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:
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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
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
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.
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
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