Image-guided Interventions Research Articles

Artifact characterization of Nitinol needles in magnetic resonance imaging-guided musculoskeletal interventions at 3.0 tesla: a phantom study.

To characterize the artifacts of an 18-gauge coaxial nickel-titanium needle using a balanced steady-state free precession sequence in magnetic resonance imaging-guided interventions at 3.0 tesla. The influence of flip angle (FA), bandwidth, matrix, slice thickness (ST), and read-out direction on needle artifact behavior was investigated for different intervention angles (IA). Artifact diameters were rated at predefined positions. Subgroup differences were assessed using Bonferroni-corrected non-parametric tests and correlations between continuous variables were expressed using the Bravais-Pearson coefficient. Interrater reliability was quantified using intraclass correlation coefficients (ICCs), and a contrast-enhanced target lesion to non-enhanced muscle tissue contrast ratio was quantified. The artifact diameters decreased with an increase in FA for all IAs (P < 0.001) and with an increase in ST for IAs of 45°-90° (all P < 0.05). Tip artifacts occurred at low IAs (0°-45°) and gradually increased in size with a decrease in IA (P = 0.022). The interrater reliability was high (ICC: 0.994-0.999). The contrast-enhanced target lesion to non-enhanced muscle tissue contrast ratio presented positive correlations with increasing FAs and matrices (P < 0.001; P = 0.003) and a negative correlation with increasing STs (P = 0.007). To minimize needle artifacts, it is recommended to use FAs of 40°-60°, a ST of >7 mm, and, if possible, an IA of 45°-60°. The visibility of the target lesion and the needle's artifact behavior must be weighed up against each other when choosing the ST, while higher FAs (40°-60°) and matrices (224 × 224/256 × 256) are associated with low artifacts and sufficient lesion visibility.

Ultrasound Probe Calibration Method of Single-Wire Phantom Using Levenberg-Marquardt Algorithm

A freehand three-dimensional (3D) ultrasound system is a method of acquiring images using a 3D ultrasound probe or conventional two-dimensional (2D) ultrasound probe to give a 3D visualization of an object inside the body. Ultrasounds are used extensively in clinical applications since they are advantageous in that they do not bring dangerous radiation effects and have a low cost. However, a probe calibration method is needed to transform the coordinate position into a 3D visualization display, especially for image-guided intervention. The current ultrasound probe calibration system usually uses the numerical regression method for the N-wire phantom, which has problems in accuracy and reliability due to nonlinear point scattered ultrasound image data. Hence, a method for ultrasound probe positional calibration of single-wire phantom using the Levenberg-Marquardt algorithm (LMA) was proposed to overcome this weakness. This experiment consisted of an optical tracking system setup, a 2D ultrasound probe with marker, an ultrasound machine, and a single-wire object in a water container equipped with a marker. The position and orientation of the marker in a 2D ultrasound probe and the marker in the water container were tracked using the optical tracking system. A 2D ultrasound probe was equipped with a marker connected wirelessly using an optical tracking system to capture the single-wire object. The resulting sequences of 2D ultrasound images were reconstructed and visualized into 3D ultrasound images using three transformations, ultrasound beam to ultrasound probe’s marker, single-wire phantom position to container’s marker, and the 3D visualization transformation. The LMA was used to determine the best optimization parameters for determining the exact position and representing that 3D visualization. The experiment result showed that the lowest mean square error (MSE), rotation error, and translation error were 0.45 mm, 0.25°, and 0.3828 mm, respectively.

Review of Image-Guided Pleural Interventions

Review of Image-Guided Pleural Interventions

Arc-to-line frame registration method for ultrasound and photoacoustic image-guided intraoperative robot-assisted laparoscopic prostatectomy.

To achieve effective robot-assisted laparoscopic prostatectomy, the integration of transrectal ultrasound (TRUS) imaging system which is the most widely used imaging modality in prostate imaging is essential. However, manual manipulation of the ultrasound transducer during the procedure will significantly interfere with the surgery. Therefore, we propose an image co-registration algorithm based on a photoacoustic marker (PM) method, where the ultrasound/photoacoustic (US/PA) images can be registered to the endoscopic camera images to ultimately enable the TRUS transducer to automatically track the surgical instrument. An optimization-based algorithm is proposed to co-register the images from the two different imaging modalities. The principle of light propagation and an uncertainty in PM detection were assumed in this algorithm to improve the stability and accuracy of the algorithm. The algorithm is validated using the previously developed US/PA image-guided system with a da Vinci surgical robot. The target-registration-error (TRE) is measured to evaluate the proposed algorithm. In both simulation and experimental demonstration, the proposed algorithm achieved a sub-centimeter accuracy which is acceptable in practical clinics (i.e., 1.15 ± 0.29mm from the experimental evaluation). The result is also comparable with our previous approach (i.e., 1.05 ± 0.37mm), and the proposed method can be implemented with a normal white light stereo camera and does not require highly accurate localization of the PM. The proposed frame registration algorithm enabled a simple yet efficient integration of commercial US/PA imaging system into laparoscopic surgical setting by leveraging the characteristic properties of acoustic wave propagation and laser excitation, contributing to automated US/PA image-guided surgical intervention applications.

Intelligent contour extraction approach for accurate segmentation of medical ultrasound images.

Introduction: Accurate contour extraction in ultrasound images is of great interest for image-guided organ interventions and disease diagnosis. Nevertheless, it remains a problematic issue owing to the missing or ambiguous outline between organs (i.e., prostate and kidney) and surrounding tissues, the appearance of shadow artifacts, and the large variability in the shape of organs. Methods: To address these issues, we devised a method that includes four stages. In the first stage, the data sequence is acquired using an improved adaptive selection principal curve method, in which a limited number of radiologist defined data points are adopted as the prior. The second stage then uses an enhanced quantum evolution network to help acquire the optimal neural network. The third stage involves increasing the precision of the experimental outcomes after training the neural network, while using the data sequence as the input. In the final stage, the contour is smoothed using an explicable mathematical formula explained by the model parameters of the neural network. Results: Our experiments showed that our approach outperformed other current methods, including hybrid and Transformer-based deep-learning methods, achieving an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 95.7 ± 2.4%, 94.6 ± 2.6%, and 95.3 ± 2.6%, respectively. Discussion: This work develops an intelligent contour extraction approach on ultrasound images. Our approach obtained more satisfactory outcome compared with recent state-of-the-art approaches . The knowledge of precise boundaries of the organ is significant for the conservation of risk structures. Our developed approach has the potential to enhance disease diagnosis and therapeutic outcomes.

BOSS: Bones, organs and skin shape model

A virtual anatomical model of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. For these tasks, it is highly desirable that the patient’s surface and internal organs are of high quality for any pose and shape estimate. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can be beneficial for automation in various medical applications. In an interventional setup, our model could, for example, facilitate automatic system/patient positioning, organ-specific iso-centering, automated collimation or collision prediction. We assessed our model’s performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. By utilizing solely skin surface data or patient metadata like height and weight, we find that the overall combined error for bone-organ measurement is 8.68 mm and 8.11 mm, respectively. To further verify our findings, we conducted additional tests on publicly available datasets with multi-part segmentations, which confirmed the effectiveness of our model. In the diverse TotalSegmentator dataset, the errors for bones and organs are observed to be 5.10mm and 8.72mm, respectively. Our work shows that anatomically parameterized statistical shape models can be created accurately and in a computationally efficient manner. The proposed approach enables the construction of shape models that can be directly integrated into to various medical applications.

FPGA Implementation of Image Registration Using Accelerated CNN.

Accurate and fast image registration (IR) is critical during surgical interventions where the ultrasound (US) modality is used for image-guided intervention. Convolutional neural network (CNN)-based IR methods have resulted in applications that respond faster than traditional iterative IR methods. However, general-purpose processors are unable to operate at the maximum speed possible for real-time CNN algorithms. Due to its reconfigurable structure and low power consumption, the field programmable gate array (FPGA) has gained prominence for accelerating the inference phase of CNN applications. This study proposes an FPGA-based ultrasound IR CNN (FUIR-CNN) to regress three rigid registration parameters from image pairs. To speed up the estimation process, the proposed design makes use of fixed-point data and parallel operations carried out by unrolling and pipelining techniques. Experiments were performed on three US datasets in real time using the xc7z020, and the xcku5p was also used during implementation. The FUIR-CNN produced results for the inference phase 139 times faster than the software-based network while retaining a negligible drop in regression performance of under 200 MHz clock frequency. Comprehensive experimental results demonstrate that the proposed end-to-end FPGA-based accelerated CNN achieves a negligible loss, a high speed for registration parameters, less power when compared to the CPU, and the potential for real-time medical imaging.

Impact of cardiac and respiratory motion on the 3D accuracy of image-guided interventions on monoplane systems

PurposeImage-guided intervention (IGI) systems have the potential to increase the efficiency in interventional cardiology but face limitations from motion. Even though motion compensation approaches have been proposed, the resulting accuracy has rarely been quantified using in vivo data. The purpose of this study is to investigate the potential benefit of motion-compensation in IGS systems.MethodsPatients scheduled for left atrial appendage closure (LAAc) underwent pre- and postprocedural non-contrast-enhanced cardiac magnetic resonance imaging (CMR). According to the clinical standard, the final position of the occluder device was routinely documented using x-ray fluoroscopy (XR). The accuracy of the IGI system was assessed retrospectively based on the distance of the 3D device marker location derived from the periprocedural XR data and the respective location as identified in the postprocedural CMR data.ResultsThe assessment of the motion-compensation depending accuracy was possible based on the patient data. With motion synchronization, the measured accuracy of the IGI system resulted similar to the estimated accuracy, with almost negligible distances of the device marker positions identified in CMR and XR. Neglection of the cardiac and/or respiratory phase significantly increased the mean distances, with respiratory motion mainly reducing the accuracy with rather low impact on the precision, whereas cardiac motion decreased the accuracy and the precision of the image guidance.ConclusionsIn the presented work, the accuracy of the IGI system could be assessed based on in vivo data. Motion consideration clearly showed the potential to increase the accuracy in IGI systems. Where the general decrease in accuracy in non-motion-synchronized data did not come unexpected, a clear difference between cardiac and respiratory motion-induced errors was observed for LAAc data. Since sedation and intervention location close to the large vessels likely impacts the respiratory motion contribution, an intervention-specific accuracy analysis may be useful for other interventions.

Awareness of interventional radiology and image guided musculoskeletal interventions among orthopaedic surgeons, rheumatologists, and physiatrists: A multi-site analysis

PurposeEvaluate physicians who treat musculoskeletal (MSK) disorders in their knowledge of image-guided MSK interventions, and identify areas that could benefit from education. Materials and methodsA 17-question survey was distributed to orthopaedic surgeons, physiatrists, and rheumatologists in the 14-hospital health system. It inquired about demographics, practice environment, awareness of interventional radiology (IR) and MSK radiology (MSKR) training, referral patterns, and knowledge of image-guided MSK interventions. ResultsIn total, 59 of 303 physicians completed the survey (41% orthopaedists, 35% physiatrists, and 24% rheumatologists). Most (93%) were attendings and 41% were female. A minority of survey respondents (17%) recognized the designation of IR as a distinct specialty of medicine per the American Board of Medical Specialties, in contrast to MSKR, which is not designated as a distinct specialty. When queried about IR procedures not under investigation, 24% selected genicular artery embolization and 31% selected embolization for adhesive capsulitis. Barriers to referral were as follows: 21% of specialists performed the procedure, 17% listed electronic medical record challenges, 14% reported scheduling difficulty, 13% reported no barriers, 11% reported difficulty consulting, 11% referred to another specialty, 10% did not have enough knowledge of image guided procedures, and 3% reported the procedure is not performed by IR or MSKR. ConclusionsSurvey data reveal a knowledge gap among surveyed physicians regarding IR as a specialty as well as areas of IR research in MSK disorders. Findings suggest areas where referring physicians can be educated and identify barriers to referral.

Basic Concepts and Insights into Aortopulmonary Collateral Arteries in Congenital Heart Diseases.

Aortopulmonary collateral arteries are persistent embryological vessels supplying lung parenchyma in various cardiopulmonary diseases with underlying pulmonary hypoperfusion. Their identification and mapping are important because of associated clinical implications and tendency to affect the surgical outcome. This article describes the embryological development and clinical relevance of aortopulmonary collaterals in various congenital cardiopulmonary conditions, along with the significance for treatment planning. Roles, strength, and shortcomings of the various imaging options and image-guided interventions are discussed, with a focus on presurgical planning and preparation, as well as postsurgical management.

Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction

Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guided interventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings because they are limited to specific breathing phases, lack temporal/spatial resolution, and have long prior acquisitions or reconstruction times. Deep learning-based (DL) 4D MRI approaches promise to overcome these shortcomings but are sensitive to domain shift. This work shows that transfer learning (TL) combined with an ensembling strategy can help alleviate this key challenge. We evaluate four approaches: pre-trained models from the source domain, models directly trained from scratch on target domain data, models fine-tuned from a pre-trained model and an ensemble of fine-tuned models. For that the data base was split into 16 source and 4 target domain subjects. Comparing ensemble of fine-tuned models (N = 10) with directly learned models, we report significant improvements (P < 0.001) of the root mean squared error (RMSE) of up to 12% and the mean displacement (MDISP) of up to 17.5%. The smaller the target domain data amount, the larger the effect. This shows that TL + Ens significantly reduces beforehand acquisition time and improves reconstruction quality, rendering it a key component in making 4D MRI clinically feasible for the first time in the context of 4D organ motion models of the liver and beyond.

Review of genicular artery embolization, radiofrequency ablation, and cryoneurolysis in the management of osteoarthritis-related knee pain.

Osteoarthritis (OA) of the knee represents one of the most common diseases in the world, affecting an estimated 14 million people in the United States alone. Exercise therapy and oral pain medication are first-line treatments but have limited efficacy. Next-line treatments such as intra-articular injections are limited in durability. Moreover, total knee replacements, although effective, require surgical intervention, which has considerable variability in patient satisfaction. Novel minimally invasive image-guided interventions are becoming more widespread for treating OA-related knee pain. Recent studies of these interventions have revealed promising results, minor complications, and reasonable patient satisfaction. In this study, published manuscripts were reviewed in the field of minimally invasive, image-guided interventions for OA-related knee pain, with a focus on genicular artery embolization, radiofrequency ablation, and cryoneurolysis. Recent studies have demonstrated a significant decrease in pain-related symptoms following these interventions. Reported complications were mild in the reviewed studies. Image-guided interventions for OA-related knee pain exist as valuable options for patients who fail other therapies, may not be good surgical candidates, or wish to avoid surgical intervention. Further studies with randomization and an increased length of follow-up are needed to better characterize outcomes following these minimally invasive therapies.

The role of percutaneous radiological guided interventions in the management of biliary leakage

Background Percutaneous image-guided biliary interventions is an important tool to deal with bile leaks. This prospective study was conducted from March 2021 to April 2023 aiming to clarify the role of image-guided interventions in the management of bile leaks, preventing its devastating consequences. Patients and methods We prospectively studied 30 patients diagnosed with bile leakage referred to our department between March 2021 to April 2023. We analyzed cause and time of bile leak presentation, percutaneous biliary drainage technique, and additional procedures when required, the technical and clinical success of the procedures, leak healing timing, and complication rates. Results Bile leak was suspected within a mean of 7 days (range from day 0–17 days) after the initial incident. In all patients image-guided biliary tree percutaneous access was attempted. An external biliary drain was inserted in 13 cases, 17 patients had an internal-external biliary drain crossing the leaking site. Complete leakage healing following PTBD was achieved in 20 of 30 patients (66.7%) which occurred in an average of 87 days (range 45–130 days). No major complications were encountered after the procedure. Conclusions PTBD is an effective, safe and feasible approach to deal with biliary leaks. It can be an alternative to tedious complex surgical attempt when ERCP is not applicable, which entails higher burden regarding patient morbidity and mortality.

Photon counting-energy integrating hybrid flat panel detector systems for image-guided interventions: an experimental proof-of-concept

Objective. Current C-arm x-ray systems equipped with scintillator-based flat panel detectors (FPDs) lack sufficient low-contrast detectability and spectral, high-resolution capabilities much desired for certain interventional procedures. Semiconductor-based direct-conversion photon counting detectors (PCDs) offer these imaging capabilities, although the cost of full field-of-view (FOV) PCD is still too high at the moment. The purpose of this work was to present a hybrid photon counting-energy integrating FPD design as a cost-effective solution to high-quality interventional imaging. Approach. In the proposed hybrid detector design, the central scintillator and thin-film transistor elements in the FPD are replaced with a semiconductor PCD module to upgrade the imaging capabilities of the C-arm system while preserving the full FOV coverage. The central PCD module can be used for high-quality 2D and 3D region-of-interest imaging with improved spatial- and temporal-resolution as well as spectral resolving capability. An experimental proof-of-concept was conducted using a 30 × 2.5 cm2 CdTe PCD and a 40 × 30 cm2 CsI(Tl)-aSi(H) FPD. Main results. Phantom and in vivo animal studies show (1) improved visualization of small stent wires in both 2D and 3D images due to the better spatial resolution of the PCD; (2) dual-energy angiography imaging capability by using the spectral PCD; (3) better conspicuity of small peripheral iodinated vessels (contrast-to-noise ratio improvement range: (29%, 151%)); (4) the central PCD outputs can be fused seamlessly with the surrounding scintillator detector outputs to provide full field imaging: A post-processing chain was developed by leveraging the PCD’s spectral information to match the image contrast of the PCD images to the surrounding scintillator detector, followed by spatial filtering of the PCD image to match noise texture and spatial resolution. Significance. The hybrid FPD design provides a cost-effective option to upgrade C-arm systems with spectral and ultra-high resolution capabilities without interfering with the clinical need for full FOV imaging.

Deep learning-based affine medical image registration for multimodal minimal-invasive image-guided interventions – A comparative study on generalizability

Multimodal image registration is applied in medical image analysis as it allows the integration of complementary data from multiple imaging modalities. In recent years, various neural network-based approaches for medical image registration have been presented in papers, but due to the use of different datasets, a fair comparison is not possible. In this research 20 different neural networks for an affine registration of medical images were implemented. The networks’ performance and the networks’ generalizability to new datasets were evaluated using two multimodal datasets - a synthetic and a real patient dataset - of three-dimensional CT and MR images of the liver. The networks were first trained semi-supervised using the synthetic dataset and then evaluated on the synthetic dataset and the unseen patient dataset. Afterwards, the networks were finetuned on the patient dataset and subsequently evaluated on the patient dataset. The networks were compared using our own developed CNN as benchmark and a conventional affine registration with SimpleElastix as baseline. Six networks improved the pre-registration Dice coefficient of the synthetic dataset significantly (p-value < 0.05) and nine networks improved the pre-registration Dice coefficient of the patient dataset significantly and are therefore able to generalize to the new datasets used in our experiments. Many different machine learning-based methods have been proposed for affine multimodal medical image registration, but few are generalizable to new data and applications. It is therefore necessary to conduct further research in order to develop medical image registration techniques that can be applied more widely.

Percutaneous cholecystostomy: techniques and applications.

Acute cholecystitis (AC) is a critical condition requiring immediate medical attention and treatment and is one of the most frequently encountered acute abdomen emergencies in surgical practice, requiring hospitalization. Laparoscopic cholecystectomy is considered the favored treatment for patients with AC who are fit for surgery. However, in high-risk patients considered poor surgical candidates, percutaneous cholecystostomy (PC) has been suggested and employed as a safe and reliable alternative option. PC is a minimally invasive, nonsurgical, image-guided intervention that drains and decompresses the gallbladder, thereby preventing its perforation and sepsis. It can act as a bridge to surgery, but it may also serve as a definitive treatment for some patients. The goal of this review is to familiarize physicians with PC and, more importantly, its applications and techniques, pre- and post-procedural considerations, and adverse events.

Radiological Imaging in Chest Diseases: Moving Away from Conventional Modes.

A chest radiograph is the most common and the initial radiological investigation for evaluating a child presenting with respiratory complaints. However, performing and interpreting chest radiography optimally requires training and skill. With the relatively easy availability of computed tomography (CT) scanning and more recently multidetector computed tomography (MDCT), these investigations are often performed. Although these can be the cross-sectional imaging modalities of choice in certain situations where detailed and exact anatomical and etiological information is required, both these investigations are associated with increased radiation exposure which has more detrimental effects on children, especially when repeated follow-up imaging is necessary to assess the disease status. Ultrasonography (USG) and magnetic resonance imaging (MRI) have evolved as radiation-free radiological investigations for evaluating the pediatric chest pathologies over the last few years. In the present review article, the utility and the current status, as well as the limitations of USG and MRI for evaluation of pediatric chest pathologies, are discussed. Radiology has grown beyond having just the diagnostic capabilities in managing children with chest disorders in the last two decades. Image-guided therapeutic procedures (percutaneous and endovascular) are routinely performed in children with pathologies in the mediastinum and lungs. The commonly performed image-guided pediatric chest interventions, including biopsies, fine needle aspiration, drainage procedures and therapeutic endovascular procedures, are also discussed in the current review.

In-Vivo Validation of a Novel Robotic Platform for Endovascular Intervention.

In-vivo validation on animal setting of a pneumatically propelled robot for endovascular intervention, to determine safety and clinical advantage of robotic cannulations compared to manual operation. Robotic assistance and image-guided intervention are increasingly used for improving endovascular procedures with enhanced navigation dexterity and accuracy. However, most platforms developed in the past decade still present inherent limitations in terms of altered clinical workflow, counterintuitive human-robot interaction, and a lack of versatility. We have created a versatile, highly integrated platform for robot-assisted endovascular intervention aimed at addressing such limitations, and here we demonstrate its clinical usability through in-vivo animal trials. A detailed in-vivo study on four porcine models conducted with our robotic platform is reported, involving cannulation and balloon angioplasty of five target arteries. The trials showed a 100% success rate, and post-mortem histopathological assessment demonstrated a reduction in the incidence and severity of vessel trauma with robotic navigation versus manual manipulation. In-vivo experiments demonstrated that the applicability of our robotic system within the context of this study was well tolerated, with good feasibility, and low risk profile. Comparable results were observed with robotics and manual cannulation, with clinical outcome potentially in favor of robotics. This study showed that the proposed robotic platform can potentially improve the execution of endovascular procedures, paving the way for clinical translation.

Conditional-based Transformer network with learnable queries for 4D deformation forecasting and tracking.

Real-time motion management for image-guided radiation therapy interventions plays an important role for accurate dose delivery. Forecasting future 4D deformations from in-plane image acquisitions is fundamental for accurate dose delivery and tumor targeting. However, anticipating visual representations is challenging and is not exempt from hurdles such as the prediction from limited dynamics, and the high-dimensionality inherent to complex deformations. Also, existing 3D tracking approaches typically need both template and search volumes as inputs, which are not available during real-time treatments. In this work, we propose an attention-based temporal prediction network where features extracted from input images are treated as tokens for the predictive task. Moreover, we employ a set of learnable queries, conditioned on prior knowledge, to predict future latent representation of deformations. Specifically, the conditioning scheme is based on estimated time-wise prior distributions computed from future images available during the training stage. Finally, we propose a new framework to address the problem of temporal 3D local tracking using cine 2D images as inputs, by employing latent vectors as gating variables to refine the motion fields over the tracked region. The tracker module is anchored on a 4D motion model, which provides both the latent vectors and the volumetric motion estimates to be refined. Our approach avoids auto-regression and leverages spatial transformations to generate the forecasted images. The tracking module reduces the error by 63% compared to a conditional-based transformer 4D motion model, yielding a mean error of 1.5±1.1 mm. Furthermore, for the studied cohort of abdominal 4D MRI images, the proposed method is able to predict future deformations with a mean geometrical error of 1.2±0.7 mm.

Ultrasound Fusion: Applications in Musculoskeletal Imaging.

Ultrasound fusion is an established technique that pairs real time B-scan ultrasound (US) with other forms of cross-sectional imaging, including computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET). Each of these imaging modalities has distinct advantages. CT provides superior anatomic resolution, with improved imaging of bone and calcified structures; MRI has superior contrast resolution; and PET provides physiologic information, identifying processes that are metabolically active (i.e., tumor, inflammatory conditions). However, these modalities are static. A key highlight of ultrasound is its capability of dynamic, real-time scanning. The ability to pair CT, MRI or PET with ultrasound can have significant advantages, both in diagnostic evaluation and when performing difficult or challenging image-guided interventions. Percutaneous interventions using ultrasound fusion have been described in the abdominal imaging literature; however, there have been very few musculoskeletal applications detailed in the literature. The purpose of this article is to review the basic concepts of real-time ultrasound fusion, and to detail, through the use of multiple case examples, its potential use as a safe and effective method for performing image-guided musculoskeletal interventions.