЗОБРАЖЕНЬ СПЕКТРА

Subject and Purpose. In this paper, an algorithm is developed to improve the spectrum image resolution in the atomic emission spectrum registration using multi-element detectors and taking multiple exposures with image shifting by a fractional part of a pixel. Methods and Methodology. Mathematical modeling techniques are employed in the coordinate representation dictated by the prior information. The modeling results are checked by experiment. Results. The importance of the resolution value of spectral image registration systems and its impact on the quality of the obtained results have been highlighted. The spectral informativeness of the regularly (matrix- or linearly) structured multi-element detectors has been evaluated against the irregular-structure sensors, such as photographic emulsions. As it has been found that the process of pixel size reduction is demanding, an alternative approach through subpixel image shifting has been proposed. Unlike the case of the corresponding instruments implemented as a rule in the frequency domain, here the relevant theoretical problem reduces to the over- determined system of linear equations. The proposed solution algorithm employs the least squares method having regard to the prior data specificity posed by the isolation of the spectral lines and a smooth flow of the background between them. The digital simulation results and the real experimental evidence from the atomic emission spectral analysis have been presented, illustrating the algorithm operation. The experimental research was carried out for one-dimensional spectrum images registered with linear charge-coupling photodetectors. The so gained resolution was twice the spatial resolution of the photodetector. Conclusions. It has been demonstrated that controlled image shifting not only improves the photometry accuracy but also significantly enhances the detector resolution. The general comparisons of the theory with the experiment have proved the feasibility of bringing the proposed methodology into atomic emission analysis.

Automated marker-free longitudinal infrared breast image registration by GA-PSO

The automated marker-free longitudinal Infrared (IR) breast image registration overcomes several challenges like no anatomic fiducial markers on the body surface, blurry boundaries, heat pattern variation by environmental and physiological factors, nonrigid deformation, etc, has the ability of quantitative pixel-wise analysis with the heat energy and patterns change in a time course study. To achieve the goal, scale-invariant feature transform, Harris corner, and Hessian matrix were employed to generate the feature points as anatomic fiducial markers, and hybrid genetic algorithm and particle swarm optimization minimizing the matching errors was used to find the appropriate corresponding pairs between the 1st IR image and the nth IR image. Moreover, the mechanism of the IR spectrogram hardware system has a high level of reproducibility. The performance of the proposed longitudinal image registration system was evaluated by the simulated experiments and the clinical trial. In the simulated experiments, the mean difference of our system is 1.64 mm, which increases 57.58% accuracy than manual determination and makes a 17.4% improvement than the previous study. In the clinical trial, 80 patients were captured several times of IR breast images during chemotherapy. Most of them were well aligned in the spatiotemporal domain. In the few cases with evident heat pattern dissipation and spatial deviation, it still provided a reliable comparison of vascular variation. Therefore, the proposed system is accurate and robust, which could be considered as a reliable tool for longitudinal approaches to breast cancer diagnosis.

Performance evaluation of underwater vision systems

The article describes a methodology for performance evaluation of vision systems for remotely operated underwater vehicles. The methodology is based on a system approach and uses mathematical models of the aqueous medium where an optical signal propagates, the underwater object image registration system, and the mathematical model of the human visual system. The detection and recognition probabilities of underwater object image at a given registration range are used as performance evaluation indicators of underwater vision systems. The mathematical model of the aqueous medium developed by the authors allows quantitative evaluation of the influence of backscattering interference arising during objects illumination on the underwater vision system performance. The results of numerical experiments presented in the paper illustrate the possibility of using the proposed technique to optimize the underwater object image registration system parameters in order to achieve the required values of detection or recognition probabilities at the given ranges.

Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance.

Fusing intraoperative 2-D ultrasound (US) frames with preoperative 3-D magnetic resonance (MR) images for guiding interventions has become the clinical gold standard in image-guided prostate cancer biopsy. However, developing an automatic image registration system for this application is challenging because of the modality gap between US/MR and the dimensionality gap between 2-D/3-D data. To overcome these challenges, we propose a novel US frame-to-volume registration (FVReg) pipeline to bridge the dimensionality gap between 2-D US frames and 3-D US volume. The developed pipeline is implemented using deep neural networks, which are fully automatic without requiring external tracking devices. The framework consists of three major components, including one) a frame-to-frame registration network (Frame2Frame) that estimates the current frame's 3-D spatial position based on previous video context, two) a frame-to-slice correction network (Frame2Slice) adjusting the estimated frame position using the 3-D US volumetric information, and three) a similarity filtering (SF) mechanism selecting the frame with the highest image similarity with the query frame. We validated our method on a clinical dataset with 618 subjects and tested its potential on real-time 2-D-US to 3-D-MR fusion navigation tasks. The proposed FVReg achieved an average target navigation error of 1.93 mm at 5-14 fps. Our source code is publicly available at https://github.com/DIAL-RPI/Frame-to-Volume-Registration.

Multimodal Image Registration Using a Viscous Fluid Model with the Bhattacharyya Distance.

Image registration is an elementary task in medical image processing and analysis, which can be divided into monomodal and multimodal. Direct 3D multimodal registration in volumetric medical images can provide more insight into the interpretation of subsequent image processing applications than 2D methods. This paper is dedicated to the development of a 3D multimodal image registration algorithm based on a viscous fluid model associated with the Bhattacharyya distance. In our approach, a modified Navier-Stoke's equation is exploited as the foundation of the multimodal image registration framework. The hopscotch method is numerically implemented to solve the velocity field, whose values at the explicit locations are first computed and the values at the implicit positions are solved by transposition. The differential of the Bhattacharyya distance is incorporated into the body force function, which is the main driving force for deformation, to enable multimodal registration. A variety of simulated and real brain MR images were utilized to assess the proposed 3D multimodal image registration system. Preliminary experimental results indicated that our algorithm produced high registration accuracy in various registration scenarios and outperformed other competing methods in many multimodal image registration tasks.Clinical Relevance- This facilitates the disease diagnosis and treatment planning that requires accurate 3D multimodal image registration without massive image data and extensive training regardless of the imaging modality.

Realistic C-arm to pCT registration for vertebral localization in spine surgery

Spine surgeries are vulnerable to wrong-level surgeries and postoperative complications because of their complex structure. Unavailability of the 3D intraoperative imaging device, low-contrast intraoperative X-ray images, variable clinical and patient conditions, manual analyses, lack of skilled technicians, and human errors increase the chances of wrong-site or wrong-level surgeries. State of the art work refers 3D-2D image registration systems and other medical image processing techniques to address the complications associated with spine surgeries. Intensity-based 3D-2D image registration systems had been widely practiced across various clinical applications. However, these frameworks are limited to specific clinical conditions such as anatomy, dimension of image correspondence, and imaging modalities. Moreover, there are certain prerequisites for these frameworks to function in clinical application, such as dataset requirement, speed of computation, requirement of high-end system configuration, limited capture range, and multiple local maxima. A simple and effective registration framework was designed with a study objective of vertebral level identification and its pose estimation from intraoperative fluoroscopic images by combining intensity-based and iterative control point (ICP)–based 3D-2D registration. A hierarchical multi-stage registration framework was designed that comprises coarse and finer registration. The coarse registration was performed in two stages, i.e., intensity similarity-based spatial localization and source-to-detector localization based on the intervertebral distance correspondence between vertebral centroids in projected and intraoperative X-ray images. Finally, to speed up target localization in the intraoperative application, based on 3D-2D vertebral centroid correspondence, a rigid ICP-based finer registration was performed. The mean projection distance error (mPDE) measurement and visual similarity between projection image at finer registration point and intraoperative X-ray image and surgeons’ feedback were held accountable for the quality assurance of the designed registration framework. The average mPDE after peak signal to noise ratio (PSNR)–based coarse registration was 20.41mm. After the coarse registration in spatial region and source to detector direction, the average mPDE reduced to 12.18mm. On finer ICP-based registration, the mean mPDE was finally reduced to 0.36 mm. The approximate mean time required for the coarse registration, finer registration, and DRR image generation at the final registration point were 10 s, 15 s, and 1.5 min, respectively. The designed registration framework can act as a supporting tool for vertebral level localization and its pose estimation in an intraoperative environment. The framework was designed with the future perspective of intraoperative target localization and its pose estimation irrespective of the target anatomy.Graphical abstract

MO413: Registration of Image Modalities for Analyses of Tissue Samples Using 3D Image Modelling

Abstract BACKGROUND AND AIMS Biopsies are a diagnostic tool for the diagnosis of histopathological, molecular biological, proteomic and imaging data, to narrow down disease patterns or identify diseases. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) provides an emerging state-of-the-art technique for molecular imaging of biological tissue. The aim of this study is the registration of MALDI MSI data sets and data acquired from different histological stainings to create a 3D model of biopsies and whole organs. METHOD The registration of the image modalities is achieved by using a variant of our global, deformable Schatten-q-Norm registration approach. Utilizing a connected-component segmentation for background removal followed by a principal-axis-based linear pre-registration, the images are adjusted into a homogeneous alignment. This registration approach is accompanied by the 3D reconstruction of histological and MALDI MSI data. RESULTS With this, a system of automatic registration for cross-process evaluation, as well as for creating 3D models, is developed and established. The registration of MALDI MSI data with different histological image data is evaluated by using the established global image registration system. CONCLUSION In conclusion, this multimodal image approach offers the possibility of molecular analyses of tissue specimens in clinical research and diagnosis.

Frameless neuronavigation with computer vision and real-time tracking for bedside external ventricular drain placement: a cadaveric study.

A major obstacle to improving bedside neurosurgical procedure safety and accuracy with image guidance technologies is the lack of a rapidly deployable, real-time registration and tracking system for a moving patient. This deficiency explains the persistence of freehand placement of external ventricular drains, which has an inherent risk of inaccurate positioning, multiple passes, tract hemorrhage, and injury to adjacent brain parenchyma. Here, the authors introduce and validate a novel image registration and real-time tracking system for frameless stereotactic neuronavigation and catheter placement in the nonimmobilized patient. Computer vision technology was used to develop an algorithm that performed near-continuous, automatic, and marker-less image registration. The program fuses a subject's preprocedure CT scans to live 3D camera images (Snap-Surface), and patient movement is incorporated by artificial intelligence-driven recalibration (Real-Track). The surface registration error (SRE) and target registration error (TRE) were calculated for 5 cadaveric heads that underwent serial movements (fast and slow velocity roll, pitch, and yaw motions) and several test conditions, such as surgical draping with limited anatomical exposure and differential subject lighting. Six catheters were placed in each cadaveric head (30 total placements) with a simulated sterile technique. Postprocedure CT scans allowed comparison of planned and actual catheter positions for user error calculation. Registration was successful for all 5 cadaveric specimens, with an overall mean (± standard deviation) SRE of 0.429 ± 0.108 mm for the catheter placements. Accuracy of TRE was maintained under 1.2 mm throughout specimen movements of low and high velocities of roll, pitch, and yaw, with the slowest recalibration time of 0.23 seconds. There were no statistically significant differences in SRE when the specimens were draped or fully undraped (p = 0.336). Performing registration in a bright versus a dimly lit environment had no statistically significant effect on SRE (p = 0.742 and 0.859, respectively). For the catheter placements, mean TRE was 0.862 ± 0.322 mm and mean user error (difference between target and actual catheter tip) was 1.674 ± 1.195 mm. This computer vision-based registration system provided real-time tracking of cadaveric heads with a recalibration time of less than one-quarter of a second with submillimetric accuracy and enabled catheter placements with millimetric accuracy. Using this approach to guide bedside ventriculostomy could reduce complications, improve safety, and be extrapolated to other frameless stereotactic applications in awake, nonimmobilized patients.

A robust multimodal remote sensing image registration method and system using steerable filters with first- and second-order gradients

Co-registration of multimodal remote sensing (RS) images (e.g., optical, infrared, LiDAR, and SAR) is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions (e.g., scale and rotation changes) between these images. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. First, to address severe NRD, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed, which combines the first- and second-order gradient information by using the steerable filters with a multi-scale strategy to depict more discriminative structure features of images. Then, a fast similarity measure is established called Fast Normalized Cross-Correlation (Fast-NCCSFOC), which employs the Fast Fourier Transform (FFT) technique and the integral image to improve the matching efficiency. Furthermore, to achieve reliable registration performance, a coarse-to-fine multimodal registration system is designed consisting of two pivotal modules. The local coarse registration is first conducted by involving both detection of interest points (IPs) and local geometric correction, which effectively utilizes the prior georeferencing information of RS images to address global geometric distortions. In the fine registration stage, the proposed SFOC is used to resist significant NRD, and to detect control points (CPs) between multimodal images by a template matching scheme. The performance of the proposed matching method has been evaluated with many different kinds of multimodal RS images. The results show its superior matching performance compared with the state-of-the-art methods. Moreover, the designed registration system also outperforms the popular commercial software (e.g., ENVI, ERDAS, and PCI) in both registration accuracy and computational efficiency. Our system is available at https://github.com/yeyuanxin110/SFOC-Multimodal_Remote_Sensing_Image_Registration_System.

Development of Novel Image Processing System Using Super-Resolution to Reduce Cone-Beam CT Imaging Dose in Radiation Therapy

To develop an image processing system which can improve the image quality of low-dose Cone-bean CT (CBCT) images for Image Guided Radiation Therapy (IGRT) by using super-resolution technique which can predict high-resolution CBCT images from low-resolution CBCT images.The developed system was using Single Image Super-Resolution (SISR) based on Convolutional Neural Network (CNN). We used CBCT images of the pelvic region of 123 patients for training of the developed system and its evaluation: 104 patients for training and 19 patients for performance evaluation. Original images (512×512 pixels, IMorg) were downsampled to multiple low matrix sizes (256×256 pixels (IM256), 128×128 pixels (IM128)), and we trained two types of the system to output super-resolution images (512×512 pixels) from each low matrix size image. Then, for performance evaluation, super-resolution images (IMsr2x, IMsr4x) were generated by the trained systems and evaluated them by Peak Signal-to-Noise Ratio (PSNR) and structural similarity (SSIM). Image registration using bone matching and target matching were performed to evaluate the impact of the developed system for image registration. The generated images were also compared with the images obtained by the conventional upsampling method, Bi-Linear (IMlin2x, IMlin4x) and Bi-Cubic (IMcub2x, IMcub4x) methods, using PSNR and SSIM.The mean values and standard deviation of PSNR and SSIM of IMsr2x, IMlin2x and IMcub2x were 51.34 ± 0.87 [dB] and 0.995 ± 0.001, 43.05 ± 0.61 [dB] and 0.985 ± 0.002, 45.25 ± 0.66 [dB] and 0.989 ± 0.001, respectively. Similarly, the PSNR and SSIM of IMsr4x, IMlin4x and IMcub4x were 46.27 ± 0.95 [dB] and 0.988 ± 0.002, 37.63 ± 0.61 [dB] and 0.956 ± 0.005, 38.66 ± 0.66 [dB] and 0.960 ± 0.004, respectively. As a result of frequency component analysis, IMsr was added high frequency components that were not included in low matrix size CBCT images. Therefore, the PSNR and SSIM values were significantly higher in IMsr. The registration errors, which were defined as the errors from the registration results of IMorg, of IMsr2x and IMsr4x were 0.20 ± 0.48 [mm] and 1.25 ± 1.45 [mm] for target matching, 0.10 ± 0.31 [mm] and 0.53 ± 0.73 [mm] for bone matching respectively. With IMsr4x, the values of PSNR and SSIM were reduced, but the accuracy of image registration was not significantly affected.We developed the image processing system using SISR based on CNN to reduce the CBCT imaging dose in radiation therapy. The developed system could add the high-frequency components which were not included in low matrix size CBCT images. CBCT imaging dose could be reduced to 1/16 and 1/4 when CBCT scans were taken with 128×128 and 256×256 pixels, respectively. The developed system enables CBCT imaging using a low radiation dose without degrading the image quality.

A Rapid Beam Pointing Determination and Beam-Pointing Error Analysis Method for a Geostationary Orbiting Microwave Radiometer Antenna in Consideration of Antenna Thermal Distortions.

When observing the Earth’s radiation signal with a geostationary orbiting (GEO) mechanically scanned microwave radiometer, it is necessary to correct the antenna beam pointing (ABP) in real time for the deviation caused by thermal distortions of antenna reflectors with the help of the on-board Image Navigation and Registration (INR) system during scanning of the Earth. The traditional ABP determination and beam-pointing error (BPE) analysis method is based on the electromechanical coupling principle, which usurps time and computing resources and thus cannot meet the requirement for frequent real-time on-board INR operations needed by the GEO microwave radiometer. For this reason, matrix optics (MO), which is widely used in characterizing the optical path of the visible/infrared sensor, is extended to this study so that it can be applied to model the equivalent optical path of the microwave antenna with a much more complicated configuration. Based on the extended MO method, the ideal ABP determination model and the model for determining the actual ABP affected by reflector thermal distortions are deduced for China’s future GEO radiometer, and an MO-based BPE computing method, which establishes a direct connection between the reflector thermal distortion errors (TDEs) and the thermally induced BPE, is defined. To verify the overall performance of the extended MO method for rapid ABP determination, the outputs from the ideal ABP determination model were compared to calculations from GRASP 10.3 software. The experimental results show that the MO-based ABP determination model can achieve the same results as GRASP software with a significant advantage in computational efficiency (e.g., at the lowest frequency band of 54 GHz, our MO-based model yielded a 4,730,000 times faster computation time than the GRASP software). After validating the correctness of the extended MO method, the impacts of the reflector TDEs on the BPE were quantified on a case-by-case basis with the help of the defined BPE computing method, and those TDEs that had a significant impact on the BPE were therefore identified. The methods and results presented in this study are expected to set the basis for the further development of on-board INR systems to be used in China’s future GEO microwave radiometer and benefit the ABP determination and BEP analysis of other antenna configurations to a certain extent.

The diagnostics to study the spatial-angular distribution of radiation scattered by plasma at the "Kanal-2" facility

A fiber-optics-based diagnostics to study the spatial-angular scattering of radiation by plasma in the spectral range of 400–1900 nm is presented. Introducing light filters into the image registration system enables to choose the entire spectral range or a selected one. The proposed method allows to carry out studies simultaneously in different planes, which gives information about the scattering process isotropy. This method can be used to record on digital camera matrix the energy evenly distributed over the area of the fiber input end face, starting from values of 10−12 J at a wavelength of 0.532 µm, 10−11 J - at 0.632 µm, and 10−7 J - at 1.064 µm. Experiments carried out with the interaction of Nd:glass laser radiation with a solid copper target have shown that the scattering process is not isotropic, and the scatterplots in experiments with the fundamental frequency of laser radiation and its second harmonic differ.

Are fiducial registration error and target registration error correlated? SciKit-SurgeryFRED for teaching and research.

Understanding the relationship between fiducial registration error (FRE) and target registration error (TRE) is important for the correct use of interventional guidance systems. Whilst it is well established that TRE is statistically independent of FRE, system users still struggle against the intuitive assumption that a low FRE indicates a low TRE. We present the SciKit-Surgery Fiducial Registration Educational Demonstrator and describe its use. SciKit-SurgeryFRED was developed to enable remote teaching of key concepts in image registration. SciKit-SurgeryFRED also supports research into user interface design for image registration systems. SciKit-SurgeryFRED can be used to enable remote tutorials covering the statistics relevant to image guided interventions. Students are able to place fiducial markers on pre and intra-operative images and observe the effects of changes in marker geometry, marker count, and fiducial localisation error on TRE and FRE. SciKit-SurgeryFRED also calculates statistical measures for the expected values of TRE and FRE. Because many registrations can be performed quickly the students can then explore potential correlations between the different statistics. SciKit-SurgeryFRED also implements a registration based game, where participants are rewarded for complete treatment of a clinical target, whilst minimising the treatment margin. We used this game to perform a remote study on registration and simulated ablation, measuring how user performance changes depending on what error statistics are made available. The results support the assumption that knowing the exact value of target registration error leads to better treatment. Display of other statistics did not have a significant impact on the treatment performance.

Применение метода экспансионной микроскопии в нейробиологии

Many biological studies require the analysis of ultrastructural changes at the cellular organelles and molecular interaction. Since the modern confocal microscope resolution is limited by the diffraction limit (200-300nm), it is impossible to study such small objects using standard fluorescence microscopy. Ultra-high resolution microscopy methods require expensive equipment and are technically difficult in use, which in turn limits their widespread practical application. However, recently appeared methods make it possible to increase the resolution of microscopy not by improving the image registration system, but by physically isotropic expansion of a biological sample using a controlled chemical process. Thereby to this method, called expansion or expansive microscopy (EM), it became possible to obtain three-dimensional images of samples with a resolution sufficient for studying individual cell organelles using a conventional confocal microscope. This review covers the history of this method, its basic principles and examples of use in various fields of biology and medicine, as well as reflects future directions for improving this technology. The article discusses the methodological features of the EM applying in the study of brain tissue samples and primary neuronal cell culture with an algorithm that can be used to adapt the standard protocol to the goals and objectives of a particular study.

Recovery of dust particles trajectories in modeling physical processes in the near-surface exosphere of the moon

This study demonstrates the visualization and recovery of the 3D dynamic trajectories of charged microparticles in electric field conditions. The main aim of this work is to simulate plasma-dust processes above the surfaces of the Moon and other Solar system bodies without atmospheres. The experimental setup includes two parts: a vacuum chamber, in which microparticles imitate lunar dust dynamics under electrostatic field conditions, and a stereo camera system for image registration combined with laser and optics for illuminating the investigation volume. Image processing techniques for estimating the 3D particle trajectory were developed. Examples of processing results and their prospective application are discussed.

Registration of Image Modalities for Analyses of Tissue Samples Using 3D Image Modelling.

Biopsies are a diagnostic tool for the diagnosis of histopathological, molecular biological, proteomic, and imaging data, to narrow down disease patterns or identify diseases. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) provides an emerging state-of-the-art technique for molecular imaging of biological tissue. The aim of this study is the registration of MALDI MSI data sets and data acquired from different histological stainings to create a 3D model of biopsies and whole organs. The registration of the image modalities is achieved by using a variant of the authors' global, deformable Schatten-q-Norm registration approach. Utilizing a connected-component segmentation for background removal followed by a principal-axis based linear pre-registration, the images are adjusted into a homogeneous alignment. This registration approach is accompanied by the 3D reconstruction of histological and MALDI MSI data. With this, a system of automatic registration for cross-process evaluation, as well as for creating 3D models, is developed and established. The registration of MALDI MSI data with different histological image data is evaluated by using the established global image registration system. In conclusion, this multimodal image approach offers the possibility of molecular analyses of tissue specimens in clinical research and diagnosis.