Image-based System Research Articles

An Image-Based Augmented Reality System for Achieving Accurate Bone Resection in Total Knee Arthroplasty.

Background and objective With the steady advancement of computer-assisted surgical techniques, the importance of assessing and researching technology related to total knee arthroplasty (TKA) procedures has increased. Augmented reality (AR), a recently proposed next-generation technology, is expected to enhance the precision of orthopedic surgery by providing a more efficient and cost-effective approach. However, the accuracy of image-based AR in TKA surgery has not been established. Therefore, this study aimed to determine whether accurate bone resection can be achieved in TKA surgery using image-based AR. Methods In this study, we replaced traditional CT imaging and reconstructions for creating a bone 3D modelby direct 3D scanning of the femur and tibia. The preoperative planning involved identifying anatomical landmarks and determining the surgical details. During surgery, markers were employed to create a local coordinate system for an AR-assisted surgical system using a Polaris camera. This approach helped minimizediscrepancies between the 3D model and actual positioning, ensuring accurate alignment. Results The AR-assisted surgery using the image method resulted in fewer errors [average error: 0.32 mm; standard deviation (SD): 0.143] between the bone resection depth of the preoperative surgical plan and the bone model test results. Conclusions Our findings demonstrated the accuracy of bone resectioning by using image-based AR-assisted navigation for TKA surgery. Image-based AR-assisted navigation in TKA surgery is a valuable tool not only for enhancing accuracy by using smart glasses and sensors but also for improving the efficiency of the procedure. Therefore, we anticipate that image-based AR-assisted navigation in TKA surgery will gain wideacceptance in practice.

F2M: Ensemble-based uncertainty estimation model for fire detection in indoor environments

Early fire detection and timely notification are paramount for preventing human and material casualties caused by fire. As a result, scientists have developed various fire monitoring systems based on sensors and images. Image-based systems have proven more advantageous than sensor-based ones as they provide additional details about the fire, such as its location, intensity, and progression. However, accurately determining the shape and boundaries of flames in image-based systems is difficult due to various factors, including backgrounds, different fire sizes, and interference from objects that resemble flames. In this study, we first evaluate convolutional neural networks used in related research and benchmark them on our challenging indoor fire dataset. Secondly, we propose a novel Feature Merging Model (F2M), which combines the output fire segmentation masks of the top five segmentation models obtained through the performed evaluation. The F2M relies on a novel mechanism that introduces bias into the mask estimation and achieves better results than all the tested methods, along with Monte-Carlo dropout for uncertainty estimation. The F2M ensemble-based uncertainty estimation model achieved improvements in comparison to the best performance convolutional neural network U-Net++, on three different image resolutions: 256 × 256, 640 × 640 and 800 × 800, according to the following metrics: Total error, Dice coefficient, and IoU score.

Mosaic crack mapping of footings by convolutional neural networks

Cracks are the primary indicator informing the structural health of concrete structures. Frequent inspection is essential for maintenance, and automatic crack inspection offers a significant advantage, given its efficiency and accuracy. Previously, image-based crack detection systems have been utilized for individual images, yet these systems are not effective for large inspection areas. This paper thereby proposes an image-based crack detection system using a Deep Convolution Neural Network (DCNN) to identify cracks in mosaic images composed from UAV photos of concrete footings. UAV images are transformed into 3D footing models, from which the composite images are created. The CNN model is trained on 224 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 224 pixel patches, and training samples are augmented by various image transformation techniques. The proposed method is applied to localize cracks on composite images through the sliding window technique. The proposed VGG16 CNN detection system, with 95% detection accuracy, indicates superior performance to feature-based detection systems.

A Comparative Study of Human Pluripotent Stem Cell-Derived Macrophages in Modeling Viral Infections.

Macrophages play multiple roles in innate immunity including phagocytosing pathogens, modulating the inflammatory response, presenting antigens, and recruiting other immune cells. Tissue-resident macrophages (TRMs) adapt to the local microenvironment and can exhibit different immune responses upon encountering distinct pathogens. In this study, we generated induced macrophages (iMACs) derived from human pluripotent stem cells (hPSCs) to investigate the interactions between the macrophages and various human pathogens, including the hepatitis C virus (HCV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and Streptococcus pneumoniae. iMACs can engulf all three pathogens. A comparison of the RNA-seq data of the iMACs encountering these pathogens revealed that the pathogens activated distinct gene networks related to viral response and inflammation in iMACs. Interestingly, in the presence of both HCV and host cells, iMACs upregulated different sets of genes involved in immune cell migration and chemotaxis. Finally, we constructed an image-based high-content analysis system consisting of iMACs, recombinant GFP-HCV, and hepatic cells to evaluate the effect of a chemical inhibitor on HCV infection. In summary, we developed a human cell-based in vitro model to study the macrophage response to human viral and bacterial infections; the results of the transcriptome analysis indicated that the iMACs were a useful resource for modeling pathogen-macrophage-tissue microenvironment interactions.

Developing an easy-to-use image-based system for offline tool-wear detection

Tool wear plays a critical role in the machining industry. Worn tools can significantly affect the workpiece's dimensional accuracy. However, with the increasing popularity of operating cutting machines without enough machining background, such as the technician operating the milling machine in a dental clinic, nonprofessionals may not know the tool replacement timings to reduce the production waste caused by the worn tools. If a tool wears too much so that the finished workpiece is dimensionally inaccurate, then rebuilding another workpiece will cost valuable time and resources. On the other hand, if a tool is replaced too early, the cost of purchasing tools will increase. This research aims to address these challenges. The objective of this research was to develop an easy-to-use image-based system for tool wear detection, which was built with inexpensive components and can automatically measure cutting-edge widths of cutting tools using image processing techniques. The practical implication of the proposed tool wear detection system is that it can automatically measure cutting-edge widths at various cutting depths of a tool, which can allow nonprofessionals to understand how much dimensional change has been made before and after the tool is used. The repeatability of the proposed system was only 4 µm, which means that the system can effectively detect the width change greater than 8 µm at a 95% confidence level. For the 2 mm ball end milling tool used in the research, the width difference from a new tool to an end-of-life tool was 57 µm. The proposed system can detect the width difference caused by tool wear so that the technician can replace the tool in time using the proposed system.

Comparative review of advanced seatbelt detection: Infrared sensors vs. image-based systems

The seatbelt, often heralded as the first line of defense in vehicular safety, plays an indispensable role in mitigating the risks associated with traffic incidents. Consequently, the push towards enhancing transportation safety has led to the emergence of sophisticated seatbelt detection systems. In this study, we undertake a comprehensive examination of four cutting-edge detection methodologies: RF (Radio Frequency), Infrared Marker Vision, Convolutional Neural Networks (CNN), and the You Only Look Once (YOLO) approach. Each method is dissected to understand its efficacy in determining if a passenger has properly donned their seatbelt. Beyond just immediate detection, this paper also casts a vision towards the horizon of seatbelt detection advancements. We explore how such detection systems might seamlessly integrate with advanced vehicle safety infrastructures, and postulate on their pivotal role in the burgeoning domain of autonomous vehicles. As self-driving cars become an imminent reality, the importance of reliable seatbelt detection mechanisms will only magnify. Thus, through this paper, we not only shed light on current methodologies but also endeavor to chart the trajectory of future innovations in the realm of seatbelt safety detection.

An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks

This study proposes the utilization of an optical fiber vibration sensor for detecting the superposition of extremely close frequencies in vibration signals. Integration of deep neural networks (DNN) proves to be meaningful and efficient, eliminating the need for signal analysis methods involving complex mathematical calculations and longer computation times. Simulation results of the proposed model demonstrate the remarkable capability to accurately distinguish frequencies below 1 Hz. This underscores the effectiveness of the proposed image-based vibration signal recognition system embedded in DNN as a streamlined yet highly accurate method for vibration signal detection, applicable across various vibration sensors. Both simulation and experimental evaluations substantiate the practical applicability of this integrated approach, thereby enhancing electric motor vibration monitoring techniques.

Spacecraft Formation Keeping and Reconfiguration Using Optimal Visual Servoing

This paper proposes a direct visual servoing system for spacecraft guidance in formation flying scenarios. The proposed image-based visual servoing system uses image information for planning and executing formation acquisition, reconfiguration, and maintenance maneuvers. The system assumes that LEDs are located at specific points on the satellites, enabling the visual servoing controller to rely on continuous tracking of these features in the camera’s image plane. Analytical developments demonstrate that the proposed optimal visual control system is stable and optimal, and it acts on both the orbital and attitude dynamics of the spacecraft, considering circular and elliptical reference orbits. The distributed image-based controller defines a cost function that minimizes control efforts, and the paper proses an optimal framework for developing controllers that address the issue. A ROS-based simulation tool was used to test the proposed visual servoing controller in a realistic small-sat formation flying scenario. Results indicate that the proposed distributed control strategy is viable and robust against environmental perturbations and disturbances in sensing and actuation.

Skin Cancer Recognition Using Unified Deep Convolutional Neural Networks.

The incidence of skin cancer is rising globally, posing a significant public health threat. An early and accurate diagnosis is crucial for patient prognoses. However, discriminating between malignant melanoma and benign lesions, such as nevi and keratoses, remains a challenging task due to their visual similarities. Image-based recognition systems offer a promising solution to aid dermatologists and potentially reduce unnecessary biopsies. This research investigated the performance of four unified convolutional neural networks, namely, YOLOv3, YOLOv4, YOLOv5, and YOLOv7, in classifying skin lesions. Each model was trained on a benchmark dataset, and the obtained performances were compared based on lesion localization, classification accuracy, and inference time. In particular, YOLOv7 achieved superior performance with an Intersection over Union (IoU) of 86.3%, a mean Average Precision (mAP) of 75.4%, an F1-measure of 80%, and an inference time of 0.32 s per image. These findings demonstrated the potential of YOLOv7 as a valuable tool for aiding dermatologists in early skin cancer diagnosis and potentially reducing unnecessary biopsies.

Abstract 4247: Miniaturizing patient-derived organoid screening assays for predictive drug testing

Abstract Colorectal cancer (CRC) ranks as the third leading cause of both cancer incidence and cancer-related mortality. A significant portion of CRC patients diagnosed with de novo metastatic disease face limited benefits from conventional treatment options, often accompanied by substantial side effects. This underscores the pressing need for more effective preclinical models to predict patient responses in clinical settings. Patient-derived organoids (PDOs or HUB Organoids®) represent a pivotal breakthrough in preclinical modeling. These organoids are directly derived from patient tissue, faithfully mirroring the patient's disease. While HUB Organoid Technology is currently applicable for preclinical drug screening, it is essential to expedite the transition from patient diagnosis to the delivery of PDO-based results to directly benefit patients. In a collaborative effort with Yamaha Motor, we have optimized the Yamaha CELL HANDLERTM for the automated transfer of organoids, offering enhanced precision and efficiency in handling a significantly reduced number of organoids per screening plate, compared to conventional methods. Moreover, we have developed an image-based readout system that enables precise quantification of organoid numbers, ensuring high assay quality. Utilizing this developed automated platform, we conducted a proof-of-concept investigation into PDO responses to chemotherapy in comparison to corresponding patient responses. The correlation between PDO and patient responses demonstrated a strong connection, highlighting the potential of the developed automated platform for predictive drug testing. In summary, this work signifies the development of an automated workflow that synergizes patient-representative HUB Organoids® with cutting-edge robotics from Yamaha Motor. The described workflow reduces the number of organoids needed per screening well, streamlining patient response prediction, potentially leading to faster diagnoses, and ultimately optimizing patient outcomes. Citation Format: Yasmine Abouleila, Roel Verkerk, Mayke Doorn, Timo Voskuilen, Gakuro Harada, Masahiko Watanabe, Lidwien Smabers, Hideaki Kyan, Takahiko Kumagai, Yuichi Hikichi, Rene Overmeer, Jeanine Roodhart, Kiyotaka Matsuno, Carla Verissimo, Sylvia Boj. Miniaturizing patient-derived organoid screening assays for predictive drug testing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4247.

Computerized Optical Impression Making of Fully Dentate Upper and Lower Jaws: An In Vitro Study

Objectives: The aim of this experimental study was to evaluate the accuracy of five intraoral scanners for digitizing fully dentate unprepared maxillae and mandibulae in vitro. Materials and Methods: One maxillary and one mandibular reference model with acrylic teeth, an industrial grade reference scanner, 3D evaluation software and the intraoral scanners CS 3500, iTero HD2.9, Planmeca PlanScan, TRIOS Standard and 3M True Definition were used. Scans of the entire arches, one front and two side segments of each arch scan of maxilla and mandibula were evaluated separately for trueness and precision. In addition, visual analyses of deviation patterns, surface properties and approximal areas were performed with the aid of 3D evaluation software. Results: The intraoral scanners CS 3500, TRIOS Standard and iTero HD2.9 showed a similar level of trueness. The True Definition scanner showed lower full arch trueness compared to the TRIOS Standard and to the iTero HD2.9 (p < 0.05). Full arch trueness of the PlanScan was lower compared to the other scanners. Video-based systems showed higher numbers of datapoints per scan (127,300–169,730) compared to single image-based systems (64,115–88,124). The acquisition of interproximal areas was insufficient across all scanners. Limitations: The intraoral scanners were not tested under clinical conditions in this study. Conclusions: Apart from interproximal areas, clinically acceptable full arch trueness was achieved by the CS 3500, the iTero HD2.9 and the TRIOS Standard.

Artificial and Deceitful Faces Detection Using Machine Learning

Security certification is becoming popular for many applications, such as significant financial transactions. PIN and password authentication is the most common method of authentication. Due to the finite length of the password, the security level is low and can be easily damaged. Adding a new dimension to the sensing mode-driven state-of-the-art multi-modal boundary face recognition system of the image-based solutions. It combines the active complex visual features extracted from the latest facial recognition model and uses a custom Convolution Neural Network issue facial authentications and extraction capabilities to ensure the safety of face recognition. The Echo function is dependent on the geometry and material of the face, not disguised by the pictures and videos, such as multi-modal design is easy to image-based face recognition system

A multi-level privacy-preserving scheme for extracting traffic images

Traffic images are constantly used as a stick to assess traffic conditions. Traffic flow statistical analysis, road condition safety monitoring, vehicle violation detection, accident surveillance, and the driving environment perception of autonomous vehicles are all functionalities that depend on the processing of traffic images. However, traffic images contain privacy-sensitive information related to users, such as license plate numbers, drivers and passengers. Extracting, analyzing and sharing such privacy-sensitive information without any security measures may raise concerns among users about potential privacy violations. This paper proposes a multi-level privacy protection scheme for traffic image extraction based on compressive sensing, which has the advantages of data undersampling, privacy protection and data access control. Detailed compression rate analysis demonstrates that the proposed solution can effectively reduce the transmission load of image information. Security analysis demonstrates that the proposed approach achieves multi-level reconstruction qualities and high-security strength for users with different authorities. Thus, it has a good application prospect in an image-based intelligent traffic management system.

Imageless robotic total knee arthroplasty determines similar coronal plane alignment of the knee (CPAK) parameters to long leg radiographs

BackgroundThe coronal plane alignment of the knee (CPAK) classification was first developed using long leg radiographs (LLR) and has since been reported using image-based and imageless robotic total knee arthroplasty (TKA) systems. However, the correspondence between imageless robotics and LLR-derived CPAK parameters has yet to be investigated. This study therefore examined the differences in CPAK parameters determined with LLR and imageless robotic navigation using either generic or optimized cartilage wear assumptions.MethodsMedial proximal tibial angle (MPTA) and lateral distal femoral angle (LDFA) were determined from the intraoperative registration data of 61 imageless robotic TKAs using either a generic 2 mm literature-based wear assumption (Navlit) or an optimized wear assumption (Navopt) found using an error minimization algorithm. MPTA and LDFA were also measured from preoperative LLR by two observers and intraclass correlation coefficients (ICCs) were calculated. MPTA, LDFA, joint line obliquity (JLO), and arithmetic hip-knee-ankle angle (aHKA) were compared between the robotic and the average LLR measurements over the two observers.ResultsICCs between observers for LLR were over 0.95 for MPTA, LDFA, JLO, and aHKA, indicating excellent agreement. Mean CPAK differences were not significant between LLR and Navlit (all differences within 0.6°, P > 0.1) or Navopt (all within 0.1°, P > 0.83). Mean absolute errors (MAE) between LLR and Navlit were: LDFA = 1.4°, MPTA = 2.0°, JLO = 2.1°, and aHKA = 2.7°. Compared to LLR, the generic wear classified 88% and the optimized wear classified 94% of knees within one CPAK group. Bland–Altman comparisons reported good agreement for LLR vs. Navlit and Navopt, with > 95% and > 91.8% of measurements within the limits of agreement across all CPAK parameters, respectively.ConclusionsImageless robotic navigation data can be used to calculate CPAK parameters for arthritic knees undergoing TKA with good agreement to LLR. Generic wear assumptions determined MPTA and LDFA with MAE within 2° and optimizing wear assumptions showed negligible improvement.

IGDM: Image-Based Grading System of Downy Mildew in Cucumber Using Digital Image Processing and Unsupervised Learning

IGDM: Image-Based Grading System of Downy Mildew in Cucumber Using Digital Image Processing and Unsupervised Learning

Predicting Time-to-Healing from a Digital Wound Image: A Hybrid Neural Network and Decision Tree Approach Improves Performance

Despite the societal burden of chronic wounds and despite advances in image processing, automated image-based prediction of wound prognosis is not yet in routine clinical practice. While specific tissue types are known to be positive or negative prognostic indicators, image-based wound healing prediction systems that have been demonstrated to date do not (1) use information about the proportions of tissue types within the wound and (2) predict time-to-healing (most predict categorical clinical labels). In this work, we analyzed a unique dataset of time-series images of healing wounds from a controlled study in dogs, as well as human wound images that are annotated for the tissue type composition. In the context of a hybrid-learning approach (neural network segmentation and decision tree regression) for the image-based prediction of time-to-healing, we tested whether explicitly incorporating tissue type-derived features into the model would improve the accuracy for time-to-healing prediction versus not including such features. We tested four deep convolutional encoder–decoder neural network models for wound image segmentation and identified, in the context of both original wound images and an augmented wound image-set, that a SegNet-type network trained on an augmented image set has best segmentation performance. Furthermore, using three different regression algorithms, we evaluated models for predicting wound time-to-healing using features extracted from the four best-performing segmentation models. We found that XGBoost regression using features that are (i) extracted from a SegNet-type network and (ii) reduced using principal components analysis performed the best for time-to-healing prediction. We demonstrated that a neural network model can classify the regions of a wound image as one of four tissue types, and demonstrated that adding features derived from the superpixel classifier improves the performance for healing-time prediction.

Fast and high-precision tracking technology for image-based closed-loop cascaded control system with a Risley prism and fast steering mirror.

The Risley prism's compact structure, dynamic responsiveness, and high tracking accuracy make it ideal for photoelectric image tracking. To realize fast and high-precision tracking of the target, we propose an image-based closed-loop tracking cascade control (IBCLTCR-F) system using a single image detector that integrates the Risley prism and fast steering mirror (FSM). Firstly, We propose a cascade control input-decoupling method (CCIDM) for the IBCLTCR-F system to solve the complex problem of coarse-fine control input decoupling in traditional single detector cascaded control systems. Moreover, the CCIDM method ensures that the FSM deflection angle is small and does not exceed its range during the fine tracking process, by using the Risley prism to compensate for the FSM deflection angle. Next, we design the image-based closed-loop tracking controllers of the Risley prism system and FSM system and analyze the stability of the IBCLTCR-F system. Finally, we track static and moving targets through experiments. The experimental results verify the feasibility of the IBCLTCR-F system, the effectiveness of the decoupling method, and the fast and high-precision tracking of the targets.

Metrological characterisation of tool pre-setting optical systems based on 2D imaging

Accurate tool geometry is vital in modern computer numerical control machine tools for ensuring the accuracy of the manufactured parts in machining processes. The tool pre-setting optical systems play an important role by establishing accurate, efficient, and repeatable tool data for cutting tools varying from macro to micro size tools. The tool data is later utilised by the numerical control programs to accomplish precision machining operations with the required accuracy. However, the traceability of tool pre-setting optical systems is not guaranteed due to the lack of specific international standards, and there is a strong need of establishing a traceable measurement process which requires calibrated reference artefacts. In this work, we have established the metrological characteristics of an image-based tool pre-setting optical system based on ISO 15530 part 3. For this purpose, an artefact with sharp cutting edges was manufactured from a calibrated cylindrical gauge pin, measured on a coordinate measuring machine by two different approaches, and the impact of the edge radius is incorporated in the measurement process. The artefact was later employed in the performance verification of the optical system for on-machine tool characterization analogous to the production workshop environment, and the measured outcome of the optical sensor was evaluated with the developed metrological approaches. Experiments have shown the applicability of the measurement procedures and the relevance of the proposed reference artefact for the characterisation of image-based tool pre-setting optical systems.

A Survey on Comprehensive Exploration of Modern Perception System

This review paper offers an in-depth analysis of recent advancements in image-based intelligent transportation systems. It provides insights into various approaches employed for detecting false images, license plate recognition, distributing emergency messages, as well as detecting and classifying vehicle accidents and road accidents. The research critically evaluates the pros and cons of deep learning techniques, including position-based routing, enhanced YOLOv8, optimal K-means with a convolutional neural network, and gated hierarchical multi-task learning. The investigation of an Internet of Things-based vehicle accident detection and classification system highlights the benefits of sensor fusion for precise accident detection; nonetheless, the evaluation raises questions regarding the scalability and reliability of smartphone sensors. The utility of a deep learning system for road accident identification utilizing artificially created multi-perspective movies is emphasized, along with warnings about possible limits in capturing real-world circumstances

Tripod Fixation of Periacetabular Metastatic Lesions Using the IlluminOss Device.

Percutaneous tripod fixation of periacetabular lesions is performed at our institution for patients with metastatic bone disease and a need for quick return to systemic therapy. We have begun to use the IlluminOss Photodynamic Bone Stabilization System instead of the metal implants previously described in the literature because of the success of the IlluminOss implant in fixing fragility fractures about the pelvis. At our institution, the procedure is performed in the interventional radiology suite in order to allow for the use of 3D radiographic imaging and vector guidance systems. The patient is positioned prone for the transcolumnar PSIS-to-AIIS implant and posterior column/ischial tuberosity implant or supine for the anterior column/superior pubic ramus implant. Following a small incision, a Jamshidi needle with a trocar is utilized to enter the bone at the chosen start point. A hand drill is utilized to advance the Jamshidi needle according to the planned vector; alternatively, a curved or straight awl can be utilized. The 1.2-mm guidewire is placed and reamed. We place both the transcolumnar and posterior column wires at the same time to ensure that there is no interference. The balloon catheter for the IlluminOss is assembled on the back table and inserted according to the implant technique guide. The balloon is inflated and observed on radiographs in order to ensure that the cavity is filled. Monomer is then cured, and the patient is flipped for the subsequent implant. Following placement of the 3 IlluminOss devices, adjunct treatments such as cement acetabuloplasty or cryoablation can be performed. Alternative treatments include traditional open fixation of impending or nondisplaced acetabular fractures in the operating room, or percutaneous implant placement in the operating room. Implant placement may be performed with the patient in the supine, lateral, or prone position, depending on surgeon preference. Alternative implants include standard metal implants such as plates and screws, or cement augmentation either alone or with percutaneous screws. Finally, ablation alone may be an alternative option, depending on tumor histology. Open treatment of acetabular fractures is a more morbid procedure, given the larger incision, increased blood loss, longer time under anesthesia, and increased length of recovery. Percutaneous fixation may be performed in either the operating room or interventional radiology suite, depending on the specific equipment setup at an individual institution. At our institution, we prefer utilizing the interventional radiology suite as it allows for more precise implant placement through the use of an image-based vector guidance system and 3D fluoroscopy to accurately identify safe corridors. The use of percutaneous fixation allows for faster recovery and earlier return to systemic therapy. Because the IlluminOss implant is radiolucent, it allows for better evaluation of disease progression and can better accommodate nonlinear corridors or fill a lytic lesion to provide stability. Postoperatively, we expect the patient to be weight-bearing as tolerated with use of an assistive device. We expect the small incisions to fully heal within 2 weeks. Patients should be able to return to systemic therapy as indicated earlier than with an open procedure. The use of a hand drill with the Jamshidi needle and trocar can help adjust a drilled pathway and allow for close adherence to a planned vector.Vector guidance systems can be useful to fully capture the area at risk for fracture and to provide maximal stability with the expandable implant, but they are not necessary to perform the procedure.Placing both posterior implants at the same time can be helpful to avoid interference. This is accomplished by drilling and placing the guidewire for both implants prior to reaming and placing the balloon implant. CT = Computed tomographyPSIS = posterior superior iliac spineAIIS = anterior inferior iliac spine.