Consecutive Images Research Articles

Three-Dimensional Tooth Model Reconstruction Using Statistical Randomization-Based Particle Swarm Optimization

The registration between images is a crucial part of the 3-D tooth reconstruction model. In this paper, we introduce a registration method using our proposed statistical randomization-based particle swarm optimization (SR-PSO) algorithm with the iterative closet point (ICP) method to find the optimal affine transform between images. The hierarchical registration is also utilized in this paper since there are several consecutive images involving in the registration. We implemented this algorithm in the scanned commercial regular-tooth and orthodontic-tooth models. The results demonstrated that the final 3-D images provided good visualization to human eyes with the mean-squared error of 7.37 micrometer2 and 7.41 micrometer2 for both models, respectively. From the results compared with the particle swarm optimization (PSO) algorithm with the ICP method, it can be seen that the results from the proposed algorithm are much better than those from the PSO algorithm with the ICP method.

Tracking Keypoints from Consecutive Video Frames Using CNN Features for Space Applications

Hard time constraints in space missions bring in the problem of fast video processing for numerous autonomous tasks. Video processing involves the separation of distinct image frames, fetching image descriptors, applying different machine learning algorithms for object detection, obstacle avoidance, and many more tasks involved in the automatic maneuvering of a spacecraft. These tasks require the most informative descriptions of an image within the time constraints. Tracking these informative points from consecutive image frames is needed in flow estimation applications. Classical algorithms like SIFT and SURF are the milestones in the feature description development. But computational complexity and high time requirements force the critical missions to avoid these techniques to get adopted in real-time processing. Hence a time conservative and less complex pre-trained Convolutional Neural Network (CNN) model is chosen in this paper as a feature descriptor. 7-layer CNN model is designed and implemented with pre-trained VGG model parameters and then these CNN features are used to match the points of interests from consecutive image frames of a lunar descent video. The performance of the system is evaluated based on visual and empirical keypoints matching. The scores of matches between two consecutive images from the video using CNN features are then compared with state-of-the-art algorithms like SIFT and SURF. The results show that CNN features are more reliable and robust in case of time-critical video processing tasks for keypoint tracking applications of space missions.

A Posteriori Outlier Rejection Approach Owing to the Well-ordering Property of a Sample Consensus Method for the Stitching of Drone-based Thermal Aerial Images

Abstract In this study, the authors generate panoramic images using feature-based registration for drone-based aerial thermal images. In the case of drone aerial images, the distortion of the photographing angle due to the unstableness in the shooting altitude deteriorates the performance of the stitching. Furthermore, for the thermal aerial images, the same objects photographed at the same time zone may have different colors due to the relative temperature, which may lead to a more severe condition to be stitched. Applying the scale-invariant feature transform descriptor, they propose a posteriori outlier rejection scheme to estimate the hypothesis of the mapping function for the stitching of consecutive thermal aerial images. By extension of the method of optimal choice of initial candidate inliers (OCICI) and a posteriori outlier rejection scheme using cross-correlation calculus, the authors obtained elaborate stitching of thermal aerial images. Their proposed method is numerically verified for its quality by comparing it with other possible approaches of post-outlier rejection treatments employed of OCICI. Also, after the Poisson blending using the finite difference method is conducted, the stitching performance is compared with some benchmark software such as Matlab-toolbox, OpenCV, Autopano Giga, Hugin, and PTGui.

Peripapillary Hyper-reflective Ovoid Mass-like Structure (PHOMS): An Optical Coherence Tomography Marker of Axoplasmic Stasis in the Optic Nerve Head.

With the development and widespread adoption of spectral-domain optical coherence tomography (OCT), peripapillary hyper-reflective ovoid mass-like structures (PHOMS) have become a frequent OCT finding in neuro-ophthalmic practice. Although originally assumed to represent a form of buried optic disc drusen (ODD), PHOMS differ from ODD in many important ways. The histopathological underpinnings of PHOMS are now becoming more clearly understood. Review of literature. PHOMS can be broadly classified as disk edema-associated PHOMS, ODD-associated PHOMS, or anomalous disk-associated PHOMS. PHOMS are seen in many conditions, including papilledema, nonarteritic anterior ischemic optic neuropathy, central retinal vein occlusion, acute demyelinating optic neuritis, ODD, and tilted disks (myopic obliquely inserted disks) and in many cases resolve along with the underlying condition. The histopathological study of these diverse entities reveals the common feature of a bulge of optic nerve fibers herniating centrifugally over Bruch membrane opening into the peripapillary space, correlating exactly with the location, shape, and space-occupying nature of PHOMS on OCT. Because of the radial symmetry of these herniating optic nerve fibers, PHOMS are best thought of as a complete or partial torus (i.e., donut) in 3 dimensions. PHOMS are a common but nonspecific OCT marker of axoplasmic stasis in the optic nerve head. They are not themselves ODD or ODD precursors, although they can be seen in association with ODD and a wide spectrum of other conditions. They do not exclude papilledema and often accompany it. The circumferential extent and characteristic 3D toroidal nature of a PHOMS are best appreciated by scrolling through consecutive OCT images.

Inspiratory Efforts, Positive End-Expiratory Pressure, and External Resistances Influence Intraparenchymal Gas Redistribution in Mechanically Ventilated Injured Lungs

BackgroundPotentially harmful lung overstretch can follow intraparenchymal gas redistribution during mechanical ventilation. We hypothesized that inspiratory efforts characterizing spontaneous breathing, positive end-expiratory pressure (PEEP), and high inspiratory resistances influence inspiratory intraparenchymal gas redistribution.MethodsThis was an experimental study conducted on a swine model of mild acute respiratory distress syndrome. Dynamic computed tomography and respiratory mechanics were simultaneously acquired at different PEEP levels and external resistances, during both spontaneous breathing and controlled mechanical ventilation. Images were collected at two cranial–caudal levels. Delta-volume images (ΔVOLs) were obtained subtracting pairs of consecutive inspiratory images. The first three ΔVOLs, acquired for each analyzed breath, were used for the analysis of inspiratory pendelluft defined as intraparenchymal gas redistribution before the start of inspiratory flow at the airway opening. The following ΔVOLs were used for the analysis of gas redistribution during ongoing inspiratory flow at the airway opening.ResultsDuring the first flow-independent phase of inspiration, the pendelluft of gas was observed only during spontaneous breathing and along the cranial-to-caudal and nondependent-to-dependent directions. The pendelluft was reduced by high PEEP (p < 0.04 comparing PEEP 15 and PEEP 0 cm H2O) and low external resistances (p < 0.04 comparing high and low external resistance). During the flow-dependent phase of inspiration, two patterns were identified: (1) gas displacing characterized by large gas redistribution areas; (2) gas scattering characterized by small, numerous areas of gas redistribution. Gas displacing was observed at low PEEP, high external resistances, and it characterized controlled mechanical ventilation (p < 0.01, comparing high and low PEEP during controlled mechanical ventilation).ConclusionsLow PEEP and high external resistances favored inspiratory pendelluft. During the flow-dependent phase of the inspiration, controlled mechanical ventilation and low PEEP and high external resistances favored larger phenomena of intraparenchymal gas redistribution (gas displacing) endangering lung stability.

Sparse self-attention aggregation networks for neural sequence slice interpolation

BackgroundMicroscopic imaging is a crucial technology for visualizing neural and tissue structures. Large-area defects inevitably occur during the imaging process of electron microscope (EM) serial slices, which lead to reduced registration and semantic segmentation, and affect the accuracy of 3D reconstruction. The continuity of biological tissue among serial EM images makes it possible to recover missing tissues utilizing inter-slice interpolation. However, large deformation, noise, and blur among EM images remain the task challenging. Existing flow-based and kernel-based methods have to perform frame interpolation on images with little noise and low blur. They also cannot effectively deal with large deformations on EM images.ResultsIn this paper, we propose a sparse self-attention aggregation network to synthesize pixels following the continuity of biological tissue. First, we develop an attention-aware layer for consecutive EM images interpolation that implicitly adopts global perceptual deformation. Second, we present an adaptive style-balance loss taking the style differences of serial EM images such as blur and noise into consideration. Guided by the attention-aware module, adaptively synthesizing each pixel aggregated from the global domain further improves the performance of pixel synthesis. Quantitative and qualitative experiments show that the proposed method is superior to the state-of-the-art approaches.ConclusionsThe proposed method can be considered as an effective strategy to model the relationship between each pixel and other pixels from the global domain. This approach improves the algorithm’s robustness to noise and large deformation, and can accurately predict the effective information of the missing region, which will greatly promote the data analysis of neurobiological research.

Magnetic Resonance Imaging Findings After Superior Capsule Reconstruction

PurposeTo evaluate graft properties on magnetic resonance imaging (MRI) after superior capsular reconstruction (SCR) to help improve our understanding of postoperative imaging.MethodsWe identified consecutive patients who underwent SCR by a single surgeon and who had postoperative MRIs available. MRIs were analyzed to look for common postoperative findings on imaging.ResultsTen consecutive patients with an average age of 58 years who underwent SCR by a single surgeon had postoperative MRIs on average 404 days from surgery. Eight patients had completely intact grafts on follow-up MRI. All intact grafts were similar with homogenous appearance on all coronal images. All patients displayed some trace fluid with mild heterogenous signal at the level of the glenoid, which could represent failure of the graft to completely incorporate at the level of the glenoid or could be normal in the postoperative setting since all eight intact grafts displayed this finding. None of the patients with intact grafts had bony edema noted on either the glenoid or humeral side. Four of 8 patients were noted to have trace bony edema at the level of the lateral acromion. One patient had complete disruption on the glenoid side. One patient had partially intact graft that revealed heterogenous appearance of graft.ConclusionsAn intact graft displays a more homogenous signal on consecutive postoperative MRI coronal images than disrupted grafts or partially intact grafts. This suggests that intact grafts have better clinical outcomes than a partially disrupted or completely disrupted graft. However, the finding of heterogenous signal/fluid at the glenoid graft interface in all intact grafts could not be explained in this study.Level of EvidenceLevel IV, therapeutic case series.

Temporal image analytics for abnormal construction activity identification

Abnormal activities on construction jobsites may compromise productivity and pose threat to workers' safety. This paper proposes the analysis of consecutive image sequences for automatic identification of irregular operations and their visualization. The data analytics is composed of four steps: object detection, object tracking, action recognition, and operational analysis. The Faster Region-proposal Convolutional Neural Network (Faster R-CNN) is adapted with transfer learning for detection of workers and pieces of construction equipment on the jobsite, while the Simple Online and Realtime Tracking (SORT) approach is applied for object tracking. A hybrid model integrating CNN and Long Short Term Memory (LSTM) is employed for action recognition. An alternative form of the Crew-balance Chart (CBC), called line chart in which anomalies are pre-screened, is utilized for recognized actions. Validation was carried out with earthmoving operations. The trained Faster R-CNN reached a 73% Average Precision (AP), and the SORT algorithm modified by this work successfully reduced identity switches. Irregular operations in the testing videos were identified, and truck exchanges were filtered. In addition, an activity log was produced with basic information along with starting and ending times of the identified irregular operations. With the line chart and the log provided by the proposed framework, field managers can efficiently identify potential abnormal activities, providing the opportunity for further investigations and adjustments accordingly.

Accuracy of intraoral scan images in full arch with orthodontic brackets: a retrospective in vivo study

ObjectivesThe purpose of this retrospective study was to evaluate the accuracy of intraoral scan (IOS) images in the maxillary and mandibular arches with orthodontic brackets.Material and methodsFrom digital impressions of 140 patients who underwent orthodontic treatment, consecutive IOS images were selected based on standardized inclusion criteria: Two pre-orthodontic IOS images (IOS1 and IOS2) of permanent dentition with fully erupted second molars and IOS images obtained immediately after orthodontic bracket bonding (IOSb). Superimpositions were performed to evaluate the reproducibility of repeated IOS images. Accuracy of IOSb images was analyzed by comparing the average surface errors between IOS1c and IOS2c images, which were IOS images cut based on the same region of the interest as between IOS1 and IOSb images.ResultsA total of 84 IOS images was analyzed. The average surface errors between IOS1 and IOS2 images were 57 ± 8 μm and 59 ± 14 μm in the maxillary and mandibular arch, respectively, and their reliability was almost perfect. The average errors between IOSb and IOS1c images exhibited an increase, which measured 97 ± 28 μm in the maxillary arch and 95 ± 29 μm in the mandibular arch. These surface deviations between IOSb and IOS1c images were significantly larger in each region as well as entire dentition (P < 0.001) compared to those between IOS1c and IOS2c images.ConclusionsThe average surface errors of the scans with brackets showed increased values compared with those without brackets. This suggests that orthodontic brackets could affect the trueness of intraoral scan images.Clinical relevanceIt is necessary for clinicians to consider the effect of brackets on digital impression when using IOS images in orthodontic patients.

OUTER RETINAL TUBULATION MAY RESULT FROM FIBROSED TYPE 2 NEOVASCULARIZATION: Clinical Observations and Model of Pathogenesis.

To investigate the role of Type 2 macular neovascularization with subsequent subretinal fibrosis in the pathogenesis of outer retinal tubulation (ORT). We conducted a retrospective cohort study of patients with stabilized inactive exudative macular degeneration who had been treated with intravitreal injections of antivascular endothelial growth factor agents. Baseline fluorescein and optical coherence tomography images were included. Macular neovascularizations (MNVs) were classified by type and size. Consecutive optical coherence tomography images analyzed for ORT development. One hundred forty-four eyes of 134 patients were included in this study. Sixty eyes presented with pure Type 1 MNV. Eighty-four eyes presented with some Type 2 component of MNV. In total, evidence of ORT is shown in 55 (38%) eyes. In the Type 1 group, 6.7% developed ORT. Outer retinal tubulation developed in 61% of eyes with some Type 2 component of the MNV. Among eyes that developed ORT, 92.7% presented with some Type 2 component. In a multivariate analysis, Type 2 membranes on optical coherence tomography (22.2 [6.1-80.8]; P < 0.001), larger MNV size {>1 DA (5.1 [1.1-24.2]; P = 0.041) and >1.5 DA (9.0 [1.8-44.0]; P = 0.007)}, and presence of subretinal fibrovascular material (3.1 [1.1-8.5]; P = < 0.03) are associated with higher odds of ORT formation. Once the ORT is formed, fibrosis was observed directly underlying the ORT on SD-optical coherence tomography in 70.9% of cases. Type 2 membranes at presentation predict ORT formation. Fibrosis often underlies ORT. This suggests that contraction of Type 2 MNV-derived fibrosis may be important in ORT formation.

Ego-Motion Estimation Using Recurrent Convolutional Neural Networks through Optical Flow Learning

Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent subspace of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF subspace is used to compute the regression of the 6-dimensional pose vector. We derive three models with different network structures and different training schemes: LS-CNN-VO, LS-AE-VO, and LS-RCNN-VO. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our LS-RCNN-VO outperforms the existing learning-based VO approaches.

Wood species automatic identification from wood core images with a residual convolutional neural network

This paper tackles the problem of automatic tree species identification from scanned images of wood cores. A convolutional neural network with residual connections is proposed to perform this task. The model is applied to consecutive image patches following the sliding window strategy to recognize a patch central pixel’s membership. It then decides about the resulting tree species via a majority voting. The model’s performance was assessed concerning a dataset of 312 wood core images representing 14 European tree species, including both conifer and angiosperm (ring-porous and diffuse-porous) wood. Two tasks were considered, including wood patch classification and wood core classification. In these tasks, the proposed model correctly recognized species of almost 93% the wood image patches and 98.7% of wood core images. It also outperformed the state-of-the-art convolutional neural network-based competitor by 9% and 3%, respectively. The influence of the model’s parameters and training set-up on its performance is analyzed in the manuscript to ensure the highest recognition rates of wood species. The source code of the proposed method is released together with the corresponding image dataset to facilitate the reproduction of results.

Calibration-based minimalistic multi-exposure digital sensor camera robust linear high dynamic range enhancement technique demonstration

Demonstrated for a digital image sensor-based camera is a calibration target optimized method for finding the Camera Response Function (CRF). The proposed method uses localized known target zone pixel outputs spatial averaging and histogram analysis for saturated pixel detection. Using the proposed CRF generation method with a 87 dB High Dynamic Range (HDR) silicon CMOS image sensor camera viewing a 90 dB HDR calibration target, experimentally produced is a non-linear CRF with a limited 40 dB linear CRF zone. Next, a 78 dB test target is deployed to test the camera with this measured CRF and its restricted 40 dB zone. By engaging the proposed minimal exposures, weighting free, multi-exposure imaging method with 2 images, demonstrated is a highly robust recovery of the test target. In addition, the 78 dB test target recovery with 16 individual DR value patches stays robust over a factor of 20 change in test target illumination lighting. In comparison, a non-robust test target image recovery is produced by 5 leading prior-art multi-exposure HDR recovery algorithms using 16 images having 16 different exposure times, with each consecutive image having a sensor dwell time increasing by a factor of 2. Further validation of the proposed HDR image recovery method is provided using two additional experiments, the first using a 78 dB calibrated target combined with a natural indoor scene to form a hybrid design target and a second experiment using an uncalibrated indoor natural scene. The proposed technique applies to all digital image sensor-based cameras having exposure time and illumination controls. In addition, the proposed methods apply to various sensor technologies, spectral bands, and imaging applications.

ASRNet: Adversarial Segmentation and Registration Networks for Multispectral Fundus Images

Multispectral imaging (MSI) technique is often used to capture images of the fundus by illuminating it with different wavelengths of light. However, these images are taken at different points in time such that eyeball movements can cause misalignment between consecutive images. The multispectral image sequence reveals important information in the form of retinal and choroidal blood vessel maps, which can help ophthalmologists to analyze the morphology of these blood vessels in detail. This in turn can lead to a high diagnostic accuracy of several diseases. In this paper, we propose a novel semi-supervised end-to-end deep learning framework called “Adversarial Segmentation and Registration Nets” (ASRNet) for the simultaneous estimation of the blood vessel segmentation and the registration of multispectral images via an adversarial learning process. ASRNet consists of two subnetworks: (i) A segmentation module S that fulfills the blood vessel segmentation task, and (ii) A registration module R that estimates the spatial correspondence of an image pair. Based on the segmention-driven registration network, we train the segmentation network using a semi-supervised adversarial learning strategy. Our experimental results show that the proposed ASRNet can achieve state-of-the-art accuracy in segmentation and registration tasks performed with real MSI datasets.

Deep Learning Architectures for Navigation Using Forward Looking Sonar Images

This paper investigates the use of supervised Deep Learning (DL) networks to process sonar images for underwater navigation. State-of-the-art DL techniques for micro-navigation using sequences of optical images have been adapted to work with sonar images. Specifically, the DL networks estimate the Forward-Looking Sonar (FLS) motion in three degrees of freedom corresponding to $x$ - and $y$ -translation and rotation around $z$ -axis. The state-of-the-art DL architectures and a proposed new architecture are investigated for motion estimation. They are trained using images generated by a FLS simulator. The data sets are made using pairs of consecutive images associated with labels that represent the motion of the sonar platform between images. The results show the effectiveness of using the DL architectures, which can provide millimeter accuracy for translation motion and below 0.1° for rotation motion between two consecutive sonar images. Examples of trajectory estimation and mosaic building using simulated and real sonar images are also presented.

Motion tracking and analysis of spray water droplets studied by high-speed photography using an iPhone X

Smartphones are not only becoming an inseparable part of our daily lives, but also a low-cost, powerful optical imaging tool for more and more scientific research applications. In this work, we hypothesized that some smartphones could be used as a low-cost, high-speed, photographic alternative to expensive equipment, such as those typically found in scientific research labs, to accurately perform motion tracking and analysis of fast-moving objects. This demonstrative experiment was done at home at very low cost by using a low-power green laser (&amp;lt;0.1 W) purchased from Amazon, an iPhone X, and a water spray bottle. The sprayed water droplets were illuminated by a divergent green laser beam and recorded with a smartphone using the Slow-Motion Video function (240 frames per second), which allows us to record high-speed events. The Slow-Motion Video was converted to a series of consecutive images taken every 1/240 second (or 4.16 ms), enabling tracking and motion analysis of the sprayed water droplets. By analyzing these consecutive images, the speed and flight trajectory of water droplets in the air were obtained, thereby enabling us to estimate the area of the water droplets landing on the ground.

Bunch-of-Keys Module for Optimizing a Single Image Detector Based on the Property of Sequential Images

In image processing, deep learning networks have been continuously developed and are used in many fields. However, most networks do not reflect image continuity. In this paper, we propose a novel bunch-of-keys module connected to the backend of a deep learning network to improve the detector performance on sequential images. This module optimizes existing deep learning networks to detect sequential images without retraining. This procedure reduces time and computing costs, and the average precision improves with a minimal drop in the frames per second. By adopting a sliding window method that uses three consecutive images, the keys are generated by comparing the positions of the detected boxes for each of the images using generalized intersection over union. The two key types perform correcting operations. The rectifying key has the effect of adding or merging undetected bounding boxes in mid-frame. The tracking key has the effect of compensating for bounding boxes lost for no reason in the third frame. The candidate box extracted using each key determines whether to add or merge to the target image in the correction task. This task calculates the complete intersection over union (CIoU) score between candidate boxes and all boxes of the target image and is divided into add or merge cases according to a set of CIoU criterion. As a result of adding or merging the bounding box to the missing object, detection performance was improved up to 3% in terms of the average precision.

Counting People by Estimating People Flows.

Modern methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness constraints across consecutive frames. In this paper, we advocate estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing them. This enables us to impose much stronger constraints encoding the conservation of the number of people. As a result, it significantly boosts performance without requiring a more complex architecture. Furthermore, it allows us to exploit the correlation between people flow and optical flow to further improve the results. We also show that leveraging people conservation constraints in both a spatial and temporal manner makes it possible to train a deep crowd counting model in an active learning setting with much fewer annotations. This significantly reduces the annotation cost while still leading to similar performance to the full supervision case.

Predicting brain atrophy from tau pathology: a summary of clinical findings and their translation into personalized models

For more than 25 years, the amyloid hypothesis–the paradigm that amyloid is the primary cause of Alzheimer’s disease–has dominated the Alzheimer’s community. Now, increasing evidence suggests that tissue atrophy and cognitive decline in Alzheimer’s disease are more closely linked to the amount and location of misfolded tau protein than to amyloid plaques. However, the precise correlation between tau pathology and tissue atrophy remains unknown. Here we integrate multiphysics modeling and Bayesian inference to create personalized tau-atrophy models using longitudinal clinical images from the Alzheimer’s Disease Neuroimaging Initiative. For each subject, we infer three personalized parameters, the tau misfolding rate, the tau transport coefficient, and the tau-induced atrophy rate from four consecutive annual tau positron emission tomography scans and structural magnetic resonance images. Strikingly, the tau-induced atrophy coefficient of 0.13/year (95% CI: 0.097-0.189) was fairly consistent across all subjects suggesting a strong correlation between tau pathology and tissue atrophy. Our personalized whole brain atrophy rates of 0.68-1.68%/year (95% CI: 0.5-2.0) are elevated compared to healthy subjects and agree well with the atrophy rates of 1-3%/year reported for Alzheimer’s patients in the literature. Once comprehensively calibrated with a larger set of longitudinal images, our model has the potential to serve as a diagnostic and predictive tool to estimate future atrophy progression from clinical tau images on a personalized basis. Statement of SignificanceDeveloping predictive, patient-specific models of Alzheimer’s disease pathology and progression is of paramount importance for effective patient care and potential treatment. Tissue atrophy, the reduction of brain volume, is an important biomarker for Alzheimer’s disease. Similarly, the pathology associated with tau proteins is thought to play a central role in Alzheimer’s disease progression, local atrophy, and a patient’s cognitive decline. The main question is: how do combine the mechanisms of tau propagation and atrophy in a single model that can make the best use of existing data? Here, we first review the dynamics of atrophy in for Alzheimer’s disease and describe a mathematical model that couples tau propagation and atrophy. We then investigate how to fit the model parameters using the longitudinal structural neuroimaging data of four subjects, from the ADNI database, and a Bayesian Markov Chain Monte Carlo inference method. Our approach shows that network neurodegeneration models may hold promise for the predictive, patient-specific modeling of AD using AV-1451 tau PET and T1 structural MRI data.

МЕТОД КОМПЕНСАЦІЇ ПРОСТОРОВОЇ НЕВИЗНАЧЕНОСТІ ПРИ ВИМІРЮВАННІ У НАНОМЕТРОВОМУ ДІАПАЗОНІ

The article analyzes the main destabilizing factors that cause drift when measuring with a scanning probe microscope (SPM) in the nanometer range. It is determined that thermal drift is the main cause of spatial uncertainty in the mechanical system of SPM, because due to thermal expansion and compression can be displaced SPM probe or sample without applying any driving voltage to the piezoelectric drive mechanism. It has been established that thermal drift can have a significant effect on the reliability of measurement results in the form of a digital image, as it significantly distorts the position of the nanoobject during sequential scanning. Methods have been developed to compensate for the spatial uncertainty of the SPM and its piezoelectric drive mechanism, namely: hysteresis, creep, and thermal drift. It has been shown that hysteresis can be reduced by scanning in one direction, and it is recommended to pause for a few minutes after each major scan movement to eliminate creep. It is proved that drift in the z-direction can be considered as an uncorrelated value to the xy plane and can be eliminated by converting the original digital images to the gradient. Experiments have shown that the use of a phase correlation method to measure the offset of two gradient images is more effective than the use of original images. In addition, in two consecutive gradient images, there are no topographic changes, and using the Fourier transform shear property, the phase correlation method can convert the shift of the spatial region to the phase difference in the frequency domain. Calculation methods are proposed that analyze the effect of thermal expansion of the sensor drive on the overall drift of the system. The results showed that although the extension length of the sensor drive is the largest among all major system components, this may not be the main cause of overall system drift.