A Freehand 3D Ultrasound Carotid Scanning System for Point-of-Care Ultrasonography

Stroke is a leading cause of morbidity and mortality throughout the world. Three-dimensional ultrasound (3DUS) imaging was shown to be more sensitive to treatment effect and more accurate in stratifying stroke risk than two-dimensional ultrasound (2DUS) imaging. Point-of-care ultrasound screening (POCUS) is important for patients with limited mobility and at times when the patients have limited access to the ultrasound scanning room, such as in the COVID-19 era. We used an optical tracking system to track the 3D position and orientation of the 2DUS frames acquired by a commercial wireless ultrasound system and subsequently reconstructed a 3DUS image from these frames. The tracking requires spatial and temporal calibrations. Spatial calibration is required to determine the spatial relationship between the 2DUS machine and the tracking system. Spatial calibration was achieved by localizing the landmarks with known coordinates in a custom-designed Z-fiducial phantom in an 2DUS image. Temporal calibration is needed to synchronize the clock of the wireless ultrasound system and the optical tracking system so that position and orientation detected by the optical tracking system can be registered to the corresponding 2DUS frame. Temporal calibration was achieved by initiating the scanning by an abrupt motion that can be readily detected in both systems. This abrupt motion establishes a common reference time point, thereby synchronizing the clock in both systems. We demonstrated that the system can be used to visualize the three-dimensional structure of a carotid phantom. The error rate of the measurements is 2.3%. Upon in-vivo validation, this system will allow POCUS carotid scanning in clinical research and practices. © 2023 SPIE.

CCTwins: A Weakly-Supervised Transformer-based Crowd Counting Method with Adaptive Scene Consistency Attention

Recently, crowd counting has attracted significant attention, particularly in the context of the COVID-19 pandemic, due to its ability to automatically provide accurate crowd numbers in images. To address the challenges of location-level labeling, several transformer-based crowd counting methods have been proposed with only count-level supervision. However, these methods directly use the transformer as an encoder without considering the uneven crowd distribution. To address this issue, we propose CCTwins, a novel transformer-based crowd counting method with only count-level supervision. Specifically, we introduce an adaptive scene consistency attention mechanism to enhance the transformer-based model Twins-SVT-L for feature extraction in crowded scenes. Additionally, we design a multi-level weakly-supervised loss function that generates estimated crowd numbers in a coarse-to-fine manner, making it more appropriate for weakly-supervised settings. Moreover, intermediate features supervised by count-level labels are utilized to fuse multi-scale features. Experimental results on four public datasets demonstrate that our proposed method outperforms the state-of-the-art weakly-supervised methods, achieving up to a 16.6% improvement in MAE and up to a 13.8% improvement in RMSE across all evaluation settings. Moreover, the proposed CCTwins obtains competitive counting performance, even when compared to the state-of-the-art fully-supervised methods. IEEE

Image Reconstruction for COVID-19 Using Multifrequency Electrical Impedance Tomography

Multifrequency electrical impedance tomography (MfEIT) is a technique that allows the visualization of images inside the body through the characterization of electrical impedance, conductivity or permissiveness in a given frequency range, as well as the characterization of body tissue analyzed. Usually, several alternating electrical currents are injected through electrodes connected to the surface of the body under study, and the resulting voltages are measured and stored for processing and obtaining an image. The image reconstruction algorithm uses the data set of measurements of applied currents and voltages measured at each electrode, calculating the distributions of conductivity, permittivity, or resistivity within the conductive volume studied. The reconstruction of images by direct methods is widely used in applications that require rapid reconstruction and lower computational cost, such as the monitoring of pulmonary mechanical ventilation in ICU beds in patients intubated due to COVID-19. In this chapter, we present the basic characteristics so that a wireless, low-cost, and portable MfEIT system can be implemented, as well as the definitions and modeling of the two-dimensional D-bar method for image reconstruction. Clinical parameters of patients diagnosed with COVID-19 are used to implement some reconstructions of images, as well as to bring a discussion about the efficiency of this technology for this clinical condition. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

AIGAN: Attention-encoding Integrated Generative Adversarial Network for the reconstruction of low-dose CT and low-dose PET images.

X-ray computed tomography (CT) and positron emission tomography (PET) are two of the most commonly used medical imaging technologies for the evaluation of many diseases. Full-dose imaging for CT and PET ensures the image quality but usually raises concerns about the potential health risks of radiation exposure. The contradiction between reducing the radiation exposure and remaining diagnostic performance can be addressed effectively by reconstructing the low-dose CT (L-CT) and low-dose PET (L-PET) images to the same high-quality ones as full-dose (F-CT and F-PET). In this paper, we propose an Attention-encoding Integrated Generative Adversarial Network (AIGAN) to achieve efficient and universal full-dose reconstruction for L-CT and L-PET images. AIGAN consists of three modules: the cascade generator, the dual-scale discriminator and the multi-scale spatial fusion module (MSFM). A sequence of consecutive L-CT (L-PET) slices is first fed into the cascade generator that integrates with a generation-encoding-generation pipeline. The generator plays the zero-sum game with the dual-scale discriminator for two stages: the coarse and fine stages. In both stages, the generator generates the estimated F-CT (F-PET) images as like the original F-CT (F-PET) images as possible. After the fine stage, the estimated fine full-dose images are then fed into the MSFM, which fully explores the inter- and intra-slice structural information, to output the final generated full-dose images. Experimental results show that the proposed AIGAN achieves the state-of-the-art performances on commonly used metrics and satisfies the reconstruction needs for clinical standards.

Computed Tomography Simulator (Ctlab)

University of Oulu and Oulu University of Applied Sciences have established a unique medical imaging teaching and testing laboratory in collaboration with Oulu University Hospital in a European Regional Development Fund -project. Virtually implemented medical imaging devices (CT, MRI, radiography) are unique features of the lab. Many of the virtual tools have been developed by the universities themselves. One of the virtual tools implemented during the project is the CTlab simulator, which can be widely used in computed tomography training for all professionals who use radiation in their work. The CTlab provides fast, comprehensive, and efficient solutions for numerical CT simulations with low hardware requirements. The simulator has been developed to introduce the basic operations and workflow behind the CT imaging modality and to illustrate how the polychromatic x-ray spectrum, various imaging parameters, scan geometry and CT reconstruction algorithm affect the quality of the detected images. Key user groups for the simulator include medical physics, engineering, and radiographer students. CTlab has been created with MATLAB's app designer feature. It offers its user the opportunity to select the virtual imaging target, to adjust CT imaging parameters (image volume, scan angles, detector element size and detector width, noise, algorithm/geometry specific parameters), to select specific scan geometry, to observe projection data from selected imaging target with polychromatic x-ray spectrum, and to select the specific algorithm for image reconstruction (FBP, least squares, Tikhonov regularization). The CTlab has so far been used at a postgraduate course on computed tomography technology with encouraging feedback from the students. At the course, teaching of CT modality were performed by using the simulator, giving students unlimited opportunity to practice the use of virtual imaging device and participate demonstrations remotely during the Covid-19 pandemic. Using CTlab in teaching enhances and deepens the learning experience in the physics behind computed tomography. CTlab can be used remotely (https://www.oulu.fi/fi/projektit/laaketieteellisen-kuvantamisen-opetus-ja-testilaboratorio-0), which makes teaching and training of CT scanner usage successful regardless of time and place. The simulator enables more illustrative and in-depth teaching and offers cost-effectiveness, versatility, and flexibility in education. CTlab can also be used to support teaching in special situations, such as during the Covid-19 pandemic when simulator is utilized remotely to perform teaching-related demonstrations flexibly and safely.Copyright © 2023 Southern Society for Clinical Investigation.

LCA-GAN: Low-Complexity Attention-Generative Adversarial Network for Age Estimation with Mask-Occluded Facial Images

Facial-image-based age estimation is being increasingly used in various fields. Examples include statistical marketing analysis based on age-specific product preferences, medical applications such as beauty products and telemedicine, and age-based suspect tracking in intelligent surveillance camera systems. Masks are increasingly worn for hygiene, personal privacy concerns, and fashion. In particular, the acquisition of mask-occluded facial images has become more frequent due to the COVID-19 pandemic. These images cause a loss of important features and information for age estimation, which reduces the accuracy of age estimation. Existing de-occlusion studies have investigated masquerade masks that do not completely occlude the eyes, nose, and mouth;however, no studies have investigated the de-occlusion of masks that completely occlude the nose and mouth and its use for age estimation, which is the goal of this study. Accordingly, this study proposes a novel low-complexity attention-generative adversarial network (LCA-GAN) for facial age estimation that combines an attention architecture and conditional generative adversarial network (conditional GAN) to de-occlude mask-occluded human facial images. The open databases MORPH and PAL were used to conduct experiments. According to the results, the mean absolution error (MAE) of age estimation with the de-occluded facial images reconstructed using the proposed LCA-GAN is 6.64 and 6.12 years, respectively. Thus, the proposed method yielded higher age estimation accuracy than when using occluded images or images reconstructed using the state-of-the-art method.

Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging.

Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality. Thus, improving the accuracy of material decomposition while suppressing noise is pivotal for spectral CT imaging. This paper proposes a one-step multi-material reconstruction model as well as an iterative proximal adaptive decent method. In this approach, a proximal step and a descent step with adaptive step size are designed under the forward-backward splitting framework. The convergence analysis of the algorithm is further discussed according to the convexity of the optimization objective function. For simulation experiments with different noise levels, the peak signal-to-noise ratio (PSNR) obtained by the proposed method increases approximately 23 dB, 14 dB, and 4 dB compared to those of other algorithms. Magnified areas of thorax data further demonstrated that the proposed method has a better ability to preserve details in tissues, bones, and lungs. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise and beam hardening artifacts compared with the state-of-the-art methods.

Visibility Improvement in Air Pollution Scene by Joint Sharpness - Contrast Enhanced Dehazing

In the latter half of the 1980s, PM2.5 pollution in Beijing became a serious problem, and there were concerns about health hazards. It was expected that China's emissions must be reduced from 2013 to 2016, and the lockdown effect of Covid-19 would bring about an end, but it is still reluctant to regulate CO2 emissions. Again, in Beijing in November 2021, a visibility of 500 m or less has been observed, then road traffic is dangerous in addition to health. After that, the center of pollution has moved from India to Mongolia, and now Nepal, Qatar and Saudi Arabia. The situation is still serious in developing countries. Image restoration to remove the effects of haze and fog has been a long-standing concern of NASA, and their original Visual Servo has been put into practical use. Though the mainstream moved to the technique based on atmospheric physics. He et al.'s Dark Channel Priority (DCP) logic has had a certain effect on heavily polluted PM 2.5 scenes, but there is a limit to the restoration of detailed visibility. The observed images are affected by two spatial inhomogeneities of 1) atmospheric layer and 2) illumination. As a countermeasure, we have improved DCP process with the help of Retinex and introduced the veil coefficient as reported in CIC24. Recently, a variety of improvements in single image Dehazing, using FFA-net, BPP-net, LCA-net, or Vision-based model are in progress. However, in each case, visibility of details is still a common problem. This paper proposes an improvement in detail visibility by (1) joint sharpness-contrast preprocess (2) adjustment in Dehaze effect with veil coefficient v Lastly, we challenge numerical evaluation of improvement in detail visibility by the two ways of attenuation of high-frequency Fourier spectrum and the expansion rate of the color gamut. © 2022 Society for Imaging Science and Technology.

Face Completion with Multi-Output Estimators

Since the beginning of the COVID-19 pandemic, images of faces with obscured bottom halves have become more common due to masking. Now more than ever, end-users are looking toward machine learning and data science to create high-quality replacements for missing facial data. For face completion, we evaluate multiple machine learning algorithms, including Decision Trees, K-Nearest Neighbors, and Support Vector Machines. Since most of the existing work in this field uses deep learning, we explore the impact of using multiple deep learning techniques and use them as a point of comparison. Our study indicates that despite the conventional norm that deep learning algorithms outperform their machine learning counterparts, the non-deep learning techniques perform better for this application.11Code is available at https://github.com/nickfons/fcwmoe. © 2022 IEEE.

Recreation and Virtual Animation as an Active Learning Aid of the Aircraft Engines Course

The virtual modelling and animation of a turbo propeller with Catia is of great significance in regard to facilitate its visualization and analysis, offering multiple possibilities in the field of teaching–learning of air-breathing jet engines in aeronautical engineering studies. This work shares the teaching experience carried out, during the two academic courses of the pandemic (19/20 and 20/21), to explain the assembly and operation of the Garrett TPE331 turbo propeller, without the need to own it physically, only using its virtual modelling and animation and the Blackboard Collaborate virtual teaching platform. The results achieved in these courses are presented to be compared to those prior to Covid to verify the effectiveness of the teaching process applied. Lastly, it is intended that this model serve as support for its implementation through apps to the world of virtual reality, being able to combine the theory related to the engine with three-dimensional representations, thus facilitating the complex educational task that involves explaining the operation of this or any other engine that are not physically available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Evaluation of the Association between Multilobar Involvement and ACE Inhibitor Use in SARS-CoV-2 Patients

Aim: Angiotensin-converting enzyme 2 (ACE2) acts not only as an enzyme but also as a thought to be central receptor by which severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) enters host cells. Angiotensin-converting enzyme inhibitors (ACEIs) are thought to $1 are central to SARS-CoV-2 progression. However, its effect on clinical outcomes is still not fully explained. In this study, we investigated the effects of ACEIs use on pulmonary computed tomography findings. Method(s): The data of the patients who were hospitalized for SARS-CoV-2 pneumonia and were using medications for the diagnosis of hypertension from 20th March to 20th June 2020 were evaluated retrospectively. Patients were divided into 2 groups patients using ACEIs and not using ACEIs. Result(s): The study was conducted with 107 patients. Mild cases without signs of pneumonia were excluded from this study. Moderate cases were accepted as patients with symptoms related to the respiratory system and pneumonia detected on imaging. SpO2<=93%, >=30 breaths/min respiratory rate, and patients who developed respiratory failure, mechanical ventilator need, shock, or multiorgan failure were included in the severe and critically ill cases group. Severe and critical cases were evaluated as a single group. When the radiological images of the patients were examined, it was remarkable that multilobar findings were less common in the ACEIs using group (p<0.001). At the clinical end point, mortality rates in patients using ACEIs (12.7%) were significantly lower than patients without using ACEIs (32.7%). Conclusion(s): In our study, we showed that SARS-CoV-2 progresses with less multilobar involvement in pulmonary computed tomography in patients using ACEI.Copyright © 2023 by The Medical Bulletin of Istanbul Haseki Training and Research Hospital The Medical Bulletin of Haseki published by Galenos Yayinevi.

An optimized EBRSA-Bi LSTM model for highly undersampled rapid CT image reconstruction.

COVID-19 has spread all over the world, causing serious panic around the globe. Chest computed tomography (CT) images are integral in confirming COVID positive patients. Several investigations were conducted to improve or maintain the image reconstruction quality for the sample image reconstruction. Deep learning (DL) methods have recently been proposed to achieve fast reconstruction, but many have focused on a single domain, such as the image domain of k-space. In this research, the highly under-sampled enhanced battle royale self-attention based bi-directional long short-term (EBRSA-bi LSTM) CT image reconstruction model is proposed to reconstruct the image from the under-sampled data. The research is adapted with two phases, namely, pre-processing and reconstruction. The extended cascaded filter (ECF) is proposed for image pre-processing and tends to suppress the noise and enhance the reconstruction accuracy. In the reconstruction model, the battle royale optimization (BrO) is intended to diminish the loss function of the reconstruction network model and weight updation. The proposed model is tested with two datasets, COVID-CT- and SARS-CoV-2 CT. The reconstruction accuracy of the proposed model with two datasets is 93.5 % and 97.7 %, respectively. Also, the image quality assessment parameters such as Peak-Signal to Noise Ratio (PSNR), Root Mean Square Error (RMSE) and Structural Similarity Index metric (SSIM) are evaluated, and it yields an outcome of (45 and 46 dB), (0.0026 and 0.0022) and (0.992, 0.996) with two datasets.

Real-time Semantic 3D Reconstruction for High- Touch Surface Recognition for Robotic Disinfection

Disinfection robots have applications in promoting public health and reducing hospital acquired infections and have drawn considerable interest due to the COVID-19 pan-demic. To disinfect a room quickly, motion planning can be used to plan robot disinfection trajectories on a reconstructed 3D map of the room's surfaces. However, existing approaches discard semantic information of the room and, thus, take a long time to perform thorough disinfection. Human cleaners, on the other hand, disinfect rooms more efficiently by prioritizing the cleaning of high-touch surfaces. To address this gap, we present a novel GPU-based volumetric semantic TSDF (Truncated Signed Distance Function) integration system for semantic 3D reconstruction. Our system produces 3D reconstructions that distinguish high-touch surfaces from non-high-touch surfaces at approximately 50 frames per second on a consumer-grade GPU, which is approximately 5 times faster than existing CPU-based TSDF semantic reconstruction methods. In addition, we extend a UV disinfection motion planning algorithm to incorporate semantic awareness for optimizing coverage of disinfection tra-jectories. Experiments show that our semantic-aware planning outperforms geometry-only planning by disinfecting up to 20% more high-touch surfaces under the same time budget. Further, the real-time nature of our semantic reconstruction pipeline enables future work on simultaneous disinfection and mapping. Code is available at: https://github.com/uiuc-iml/RA-SLAM © 2022 IEEE.

Compression of images of dermatological manifestations of paucisymptomatic COVID-19 patients through periodic principal components

Introduction: The management of medical images has been gaining followers based on the advantages it offers for the diagnosis of diseases, which, like COVID-19, present with clinical manifestations that can be captured in the form of images.Objective: Take advantage of the quasi-periodicity of the principal components (PCs) in the decomposition into PCs of medical images, which represent dermatological manifestations in paucisymptomatic patients of COVID-19.Methodology: Here, a set of photos was taken of one of the most frequent patterns in COVID-19, the maculopapular pattern, characterized by an erythmatopapular rash, and compression of one of the medical images was performed. Said compression was carried out in different ways: (1) using two PCs, (2) using both a periodic PC and a non-periodic PC, (3) using two periodic PCs, (4) using a single PC, and (5) using a single periodic PC. Result: The results of this research proved that it is possible to work with acceptable reconstructions of compressed images in the field of dermatology, without losing the quality and characteristics that allow to reach a correct diagnosis. In addition, this achievement permits to correctly classify many diseases without fear of being wrong.Conclusion: With the method presented, the use of a robust medical image compression technique that could be very useful in the field of health is proposed. The images allow the diagnosis of diseases such as COVID-19 in paucisymptomatic patients, understanding them allows minimizing their weight without losing quality, which facilitates their use and storage.

Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

Being an efficient image reconstruction and recognition algorithm, two-dimensional PCA (2DPCA) has an obvious disadvantage in that it treats the rows and columns of images unequally. To exploit the other lateral information of images, alternative 2DPCA (A2DPCA) and a series of bilateral 2DPCA algorithms have been proposed. This paper proposes a new algorithm named direct bilateral 2DPCA (DB2DPCA) by fusing bilateral information from images directly-that is, we concatenate the projection results of 2DPCA and A2DPCA together as the projection result of DB2DPCA and we average between the reconstruction results of 2DPCA and A2DPCA as the reconstruction result of DB2DPCA. The relationships between DB2DPCA and related algorithms are discussed under some extreme conditions when images are reshaped. To test the proposed algorithm, we conduct experiments of image reconstruction and recognition on two face databases, a handwritten character database and a palmprint database. The performances of different algorithms are evaluated by reconstruction errors and classification accuracies. Experimental results show that DB2DPCA generally outperforms competing algorithms both in image reconstruction and recognition. Additional experiments on reordered and reshaped databases further demonstrate the superiority of the proposed algorithm. In conclusion, DB2DPCA is a rather simple but highly effective algorithm for image reconstruction and recognition.

Exploring digital tourism application for medieval period reconstruction

Europe, with all its common sights, has an enviable wealth of history and cultural heritage. With its many monuments, sites, traditions, history, art, and culture, it has always attracted curious views and tells centuries-old stories to many tourists and visitors. At the heart of Europe, Bosnia and Herzegovina (BiH), founded in the 11th century, with its picturesque past, has always been at the crossroads of faith and civilizations. The key audience of tourism in BiH are nature lovers, adventurers and young and digital nomads, who represent great potential for the development of this sector given their nature of work, to be able to work from any location, and during the COVID-19 period. Furthermore, the importance of the diaspora for the development of tourism in BiH goes beyond tourist visits and helps BiH on its path to digital transformation. Digital tourism refers to how we use digital tools to organize, manage and even enjoy the travel experience. It uses all of the tools of digital transformation to change how we travel and experience different sites. Through digitalization, we want to reach every individual who passes through this country and further attract lovers of European history and culture, offer them a different, more creative, and innovative approach to learning about the cultural and historical treasures it hides. The goal of digital tourism is to raise awareness of the importance of cultural heritage, provide new opportunities for visitors and bring new knowledge. Therefore, this paper provides an overview of the possibilities of digital representations of the medieval historical period of BiH through identified pillars of digital reconstruction, and ways to connect the movable cultural heritage residing in the museums with real sites in an attempt to contribute to its promotion through digital tourism. © 2022 Copyright for this paper by its authors.

Automatic and Fast Whole Heart Segmentation for 3D Reconstruction

Accurate and fast whole cardiac substructures segmentation from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) is crucial in developing clinical applications, such as computer-aided surgery and computer-aided diagnosis. However, the segmentation of different substructures is challenging because of the amount of data that should be annotated by experts, the diversity of sizes and shapes of cardiac substructures, and the complexity of the background. This work aims to develop an automatic and fast whole heart segmentation including all cardiac substructures as well as the great vessels. The proposed approach used mainly image processing methods that enable the heart segmentation from sagittal, axial, and coronal views to obtain a 3D reconstruction. Finally, the experiments are conducted on both Automated Cardiac Diagnosis Challenge and CT scans acquired from a patient with COVID-19 at the Cheikh Zaid International University Hospital in Rabat Morocco. © 2022 IEEE.

Exploration of the Emergency Renovation of Sports Venues Resilience Space Based on Green Building Technology Under the Normal Epidemic Situation

The sudden outbreak of COVID-19 has caused a surge in medical demand. It has inspired people to continuously explore how to transform public buildings such as gymnasiums in a fast, low-cost and green way during emergencies. The article studies the feasibility of applying gymnasium to sudden public events, discusses the design methods for the renovation of gymnasium space, water supply and drainage system, ventilation system and intelligent system in emergency situations. The focus is on preventing cross-contamination, preventing backflow contamination, pressure shaving, airflow organization, and system control. Through these design methods, the gymnasium has the characteristics of efficient, adaptable and inclusive epidemic prevention. The application prospect of green building technology in emergency reconstruction was explored, and a reference is put forward for the design and reconstruction of gymnasiums in the post-epidemic era with “combination of epidemic control” and improving the resilience space of gymnasiums. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Performance Analysis of Wiener filter in Restoration of Covid-19 Chest X-Ray Images, Ultrasound Images and Mammograms

Medical images such as X-Ray images, Mammograms and Ultrasound images are very useful diagnostic techniques used for understanding the functions of different internal organs, bones, tissues, etc. Most of the times these medical images are degraded by some noises and different kinds of blur. Image blurring and degradation leads to loss of quality of images which in hand causes difficulty in proper diagnosis. This paper emphases on the efficacy of Wiener filter in image de blurring and denoising Chest X-Ray of Covid-19 patients, ultrasound images of fetal abdominal cyst, umbilical cord cyst and Common Carotid Artery, Mammogram of both pathological and non-pathological breasts. Performance of Wiener filter is analyzed using image restoration parameters like Structural Similarity (SSIM), Histogram, Peak Signal to Noise Ratio and Mean Square Error. © 2022 IEEE.

Resolving the Urban Dilemma of Two Adjacent Rivers through a Dialogue between GIS and Augmented Reality (AR) of Fabrics

Keelung Harbor, which is the most important center of sea freight in northern Taiwan, suffers from deteriorating urban development due to limited land supply. A dilemma arose from the Asahikawa River and the Tianliao River fronts, which evolved from cultural landscapes to buried and truncated rivers. This research was aimed at resolving the urban dilemma of the two adjacent rivers through a dialogue between the physical and augmented interaction of fabrics in three scenarios: GIS to AR, AR to GIS, and both. The physical dynamics were used to trace development chronologically by the area and length assessed from historical maps of hydrogeography, architecture, and the railroad. The augmented dynamics involved AR-based simulations and comparisons in terms of skyline overlay, fabric substitution, and fabric disposition. The dynamics involved AR models made by UAV images and 3D drawings. The assessments and simulations determined the key event in Keelung history when the Asahikawa River was leveled up. The dilemma verified from the augmented dynamics facilitated comprehension of the evolvement of the physical dynamics. With the assistance of AR and GIS, we concluded that the specific instance of riverfront reconstruction was an important landmark of meta-relationship.