At present, orthopaedic surgeons utilize either CT, MRI or X-ray for imaging a joint. Unfortunately, CT and MRI are quite expensive, non weight-bearing and the orthopaedic surgeon does not receive revenue for these procedures. Although x-rays are cheaper, similar to CT scans, patients incur radiation. Also, all three of these imaging modalities are static. More recently, a new ultrasound technology has been developed that will allow a surgeon to image their patients in 3D. The objective of this study is to highlight the new opportunity for orthopaedic surgeons to use 3D ultrasound as alternative to CT, MRI and X-rays. The 3D reconstruction process utilizes statistical shape atlases in conjunction with the ultrasound RF data to build the patient anatomy in real-time. The ultrasound RF signals are acquired using a linear transducer. Raw RF data is then extracted across each scan line. The transducer is tracked using a 3D tracking system. The location and orientation for each scan line is calculated using the tracking data and known position of the tracker relative to the signal. For each scan line, a detection algorithm extracts the location on the signal of the bone boundary, if any exists. Throughout the scan process, a 3D point cloud is created for each detected bone signal. Using a statistical bone atlas for each anatomy, the patient specific surface is reconstruction by optimizing the geometry to match the point cloud. Missing regions are interpolated from the bone atlas. To validate reconstructed models output models are then compared to models generated from 3D imaging, including CT and MRI.Introduction
Methods
Mathematical modeling provides an efficient and easily reproducible method for the determination of joint forces under in vivo conditions. The need for these new modeling methodologies is needed in the lumbar spine, where an understanding of the loading environment is limited. Few studies using telemetry and pressure sensors have directly measured forces borne by the spine; however, only a very small number of subjects have been studied and experimental conditions were not ideal for giving total forces acting in the spine. As a result, alternative approaches for investigating the lumbar spine across different clinical pathologies are essential. Therefore, the objective of this study was to develop of an inverse dynamic mathematical model for theoretically deriving in-vivo contact forces as well as musculotendon forces in patients having healthy, symptomatic, pathological and post-operative conditions of the lumbar spine. Fluoroscopy and 3D-to-2D image registration were used to obtain kinematic data for patients performing flexion-extension of the lumbar spine. This data served as input into the multi-body, mathematical model. Other inputs included patient-specific bone geometries, recreated from CT, and ground reaction forces. Vertebral bones were represented as rigid bodies, while massless frames symbolized the lower body, torso and abdominal wall (Figure 1). In addition, ligaments were selected and modeled as linear spring elements, along with relevant muscle groups. The muscles were divided into individual fascicles and solved for using a pseudo-inverse algorithm which enabled for decoupling of the derived resultant torques defining the desired kinetic trajectory for the muscles. The largest average contact forces in the model for healthy, symptomatic, pathological, and post-operative lumbar spine conditions occurred at maximum flexion at L4L5 level and were predicted to be 2.47 BW, 2.33 BW, 3.08 BW, and 1.60 BW, respectively. The FE rotation associated with these theoretical force values was 43.0° in healthy, 40.5° in symptomatic, 44.4° in pathological, and 22.8° in post-operative patients. The smallest forces occurred as patients approached the upright, standing position, followed by slight increases in the contact force at full extension. The theoretically derived muscle forces exhibited similar contributory force profiles in the intact spine (healthy, symptomatic, and pathologic); however, surgically implanted spines experienced an increase in the contribution of the external oblique muscles accompanied with decreased slope gradients in the muscle force profiles (Figure 2). These altered force patterns may be associated with the decrease in the predicted contact forces in post-operative patients. In addition, the decreased slope gradients in surgically implanted patients corresponds with the observed difficulty of performing the prescribed motion, possibly due to improper muscle firing, thereby leading to slower motion cycles and less ranges-of-motion. On the contrary, patients having an intact spine performed the activity at a faster speed and to greater ranges-of-motion, which corresponds with the higher contact forces derived in the model. In conclusion, this research study presented the development of a mathematical modeling approach utilizing patient-specific data to generate theoretical in-vivo joint forces. This may serve to help progress the understanding for the kinetic characteristics of the native and surgically implanted lumbar spine.
Previous modalities such as static x-rays, MRI scans, CT scans and fluoroscopy have been used to diagnosis both soft-tissue clinical conditions and bone abnormalities. Each of these diagnostic tools has definite strengths, but each has significant weaknesses. The objective of this study is to introduce two new diagnostic, ultrasound and sound/vibration sensing, techniques that could be utilized by orthopaedic surgeons to diagnose injuries, defects and other clinical conditions that may not be detected using the previous mentioned modalities. A new technique has been developed using ultrasound to create three-dimensional (3D) bones and soft-tissues at the articulating surfaces and ligaments and muscles across the articulating joints (Figure 1). Using an ultrasound scan, radio frequency (RF) data is captured and prepared for processing. A statistical signal model is then used for bone detection and bone echo selection. Noise is then removed from the signal to derive the true signal required for further analysis. This process allows for a contour to be derived for the rigid body of questions, leading to a 3D recovery of the bone. Further signal processing is conducted to recover the cartilage and other soft-tissues surrounding the region of interest. A sound sensor has also been developed that allows for the capture of raw signals separated into vibration and sound (Figure 2). A filtering process is utilized to remove the noise and then further analysis allows for the true signal to be analyzed, correlating vibrational signals and sound to specific clinical conditions.INTRODUCTION:
METHODS:
The low-cost, no-harm conditions associated with vibroarthography, the study of listening to the vibrations and sound patterns of interaction at the human joints, has made this method a promising tool for diagnosing joint pathologies. This current study focuses on the knee joint and aims to synchronize computational models with vibroarthographic signals via a comprehensive graphical user interface (GUI) to find correlations between kinematics, vibration signals, and joint pathologies. This GUI is the first of its kind to synchronize computational models with vibroarthographic signals and gives researchers a new advantage of analyzing kinematics, vibration signals, and pathologies simultaneously in an easy-to-use software environment. The GUI (Figure 1) has the option to view live or previously captured fluoroscopic videos, the corresponding computational model, and/or the pre- or post-processed vibration signals. Having more than one signal axes available allows for comparison of different filtering techniques to the same signal, or comparison of signals coming from different sensor placements (ex: medial vs. lateral femoral condyle). Using computational models derived using fluoroscopic data synchronized with the vibration signals, the areas of contact between articulating surfaces can be mapped for the in vivo signal (figure 2). This new method gives the opportunity to find correlations between the different sensor signals and contact maps with the diagnosis and cartilage degeneration map, provided by a surgeon, during arthroscopy or TKA implantation (figure 3).Introduction
Methods
In-vivo data pertaining to the actual cam-post engagement mechanism in PS and Bi-Cruciate Stabilized (BCS) knees is still very limited. Therefore, the objective of this study was to determine the cam-post mechanism interaction under in-vivo, weight-bearing conditions for subjects implanted with either a Rotating Platform (RP) PS TKA, a Fixed Bearing (FB) PS TKA or a FB BCS TKA. In-vivo, weight-bearing, 3D knee kinematics were determined for eight subjects (9 knees) having a RP-PS TKA (DePuy Inc.), four subjects (4 knees) with FB-PS TKA (Zimmer Inc.), and eight subjects (10 knees) having BCS TKA (Smith&Nephew Inc.), while performing a deep knee bend. 3D-kinematics was recreated from fluoroscopic images using a previously published 3D-to-2D registration technique (Figure 1). Images from full extension to maximum flexion were analyzed at 10° intervals. Once the 3D-kinematics of implant components was recreated, the cam-post mechanism was scrutinized. The distance between the interacting surfaces was monitored throughout flexion and the predicted contact map was calculated.INTRODUCTION
METHODS
While fluoroscopic techniques have been widely utilized to study in vivo kinematic behavior of total knee arthroplasties, determination of the contact forces of large population sizes has proven a challenge to the biomedical engineering community. This investigation utilizes computational modeling to predict these forces and validates these with independent telemetric data for multiple patients, implants, and activities. Two patients with telemetric implants, the first of which was studied twice with the reexamination occurring 8 years after the first, were studied. Three-dimensional models of the patients' bones were segmented from CT and aligned with the design models of the telemetric implants. Fluoroscopy was collected for gait, deep knee bend, chair rise, and stair activities while being synchronized to the ground reaction force (GRF) plate, telemetric forces, knee flexion angles, electromyography (EMG), and vibration sensors. Registration of the implants and bones to the 2-D fluoroscopy provided the 6 degree of freedom kinematic data for each object. Orientation and position of the components, the GRFs, ligament properties, and muscle attachment locations were the only inputs to the Kane's dynamics inverse solution. Dynamic contact mapping and pseudo-inverse solution method were incorporated to output the predicted muscle forces of the vastus lateralis, rectus femoris, vastus medialis, biceps femoris long head, and gastrocnemius and contact forces at the patellofemoral and medial and lateral tibiofemoral. While every major muscle of the lower limb was incorporated into the model, these five were used in the validation process. EMG signals were processed to determine the neural excitation, muscle activation, and using the dynamic muscle length from the kinematics, the tension generated by these muscles.Introduction
Methods
Previous fluoroscopy studies have been conducted on numerous primary-type TKA, but minimal in vivo data has been documented for subjects implanted with revision TKA. If a subject requires a revision TKA, most often the ligament structures at the knee are compromised and stability of the joint is of great concern. In this present study, subjects implanted with a fixed or mobile bearing TC3 TKA are analyzed to determine if either provides the patient with a significant kinematic advantage. Ten subjects are analyzed implanted with fixed bearing PFC TC3 TKA and 10 subjects with a mobile bearing PFC TC3 TKA. Each subject underwent a fluoroscopic analysis during four weight bearing activities: deep knee bend (DKB), chair rise, gait, and stair descent. Fluoroscopic images were taken in the sagittal plane at 10 degree increments for the DKB, 30 degree increments for chair rise, and at heel strike, toe off, 33% and 66% cycle gait and stair descent.Introduction
Methods
Electromyography (EMG) is the best known method in obtaining in vivo muscle activation signals during dynamic activities, and this study focuses on comparing the EMG signals of the quadriceps muscles for different TKA designs and normal knees during maximum weight bearing flexion. It is hypothesized that the activation levels will be higher for the TKA groups than the normal group. Twenty-five subjects were involved in the study with 11 having a normal knee, five a rotating platform (RP) posterior stabilized (PS) TKA, and nine subjects with a PFC TC3 revision TKA. EMG signals were obtained from the rectus femoris, vastus medialis, and vastus lateralis as the patients performed a deep knee bend from full extension to maximum flexion. The data was synchronized with the activity so that the EMG data could be set in flexion-space and compared across the groups. EMG signals were pre-processed by converting the raw signals into neural excitations and normalizing this data with the maximum voluntary contraction (MVC) performed by the subject. The signals were then processed to find the muscle activations which, normalized by MVC, range from 0 to 1.Introduction
Methods
Kinematics tracking is the process by which the motion of the joints is studied. This motion consists of relative rotation and translation of the joint bones. Joint motion analysis is used in diagnosis of joint pathology, as well as studying the normal joint function. Currently, fluoroscopy is used in joint kinematics tracking. We are researching the use of pulse-echo A-mode ultrasound for the bone motion tracking instead of the fluoroscopy to avoid its radiation. In this work we performed feasibility study using simulation, and concluded that it is feasible to perform knee motion tracking with accuracy of 2 mm. The idea of the proposed system is to attach a number of single-element ultrasound transducers to a brace as shown in Figure 1. This brace will have a commercially available optical or electromagnetic tracking system's probe attached to it to track the global motion of the brace. The ultrasound transducers will be responsible for transcutaneously detecting points over the surface of the bone. The bone's echo extracted from each signal at each transducer will be registered in the optical or electromagnetic tracker's coordinate frame to create a set of points acquired over the surface of the bone. These points represent the bone's position at that point of time. A 3D model of the bone is then registered to these points using the iterative closest point method (ICP) to estimate the bone's position. At each tracking step, the 3D model will be at a position close to the new position of the points set, because this process will be repeated at a rate of 100 Hz or more in order to ensure that the change in the bone's position between every two successive tracking steps is small enough to guarantee high tracking accuracy. In this work we simulated the mentioned process using real kinematics data obtained for a patient using fluoroscopy. 3D models of the proximal tibia and distal femur were segmented from CT scans of the patient's knee. These models were then moved using the kinematic data in incremental steps. Simulated points over the surface of the bones (simulating the points on the bone's surface to be acquired using ultrasound) were used to track the bones' simulated motion using another set of the bones 3D models which move only according to the registration with the simulated points. In other words, the tracking models follow the simulated points' motion. Simulation was performed using deep knee bend kinematics data.Introduction
Methods
Previous fluoroscopic studies compared total knee arthroplasty (TKA) kinematics to normal knees. It was our hypothesis that comparing TKA directly to its non-replaced controlateral knee may provide more realistic kinematics information. Using fluoroscopic analysis, we aimed to compare knee flexion angles, femoral roll-back, patellar tracking and internal and external rotation of the tibia. 15 patients (12 women and 3 men) with a mean age of 71.8 years (SD=7.4) operated by the same surgeon were included in this fluoroscopic study. For each patient at a minimum one year after mobile-bearing TKA, kinematics of the TKA was compared to the controlateral knee during three standardized activities: weight-bearing deep-knee bend, stair climbing and walking. A history of trauma, pain, instability or infection on the non-replaced knee was an exclusion criteria. A CT-scan of the non-replaced knee was performed for each patient to obtain a 3-D model of the knee. The Knee Osteoarthitis Outcome Score (KOOS) was also recorded.Introduction
Material and methods
Posterior stabilized (PS) total knee arthroplasty (TKA) provides posterior stability with the use of a cam-post mechanism which performs the function of the posterior cruciate ligament. The tibial post engages with the femoral cam, prevents the femur from sliding anteriorly and provides the posterior femoral rollback necessary for achieving deep flexion of the knee. However, these designs do not substitute the resection of the anterior cruciate ligament. In order to overcome this deficit, other TKA designs have been recently introduced to provide dual support, with the help of dual cam-post engagement mechanism. Various studies conducted on the PS TKA have suggested that the cam-post mechanism does not engage as designed, resulting in tibial post wear and increased stresses resulting in backside wear of the polyethylene insert component. Also, the in vivo data pertaining to the actual cam-post engagement mechanism in bi-cruciate stabilized knees is still very limited. Therefore, the objective of this study was to determine the cam-post mechanism interaction under in vivo, weight bearing conditions for subjects implanted with either a Rotating Platform (RP) Posterior Stabilized (PS) TKA or a bi-cruciate stabilizing TKA (BCS). In-vivo, weight-bearing, 3D knee kinematics were determined for eight subjects (9 knees) having a RP-PS TKA (DePuy Inc.) and eight subjects (10 knees) having BCS TKA (Smith&Nephew Inc.), while performing a deep knee bend. 3D kinematics was recreated from the fluoroscopic images using a previously published 3D-to-2D registration technique (Figure 1). Images from full extension to maximum flexion were analyzed at 10° intervals. Once the 3D kinematics of all implant components was recreated, the cam-post mechanism was scrutinized. The distance between the interacting surfaces was monitored throughout the flexion and the predicted contact map was calculated. The instances, when the minimum distance between the cam and post surfaces dropped to zero was considered to indicate the engagement of the mechanism. This analysis was carried out for both the, anterior and posterior cam-post engagement sites.INTRODUCTION
METHODS
Anterior knee pain is one of the most frequently reported musculoskeletal complaints in all age groups. However, patient's complaints are often nonspecific, leading to difficulty in properly diagnosing the condition. One of the causes of pain is the degeneration of the articular cartilage. As the cartilage deteriorates, its ability to distribute the joint reaction forces decreases and the stresses may exceed the pain threshold. Unfortunately, the assessment of the cartilage condition is often limited to a detailed interview with the patient, careful physical examination and x-ray imaging. The X-ray screening may reveal bone degeneration, but does not carry sufficient information of the soft tissues' conditions. More advanced imaging tools such as MRI or CT are available, but these are expensive, time consuming and are only suitable for detection of advanced arthritis. Arthroscopic surgery is often the only reliable option, however due to its semi-invasive nature, it cannot be considered as a practical diagnostic tool. However, as the articular cartilage degenerates, the surfaces become rougher, they produce higher vibrations than smooth surfaces due to higher friction during the interaction. Therefore, it was proposed to detect vibrations non-invasively using accelerometers, and evaluate the signals for their potential diagnostic applications. Vibration data was collected for 75 subjects; 23 healthy and 52 subjects suffering from knee arthritis. The study was approved by the IRB and an Informed Consent was obtained prior to data collection. Five accelerometers were attached to skin around the knee joint (at the patella, medial and lateral femoral condyles, tibial tuberosity and medial tibial plateau). Each subject performed 5 activities; (1) flexion-extension, (2) deep knee bend, (3) chair rising, (4) stair climbing and (5) stair descent. The vibration and motion components of the signals were separated by a high pass filter. Next, 33 parameters of the signals were calculated and evaluated for their discrimination effectiveness (Figure 1). Finally the pattern recognition method based on Baysian classification theorem was used for classify each signal to either healthy or arthritic group, assuming equal prior probabilities. The variance and mean of the vibration signals were significantly higher in the arthritic group (p=2.8e-7 and p=3.7e-14, respectively), which confirms the general hypothesis that the vibration magnitudes increase as the cartilage degenerates. Other signal features providing good discrimination included the 99th quantile, the integral of the vibration signal envelope, and the product of the signal envelope and the activity duration. The pattern classification yielded excellent results with the success rate of up to 92.2% using only 2 features, up to 94.8% using 3 (Figure 2), and 96.1% using 4 features. The current study proved that the vibrations can be studied non-invasively using a low-cost technology. The results confirmed the hypothesis that the degeneration of the cartilage increases the vibration of the articulating bones. The classification rate obtained in the study is very encouraging, providing over 96% accuracy. The presented technology has certainly a potential of being used as an additional screening methodology enhancing the assessment of the articular cartilage condition.
Total shoulder arthroplasty (TSA) implants are used to restore function to individuals whose shoulder motions are impaired by osteoarthritis. To improve TSA implant designs, it is crucial to understand the kinematics of healthy, osteoarthritic (OA), and post-TSA shoulders. Hence, this study will determine in vivo kinematic trends of the glenohumeral joints of healthy, OA, and post-TSA shoulders. In vivo shoulder kinematics were determined pre and post-operatively for five unilateral TSA subjects with one healthy and a contralateral OA glenohumeral joint. Fluoroscopic examinations were performed for all three shoulder categories (healthy, OA, and post-TSA) for each subject shoulder abduction and external rotation. Then, three-dimensional (3D) models of the left and right scapula and humerus were constructed using CT scans. For post-operative shoulders, 3D computer-aided design models of the implants were obtained. Next, the 3D glenohumeral joint kinematics were determined using a previously published 3D to 2D registration technique. After determining kinematics, relative Euler rotation angles between the humerus and scapula were calculated in MATLAB® to determine range of motion (ROM) and kinematic profiles for all three shoulder categories. The ROMs for each category were compared using paired t-tests for each exercise. Also, the location of the contact point of the humerus on the glenoid was found. This allowed the vertical translation from the most superior to most inferior contact point (SI contact range) to be calculated as well as the horizontal translation from the most anterior to most posterior contact point (AP contact range). The SI and AP contact ranges for all shoulder categories were compared using paired t-tests for each exercise.INTRODUCTION
Methods
Numerous studies have been conducted to investigate the kinematics of the lumbar spine, and while many have documented its intricacies, few have analyzed the complex coupled out-of-plane rotations inherent in the low back. Some studies have suggested a possible relationship between patients having low back pain (LBP) or degenerative conditions in the lumbar region and various degrees of restricted, excessive, or poorly-controlled lumbar motion. Conversely, others in the orthopedic community maintain there has been no distinct correlation found between spinal mobility and clinical symptoms. The objective of this study was to evaluate both the in-plane and coupled out-of-plane rotational magnitudes about all three motion axes in both symptomatic and asymptomatic patients. Ten healthy, 10 LBP, and 10 degenerative patients were CT scanned and evaluated under fluoroscopic surveillance while performing flexion/extension of the lumbar spine. Three-dimensional, patient-specific bone models were created and registered to fluoroscopic images using a 3D-to-2D model fitting algorithm. Introduction
Methods
In this work, we present the first real-time fully automatic system for reconstruction of patient-specific 3D knee bones models using ultrasound raw RF data. The system was experimented on two cadaveric knees, and reconstruction accuracy of 2 mm was achieved. To use the highest available contrast and spatial resolution in the ultrasound data, the raw RF signals were used directly to automatically extract the bone contours from the ultrasound scans. Figure 1 shows a sample ultrasound B-mode image for cadaver's distal femur, showing some of the scan lines raw RF signals as well as the final extracted contour using our method. An ultrasound machine (SonixRP, Ultrasonix Inc) was used to scan the knee joint and the RF data of the scans are acquired by custom-built (using Visual C++) software running on the ultrasound machine. An optical tracker (Polaris Spectra, Northern Digital Inc) was attached to the ultrasound probe to track its motion while being used in scanning. The scanning of the knee was performed at two flexion angles (full extension, and deep knee bend). At each position, the knee was fixed in order to collect scans that represent a partial surface of the bone (which will be later mutually registered to represent the whole bone's surface). Figure 4 shows fluoroscopy images of a patient's knee, showing the different articulating surfaces of the knee bones visible to the ultrasound at different flexion angles. Figure 5 shows a dissected cadaver's knee showing the articulating surfaces visible to ultrasound at 90 degrees flexion. The custom-built software collects the RF data synchronized with the probe tracking data for each ultrasound frame. Each frame of the RF data is then processed to extract the bone contour. The bone contours are automatically extracted from the RF data frame with frame rate of 25 frames per second. Figure 2 shows a flowchart for the contour extraction process. The extracted bone contours were then used by the our software, along with the ultrasound probe's tracking data, to reconstruct point clouds representing the bones' surfaces. These point clouds were then aligned to the mean model of the bone's atlas using ICP and integrated together to form 3D point cloud of the bone's surface. A 3D model of the bone is then reconstructed by morphing the mean model to match the point cloud. Figure 3 shows a flowchart for the point cloud and 3D model reconstruction process.Introduction
Methods
