Co-registration Of Data Research Articles

Comparison of five methods for improving the accuracy of SRTM3 DEM and TanDEM-X DEM in the Qinghai-Tibet Plateau using ICESat-2 data

ABSTRACT The accuracy of digital elevation models (DEMs) is crucial for practical applications in complex terrain. In this study, various correction models were employed to evaluate and correct the 90 m SRTM3 DEM and TanDEM-X DEM data in the Qinghai-Tibet Plateau region. A simple and universal three-dimensional data co-registration method was utilized for horizontal alignment. The study demonstrated a significant reduction in horizontal discrepancies between datasets after data co-registration. By comparing the performance of five different correction methods, it was found that the RF model exhibited excellent accuracy and robustness, making it particularly suitable for applications that require high precision. The XGB and BPNN models offer a good balance between accuracy and time efficiency, making them suitable for research scenarios that are less sensitive to precision requirements. In contrast, the MLR and IDW methods performed poorly and are not recommended for high-precision applications. Furthermore, the corrected DEMs showed improved resistance to the influence of high slopes, enhancing their applicability in complex terrain areas. This study provides an important reference for the correction of high-quality DEMs in the Qinghai-Tibet Plateau region and offers valuable insights for DEM research in similar areas.

Abstract 7429: A web-based application to co-register elemental imaging with histopathology to enhance the study of metal bioaccumulation within tumors

Abstract Essential trace elements are crucial in various biological processes, including tumor growth, migration, and metastasis. For instance, copper ions are involved in mitochondrial metabolism, cell proliferation, tumor migration, metastasis and proangiongenic pathways. Localizing where metal ion homeostasis is disrupted is vital for understanding these processes beyond what bulk analysis can reveal. Elemental imaging offers high-resolution, quantifiable multi-elemental distribution maps, providing a more detailed analysis compared to traditional bulk measurements. However, aligning findings with tissue structures identified in histopathological data can be challenging. To address this, we developed a web-based application for the co-registration and analysis of Whole Slide Images (WSI) and elemental imaging data at the cellular level. The application integrates WSI with elemental maps generated by technologies such as ultrafast laser ablation and inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS), as well as a range of other elemental imaging techniques. Hosted by the Biomedical National Elemental Imaging Resource, the current application allows for a streamlined data integration, co-registration, and analysis, including: 1) uploading WSI, elemental maps, and pathologist annotations, 2) automated preprocessing to identify tissue regions, 3) co-registration to merge WSI and elemental maps, 4) custom import, real-time overlay/editing of pathologist annotations transferred from WSI, 5) plotting, statistical tools and 6) data export. This application enables users to compare elemental abundance within/between tissue structures annotated on H&E or immunohistochemical slides. Derived data can pinpoint elements and mixtures whose abundance differs significantly across various pathological conditions. As an initial test, we applied the software on breast HER2/ER/PR tumors, varying in molecular subtype/histology. Preliminary results, using Bayesian lognormal regression models, adjusting for tumor subtypes, indicated a subtype-dependent variation in copper abundance. The relative difference in copper levels between tumor and stromal regions was notably less in HER2 tumors (p=0.026) compared to HR tumors (p<0.0001). Conversely, iron showed a consistently higher abundance in stromal areas across both subtypes (p<0.0001). Future enhancements include improved co-registration capabilities, hotspot analysis, machine learning, mixture effects, and cloud-based architecture. This method uniquely enables integration of elemental data with spatial transcriptomics, mapping gene transcripts on tissue slides. With current ST assays aligned with H&E slides, this tool facilitates co-registration, offering insights into biological pathways affected by metal homeostasis disruptions. Citation Format: Yunrui Lu, Ramsey Steiner, Serin Han, Matthew Chan, Tracy Punshon, Brian Jackson, Linda Vahdat, Jonathan Marotti, Joshua Levy. A web-based application to co-register elemental imaging with histopathology to enhance the study of metal bioaccumulation within tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7429.

UAV-BASED ARCHAEOLOGICAL 3D MONITORING: A RURALSCAPE CASE IN IRAQI KURDISTAN

Abstract. Recently rapid mapping techniques based on UAV photogrammetry increasingly help on-site archaeological documentation works. Multi-temporal data are specifically crucial in diachronic investigation research, and for this purpose the data co-registration and integration can support the accurate 3D digitization of excavation phases and make coherent topological relation among them and phases-related stratigraphic units’ data. In this framework, the level of automation and accuracy control are challenging aspects to streamline the documentation process during excavation activities, however all experimentation phases must be tested and validated in the actual archaeological context, where the boundary conditions are typically demanding. This research is developed during the collaboration project with Cà Foscari University of Venice, started in 2022 campaign, in the excavation site of Tell Zeyd, in Iraqi Kurdistan, The Tell Zeyd Archaeological Project (ZAP) aims to study the rural landscape of the hinterland of Mosul in the long Islamic period, from the Arab conquest in the 7th century to the disintegration of the Ottoman Empire with the First World War, as an ideal observatory on the characteristics of the settlement in its spatial organisation, places of worship, production installations and facilities for storing foodstuffs. The research aims to present the preliminary test and results on an experimental documentation activity based on multi-scale UAV mapping strategy and training with the archaeological expert group, and particularly for automatic co-registration of multi-temporal data, considering different images datasets epochs belonging to subsequent excavation phases.

Source connectivity patterns in the default mode network differ between elderly golf-novices and non-golfers

Learning to play golf has high demands on attention and therefore may counteract age-related changes of functional brain networks. This cross-sectional study compared source connectivity in the Default Mode Network (DMN) between elderly golf novices and non-golfers. Four-minute resting-state electroencephalography (128 channels) from 22 elderly people (mean age 67 ± 4.3 years, 55% females) were recorded after completing a 22-week golf learning program or after having continued with normal life. Source connectivity was assessed after co-registration of EEG data with native MRI within pre-defined portions of the DMN in the beta band (14–25 Hz). Non-golfers had significantly higher source connectivity values in the anterior DMN compared to non-golfers. Exploratory correlation analyses did not indicate an association to cognitive performance in either group. Inverse correlations between a marker of external attention with source connectivity of the anterior DMN may suggest a trend in the golf group only, but have to be replicated in future studies. Clinical relevance of these findings remains to be elucidated, but the observed difference in the anterior DMN may provide a starting point to further investigate if and how learning golf may have an impact on physiological age-related cognitive changes.

Efficient co-registration of UAV and ground LiDAR forest point clouds based on canopy shapes

• Time-efficient co-registration of UAV and ground LiDAR data is proposed for plantation forests. • Coarse alignment of forest point clouds is divided into ground alignment and canopy image matching. • Canopy images are matched by two pairs of canopy corner points and the canopy overlap rate. • The runtime of coarse alignment is less than 1 s for millions of forest points. Registration of unmanned aerial vehicle laser scanning (ULS) and ground light detection and ranging (LiDAR) point clouds in forests is critical to create a detailed representation of a forest structure and an accurate retrieval of forest parameters. However, tree occlusion poses challenges for those registration methods used artificial markers, and some automated registration methods have low time-efficiency due to the process of object (e.g., tree, crown) segmentation. In this study, we propose an automated and time-efficient method to register ULS and ground LiDAR (including terrestrial and backpack laser scanning) forest point clouds. Registration involves coarse alignment and fine registration, where the coarse alignment is divided into vertical and horizontal alignment. The vertical alignment is implemented by rotating grounds to the horizontal plane, and the horizontal alignment is achieved by canopy image matching. During image matching, vegetation points are projected onto the horizontal plane to obtain two binary images, and then, canopy shape feature, which is described by a two-point congruent set and canopy overlap, is used to match the binary images. Finally, we implement coarse alignment of ULS and ground LiDAR datasets by combining the results of ground alignment and image matching and finish fine registration in six plantation forest plots with sizes of 0.03 ha to 0.25 ha. Experimental results show that the ULS and ground LiDAR data in different plots are registered, of which the coarse alignment errors are less than 0.20 m in the horizontal direction, the final registration accuracy is less than 0.15 m, and the average runtime is less than 1 s. Our study demonstrates the effectiveness of the proposed strategy and has able to perform accurate and quick registration of ULS and ground LiDAR data from plantation forests with different attributes.

Effects of variability in manually contoured spinal cord masks on fMRI co-registration and interpretation.

Functional magnetic resonance imaging (fMRI) of the human spinal cord (SC) is a unique non-invasive method for characterizing neurovascular responses to stimuli. Group-analysis of SC fMRI data involves co-registration of subject-level data to standard space, which requires manual masking of the cord and may result in bias of group-level SC fMRI results. To test this, we examined variability in SC masks drawn in fMRI data from 21 healthy participants from a completed study mapping responses to sensory stimuli of the C7 dermatome. Masks were drawn on temporal mean functional image by eight raters with varying levels of neuroimaging experience, and the rater from the original study acted as a reference. Spatial agreement between rater and reference masks was measured using the Dice Similarity Coefficient, and the influence of rater and dataset was examined using ANOVA. Each rater's masks were used to register functional data to the PAM50 template. Gray matter-white matter signal contrast of registered functional data was used to evaluate the spatial normalization accuracy across raters. Subject- and group-level analyses of activation during left- and right-sided sensory stimuli were performed for each rater's co-registered data. Agreement with the reference SC mask was associated with both rater (F(7, 140) = 32.12, P < 2 × 10-16, η2 = 0.29) and dataset (F(20, 140) = 20.58, P < 2 × 10-16, η2 = 0.53). Dataset variations may reflect image quality metrics: the ratio between the signal intensity of spinal cord voxels and surrounding cerebrospinal fluid was correlated with DSC results (p < 0.001). As predicted, variability in the manually-drawn masks influenced spatial normalization, and GM:WM contrast in the registered data showed significant effects of rater and dataset (rater: F(8, 160) = 23.57, P < 2 × 10-16, η2 = 0.24; dataset: F(20, 160) = 22.00, P < 2 × 10-16, η2 = 0.56). Registration differences propagated into subject-level activation maps which showed rater-dependent agreement with the reference. Although group-level activation maps differed between raters, no systematic bias was identified. Increasing consistency in manual contouring of spinal cord fMRI data improved co-registration and inter-rater agreement in activation mapping, however our results suggest that improvements in image acquisition and post-processing are also critical to address.

Adaptive Micro-liter Fiducials for Pre-clinical MPI and MRI Imaging.

Magnetic Particle imaging (MPI) allows to measure and quantify background-free tracer distribution with a high temporal resolution. Anatomical structures are not displayed in MPI, rendering orientation within a sample error-prone and necessitating co-registration with other imaging modalities such as MRI. To support this challenge, defined external landmarks (fiducials) made from materials visible in each of the imaging modalities respectively were used in this work. Resulting signals can be aligned with the merged image containing both anatomical data and information about the tracer distribution. Defining the optimal fiducial placement is demanding and can drastically impact the 3D MPI-MRI image presentation. Here we present an adaptable 3D-printed fiducial system for preclinical co-registration of MRI and MPI data designed for easy visualisation. Clinical relevance- MPI is a promising imaging modality with many conceivable clinical applications. Simple and reliable co-registration with other imaging modalities will be crucial for a seamless transition into the clinic.

Hippocampal subfield involvement in patients with transient global amnesia.

Transient global amnesia (TGA) is a rare neurological disorder causing a transient disturbance of episodic long-term memory. Its etiology remains yet to be identified; the only consistently reported findings in patients with TGA are small hyperintense lesions in the hippocampus on diffusion-weighted magnetic resonance imaging (DWI). The aim of this study was to define whether these lesions are subfield specific, as suggested previously. High-resolution multiplanar reformation T1 and DWI of the hippocampus were acquired in 25 patients after TGA with a total of 43 hippocampal lesions. Hippocampal subfields were determined using the FreeSurfer software and the location of the DWI lesions was transformed to the T1 images after data co-registration. Additionally, hippocampal subfield volumes in each patient were calculated and compared with that of 20 healthy controls. Hippocampal lesions were most frequently detected in the cornu ammonis area 1 (CA1) subfield (30.2%), the hippocampal tail (28.0%), and the subiculum (21.0%); however, lesions were also found in other subfields. There was no significant difference between patients and controls concerning the volumes of the hippocampal subfields. Contrasting previous assumptions, we found DWI hyperintense lesions not to be restricted to the CA1 subfield. The visualization of focal hippocampal lesions on diffusion imaging located to several different hippocampal subfields suggests a potential pathophysiology of TGA independent of microstructural hippocampal anatomy and subfield-specific vulnerability.

Co-registration of single tree maps and data captured by a moving sensor using stem diameter weighted linking

A new method for the co-registration of single tree data in forest stands and forest plots applicable to static as well as dynamic data capture is presented. This method consists of a stem diameter weighted linking algorithm that improves the linking accuracy when operating on diverse diameter stands with stem position errors in the single tree detectors. A co-registration quality metric threshold, QT, is also introduced which makes it possible to discriminate between correct and incorrect stem map co-registrations with high probability (&gt;99%). These two features are combined to a simultaneous location and mapping-based co-registration method that operates with high linking accuracy and that can handle sensors with drifting errors and signal bias. A test with simulated data shows that the method has an 89.35% detection rate. The statistics of different settings in a simulation study are presented, where the effect of stem density and position errors were investigated. A test case with real sensor data from a forest stand shows that the average nearest neighbor distances decreased from 1.90 m to 0.51 m, which indicates the feasibility of this method.

PS-ESD: Persistent Scatterer-Based Enhanced Spectral Diversity Approach for Time-Series Sentinel-1 TOPS Data Co-Registration

In Sentinel-1 terrain observation with progressive scan (TOPS) mode, this azimuth sweeping introduces an extra high-frequency Doppler term into the impulse response function, which requires the azimuth co-registration accuracy of 0.001 pixels to make the interferometric phase difference of adjacent bursts less than 3&#x00B0;. The enhanced spectral diversity (ESD) and developing versions are usually used to correct such residual azimuth misregistration. However, in fast decorrelation scenario, the distributed scatterers (DSs)-based ESDs need to perform lots of complex data processing to reduce the decorrelation impact on high-precision co-registration, including multilooking, spatial filtering, network construction, and least square adjustment. The DS-based co-registration methods are complicated and inefficient, which is not suitable for big SAR data processing. Therefore, this paper proposes a persistent scatterers-based ESD (PS-ESD): to estimate the amplitude dispersion index (ADI) with double samples over the burst overlap regions and select the satisfied PS pixels to compute the double differential phase under the one-master interferometric framework for correcting the time-series azimuth shifts. Based on the Sentinel-1 TOPS SAR data over the mountainous area in Three Gorges, China, experimental results have demonstrated that the proposed PS-ESD can achieve higher co-registration accuracy and faster convergence rate, especially in the case of dynamic co-registration of newly added TOPS images.

Ground-based/UAV-LiDAR data fusion for quantitative structure modeling and tree parameter retrieval in subtropical planted forest

Light detection and ranging (LiDAR) has contributed immensely to forest mapping and 3D tree modelling. From the perspective of data acquisition, the integration of LiDAR data from different platforms would enrich forest information at the tree and plot levels. This research develops a general framework to integrate ground-based and UAV-LiDAR (ULS) data to better estimate tree parameters based on quantitative structure modelling (QSM). This is accomplished in three sequential steps. First, the ground-based/ULS LiDAR data were co-registered based on the local density peaks of the clustered canopy. Next, redundancy and noise were removed for the ground-based/ULS LiDAR data fusion. Finally, tree modeling and biophysical parameter retrieval were based on QSM. Experiments were performed for Backpack/Handheld/UAV-based multi-platform mobile LiDAR data of a subtropical forest, including poplar and dawn redwood species. Generally, ground-based/ULS LiDAR data fusion outperforms ground-based LiDAR with respect to tree parameter estimation compared to field data. The fusion-derived tree height, tree volume, and crown volume significantly improved by up to 9.01%, 5.28%, and 18.61%, respectively, in terms of r RMSE. By contrast, the diameter at breast height (DBH) is the parameter that has the least benefits from fusion, and r RMSE remains approximately the same, because stems are already well sampled from ground data. Additionally, particularly for dense forests, the fusion-derived tree parameters were improved compared to those derived from ground-based LiDAR. Ground-based LiDAR can potentially be used to estimate tree parameters in low-stand-density forests, whereby the improvement owing to fusion is not significant. • Mitigation of the limitations of tree localization in case of ground-based/ULS LiDAR data co-registration using virtual keypoints. • Fusion of ground-based/ULS LiDAR data based on removal of redundant and noisy points. • Investigation and evaluation of the utility of ground-based/ULS LiDAR data fusion for tree parameter estimation. • Ground-based/ULS LiDAR data fusion improved estimation of tree height, tree volume, and crown volume. • Ground-based/ULS LiDAR data fusion has strong potential in QSM of trees particularly in high-stand-density forest.

Accuracy Assessment of Cultural Heritage Models Extracting 3D Point Cloud Geometric Features with RPAS SfM-MVS and TLS Techniques

A proper classification of 3D point clouds allows fully exploiting data potentiality in assessing and preserving cultural heritage. Point cloud classification workflow is commonly based on the selection and extraction of respective geometric features. Although several research activities have investigated the impact of geometric features on classification outcomes accuracy, only a few works focused on their accuracy and reliability. This paper investigates the accuracy of 3D point cloud geometric features through a statistical analysis based on their corresponding eigenvalues and covariance with the aim of exploiting their effectiveness for cultural heritage classification. The proposed approach was separately applied on two high-quality 3D point clouds of the All Saints’ Monastery of Cuti (Bari, Southern Italy), generated using two competing survey techniques: Remotely Piloted Aircraft System (RPAS) Structure from Motion (SfM) and Multi View Stereo (MVS) techniques and Terrestrial Laser Scanner (TLS). Point cloud compatibility was guaranteed through re-alignment and co-registration of data. The geometric features accuracy obtained by adopting the RPAS digital photogrammetric and TLS models was consequently analyzed and presented. Lastly, a discussion on convergences and divergences of these results is also provided.

Enhancing wall-to-wall forest structure mapping through detailed co-registration of airborne and terrestrial laser scanning data in Mediterranean forests

This paper presents a new co-registration procedure of complementary point clouds captured by both Terrestrial (TLS) and Airborne Laser Scanning (ALS) technologies. Starting from the geographic position of the TLS point cloud, a geometric features recognition algorithm, which evaluates digital terrain models obtained from both ALS and TLS, was developed and implemented in a new GIS software (ForeSight®). As a case study, we tested this new approach using point clouds acquired from both hand-held mobile TLS and ALS sensors over 24 test sites located in a protected area in southern Italy, with the ultimate goal of characterizing the different forest stand structures. From each aligned point cloud, a plot-level spatially explicit index (Enhanced Structural Spatial Index, ESCI) was derived to assess the three-dimensional structure of the considered forest stands. Then, we compared structural features derived from the ESCI index with different computed ALS metrics. Finally, the most correlated ALS metrics were used as predictors to produce an ESCI-map of the entire region of interest.

Feature matching for multi-epoch historical aerial images

Historical imagery is characterized by high spatial resolution and stereoscopic acquisitions, providing a valuable resource for recovering 3D land-cover information. Accurate geo-referencing of diachronic historical images by means of self-calibration remains a bottleneck because of the difficulty to find sufficient amount of feature correspondences under evolving landscapes. In this research, we present a fully automatic approach to detecting feature correspondences between historical images taken at different times (i.e., inter-epoch), without auxiliary data required. Based on relative orientations computed within the same epoch (i.e., intra-epoch), we obtain DSMs (Digital Surface Model) and incorporate them in a rough-to-precise matching. The method consists of: (1) an inter-epoch DSMs matching to roughly co-register the orientations and DSMs (i.e, the 3D Helmert transformation), followed by (2) a precise inter-epoch feature matching using the original RGB images. The innate ambiguity of the latter is largely alleviated by narrowing down the search space using the co-registered data. With the inter-epoch feature correspondences, we refine the image orientations and quantitatively evaluate the results (1) with DoD (Difference of DSMs), (2) with ground check points, and (3) by quantifying ground displacement due to an earthquake. We demonstrate that our method: (1) can automatically georeference diachronic historical images; (2) can effectively mitigate systematic errors induced by poorly estimated camera parameters; (3) is robust to drastic scene changes. Compared to the state-of-the-art, our method improves the image georeferencing accuracy by a factor of 2. The proposed methods are implemented in MicMac, a free, open-source photogrammetric software.

Accuracy of electroanatomical mapping-guided cardiac radiotherapy for ventricular tachycardia: pitfalls and solutions.

To analyse and optimize the interobserver agreement for gross target volume (GTV) delineation on cardiac computed tomography (CCT) based on electroanatomical mapping (EAM) data acquired to guide radiotherapy for ventricular tachycardia (VT). Electroanatomical mapping data were exported and merged with the segmented CCT using manual registration by two observers. A GTV was created by both observers for predefined left ventricular (LV) areas based on preselected endocardial EAM points indicating a two-dimensional (2D) surface area of interest. The influence of (interobserver) registration accuracy and availability of EAM data on the final GTV and 2D surface location within each LV area was evaluated. The median distance between the CCT and EAM after registration was 2.7 mm, 95th percentile 6.2 mm for observer #1 and 3.0 mm, 95th percentile 7.6 mm for observer #2 (P = 0.9). Created GTVs were significantly different (8 vs. 19 mL) with lowest GTV overlap (35%) for lateral wall target areas. Similarly, the highest shift between 2D surfaces was observed for the septal LV (6.4 mm). The optimal surface registration accuracy (2.6 mm) and interobserver agreement (Δ interobserver EAM surface registration 1.3 mm) was achieved if at least three cardiac chambers were mapped, including high-quality endocardial LV EAM. Detailed EAM of at least three chambers allows for accurate co-registration of EAM data with CCT and high interobserver agreement to guide radiotherapy of VT. However, the substrate location should be taken in consideration when creating a treatment volume margin.

Automatic Segmentation of the Olfactory Bulb.

The olfactory bulb (OB) has an essential role in the human olfactory pathway. A change in olfactory function is associated with a change of OB volume. It has been shown to predict the prognosis of olfactory loss and its volume is a biomarker for various neurodegenerative diseases, such as Alzheimer’s disease. Thus far, obtaining an OB volume for research purposes has been performed by manual segmentation alone; a very time-consuming and highly rater-biased process. As such, this process dramatically reduces the ability to produce fair and reliable comparisons between studies, as well as the processing of large datasets. Our study aims to solve this by proposing a novel methodological framework for the unbiased measurement of OB volume. In this paper, we present a fully automated tool that successfully performs such a task, accurately and quickly. In order to develop a stable and versatile algorithm and to train the neural network, we used four datasets consisting of whole-brain T1 and high-resolution T2 MRI scans, as well as the corresponding clinical information of the subject’s smelling ability. One dataset contained data of patients suffering from anosmia or hyposmia (N = 79), and the other three datasets contained data of healthy controls (N = 91). First, the manual segmentation labels of the OBs were created by two experienced raters, independently and blinded. The algorithm consisted of the following four different steps: (1) multimodal data co-registration of whole-brain T1 images and T2 images, (2) template-based localization of OBs, (3) bounding box construction, and lastly, (4) segmentation of the OB using a 3D-U-Net. The results from the automated segmentation algorithm were tested on previously unseen data, achieving a mean dice coefficient (DC) of 0.77 ± 0.05, which is remarkably convergent with the inter-rater DC of 0.79 ± 0.08 estimated for the same cohort. Additionally, the symmetric surface distance (ASSD) was 0.43 ± 0.10. Furthermore, the segmentations produced using our algorithm were manually rated by an independent blinded rater and have reached an equivalent rating score of 5.95 ± 0.87 compared to a rating score of 6.23 ± 0.87 for the first rater’s segmentation and 5.92 ± 0.81 for the second rater’s manual segmentation. Taken together, these results support the success of our tool in producing automatic fast (3–5 min per subject) and reliable segmentations of the OB, with virtually matching accuracy with the current gold standard technique for OB segmentation. In conclusion, we present a newly developed ready-to-use tool that can perform the segmentation of OBs based on multimodal data consisting of T1 whole-brain images and T2 coronal high-resolution images. The accuracy of the segmentations predicted by the algorithm matches the manual segmentations made by two well-experienced raters. This method holds potential for immediate implementation in clinical practice. Furthermore, its ability to perform quick and accurate processing of large datasets may provide a valuable contribution to advancing our knowledge of the olfactory system, in health and disease. Specifically, our framework may integrate the use of olfactory bulb volume (OBV) measurements for the diagnosis and treatment of olfactory loss and improve the prognosis and treatment options of olfactory dysfunctions.

AUTOMATIC INTEGRATION OF LASER SCANNING AND PHOTOGRAMMETRIC POINT CLOUDS: FROM ACQUISITION TO CO-REGISTRATION

Abstract. Laser scanning systems have been developed to capture very high-resolution 3D point clouds and consequently acquire the object geometry. This object measuring technique has a high capacity for being utilized in a wide variety of applications such as indoor and outdoor modelling. The Terrestrial Laser Scanning (TLS) is used as an important data capturing measurement system to provide high quality point cloud from industrial or built-up environments. However, the static nature of the TLS and complexity of the industrial sites necessitate employing a complementary data capturing system e.g. cameras to fill the gaps in the TLS point cloud caused by occlusions which is very common in complex industrial areas. Moreover, employing images provide better radiometric and edge information. This motivated a joint project to develop a system for automatic and robust co-registration of TLS data and images directly, especially for complex objects. In this paper, the proposed methods for various components of this project including gap detection from point cloud, calculation of initial image capturing configuration, user interface and support system for the image capturing procedures, and co-registration between TLS point cloud and photogrammetric point cloud are presented. The primarily results on a complex industrial environment are promising.

MULTI-TEMPORAL IMAGE CO-REGISTRATION OF UAV BLOCKS: A COMPARISON OF DIFFERENT APPROACHES

Abstract. Traditionally, data co-registration of survey epochs in photogrammetry relied on Ground Control Points (GCP) to keep the reference system unchanged. In the last years, Unmanned Aerial Systems (UAV) are increasingly used in photogrammetric environmental monitoring. The diffusion of affordable UAV platforms equipped with GNSS (Global Navigation Satellite System) centimetre-grade receivers might reduce, but not eliminate, the need for GCP. Conversely, if GNSS-assisted orientation cannot be used or if additional ground control and reliability checks are required, alternatives to repeated GCP survey have been proposed, taking advantage of Structure from Motion (SfM) photogrammetry. In particular, co-registering different epochs image blocks together, identifying corresponding features, has been demonstrated as a viable and efficient approach. In this paper four different strategies easily implementable in a generic commercial photogrammetric software are presented and compared considering three different test sites in Italy subject to different amounts of environmental changes. The influence of the amount and distribution of inter-epoch corresponding points on the accuracy of the reconstruction is investigated. The results show that some of the tested strategies obtains very good results and can be used (although not needed) also in RTK centimetre-grade UAV surveys, leveraging the additional information coming from previous epochs survey to actually increase the survey accuracy and reliability.

Accuracy of electroanatomical mapping guided cardiac radiotherapy for ventricular arrhytmias: pitfalls and solutions

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This work was partially supported by the grant AZV NU20-02-00244 from the Ministry of Health of the Czech Republic OnBehalf WECAM Background One of the main limitations of Radiofrequency catheter ablation (RFCA) is the difficulty in targeting specific VT substrate locations when they are deep intramural, covered by epicardial fat or in the proximity of a coronary artery. Stereotactic body radiotherapy (SBRT) has emerged as an alternative treatment for VT after RFCA failure. However, the accuracy of substrate volume delineation, the gross target volume (GTV), based on electroanatomical mapping (EAM) and interobserver agreement are still unknown. Aim To analyze and optimize the interobserver agreement for GTV delineation on cardiac CT (CCT) based on EAM data acquired to guide radiotherapy for ventricular arrhythmias. Method Available EAM data was exported and merged with the segmented CCT using manual registration by two observers. A GTV was created by the observers for predefined left ventricular (LV) areas based on preselected endocardial EAM points, indicating the 2D-surface area of interest. The interobserver (non)overlapping GTV volumes were analyzed. The influence of (interobserver) registration accuracy and availability of EAM data on the final GTV and 2D-surface location within each LV area was evaluated. Results The median distance between the CCT and EAM after registration was 2.7 mm, 95%th percentile 6.2 mm for observer #1 and 3.0 mm, 95%th percentile 7.6 mm for observer #2 (p = 0.9). Created GTVs were significantly different (8 vs 19 ml) with lowest GTV overlap (35%) for lateral wall target areas. Similar, the highest shift between 2D-surfaces was observed for the lateral LV (6.4mm (3.0-9.9)). The same trend is seen in the (non) overlapping volume for lateral area (P = 0.004). The optimal surface registration accuracy (2.6 mm) and interobserver agreement (Δ interobserver EAM surface registration accuracy 1.32 mm) on final GTVs could be achieved if at least three cardiac chambers were mapped, including high-density LV EAM. Conclusion Detailed EAM of at least three chambers allows for accurate co-registration of EAM data with CCT and high interobserver agreement on GTVs to guide radiotherapy of ventricular arrythmias. However, the substrate location should be taken in consideration when creating a treatment volume margin. Gross target volume (GTV) and InterobserGTV volume Observer #1 (cc)GTV volume Observer #2 (cc)Surface shift (mm)Basal anterior, (IQR)7.6 (6.2-11.5)9.3 (5.3-10)3.8 (2.2-4.5)Lateral, (IQR)8.0 (6.9-12.7)19.1 (15.7-21.4)6.2 (5.3-6.4)Septal, (IQR)10.1 (6.2-12.8)13.9 (7.7-14.6)6.4 (3.0-9.9)Abstract Figure. Graphical abstract

Fast Automated Stereo-EEG Electrode Contact Identification and Labeling Ensemble

Introduction: Stereotactic electroencephalography (SEEG) has emerged as the preferred modality for intracranial monitoring in drug-resistant epilepsy (DRE) patients being evaluated for neurosurgery. After implantation of SEEG electrodes, it is important to determine the neuroanatomic locations of electrode contacts (ECs), to localize ictal onset and propagation, and integrate functional information to facilitate surgical decisions. Although there are tools for coregistration of preoperative MRI and postoperative CT scans, identification, sorting, and labeling of SEEG ECs is often performed manually, which is resource intensive. We report development and validation of a software named Fast Automated SEEG Electrode Contact Identification and Labeling Ensemble (FASCILE). Methods: FASCILE is written in Python 3.8.3 and employs a novel automated method for identifying ECs, assigning them to respected SEEG electrodes, and labeling. We compared FASCILE with our clinical process of identifying, sorting, and labeling ECs, by computing localization error in anteroposterior, superoinferior, and lateral dimensions. We also measured mean Euclidean distances between ECs identified by FASCILE and the clinical method. We compared time taken for EC identification, sorting, and labeling for the software developer using FASCILE, a first-time clinical user using FASCILE, and the conventional clinical process. Results: Validation in 35 consecutive DRE patients showed a mean overall localization error of 0.73 ± 0.15 mm. FASCILE required 10.7 ± 5.5 min/patient for identifying, sorting, and labeling ECs by a first-time clinical user, compared to 3.3 ± 0.7 h/patient required for the conventional clinical process. Conclusion: Given the accuracy, speed, and ease of use, we expect FASCILE to be used frequently for SEEG-driven epilepsy surgery. It is freely available for noncommercial use. FASCILE is specifically designed to expedite localization of ECs, assigning them to respective SEEG electrodes (sorting), and labeling them and not for coregistration of CT and MRI data as there are commercial software available for this purpose.