Image Registration Research Articles

Anatomical significance of the vestibular aqueduct in posterior wall of the internal auditory canal drilling through the retrosigmoid approach: a study utilizing 3D reconstruction technology.

The anatomical importance of the vestibular aqueduct and posterior semicircular canal is explored in this study, which utilizes three-dimensional(3D) image reconstruction and registration fusion technology through the retrosigmoid approach for drilling the posterior wall of the internal auditory canal. A total of 200 temporal bone high-resolution computed tomography (HRCT) scans were collected from 100 patients without inner ear diseases at the Central Hospital of the PLA's Neurosurgery and Radiology Department between 2016 and 2024. Additionally, temporal bone HRCT and brain MRI imaging data were also collected concurrently from 32 patients diagnosed with vestibular schwannomas. The primary focus of this research is on 3D reconstruction and fusion registration of temporal bone HRCT and brain MRI images to accurately display and measure the anatomical structures as well as provide spatial positioning data in 3D dimensions for important structures such as the vestibular aqueduct, posterior semicircular canal, tumors, among others. Several important anatomical measurements were obtained using the 3D Reconstruction and fusion Technology. In non-tumor patients, the internal auditory canal measures (8.408 ± 1.078mm), with P-1 (defined as the pole located near the posterior region on the long axis of an elliptical opening in the inner auditory canal) to vestibular aqueduct being (9.450 ± 1.522mm) and to posterior semicircular canal being (10.348 ± 1.542mm). In vestibular schwannoma patients, these dimensions change to (7.977 ± 0.903) mm, (7.598 ± 1.223mm), and (8.687 ± 1.061mm) respectively. Statistical analysis shows significant differences (p = 5.7416e-10 < 0.05, p = 5.8961e-9 < 0.05, p = 6.0e-6 < 0.05). ROC analysis sets a threshold of 8.438mm from the internal auditory canal to the vestibular aqueduct, warning of caution near 8mm during surgery to prevent vestibular aqueduct damage. In patients with vestibular schwannoma, the distance from the posterior internal auditory canal to the vestibular aqueduct is shorter compared to that of the posterior semicircular canal, implying a higher likelihood of damaging the vestibular aqueduct when eroding the posterior internal auditory canal during surgery.

Microscopic Imaging of Alpha Particle Trajectory and Its Application for Radionuclide Distribution Measurement in Cell.

Imaging and analyzing alpha-particle trajectories are crucial for studying alpha-particle distributions in high-energy physics experiments, such as radioisotope imaging and neutron energy spectrum measurements. This study introduces a novel method that combines an electron-multiplying charge-coupled device (EMCCD) camera, a gadolinium aluminum gallium garnet (GAGG) scintillator film, and a fluorescent microscope to measure the micro-distribution of radionuclides. A reconstruction technique was developed to determine the initial positions of alpha particles based on the pixel gray-value distributions along their trajectories. The effectiveness of this technique was validated through imaging experiments using fixed incident alpha particles. Key imaging parameters, including binning, exposure time, and optical parameters such as magnification, were systematically investigated for their impacts on imaging quality. Results indicated that increasing the binning value improved detection sensitivity but reduced spatial resolution. Shortening exposure times effectively prevented track overlap, aiding trajectory identification, counting, and analysis. Higher magnification of objective lenses enhanced the spatial resolution of the whole system but required greater sample flatness and camera sensitivity. A measurement platform was further developed to explore the cellular distribution of radioactive drugs in targeted alpha therapy. Coupled registration of trajectory and cellular images was achieved using an external Ra-223 source and A549 cells. This trajectory measurement technique is broadly applicable for analyzing the cellular distribution of radioactive drugs in targeted alpha therapy.

Bowel tracking for MR-guided radiotherapy: simultaneous optimization of small bowel imaging and tracking

Objective. The small bowel is one of the most radiosensitive organs-at-risk during radiotherapy in the pelvis. This is further complicated due to anatomical and physiological motion. Thus, its accurate tracking becomes of particular importance during therapy delivery, to obtain better dose-toxicity relations and/or to perform safe adaptive treatments. The aim of this work is to simultaneously optimize the MR imaging sequence and motion estimation solution towards improved small bowel tracking precision during radiotherapy delivery.Approach. An MRI sequence was optimized, to adhere to the respiratory and peristaltic motion frequencies, by assesing the performance of an image registration algorithm on data acquired on volunteers and patients. In terms of tracking, three registration algorithms, previously-employed in the scope of image-guided radiotherapy, were investigated and optimized. The optimized scan was acquired for 7.5 min, in 18 patients and for 15 min, in 10 volunteers at a 1.5 T MRL (Unity, Elekta AB). The tracking precision was evaluated and validated by means of three different quality assurance criteria: Structural Similarity Index Measure (SSIM), Inverse Consistency (IC) and Absolute Intensity Difference.Main results. The optimal sequence was a balanced Fast Field Echo, which acquired a 3D volume of the abdomen, with a dynamic scan time of 1.8 s. An optical flow algorithm performed best and which was able to resolve most of the motion. This was shown by mean IC values of<1 mm and a mean SSIM>0.9for the majority of the cases. A strong positive correlation (p <0.001) between the registration performance and visceral fat percentage was found, where a higher visceral fat percentage gave a better registration due to the better image contrast.Significance. A method for simultaneous optimization of imaging and tracking was presented, which derived an imaging and registration procedure for accurate small bowel tracking on the MR-Linac.

AI-driven framework to map the brain metabolome in three dimensions.

High-resolution spatial imaging is transforming our understanding of foundational biology. Spatial metabolomics is an emerging field that enables the dissection of the complex metabolic landscape and heterogeneity from a thin tissue section. Currently, spatial metabolism highlights the remarkable complexity in two-dimensional (2D) space and is poised to be extended into the three-dimensional (3D) world of biology. Here we introduce MetaVision3D, a pipeline driven by computer vision, a branch of artificial intelligence focusing on image workflow, for the transformation of serial 2D MALDI mass spectrometry imaging sections into a high-resolution 3D spatial metabolome. Our framework uses advanced algorithms for image registration, normalization and interpolation to enable the integration of serial 2D tissue sections, thereby generating a comprehensive 3D model of unique diverse metabolites across host tissues at submesoscale. As a proof of principle, MetaVision3D was utilized to generate the mouse brain 3D metabolome atlas of normal and diseased animals (available at https://metavision3d.rc.ufl.edu ) as an interactive online database and web server to further advance brain metabolism and related research.

Software-based versus visual assessment of the minimal ablative margin in patients with liver tumours undergoing percutaneous thermal ablation (COVER-ALL): a randomised phase 2 trial.

Software-based versus visual assessment of the minimal ablative margin in patients with liver tumours undergoing percutaneous thermal ablation (COVER-ALL): a randomised phase 2 trial.

Correlation of point-wise retinal sensitivity with localized features of diabetic macular edema using deep learning.

Correlation of point-wise retinal sensitivity with localized features of diabetic macular edema using deep learning.

Bone-wise rigid registration of femur, tibia, and fibula for the tracking of temporal changes.

Multiple myeloma (MM) induces temporal alterations in bone structure, such as osteolytic bone lesions, which are challenging to identify through manual image interpretation. The large variation in radiologists' assessments, even at expert centers, further complicates diagnosis. Automatic image analysis methods, including segmentation and registration, can expedite detecting and tracking these bone changes. This study presents an automated pipeline for accurately tracking temporal changes in the femurs, tibiae, and fibulae of MM patients using 3D whole-body CT images. The pipeline leverages image segmentation, rigid registration, and temporal subtraction to accelerate disease monitoring and support clinicaldecision-making. A convolutional neural network (CNN) was trained to segment bones in 3D CT images of 30 MM patients. Nine patients with pre- and post-diagnosis CT scans were used to validate the segmentation and registration process. A two-phase bone-wise rigid registration was applied, followed by temporal subtraction to generate difference images. Segmentation and registration accuracy were assessed using the Dice similarity coefficient (DSC) and mean surface distance (MSD). The proposed method was compared to a non-rigid registration method. The neural network segmentation resulted in a mean DSC of 0.93 across all bone types and all test data. The registration accuracy measured by the mean DSC across the test data was at least 0.94 for all bone types. The second phase of rigid registration improved the registration fibulae. Metric-wise, the nonrigid method performed better but diminished lesion visibility in difference images. An automated pipeline for the longitudinal tracking of bone alterations was presented. Both segmentation and registration demonstrated high accuracy as measured by DSC and MSD. In the difference images produced by temporal subtraction, osteolytic lesions were clearly visible in the femurs. The methodology lays a solid foundation for future improvements, such as inclusion of the axialspine.

A registration strategy to characterize DTI-observed changes in skeletal muscle architecture due to passive shortening.

Skeletal muscle architecture is a key determinant of muscle function. Architectural properties such as fascicle length, pennation angle, and curvature can be characterized using Diffusion Tensor Imaging (DTI), but acquiring these data during a contraction is not currently feasible. However, an image registration-based strategy may be able to convert muscle architectural properties observed at rest to their contracted state. As an initial step toward this long-term objective, the aim of this study was to determine if an image registration strategy could be used to convert the whole-muscle average architectural properties observed in the extended joint position to those of a flexed position, following passive rotation. DTI and high-resolution fat/water scans were acquired in the lower leg of seven healthy participants on a 3T MR system in + 20° and -10° ankle positions. The diffusion and anatomical images from the two positions were used to propagate DTI fiber-tracts from seed points along a mesh representation of the aponeurosis of fiber insertion. The -10° and + 20° anatomical images were registered and the displacement fields were used to transform the mesh and fiber-tracts from the + 20° to the -10° position. Student's paired t-tests were used to compare the mean architectural parameters between the original and transformed fiber-tracts. The whole-muscle average fiber-tract length, pennation angle, curvature, and physiological cross-sectional areas estimates did not differ significantly. DTI fiber-tracts in plantarflexion can be transformed to dorsiflexion position without significantly affecting the average architectural characteristics of the fiber-tracts. In the future, a similar approach could be used to evaluate muscle architecture in a contracted state.

Medical Image Registration Using Optimal Control of a Linear Hyperbolic Transport Equation with a DG Discretization

Medical Image Registration Using Optimal Control of a Linear Hyperbolic Transport Equation with a DG Discretization

Evaluation of motion mitigation strategies for carbon ion therapy of abdominal tumors based on non-periodic imaging data

Objective.To identify suitable combination strategies for treatment planning and beam delivery in scanned carbon ion therapy of moving tumors.Approach. Carbon ion treatment plans for five abdominal tumors were optimized on four-dimensional (4D) computed tomography (CT) data using the following approaches. 4DITV across all phases and within a gating window, single phase uniform dose, and an innovative 4D tracking internal target volume (ITV) strategy. Delivered single-fraction doses were calculated on time-resolved virtual CT images reconstructed from 2D cine-magnetic resonance imaging series, using a deformable image registration pipeline. Treatment plans were combined with various beam delivery techniques: three-dimensional (no motion mitigation), rescanning, gating, beam tracking, and multi-phase 4D delivery with and without residual tracking (MP4D and MP4DRT) to form in total 11 treatment modalities. Single fraction doses were accumulated to simulate a fractionated treatment.Main results. Breath-sampled treatments using the MP4D and MP4DRT delivery techniques were the only to achieveD95> 95% for hypofractionated treatments, with little dependence on the number of fractions. A combination of MP4DRT with the new 4D tracking ITV approach resulting in conformal dose distributions and demonstrated the greatest robustness against irregular motion and anatomical changes.Significance. This study demonstrates, that real-time adaptive beam delivery strategies can deliver conformal doses within single fractions, thereby enabling hypofractionated treatment schemes that are not feasible with conventional strategies.

Transverse Velocity Field Measurement of Solar High-Resolution Images Based on Unsupervised Deep Learning

Abstract Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar dynamics. This paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field, addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar images. The proposed method converts the transverse velocity field computation problem into an optical flow computation problem, uses two forward propagation of features to get rid of the reliance on optical flow labels. Additionally, it reduces the impact of the "Photometric Consistency" constraint on optical flow accuracy by identifying and handling optical flow outliers. We apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the H$\alpha$ and TiO bands, observed by the New Vacuum Solar Telescope (NVST). Comparative experiments with several well-established optical flow methods, including those based on supervised deep learning models, show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean (RMM), Residual Map Variance (RVV), Cross Correlation (CC), and Structural Similarity Index Measure (SSIM). Moreover, since optical flow captures the fundamental motion information in image sequences, the proposed method can be applied to a variety of research areas, including solar image registration, sequence alignment, image super-resolution, magnetic field calibration, and solar activity forecasting. The code is available at {\it https://github.com/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images}.

Radiation therapists' perspectives on artificial intelligence: Insights from a single institution on Improving effectiveness and educational supports.

Radiation therapists' perspectives on artificial intelligence: Insights from a single institution on Improving effectiveness and educational supports.

ETSpy: A HyperSpy extension package for electron tomography data processing and reconstruction.

ETSpy: A HyperSpy extension package for electron tomography data processing and reconstruction.

RetinaRegNet: A zero-shot approach for retinal image registration.

RetinaRegNet: A zero-shot approach for retinal image registration.

Deep learning image registration for cardiac motion estimation in adult and fetal echocardiography via a focus on anatomic plausibility and texture quality of warped image.

Temporal echocardiography image registration is important for cardiac motion estimation, myocardial strain assessments, and stroke volume quantifications. Deep learning image registration (DLIR) is a promising way to achieve consistent and accurate registration results with low computational time. DLIR seeks the image deformation that enables the moving image to be warped to match the fixed image. We propose that, during DLIR training, a greater focus on the warped moving image's anatomic plausibility and image texture can support robust results, and we show that it has sufficient robustness to be applied to both fetal and adult echocardiography. Our proposed framework includes (1) an anatomic shape-encoded constraint to preserve physiological myocardial and left ventricular anatomical topologies in the warped image, (2) a data-driven texture constraint to preserve good texture features in the warped image, and (3) a multi-scale training algorithm to improve accuracy. Our experiments demonstrate a strong correlation between the shape-encoded constraint and good anatomical topology and between the data-driven texture constraint and image textures. They improve different aspects of registration results in a non-overlapping way. We demonstrate that these methods can successfully register both fetal and adult echocardiography using our multi-demographic fetal dataset and the public CAMUS adult dataset, despite the inherent differences between adult and fetal echocardiography. Our approach also outperforms traditional non-DL gold standard registration approaches, including optical flow and Elastix, and could be translated into more accurate and precise clinical quantification of cardiac ejection fraction, demonstrating potential for clinical utility.

Impact of tumor position displacement during end-exhalation breath-hold condition on tumor dose in lung stereotactic body radiation therapy using volumetric modulated arc therapy.

Impact of tumor position displacement during end-exhalation breath-hold condition on tumor dose in lung stereotactic body radiation therapy using volumetric modulated arc therapy.

Fast and highly accurate registration of textile double-sided images: An innovative solution for the calibration of binocular measuring systems

Fast and highly accurate registration of textile double-sided images: An innovative solution for the calibration of binocular measuring systems

Molecular beam epitaxial growth of HgCdTe mid-wave infrared dual-band detectors

Molecular beam epitaxial growth of HgCdTe mid-wave infrared dual-band detectors

Iterative Geolocation based on Cross-view Image Registration (IGCIR) for long-range targets

Iterative Geolocation based on Cross-view Image Registration (IGCIR) for long-range targets

Medical image registration and classification using smell agent rat swarm optimization based deep Maxout network

Medical image registration (MIR) is a crucial task in clinical image processing, involving the alignment of images from different modalities, such as magnetic resonance imaging (MRI) and computed tomography (CT), across various time points and subjects. Despite numerous advancements, no universal method caters to all MIR applications. This paper introduces the smell agent rat swarm optimization based deep Maxout network (SARSO-DMN) for MIR and classification. This work aims to enhance the accuracy and efficiency of medical image alignment and classification, addressing the challenges posed by diverse imaging modalities and temporal variations. The problem involves effectively registering CT and MRI images, followed by inhale and exhale classification. The proposed approach begins with feeding the input images into a convolutional neural network (CNN), followed by applying a deformation field to generate an intermediate output (output-1). This output, along with the input MRI images, is further processed by a CNN to produce output-2. Subsequently, output-2 and the input MRI image are subjected to another CNN, resulting in the final registered image. The classification phase utilizes a DMN optimized by the SARSO algorithm, which combines smell agent optimization (SAO) and rat swarm optimizer (RSO). The results demonstrate that SARSO-DMN achieves a maximum accuracy of 90.7%, a minimum false positive rate (FPR) of 11.3%, and a maximum true positive rate (TPR) of 91.2%. The SARSO-DMN approach provides a robust solution for MIR and classification, leveraging advanced optimization techniques to enhance performance.