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Introduction and objectives: patients with long, sequential and diffuse coronary lesions who undergo a percutaneous coronary intervention remain at a high risk of suffering cardiovascular events despite the improved safety and efficacy of the new drug-eluting stents. The objective of this study was to analyze the utility of SyncVision/iFR-guided revascularization (SyncVision version 4.1.0.5, Philips Volcano, Belgium) in this type of lesions. Methods: Randomized, multicenter, controlled, and open-label trial designed to compare SyncVision/iFR-guided and angiography-guided revascularizations in patients with long, sequential or diffuse significant angiographic coronary stenosis (ClinicalTrials.gov identifier: NCT04283734). A total of 100 patients will be randomized (1:1, no stratification). The primary endpoint is the average length of the stent implanted. The secondary endpoint is a composite of cardiac death, myocardial infarction, definitive or probable stent thrombosis, new target lesion revascularization or new target lesion failure; and the presence of residual ischemia as seen on single-photon emission computed tomography at the 6-month follow-up. Patients will be followed for 12 months after the procedure. Results: The trial is currently in the recruitment phase, and it has already recruited the first 7 patients. We expect to complete the recruitment phase by February 2021 and the follow-up by February 2022. Conclusions: The iLARDI study is the first randomized trial to assess the potential utility of SyncVision-guided revascularization in long, sequential and diffuse coronary lesions.

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.

Background and purpose Deep brain stimulation of the thalamus is an effective treatment for multiple neurological disorders. The centromedian and parafascicular nuclei are recently emerging targets for multiple conditions, such as epilepsy and Tourette syndrome; however, their limited visibility on conventional magnetic resonance imaging sequences has been a major obstacle. The goal of this study was to demonstrate the feasibility of a high-resolution and high-contrast targeting sequence for centromedian-parafascicular deep brain stimulation using a recently described magnetic resonance imaging sequence, three-dimensional edge-enhancing gradient echo. Methods The three-dimensional edge-enhancing gradient echo sequence was performed on a normal volunteer for a total of six acquisitions. Multi-image co-registration and averaging was performed by first co-registering each of the six scans and then averaging to produce an edge-enhancing gradient echo-multi-image co-registration and averaging scan. The averaging was also performed for two, three, four and five scans to assess the change in the signal-to-noise ratio and identify the ideal balance of image quality and scan time. Results The edge-enhancing gradient echo-multi-image co-registration and averaging scan allowed clear boundary delineation of the centromedian and parafascicular nuclei. The signal-to-noise ratio increased as a function of increasing scan number, but the added gain was small beyond four scans for the imaging parameters used in this study. Conclusions The recently described three-dimensional edge-enhancing gradient echo sequence provides an easily implementable approach, using widely available magnetic resonance imaging technology without complex post-processing techniques, to delineate centromedian and parafascicular nuclei for deep brain stimulation targeting.

Magnetic resonance imaging (MRI) is a widely used non-invasive methodology for both preclinical and clinical studies. However, MRI lacks molecular specificity. Molecular contrast agents for MRI would be highly beneficial for detecting specific pathological lesions and quantitatively evaluating therapeutic efficacy in vivo. In this study, an optimized Magnetization Prepared—RApid Gradient Echo (MP-RAGE) with 2 inversion times called MP2RAGE combined with advanced image co-registration is presented as an effective non-invasive methodology to quantitatively detect T1 MR contrast agents. The optimized MP2RAGE produced high quality in vivo mouse brain T1 (or R1 = 1/T1) map with high spatial resolution, 160 × 160 × 160 µm3 voxel at 9.4 T. Test–retest signal to noise was > 20 for most voxels. Extremely small iron oxide nanoparticles (ESIONPs) having 3 nm core size and 11 nm hydrodynamic radius after polyethylene glycol (PEG) coating were intracranially injected into mouse brain and detected as a proof-of-concept. Two independent MP2RAGE MR scans were performed pre- and post-injection of ESIONPs followed by advanced image co-registration. The comparison of two T1 (or R1) maps after image co-registration provided precise and quantitative assessment of the effects of the injected ESIONPs at each voxel. The proposed MR protocol has potential for future use in the detection of T1 molecular contrast agents.

Introduction: During deep brain stimulation (DBS) surgery, computed tomography (CT) and magnetic resonance imaging (MRI) scans need to be co-registered or fused. Image fusion is associated with the error that can distort the location of anatomical structures. Co-registration in DBS surgery is usually performed automatically by proprietary software; the amount of error during this process is not well understood. Here, our goal is to quantify the error during automated image co-registration with FrameLink™, a commonly used software for DBS planning and clinical research. Methods: This is a single-center retrospective study at a quaternary care referral center, comparing CT and MR imaging co-registration for a consecutive series of patients over a 12-month period. We collected CT images and MRI scans for 22 patients with Parkinson’s disease requiring placement of DBS. Anatomical landmarks were located on CT images and MRI scans using a novel image analysis algorithm that included a method for capturing the potential error inherent in the image standardization step of the analysis. The distance between the anatomical landmarks was measured, and the error was found by averaging the distances across all patients. Results: The average error during co-registration was 1.25 mm. This error was significantly larger than the error resulting from image standardization (0.19 mm) and was worse in the anterior-posterior direction. Conclusions: The image fusion errors found in this analysis were nontrivial. Although the estimated error may be inflated, it is sig­nificant enough that users must be aware of this potential inaccuracy, and developers of proprietary software should provide details about the magnitude and direction of co-registration errors.

The estimation of forest attributes at the local scale as well as in wall-to-wall approaches benefits from the integration of remote sensing data such as airborne laser scanning (ALS). A poor level...

Background Intravascular ultrasound (IVUS) provides endoluminal views and cross-sectional images of carotid arteries but lacks overview of vascular territory provided by angiography. Co-registration of IVUS with angiographic images may provide the potential to navigate both imaging modalities in a synchronous manner. The objective of this study is to evaluate the feasibility and accuracy of co-registering both imaging modalities in the carotid vasculature of the neck. Methods Fourteen patients with 15 cervical carotid artery lesions underwent angiography and subsequent treatment. In each case, an IVUS catheter was advanced to the target lesion and a reference angiography sequence was acquired. This was followed by an electrocardiography-triggered fluoroscopy sequence that was initiated upon IVUS catheter pullback. IVUS data collected during pullback were registered with fluoroscopy and evaluated for error and clinical usability. Results A total of 32 landmarks were identified that demonstrated reasonable agreement during IVUS–angiography co-registration. There was a mean registration error distance of 3.36 mm (SD 2.82 mm) between targets. The longitudinal extent and severity of the disease through the target segment could be easily evaluated after co-registration. Conclusion Semiautomatic tracking and co-registration of angiography and IVUS is a new technology and has the potential to increase the use of IVUS in carotid disease and to proivde the opportunity to optimize procedural outcomes.

Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.

Stereotactic radiosurgery (SRS) or radiotherapy (SRT) is commonly used to treat brain metastasis (BM). While effective in achieving tumor control, a significant number of BM patients exhibit lesion growth on follow-up MR imaging after SRS which cannot reliably distinguish between tumor growth, radiation necrosis (RN) or a mixture of both. SRS retreatment is a therapeutic option for tumor regrowth but contraindicated for RN. Here, we describe an instructive case of MRI progression of a breast BM previously treated by SRS where [18F] fludeoxyglucose-positron emission tomography (FDG-PET) proved useful to anatomically delineate metabolically active tumor from RN for re-treatment planning. Post-treatment FDG-PET and MRI studies indicated decreased uptake and enhancement respectively following treatment. This case study underscores the biological heterogeneity underlying MRI based BM progression after prior SRS and the potential utility of FDG-PET to guide treatment planning for repeat SRS.

Politeknik Caltex Riau (PCR) adalah sebuah perguruan tinggi di Pekanbaru, Riau. PCR memiliki proses bisnis, salah satunya adalah Carry Over (CO) atau semester pendek. Proses bisnis CO saat ini tidak sepenuhnya dilakuka dengan optimal, seperti penjadwalan, pendaftaran CO untuk siswa, dan distribusi honor untuk dosen dan atau laboran. Ini menunjukkan bahwa proses bisnis saat ini belum efisien dan dapat mempengaruhi proses bisnis organisasi secara keseluruhan. Olehkarena itu, proses bisnis CO saat ini perlu ditingkatkan. Tujuannya adalah untuk meningkatkan proses untuk layanan CO yang lebih baik bagi mahasiswa. Metode yang digunakan untuk perbaikan adalah Business Process Improvement (BPI), dengan mengimplementasikan salah satu alatnya yang disebut streamlining. Jenis streamlining yang diterapkan dalam peningkatan proses bisnis CO antara lain upgrading, standardization, bureaucracy elimination, dan process cycle-time reduction. Setelah perbaikan proses, ada proses layanan CO yang lebih ringkas dengan total 17 jumlah prosesdari 19 jumlah prosessebelumnya. Peningkatan proses CO juga didukung oleh pemanfaatan Sistem Informasi Akademik PCR.

Intravascular imaging using both intravascular ultrasound (IVUS) and optical coherence tomography (OCT) have become important tools in the Interventional Cardiologists armamentarium. However, in some centers, intravascular imaging is not widely employed. A number of reasons for this may exist, including lack of training in the use of intravascular imaging. Co-registration with angiography may be a helpful tool for those beginning to use both IVUS and OCT in the cath lab, and may reduce the learning curve associated with its use. For experienced operators, co-registration can shorten procedure time and lessen contrast use which may be particularly important when performing complex or multivessel PCI. As a research tool, co-registration can allow for accurate comparison of interval intravascular images. In this review, we will discuss how to acquire co-registered images using both IVUS and OCT systems, and the potential advantages of this technology over non-co-registered images, and indeed angiography alone.

In this work, we discussed how to directly combine thermal infrared image (TIR) and the point cloud without additional assistance from GCPs or 3D models. Specifically, we propose a point-based co-registration process for combining the TIR image and the point cloud for the buildings. The keypoints are extracted from images and point clouds via primitive segmentation and corner detection, then pairs of corresponding points are identified manually. After that, the estimated camera pose can be computed with EPnP algorithm. Finally, the point cloud with thermal information provided by IR images can be generated as a result, which is helpful in the tasks such as energy inspection, leakage detection, and abnormal condition monitoring. This paper provides us more insight about the probability and ideas about the combining TIR image and point cloud.

Introduction Intravascular ultrasound (IVUS) is a useful adjunct to guide percutaneous coronary intervention (PCI). Correlating IVUS images with angiographic findings can be challenging. We evaluated the utility of a novel co-registration system for IVUS and coronary angiography. Methods and results A 3-D virtual catheter trajectory was constructed from separate angiographic imaging runs using bespoke software. Intravascular ultrasound images were obtained using a commercially available mechanical rotational transducer with motorized pullback. Co-registration of ultrasound and angiographic images was then performed retrospectively based on the length of pullback, the 3-D trajectory and the start position of the catheter. Validation was performed in a spherical phantom model and in vivo in the coronary circulation of patients undergoing coronary angiography and intravascular imaging for clinical purposes. 111 paired angiographic and IVUS runs were performed in 3 phantom models. The differences between the reference length and the length measured on the 3D reconstructed path was −0.01 ± 0.40 mm. Intra-observer variability was 0.4%. We enrolled 25 patients in 3 European hospitals and performed 35 co-registration attempts with an 86% success rate. 71 landmarks were selected by the first operator, 68 by the second. Differences between angiographic and IVUS landmarks were −0.22 ± 0.72 mm and 0.05 ± 1.01 mm, respectively. Inter-observer variability was 0.23 ± 0.63 mm. Conclusion We present a novel method for the co-registration of IVUS and coronary angiographic images. This system performed well in a phantom model and using images obtained from the human coronary circulation. Classifications Innovation, intravascular ultrasound, other technique

Mass spectrometry imaging (MSI) is an analytical technique for the unlabeled and multiplex imaging of molecules in biological tissue sections. It therefore enables the spatial and molecular annotations of tissues complementary to histology. It has already been shown that MSI can guide subsequent material isolation technologies such as laser microdissection (LMD) to enable a more in-depth molecular characterization of MSI-highlighted tissue regions. However, with MSI now reaching spatial resolutions at the single-cell scale, there is a need for a precise co-registration between MSI and the LMD. As proof-of-principle, MSI of lipids was performed on a breast cancer tissue followed by a segmentation of the data to detect molecularly distinct segments within its tumor areas. After image processing of the segmentation results, the coordinates of the MSI-detected segments were passed to the LMD system by three co-registration steps. The errors of each co-registration step were quantified and the total error was found to be less than 13 μm. With this link established, MSI data can now accurately guide LMD to excise MSI-defined regions of interest for subsequent extract-based analyses. In our example, the excised tissue material was then subjected to ultrasensitive microproteomics in order to determine predominant molecular mechanisms in each of the MSI-highlighted intratumor segments. This work shows how the strengths of MSI, histology, and extract-based omics can be combined to enable a more comprehensive molecular characterization of in situ biological processes. Electronic supplementary material The online version of this article (10.1007/s00216-019-01983-z) contains supplementary material, which is available to authorized users.

As lipid composition of atherosclerotic plaques is considered to be one of the primary indicators for plaque vulnerability, a diagnostic modality that can sensitively evaluate their necrotic core is highly desirable in atherosclerosis imaging. In this regard, intravascular photoacoustic (IVPA) imaging is an emerging plaque detection modality that provides lipid-specific chemical information of arterial walls. Within the near-infrared window, a 1210-nm optical source is usually chosen for IVPA applications because lipid exhibits a strong absorption peak at that wavelength. However, other arterial tissues also show some degree of absorption near 1210 nm and generate undesirable interfering PA signals. In this study, a novel wavelength-modulated Intravascular Differential Photoacoustic Radar (IV-DPAR) modality was introduced as an interference-free detection technique for a more accurate and reliable diagnosis of plaque progression. By using two low-power continuous-wave laser diodes in a differential manner, IV-DPAR could efficiently suppress undesirable absorptions and system noise, while dramatically improving system sensitivity and specificity to cholesterol, the primary ingredient of plaque necrotic core. When co-registered with intravascular ultrasound imaging, IV-DPAR could sensitively locate and characterize the lipid contents of plaques in human atherosclerotic arteries, regardless of their size and depth.

Passive acoustic mapping (PAM) techniques offer a simple means of spatio-temporal cavitation monitoring during therapeutic ultrasound procedures. Implementation with a conventional diagnostic ultrasound system allows natural integration of PAM with B-mode imaging. However, the refracting properties of diagnostic array lenses may introduce PAM image registration errors that could lead to inaccuracies in treatment monitoring and guidance. To address these concerns, this paper presents lens characterization of two different array designs, analytical estimation of lens-induced source mapping errors in simple media, and experimental demonstration and correction of lens effects, reducing the depth-averaged image co-registration errors to no more than 0.52 mm.

Repeat station imaging (RSI) is a method for specialized image collection and co-registration that facilitates rapid change detection with aerial imagery for time-critical analyses. Our previously ...

Multimodal imaging systems offer the opportunity to scrutinize different properties of biological samples. Optical coherence microscopy (OCM) is a non-invasive and high-resolution imaging technique capable of generating threedimensional images of tissue. In this work, a multimodal imaging system interleaving OCM with a dual-channel fluorescence microscopy (DC-FM) system was developed to add functional imaging capabilities to OCM. The combined system was able to simultaneously acquire both reflectance and fluorescence data from the same location of the sample at the speed of 250 kHz, and with a lateral resolution of ~ 2.1 μm. An axial resolution of 2.4 μm in sample over the imaging depth of 1 mm was achieved with OCM. The performances of the combined system were evaluated by imaging a multi-layer tape as well as a gel containing green and red fluorescent microspheres. While OCM enabled the depth localization of all fluorescent microspheres, it was not able to discriminate between green and red fluorophores, a feature that was achieved with DC-FM. Hence, the interleaved system has the potential of assessing structural as well as cellular level functional changes in biological samples. This system will be applied toward longitudinal studies in small animal models.

Purpose: Multiplex staining allows molecular co-localization analysis of same cell or tissue sections and maximizes the amount of data acquired from individual samples. We developed a non-linear registration framework for automated alignment of whole mount multiplexed histology images of prostate cancer. To achieve a precise automatic fit of bleached and restained high resolution tissue samples, a patch-based approach is proposed to improve annotation speed and analysis. Methods: The three-step co-registration process begins with a coarse low-resolution registration of the IHC stained image to the fixed H&E-stained image. The initial registration is then refined separately for each high-resolution patch using a smaller search window. Finally, registered patches are stitched back together using speeded up robust features (SURF). We apply the method to five multiplex whole mount prostate histology slides. To determine its effectiveness, we compare the automatic registration to the initial coarse registration and a manual control point based-method varying the number of control points. Results: For the control point-based approach, 25, 50, and 100 manually placed set landmarks resulted in a decrease of - 76.20%, -75.9% and -75.48% of the root mean squared error (RMSE), respectively. Compared to the initial registration an improvement of -76.29% of RMSE can be seen, illustrating the potential benefits of a patch-based automatic approach. Conclusion: The proposed method achieved excellent registration of the IHC to the input image. The automated method for registration of multiplexed histology images achieves high accuracy in shorter time and with greater reproducibility than conventional registration approaches and semi-automatic control points without the need for time consuming and subjective manual control-point setting. The accuracy of the fit especially improves in complex areas close to tissue tears and folds.

Transcranial magnetic stimulation (TMS) excites neurons in the cortex, and neural activity can be simultaneously recorded using electroencephalography (EEG). However, TMS-evoked EEG potentials (TEPs) do not only reflect transcranial neural stimulation as they can be contaminated by artifacts. Over the last two decades, significant developments in EEG amplifiers, TMS-compatible technology, customized hardware and open source software have enabled researchers to develop approaches which can substantially reduce TMS-induced artifacts. In TMS-EEG experiments, various physiological and external occurrences have been identified and attempts have been made to minimize or remove them using online techniques. Despite these advances, technological issues and methodological constraints prevent straightforward recordings of early TEPs components. To the best of our knowledge, there is no review on both TMS-EEG artifacts and EEG technologies in the literature to-date. Our survey aims to provide an overview of research studies in this field over the last 40 years. We review TMS-EEG artifacts, their sources and their waveforms and present the state-of-the-art in EEG technologies and front-end characteristics. We also propose a synchronization toolbox for TMS-EEG laboratories. We then review subject preparation frameworks and online artifacts reduction maneuvers for improving data acquisition and conclude by outlining open challenges and future research directions in the field.

In this work, a novel approach for the correction of misregistration errors in sequences of Terrain Observation with Progressive Scan (TOPS) Sentinel-1 SAR data is presented. The method represents a further evolution of the Enhanced Spectral Diversity (ESD) approaches. Remarkably, the developed algorithm is almost insensitive to the presence of large azimuth ground displacements due, for instance, to massive earthquakes, volcanic eruptions or glacier movements. Indeed, in such non-stationary contexts, the conventional ESD and network ESD approaches for the SAR TOPS data co-registration reveals problematic being co-registration errors and azimuth ground deformation components mixed out. Preliminary experiments conducted on a set of TOP SAR data related to the area hit by the Ridgecrest earthquake MW 7.1, California, on July 04 2019 confirm the validity of the theoretical framework.

Optical images of the Earth at very high spatial resolutions (VHR, typically < 5 m) are seeing rapid growth in volumes over the past 5 years, due in part to the fast-expanding constellations of CubeSats. Special preprocessing of these VHR images is required to ensure their geometric and radiometric consistency for quantitative analyses for a wide range of Earth and environmental sciences and applications. Here we describe a hierarchical normalization framework (HiNF) to achieve and evaluate geometric and radiometric normalization of these VHR images towards producing analysis ready data (ARD) of optical CubeSat images. We demonstrated HiNF at a spatially heterogeneous and temporally dynamic wetland site in northeastern Germany by generating a stack of temporally consistent ~ biweekly 5-m images over 8 years (2013–2020) at visible and near infrared bands (VNIR). The HiNF combined images from rigorously calibrated multispectral sensors onboard large satellites (Landsat-7/8 and Sentinel-2) and less well calibrated sensors onboard RapidEye (SmallSats) and PlanetScope (CubeSats). A two-stage radiometric normalization procedure produced two levels of image normalization and resulted in more normalized images that passed the quality control in time series compared to common one-stage procedures. The outcome of this novel procedure allows for downstream applications to balance between the quality and the quantity of available normalized CubeSat images in a time series. The HiNF provides a new approach to quantitative evaluations of radiometric normalizations using daily MODIS imagery as bridging benchmark data. The quantitative evaluations showed the HiNF resulted in greater normalization efficacy in the visible bands than in the NIR over the predominantly wetland area. The two normalization levels yielded statistically similar efficacy for the NIR band and the widely-used normalized difference vegetation index according to the Chow test (at significance level of 0.05) but less so for the visible bands. The HiNF facilitates generating ARD of optical CubeSat images and assuring their qualities through its demonstrated efficacy and its quantitative evaluation approach. Such ARD-quality time series of VHR images from CubeSats allow for improved analyses and quantitative applications of this new stream of multispectral images at spatial scales that are better related to ground measurements and environmental management in terrestrial ecosystems.

Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Spanish Ministry of Science, Innovation and Universities Instituto de Salud Carlos III Background. Dilation of the aortic root is a key feature of Marfan syndrome and it is related to the occurrence of aortic events and death. On top of maximum diameter, rapid annual growth rate is suggested by guidelines for indication of aortic root replacement. Current gold-standard for aortic root diameter assessment is manual quantification on multiplanar reformatted 3D computed tomography (CT) or magnetic resonance angiogram. However, inter- and intra-observer reproducibility are limited and different measurement methods, i.e. cusp-to-cusp and cusp-to-commissure, may be used in different clinical centres, leading to difficulties in the clinical assessment of progressive dilation. Purpose. We aimed to test whether aortic root growth rate during follow-up can be reliably quantified by semi-automatic co-registration of two CT angiograms. Methods. Seven Marfan syndrome patients, free from previous aortic surgery, with a total of 11 pairs of CT were identified. Manual assessment of six aortic root diameters (right-non coronary -RN- , right-left -RL- and left-non coronary -LN- cusp-to-cusp and R, L and N cusp-to-commissure) was obtained from all CTs by an experienced researcher blind to semi-automatic results. The thoracic aorta and the outflow tract were semi-automatically segmented in the baseline CT and commissure and cusps were manually located. A 10 mm-thick region of interest containing the aortic wall was automatically generated from segmentation boundary. Co-registration was obtained with three, fully-automatic steps. Firstly, baseline and follow-up CT scans were aligned by means of a rigid registration. Then, scans were co-registered with multi-resolution affine followed by b-spline non-rigid registrations based on mutual information metric. The transformation pertaining to the location of baseline commissure and cusps points was used to locate the same points in the follow-up scan (Fig. 1 top). Results. Follow-up duration was 35 ± 22 (range 12-70.3) months. Automatic quantification of diameter growth during the follow-up was obtained in 62 out of 66 (94%) diameter comparisons. High Pearson correlation coefficients (R) and ICC were found between manual and semi-automatic assessment of growth rate, both for cusp-to-cusp and cusp-to-commissure diameters: R = 0.727 and ICC = 0.678 for RN; R = 0.822 and ICC = 0.602 for RL; R = 0.648 and ICC = 0.668 for LN; R = 0.726 and ICC = 0.711 for R; R = 0.911 and ICC = 0.895 for L and R = 0.553 and ICC = 0.482 for N. Scatter and Bland-Altman plots for all growth rates (Fig. 1) confirmed very good correlation (R = 0.810) but a slight tendency (R=-0.270) for underestimation at high growth rate. No correlation was found between follow-up duration and difference between techniques (R = 0.06). Conclusions. Semi-automatic quantification of aortic root growth rate by co-registration of pairs of CT angiograms is feasible for follow-up as short as one year. Larger studies are needed to confirm these preliminary data. Abstract Figure. CT measurements. Automatic vs manual.

Imaging procedures are fundamental to disease diagnosis and prognosis, along with intervention evaluation and monitoring progression. Quantitative comparison and integration of information from different image sets require image coregistration, a process typically performed manually by clinicians. Automated and semi-automated procedures rely upon features that require extensive pre-processing or may not be visible and, as such, are not universally applicable across acquisitions and modalities, limiting applicability. We present a simple yet reliable fully-automated method that registers tomographic vascular images based only on lumen contours. The results both in multimodal and pre-/post-intervention datasets demonstrated the flexibility and robustness of our method. Excellent agreement was found between method results and manual longitudinal coregistration ($0.23\pm 0.20$ mm). Rotational co-registration accuracy was also high $(8.1^{\circ}\pm 8.0^{\circ})$, but required visible fiduciary landmarks; in the absence of such, accuracy decreased to $61.3^{\circ}\pm 66.4^{\circ}$. Sensitivity analyses indicate that, while care must be taken when overlapping segments are very short or lacking variability, lumen-based vascular image co-registration offers a robust, straightforward, and widely applicable approach.

In this paper, we propose a totally unsupervised procedure to cope with the residual local misalignment between a higher-resolution panchromatic (Pan) image and a series of lower-resolution multispectral (MS) bands, preliminarily interpolated to the pixel size of Pan. The proposed method exploits the different resolutions of the MS and Pan datasets to force the former to match a lowpass version of the latter. Specifically, the space-varying residue of the multivariate regression between resampled MS bands and lowpass-filtered Pan image, which locally measures the extent of MS-to-Pan misalignment, is injected into each the MS bands, after being weighted by the pixel-varying multiplicative injection gain of each band. Tests on a GeoEye-1 image, with space-varying shifts, highlight improvements in the spatial alignment.

Abstract We propose the co-registration of local laser profile segments to high resolution Digital Terrain Models (DTMs) as an approach for obtaining seasonal CO2 ice cover height variations on Mars. The co-registration is parameterized in instantaneous MOLA pointing angles involving a rigorous laser altimeter geolocation model. Thereby, the height bias of the MOLA footprint produced by the pointing bias could be compensated through an iterative process. The feasibility and advantages of this method are tested in an example region. The ultimate goal is to apply this method to Mars Orbiter Laser Altimeter (MOLA), SHAllow RADar (SHARAD) radar altimetry and high-resolution stereoscopic DTMs to generate long-term seasonal height change time series at the Martian poles with high spatial and temporal resolution. 1 Introduction The dynamic growth and retreat of polar CO2 frost at the Martian poles has always been one of the focuses of planetary scientists [1, 2, 3]. Accurate measurements of seasonal and long-term elevation and volume changes can serve as important constraints in Mars climate models, help tap into the density evolution of the CO2 snow once combined with gravity measurements, and constrain the degree of stability of the polar deposits. The commonly-used approach to this problem is the cross-over analysis of the laser altimetry profiles, but this method may suffer from significant interpolation errors when spacing between footprints is large, also residual pointing, timing and orbit error may translate into lateral shifts of the laser profiles and further undermine the results. Here, we propose and validate the local co-registration between laser profile segments and high resolution DTMs from stereo pairs as a solution to these limitations. 2 Data 2.1 MOLA records The MOLA Precision Experimental Data Record (PEDR) dataset features a total of 8505 profiles, acquired in the mapping and extended phases of Mars Global Surveyor (MGS) from February, 1999 to May, 2001, which spanned approximately a full Martian year [1]. The PEDR dataset was processed using MGS orbit trajectory model and a Mars rotational model by GSFC dating back to 2003. Therefore, we have incorporated a refined orbit model from [4] and IAU2015 Mars rotational model [5] in the MOLA geolocation reprocessing. Meanwhile, to account for the special relativity effect, the pointing aberration correction has also been taken care of in the reprocessing [6]. 2.2 HRSC DTMs The High Resolution Stereo Camera (HRSC) is a pushbroom camera onboard the European Space Agency (ESA) spacecraft Mars Express. A total of 34 HRSC DTM tiles are adopted which feature a grid size of 50 m and covers the majority of the Martian South Pole [7]. 3 Methods First, the reprocessed MOLA profiles are self-registered to each other to form a coherent reference in the Martian South Pole [8, 9]. Subsequently, individual HRSC DTM tiles have been aligned to the MOLA reference data and mosaiced to a self-consistent reference for the co-registration. The co-registration of the reprocessed MOLA profiles to the aligned HRSC DTM mosaic is setup by compensating for two alignment angles of the boresight and height which incorporates an analytical laser altimeter geolocation model. The benefit is that the height offset induced by bias in pointing can be simultaneously compensated during the iterative co-registration process. The height differences with respect to the DTM mosaic at either footprint, cross-overs, or pseudo cross-overs are assigned as the height corrections from the local co-registration process using segmented profiles centered at these feature points. Here, pseudo cross-overs are DTM-based and formed by two track segments that do not have to actually intersect, substantially increasing the available number of cross-overs [10]. Then, CO2 height change time series are obtained by median-binning those temporal height differences with the uncertainty quantified by scaled median absolute deviation. To get rid of a temporal systematic error, the acquired temporal trend is subtracted from the one at 60°S annulus which features unchanging topography. 4 Results Figure 1: Results using height differences at footprints before the adoption of the local co-registration procedure (“F_LC”, red line, bottom) and comparison to previous literature (lime and aqua lines). Figure 2: Results using height differences at footprints after the adoption of the local co-registration procedure (“F_LC”, red line, bottom) and comparison to previous literature (lime and aqua lines). A test has been carried out at an example region situated right on the residual ice cap where largest peak-to-peak height variation would be expected. The uncertainty of the derived height change time series using height differences at footprints decreased from ~2 m to ~0.5 m after the adoption of the local co-registration procedure, roughly a fourfold enhancement (Figures 1 and 2). The maximum height fluctuation is estimated at ~2.5 m which is similar to that of Aharonson et al. (2004) [2]. The suspicious off-season accumulation pointed out by previous results using MOLA has not been resolved. While a sharp pit-shaped feature of ~1 m depth centered at solar longitude 210° is observed. Similar results have been obtained using height differences at cross-overs and pseudo cross-overs. As a byproduct of the co-registration process, MOLA alignment angles have also been examined to follow some specific temporal patterns. 5 Summary We show the feasibility and merits of the local co-registration strategy in the application of retrieving height changes at either footprints, cross-overs, or pseudo cross-overs. As for the next step, we will put the SHARAD radar altimetry and reflectometry into test [11]. Combined with the MOLA laser altimetry, long-term CO2 height change time series spanning two decades could possibly be retrieved. Apart from height change mapping, the proposed method could also be readily adapted to the tidal Love number measuring, orbit refinement with so called “direct altimetry” [12] and others. Acknowledgements This work was supported by a research grant from Helmholtz Association and German Aerospace Center (DLR). We acknowledge the work by the MOLA and HRSC instrument and science teams. References [1] Smith et al.,Science, 2001, 294, 2141-2146. [2] Aharonson et al., JGRPlanets, 2004, 109(E5). [3] Genova, ActaAstronaut., 2020, 166, 317-329. [4] Konopliv et al., Icarus, 2006, 182, 23-50. [5] Konopliv et al., Icarus, 2016, 274, 253-260. [6] Xiao et al., Submitted to JoG. [7] Putri et al., PSS, 2019, 174, 43-55. [8] Stark et al., EPSC2018, Contrib. No. 890. [9] Stark et al., GRL, 2015, 429, 7881-7889. [10] Barker et al., Icarus, 2016, 273, 346-355. [11] Steinbrügge et al., LPSC2019, LPI Contrib. No. 2132. [12] Goossens et al., Icarus, 2020, 336, 113454.

OBJECTIVES Transcatheter mitral valve-in-valve (TMViV) implantation is an alternative treatment to surgery for high-risk patients with degenerated bioprosthetic mitral valves. Some types of bioprostheses are fluoroscopically translucent, resulting in an ‘invisible’ target deployment area. In this study, we describe the feasibility and outcomes of this procedure using intraoperative fusion of transoesophageal echocardiography (TEE) and live fluoroscopy to facilitate valve deployment in cases of invisible bioprosthetic valves. METHODS We reviewed all TMViV implantations at our centre from July 2014 to July 2019. Patient, procedure and outcome details were compared between those with a visible bioprosthesis (N = 22) to those with an invisible one (N = 12). Intra-operative TEE and live Fluoroscopy co-registration were used for real-time guidance for all invisible targets. RESULTS All valve implantations were completed successfully in both groups without cardiovascular injury, valve migration or left ventricular outflow-tract obstruction. Technical success was 100% in both groups. One-year survival was 83% [95% confidence interval (CI) 70–96] for the entire cohort, with 79% (95% CI 63–100) survival for the visible group and 92% (95% CI 77–100) for the invisible group. Probability of 1-year survival free from mitral valve reintervention, significant valve dysfunction, stroke or myocardial infraction was 78% (95% CI 63–93) for all patients whereby the probability was 72% (95% CI 54–97) in the visible group and 80% (95% CI 59–100) for the invisible group. CONCLUSIONS The use of intraoperative TEE and live fluoroscopy image fusion facilitates accurate TMViV among patients with a fluoroscopically invisible target-landing zone.

Registration or matching process aims to find the misalignment between two or more images concerning the same area to detect the values of the mapping matrix in order to transform interest points in one image to its correspondence in the others. This paper presents a dynamic approach aiming to improve the performance of the registration process for synthetic aperture radar (SAR) images. First, the noise resulting from the capturing process is reduced by using a smoothing filter based on kernel-gaussian to reduce the amplification of noise. Then; a combination of two area- based matching (ABM) methods is used. The first method is carried out using Crosscorrelation approach, acting as coarse registration step. The second method is achieved by using regular step gradient descent (RSGD) optimizer, acting as fine registration step. Evaluation of the performance concerning the proposed manner is achieved by comparing to the state-of-the art detectors as Harris, Shi-Tomasi, and Features from Accelerated Segment Test (FAST) detectors. Metric factors to achieve the comparison are mean square error (MSE) and peak signal-to-noise ratio (PSNR) between the input images. Results demonstrate a highly performance for the proposed method compared to the others where it has a high robustness and minimizes the noise of the input image.

Precision agriculture (PA) has greatly benefited from new technologies over the years. The use of multispectral and hyperspectral sensors coupled to Unmanned Aerial Vehicles (UAV) has enabled farmers to monitor crops, improve the use of resources and reduce costs. Despite being widely used, multispectral images present a natural misalignment among the various spectra due to the use of different sensors. The variation of the analyzed spectrum also leads to a loss of characteristics among the bands which hinders the feature detection process among them, which makes the alignment process complex. In this paper, is proposed a new framework for the band co-registration process based on the premise that natural misalignment is an attribute of the camera, so it does not change during the acquisition process. The results were compared with the ground-truth generated by a specialist and with other methods present in the literature. The proposed framework had an average back-projection (BP) error of 0.425 pixels, this result being 335% better than the evaluated frameworks.

Magnetoencephalography (MEG) is a neuroimaging technique that accurately captures the rapid (sub-millisecond) activity of neuronal populations. Interpretation of functional data from MEG relies upon registration to the participant’s anatomical MRI. The key remaining step is to transform the participant’s MRI into the MEG head coordinate space. Although both automated and manual approaches to co-registration are available, the relative accuracy of two approaches has not been systematically evaluated. The goal of the present study was to compare the accuracy of manual and automated co-registration. Resting MEG and T1-weighted MRI data were collected from 90 participants. Automated and manual co-registration were performed on the same subjects, and the inter-method reliability of the two methods assessed using the intra-class correlation. Median co-registration error for both methods was within acceptable limits. Inter-method reliability was in the “good” range for co-registration error, and the “good” to “excellent” range for translation and rotation. These results suggest that the output of the automated co-registration procedure is comparable to that achieved using manual co-registration.

This study analyzed how the co-registration accuracy varies according to the angles of sensors when satellite images are acquired. We used two-step co-registration; coarse co-registration was conducted by using multi-spectral images with relatively lower resolution, and precise fine co-registration was conducted in the region of interest by using panchromatic images. In this study the mutual information method was used because the search area for image matching is restricted by the initial coordinate data in the metadata file, and the mutual information method shows high matching performance in the small search area. We tested the method using data set of 120 combinations with 16 KOMPSAT-3‧3A images acquired in Daejeon, Korea. And we analyzed the effects on the image co-registration accuracy by image acquisition angle factors such as azimuth, incidence, and convergence angles. Experimental results showed that the convergence angle mostly affects on the co-registration accuracy among the angle factors, which shows the overall correlation coefficient with the registration accuracy as 0.59.

Multimodal mass spectrometry imaging (MSI) data presents unique big data challenges in handling and analysis. Here, we present a pipeline for co-registering matrix-assisted laser desorption/ionization MSI and confocal immunofluorescence imaging data for extracting single-cell metabolite signatures. We further describe methods and introduce software for the simultaneous analysis of these concatenated data sets, which are designed to establish a connection between cell traits of interest (shape metrics, position within sample) and the cells' own metabolic signatures.

Intravascular optical coherence tomography (IVOCT) provides high-resolution images of coronary calcifications and detailed measurements of acute stent deployment following stent implantation. Since pre- and post-stent IVOCT image “pull-back” acquisitions start from different locations, registration of corresponding pullbacks is needed for assessing treatment outcomes. In particular, we are interested in assessing finite element model (FEM) prediction of lumen gain following stenting, requiring registration. We used deep learning to segment calcifications in corresponding pre- and post-stent IVOCT pullbacks. We created 1D representations of calcium thickness as a function of the angle of the helical IVOCT scans. Registration of two scans was done by maximizing the cross correlation of these two 1D representations. Registration was accurate, as determined by visual comparisons of 2D image frames. We used our pre-stent calcification segmentations to create a lesion-specific FEM, which took into account balloon size, balloon pressure, and stent measurements. We then compared simulated lumen gain from FEM analysis to actual stent deployment results. Actual lumen gain across ~200 registered pre and post-stent images was 1.52 ± 0.51, while FEM prediction was 1.43 ± 0.41. Comparison between actual and FEM results showed no significant difference (p < 0.001), suggesting accurate prediction of FEM modeling. Registered image data showed good visual agreement regarding lumen gain and stent strut malapposition. Hence, we have developed a platform for evaluation of FEM prediction of lumen gain. This platform can be used to guide development of FEM prediction software, which could ultimately help physicians with stent treatment planning of calcified lesions.

A multi-modal system with dual-channel fluorescence microscopy and high-resolution optical coherence tomography was developed to enable the co-registration of structural and functional information. The system’s performance was demonstrated with microscopic beads sandwiched between multi-layer tapes.

High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important information on the risks often associated with these processes. The topographic surveying platforms, georeferencing systems, and processing tools have seen important developments in the last two decades, in particular Light Detection And Ranging (LiDAR) technology used in Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Moreover, HRT data, produced through these techniques, changed a lot in terms of point cloud density, accuracy and precision over time. Therefore, old "legacy" data sets and recent surveys can often show comparison problems, especially when multi-temporal data are not homogeneous in terms of quality and uncertainties. In this context, data co-registration should be used to guarantee the coherence among multi-temporal surveys, minimizing, on stable areas, the distance between corresponding points acquired at different epochs. Although several studies highlight that this process is fundamental to properly compare multi-temporal DTMs, it is often not addressed in LiDAR post-processing workflows. In this paper we focus on the alignment of multi-temporal surveys in a topographically complex and rugged environment as the Moscardo debris-flow catchment (Eastern Italian Alps), testing various co-registration methods to align multi-temporal ALS point clouds (i.e. years 2003, 2009 and 2013) and the derived DTMs. In particular, we tested the pairwise registration with manual correspondences, the Iterative Closest Point (ICP) algorithm and a mathematical model that allows aligning simultaneously a generic number of point clouds, the so-called Generalized Procrustes Analysis (GPA), also in its GPA-ICP variant. Then, to correct the possible small inaccuracies generated from the gridding interpolation process, a custom-developed DTM co-registration tool (GRD-CoReg) was used to align gridded data. Both alignment phases (i.e. at point cloud and DTM level) proved to be fundamental and allowed us to obtain proper and reliable DTMs of Difference (DoDs), useful to quantify the debris mobilized and to detect the spatial and temporal patterns of catchment-scale erosion and deposition. The consistency of DoDs data was verified through the comparison between the erosion estimate of DoDs and the volumes of debris-flow events measured by the monitoring station close to the Moscardo torrent catchment outlet. The GPA-ICP algorithm followed by the GRD-CoReg tool proved to be the most effective solution for improving DoDs results with a decrease of systematic trend due to vertical and horizontal uncertainties between surveys, especially at steep slopes. The net volume difference (i.e. the sediment output from the catchment) of the 2003-2013 period changed from 3,237,896 m to 135,902 m in DoDs obtained from not co-registered and co-registered DTMs. The volume of debris flows measured at the catchment outlet during the same time interval amounts to 169,660 m. The comparison with debris-flow volume measures at the monitoring station shows, therefore, that the DTMs obtained from the co-registration processes generate more reliable DoDs than those obtained from the raw DTMs (without the alignment).

1.AbstractThe registration or alignment of diffusion weighted images (DWI) with other imaging modalities is a critical step in neuroimaging analysis. Within-subject T1-DWI co-registration is particularly instrumental. DWI-derived scalar images are commonly used as intermediates for T1-DWI co-registration, and the resulting registration transforms are applied to all other scalar images for analysis. The ideal registration intermediate should register well to T1 and other multimodal images and be practically easy to obtain. It is however, currently unclear which DWI-derived scalar image serves as the best intermediate. We aim to determine the best, practical, intermediate for image co-registration. T1 and DWI images were acquired from 20 healthy subjects. DWIs were acquired with 60 directions. Six DWI-derived scalar images were compared including: 1) fractional anisotropy (FA); 2) generalized FA (GFA); 3) B0 images; 4) mean DWIs with the B0 image (MDWI); 5) anisotropic power (AP) images. AP showed the smallest variability in registration improvements across all the tested DWI derived scalar images, and show the highest average percent changes with CC registration cost function (CC=1.2%, MI=15%). In contrast, the FA and GFA transforms resulted in significantly poorer registration across DWI types. The AP image was the DWI-derived scalar image that provided the most consistent registration to all other images. Practically, it is generated easily and so could be implemented in basic and clinical research pipelines currently using other intermediates. Given these findings, it is recommended that AP images be used for T1–DWI co-registration, and that FA and GFA images in particular be avoided.

Background Ex-vivo cardiovascular magnetic resonance (CMR) imaging has played an important role in the validation of in-vivo CMR characterization of pathological processes. However, comparison between in-vivo and ex-vivo imaging remains challenging due to shape changes occurring between the two states, which may be non-uniform across the diseased heart. A novel two-step process to facilitate registration between ex-vivo and in-vivo CMR was developed and evaluated in a porcine model of chronic myocardial infarction (MI). Methods Seven weeks after ischemia-reperfusion MI, 12 swine underwent in-vivo CMR imaging with late gadolinium enhancement followed by ex-vivo CMR 1 week later. Five animals comprised the control group, in which ex-vivo imaging was undertaken without any support in the LV cavity, 7 animals comprised the experimental group, in which a two-step registration optimization process was undertaken. The first step involved a heart specific flexible 3D printed scaffold generated from in-vivo CMR, which was used to maintain left ventricular (LV) shape during ex-vivo imaging. In the second step, a non-rigid co-registration algorithm was applied to align in-vivo and ex-vivo data. Tissue dimension changes between in-vivo and ex-vivo imaging were compared between the experimental and control group. In the experimental group, tissue compartment volumes and thickness were compared between in-vivo and ex-vivo data before and after non-rigid registration. The effectiveness of the alignment was assessed quantitatively using the DICE similarity coefficient. Results LV cavity volume changed more in the control group (ratio of cavity volume between ex-vivo and in-vivo imaging in control and experimental group 0.14 vs 0.56, p p p = 0.034). Following the non-rigid co-registration step of the process, the DICE similarity coefficients for the myocardium, LV cavity and scar were 0.93 (±0.02), 0.89 (±0.01) and 0.77 (±0.07) respectively and the myocardial tissue and LV cavity volumes had a ratio of 1.03 and 1.00 respectively. Conclusions The pattern of the morphological changes seen between the in-vivo and the ex-vivo LV differs between scar and healthy myocardium. A 3D printed flexible scaffold based on the in-vivo shape of the LV cavity is an effective strategy to minimize morphological changes in the ex-vivo LV. The subsequent non-rigid registration step further improved the co-registration and local comparison between in-vivo and ex-vivo data.

We investigate how co-registration errors affect the performance of anatomical constraints in magnetoencephalographic (MEG) source modeling for source-space connectivity analysis. We simulated MEG recordings from pairs of interacting cortical sources with variable locations and with a co-registration error of 0–15 mm. We computed a beamformer-based source estimate with either a free-orientation or constrained-orientation source model and tested four popular connectivity measures: imaginary part of coherency, amplitude-envelope correlation, phase-slope index and frequency-domain Granger causality. We found that even small co-registration errors (≥2 mm) compromise the performance of source orientation constraints in connectivity estimation.

PURPOSE Presurgical fMRI is an important tool for surgery navigation in achieving maximum resection of a brain tumor. However, the functional localization accuracy may be compromised by spatial transformation from echo-planar images to high-resolution structural images. We evaluated functional localization errors associated with the spatial transformation process using three algorithms commonly applied to the presurgical fMRI in the clinic. METHODS MR images of 20 brain tumor patients for presurgical evaluation of eloquent areas near motor cortices were analyzed. All fMRI data were spatially transferred to 3D T1-weighted images using three algorithms: (a) coordinate matching (CM), (b) automated registration (AR), and (c) AR plus manual adjustment (ARadj ). Activation clusters overlaid on original echo-planar images were manually delineated on slice-matched 2D T1- weighted images and then transferred to the 3D T1-weighted image volume, and served as the reference localization. Functional localization errors were estimated by measuring the distance between the reference localization and the activation cluster after spatial transformation and then compared for the three algorithms. RESULTS The 3D Euclidean distance for AR (10.2 ± 4.9 mm) was found to be significantly larger (P < 0.05) than those for CM (5.6 ± 2.6 mm) and ARadj (5.8 ± 3.0 mm) algorithms. The difference between the localization errors in CM and ARadj was not statistically significant. CONCLUSIONS A procedure was proposed to evaluate functional localization errors associated with spatial transformation in presurgical fMRI. Our results highlighted the necessity of routine quality control for the AR processing in the clinic.