Patent Description:
In humans, the knee joint is functionally controlled by a mechanical system governed by three unique types of forces (<NUM>) active forces resulting from motion, such as those resulting from muscle flexing or relaxing, (<NUM>) constraining forces that constrain motion, such as those resulting from ligaments being in tension, and (<NUM>) interaction forces that resist motion, such as those acting upon bones In addition to these three types of forces, the soft tissue in the knee joint complex (e.g., cartilage and meniscus) produce a dampening effect distributing the compressive loads acting on the knee joint.

Knee joint motions are stabilized primarily by four ligaments, which restrict and regulate the relative motion between the femur, tibia, and patella. These ligaments are the anterior cruciate ligament (ACL), the posterior cruciate ligament (PCL), the medial collateral ligament (MCL), the lateral collateral ligament (LCL), and the patellar ligament, as shown in <FIG>. An injury to any of these ligaments or other soft-tissue structures can cause detectable changes in knee kinematics and the creation of detectable vibrations representative of the type of knee joint injury and the severity of the injury. These visual (knee kinematics) and auditory (vibrations) changes are produced by the bones while moving in a distorted kinematic pattern, and they differ significantly from the look and sound of a properly balanced knee joint moving through a range of motion. <CIT> discloses a method for performing computer-assisted orthopaedic surgery which includes the step of producing and displaying three-dimensional geometrical models of first and second bones forming a joint. <CIT> discloses an ultrasonic bone motion tracking system.

The invention is defined in claim <NUM>, with further aspects of the invention in the dependent claims. There is provided in this disclosure a method of evaluating a physiological condition of bodily tissue in accordance with the present claims. The exemplary embodiments of the present disclosure include a diagnostic system for mammalian bodies to determine what type of injury, if any, exists and extent of any such injury using kinematic data and/or sound data that is patient specific. In particular, an exemplary method and embodiment are directed to a knee joint diagnostic system for automatically determining whether an injury exists, what the injury is (i.e., diagnosis), and the extent to which ligaments, muscles, bones, meniscus, and cartilage may be affected by the injury by gathering and analyzing patient specific kinematic data of the knee joint, while also gathering and analyzing the pattern and spatial distribution of sound(s) (i.e., vibrations) produced by movement of the patient's knee joint. A low level exemplary process flow diagram for this exemplary method is shown in <FIG>.

An exemplary method of the present disclosure includes generating a patient-specific 3D tissue model of the bodily area in question (e.g., the bones of knee joint), obtaining patient-specific kinematic and sound data as the bodily area is taken through a range of motion, and finally analyzing the kinematic and sound data to discern whether an injury is present and, if so, the extent of any injury. In exemplary form, patient-specific 3D tissue models of the distal femur, proximal tibia, and the patella are constructed using conventional imaging technologies, such as computed tomography (CT) scans, fluoroscopy, magnetic resonance imaging (MRI) scans, X-rays, and the like. Alternatively, the exemplary embodiments of the present disclosure provide an alternative to conventional imaging technologies by utilizing A-mode ultrasound echo morphing technology to generate data necessary to construct 3D tissue models. After the patient-specific 3D tissue models have been generated, patient-specific kinematic data is gathered and evaluated for the motions of the femur, tibia, and patella. By way of example, this kinematic data may be obtained using the same A-mode ultrasound technology as is utilized to generate the data necessary to create the patient-specific 3D tissue models. In addition, patient-specific sound data is generated using accelerometers to monitor the knee joint while the joint is taken through a range of motion in a loaded, real-world condition. Finally, the kinematic data and sound data are analyzed to determine the most accurate diagnosis, including whether an injury exists and the extent of any such injury. In exemplary form, the kinematic data and sound data are analyzed by a neural network having actual kinematic and sound data correlated to correct diagnoses. The neural network is constantly updated with new data for cases where kinematic and sound data was obtained and the correct diagnosis was verified by in vivo assessment. Accordingly, as the neural network grows with more data, the precision of the diagnoses is correspondingly increased.

In exemplary form, the sound data and kinematic data may be obtained at the same time using a single data acquisition device. Moreover, the sound data and kinematic data are obtained in real time as the bodily area in question is taken through a range of motion. In a further exemplary embodiment, if the sound and kinematic data is acquired in a physician's office, the data may be displayed in real-time on a split screen monitor. If, however, the data is acquired outside of the doctor's office, a recording device and memory may be utilized to record the data in a time synced manner. In a yet a further exemplary embodiment, the patient may be given an actuator that is operative to note the general time frame within which the patient experienced a particular pain or severe pain to allow a diagnosis that correlates pain experienced by the patient and the kinematics and sound occurring at precisely or generally the same time.

As discussed above, the kinematic data and sound data generated on a patient-specific basis is analyzed by a trained neural network in order to provide an automated output as to the existence of an injury, the type of injury, and the severity of the injury. This neural network may be accessible via the internet or may reside on a physician's local computer. In addition, or in the alternative, patient-specific sound and kinematic data may be analyzed by a physician to make or verify the diagnosis with or without the aid of the neural network.

Using the exemplary methods and devices as disclosed herein, a physician may diagnose a bodily injury without requiring experimental surgery or requiring exposure of the patient to radiation of any form, such as X-rays or fluoroscopy. In addition, the data taken regarding each patient is continuous through a range of motion, in contrast to radiographic modalities that generate images at distinct points with significant range of motions gaps. In addition, data taken in accordance with the exemplary methods and devices disclosed herein also contracts data taken by a magnetic resonance imaging machine, not only because the data taken is continuous along the range of motion, but also because the bodily portion evaluated is acting under loaded conditions in a dynamic environment.

The exemplary embodiments of the present disclosure are described and illustrated below to encompass diagnosis of bodily abnormalities and, more particularly, devices and methods for evaluating the physiological condition of bodily tissue (such as joints) to discern whether abnormalities exist and the extent of any abnormalities. Of course, it will be apparent to those of ordinary skill in the art that the exemplary embodiments discussed below are merely examples and may be reconfigured without departing from the scope of the present claims. By way of example, the exemplary embodiments disclosed herein are described with respect to diagnosing a knee joint injury. Nevertheless, the exemplary embodiments may be utilized to diagnose other bodily tissue injuries (such as a hip joint injury or a bone fracture), as the knee joint is merely exemplary to facilitate an understanding of the embodiments disclosed.

Referencing <FIG>, a first exemplary diagnostic system <NUM> includes a plurality of modules <NUM>, <NUM>, <NUM> that output data to a computer <NUM> for data processing by way of a neural network <NUM>. The data processing, as will be discussed in more detail below, provides a visual output, an audible output, and a diagnosis by way of a visual display <NUM>. Again, as will be discussed below, the diagnosis includes detection of an injury, as well as information pertaining to the severity of the injury. But before the output from the system <NUM> can be fully explained, the modules <NUM>, <NUM>, <NUM> and the functionality of each will be described initially.

Referring to <FIG> and <FIG>, the system <NUM> includes a vibroarthography module (VM) <NUM> comprising a plurality of accelerometers <NUM> that are utilized to detect sound, specifically vibrations occurring as a result of motion of the knee joint. In this exemplary VM <NUM>, the accelerometers are mounted directly to the skin or external tissue surface of a patient in order to detect bone and soft tissue interaction. An intervening adhesive is utilized between the accelerometers <NUM> and the patient's external tissue surface in order to secure the accelerometers in a fixed position.

In the context of a knee joint, an exemplary VM module includes three accelerometers <NUM>, where one accelerometer 120A is mounted on the medial side of the knee joint, while a second accelerometer 120B is mounted on the lateral side of the knee joint, while a third accelerometer 120C is mounted on the front side of the knee joint proximate the patella. In this exemplary embodiment, the accelerometers <NUM> are mounted to the patient so that each lies along a common plane, though each could be mounted so as not to lie along a common plane It should also be understood, however, that more than three accelerometers and less than three accelerometers may be utilized to detect sound generated by dynamic interactions of the tissues against one another.

Each of the accelerometers <NUM> is in communication with signal conditioning circuits <NUM> associated with the vibroarthography module (VM) <NUM>. The accelerometers <NUM> are operative to detect sound, specifically vibrations, and output the sound detected in the form of frequency data measured in Hertz to the conditioning circuits. This frequency data is processed by the conditioning circuits <NUM> and communicated to the computer <NUM> as digital frequency data. At the same time as the accelerometers <NUM> are generating frequency data, the conditioning circuits <NUM> may include a clock that time stamps the frequency data generated. As will be discussed in more detail below, correlating the frequency data with time provides a constant against which all of the detected data can be compared against on a relative scale.

As will be discussed in more detail hereafter, the interaction between bodily tissue (e.g., bone against cartilage, bone against bone) in a dynamic environment creates certain vibrations that are indicative of the condition or state of health of the joint. Even the healthiest and youngest joints create vibrations of some sort. However, joints that exhibit degradation, through wear or injury, will exhibit vibrations much more pronounced and amplified over those of a healthy joint. The exemplary embodiment of the disclosure takes advantage of the sound, such as vibrations, exhibited by the joint during a range of motion to diagnose the condition of the joint without requiring an invasive procedure or subjecting the patient to radiation.

In this exemplary embodiment, the first accelerometer 120A is mounted on the medial side of the knee joint and is operative to detect vibrations generated by the interactions between the medial condyle of the femur against the medial cartilage on top of the medial portion of the tibia. Similarly, the second accelerometer 120B is mounted on the lateral side of the knee joint and is operative to detect vibrations generated by the interactions between the lateral condyle of the femur against the lateral cartilage on top of the lateral portion of the tibia. Finally, the third accelerometer 120C is mounted at the front of the knee joint, proximate the patella, and is operative to detect vibrations generated by the interactions between the femur against the patella. The resulting data output by the accelerometers <NUM> is wirelessly transmitted to the computer <NUM> via a wireless transmitter <NUM>, such as an ultra wide band transmitter. The data from the accelerometers <NUM> that is wirelessly transmitted to the computer is then utilized in combination with data from the other modules to ascertain the appropriate diagnosis.

Referring back to <FIG>, the system <NUM> includes a contact force module (CFM) <NUM> comprising a plurality of pressure sensors <NUM> that are utilized to detect pressure or contact forces occurring at the bottom of the foot when the knee joint is moved through a range of motion under a loaded condition. In other words, as the patient walks, jogs, runs, etc., the CFM module <NUM> detects pressure data at the bottom of the foot when the foot is partially or fully in contact with the ground. In exemplary form, the pressure sensors <NUM> are incorporated into an insole that conforms to the general shape of a patient's foot. Because humans have different sized feet, the exemplary system includes insoles that are incrementally sized to accommodate humans with different sized feet or to accommodate the shoes (or lack thereof) needed for a certain activity.

Referring to <FIG>, the exemplary CFM <NUM> includes pressure sensors <NUM> that are arranged in a grid on the insole <NUM> of a shoe <NUM>. In exemplary form, the grid comprises a series of rows and columns of pressure sensors that are exposed to the underside of a patient's foot so that contact forces applied by the foot to the shoe, by way of the insole, can be measured, as well as knowing the location where the forces were applied. As will be discussed in more detail hereafter, the location of the pressures and the relative amount of pressures provides information relevant to diagnosis of injury. For example, a patient with a limp, caused by a knee joint injury, would not apply pressure to the sole of a shoe in the same manner (amount or location) as would a patient with a healthy knee joint and a normal gait or kinematics.

Each of the capacitive sensors <NUM>, as the name implies, includes a capacitor that works on the principle that a deformable dielectric medium separates two plates. Changes in the pressure applied to the plates cause a strain (deformation) within the dielectric medium. Thus a pressure applied to the sensor changes the spacing between the plates and changes the capacitance measured between them. The sensors <NUM> are arrayed across the area of pressure measurement to provide discrete capacitive data points corresponding to strains at the various locations of the array. These strains are used to find the stresses and thus the forces to calculate the output of pressure data having units of force per unit area multiplied by time (i.e., N/m<NUM> sec).

In this exemplary embodiment, the sensors <NUM> are arranged in a grid so that the position of each of the sensors relative to another sensor is known. This data, which includes the 2D orientation and spacing between the pressure sensors <NUM>, is either stored on the computer <NUM> or stored locally with the sensors. In this exemplary embodiment, the orientation and spacing data for the sensors <NUM> is stored on the computer <NUM>. The resulting data output by the sensors <NUM> is wirelessly transmitted to the computer <NUM> via a wireless transmitter <NUM>, such as an ultra wide band transmitter. Using the orientation and spacing data for the sensors <NUM> stored on the computer <NUM>, in combination with the computer receiving sensor pressure data, the computer is operative to generate data tying pressure to position, specifically the position of one pressure sensor with respect to another.

By tying force with position, the system <NUM> includes data reflecting precisely what pressures are exerted at what locations. In addition, the computer <NUM> includes an internal clock that also associates time with the pressure data generate by the pressure sensors <NUM>. Accordingly, the system <NUM> not only knows how much pressure was exerted and the location where the pressure was applied, but also has time data indicating the duration of the applied pressures. Again, by tying the pressure data generated by the pressure sensors <NUM> to time, the pressure data can be correlated with the sound data generated by the VM module <NUM> using a time scale as a common scale. As a result, the system can evaluate how pressures exhibited at the bottom of the foot change as a function of time, along with how the vibrational data changes during the same time.

Referencing <FIG> again, the system <NUM> also includes a kinematics module (KM) <NUM> that is comprise of a plurality of submodules <NUM>, <NUM>, <NUM> that include one or a plurality of A-mode ultrasound transducers. The submodules include an ultrasound creation and positioning submodule <NUM>, an ultrasound registration submodule <NUM>, and an ultrasound dynamic movement submodule <NUM>. Specifically, the submodules include A-mode ultrasound transducers that generate sound and detect the sound that bounces back, which is representative of the structure, position, and acoustical impedance of the tissue in question. Commercially available transducers for use with the exemplary embodiments include, without limitation, the Olympus immersion unfocused <NUM>-<NUM> transducer. Those skilled in the art are familiar with the operation of ultrasound transducers in general and A-mode ultrasound transducers, which generate sound pulses and operate to detect sound that bounces back within soft tissue at the interface between tissues having different acoustic impedances. The magnitude of the sound that bounces back and the time it takes for the sound to bounce back to the ultrasound transducer are utilized to determine the distance between the ultrasound transducer and the interface between the materials having different acoustic impedances.

In this exemplary embodiment, the transducers are utilized to detect the interface between bone and surrounding tissue so that the location of the bone surface can be determined. Because the operation of ultrasound transducers and A-mode ultrasound transducers are well known to those skilled in the art, a detailed discussion of the operation of ultrasound transducers in general, and A-mode ultrasound transducers specifically, has been omitted only for purposes of brevity.

Referring to <FIG>, the ultrasound creation and positioning submodule <NUM> comprises one or more A-mode ultrasound transducers <NUM> fixedly mounted to a wand <NUM> that also has mounted thereto at least one positioning device <NUM>. In this exemplary embodiment, the ultrasound creation and positioning submodule <NUM> is physically separate from the ultrasound registration submodule <NUM> and an ultrasound dynamic movement submodule <NUM>, which are themselves mounted to a rigid knee brace <NUM> (see <FIG>). In this fashion, the ultrasound creation and positioning submodule <NUM> is repositionable with respect to the rigid knee brace <NUM> and adapted to place one or more of its A-mode ultrasound transducers <NUM> in contact with the patient's epidermis proximate the knee joint. It should be noted, however, that the knee brace <NUM> does not have to be rigid other than the linkages between certain components. Moreover, the knee should be scanned by the ultrasound wand <NUM> before the brace is put on.

One of the functions of the ultrasound creation and positioning submodule <NUM> is to generate electrical signal representative of the ultrasonic wave detected by the transducers <NUM> as the wand <NUM> is moved over the patient's epidermis proximate the knee joint. The ultrasound transducer(s) <NUM> receive electrical signal pulses based upon the magnitude of the ultrasonic wave that bounces back to the transducer as a result of the sound reaching the bone and bouncing back. As discussed previously, the magnitude of the electrical signal and the delay between the generation of the ultrasonic wave by the ultrasound transducer <NUM> until a bounce back ultrasonic wave is detected by the ultrasound transducer is indicative of the depth of the bone underneath the transducer. But this depth data alone would not be particularly useful without positioning devices <NUM> that provide a 3D coordinate system.

In an embodiment not forming part of the claimed invention, the positioning devices <NUM> of the ultrasound creation and positioning submodule <NUM> are fixedly mounted to the wand <NUM> and may comprise any of a number of devices. For example, the wand <NUM> may include optical devices <NUM> that are operative to generate, detect, or reflect pulses of light, which interacts with a corresponding detector or light generator to discern the 3D position of the wand with respect to a fixed or reference position. One such device includes a light detector operative to detect pulses of light emitted from light emitters having known positions. The light detector detects the light and sends, a representative signal to control circuitry, which also knows when the light pulses were emitted as a function of time and position. In this matter, the control circuitry is operative to determine the position of the wand in a 3D coordinate system. Because the A-mode ultrasound transducer(s) <NUM> and optical devices <NUM> are fixedly mounted to the wand <NUM>, the position of the ultrasound transducer(s) <NUM> with respect to the position of the optical devices <NUM> is known. Similarly, because the ultrasound transducers <NUM> are generating signals representative of a straight line distance from the transducer to the surface of the bone, and the position of the transducer(s) <NUM> with respect to the optical devices <NUM> is known, the position of the bone with respect to the optical devices <NUM> can be easily calculated. In other words, as the wand <NUM> is repositioned, the optical devices <NUM> generate data reflecting that the relative position of the optical devices has changed in the 3D coordinate system. This change in 3D position of the optical devices <NUM> can be easily correlated to the position of the bone in three dimensions because the position of the bone relative to the ultrasound transducer is known, as is the position of the optical devices with respect to the ultrasound transducers. Accordingly, the optical devices 3D position data is used in combination with the fixed position data (distance data for the position of the ultrasound transducer(s) with respect to the optical devices) for the ultrasound transducers <NUM> in combination with the distance data generated responsive to the signals received from the ultrasound transducers to generate composite data that is used to create a plurality of 3D points representing a plurality of distinct points on the surface of the bone. As will be discussed in more detail below, these 3D points are utilized in conjunction with a default bone model to generate a virtual, 3D representation of the patient's bone.

The positioning devices <NUM> comprise one or more inertial measurement units (IMUs). IMUs are known to those skilled in the art and include accelerometers, gyroscopes, and magnetometers that work together to determine the position of the IMUs in a 3D coordinate system. Because the A-mode ultrasound transducer(s) <NUM> and IMUs <NUM> are fixedly mounted to the wand <NUM>, the position of the ultrasound transducers <NUM> with respect to the position of the IMUs <NUM> is known. Similarly, because the ultrasound transducers <NUM> are generating signals representative of a straight line distance from the transducer to the surface of the bone, and the position of the transducer(s) <NUM> with respect to the IMUs <NUM> is known, the position of the bone with respect to the IMUs <NUM> can be easily calculated. In other words, as the wand <NUM> is repositioned, the IMUs <NUM> generate data indicating that the relative position of the IMUs has changed in the 3D coordinate system. This change in 3D position of the IMUs <NUM> can be easily correlated to the position of the bone in three dimensions because the position of the bone relative to the ultrasound transducer is known, as is the position of the IMUs with respect to the ultrasound transducers. Accordingly, the IMU 3D position data is used in combination with the fixed position data (distance data for the position of the ultrasound transducer(s) with respect to the IMUs) for the ultrasound transducers <NUM> in combination with the distance data generated responsive to the signals received from the ultrasound transducers to generate composite data that is used to create a plurality of 3D points representing a plurality of distinct points on the surface of the bone. As will be discussed in more detail below, these 3D points are utilized in conjunction with a default bone model to generate a virtual, 3D representation of the patient's bone.

Referring to <FIG>, the positioning devices <NUM> may alternatively comprise one or more ultra wide band (UWB) transmitters. UWB transmitters are known to those skilled in the art, but the use of UWB transmitters and receivers for millimeter-accuracy 3D positioning is novel. One or more UWB transmitters <NUM> is fixedly mounted to the wand <NUM> and is operative to transmit sequential UWB signals to a three or more UWB receivers (having known positions in a 3D coordinate system). The UWB positioning system is comprised of active tags or transmitters <NUM> that are tracked by the UWB receivers <NUM>. The system architecture of the UWB transmitter <NUM> is shown in <FIG>, where a low noise system clock (clock crystal) triggers a baseband UWB pulse generator (for instance a step recovery diode (SRD) pulse generator). The baseband pulse is upconverted by a local oscillator (LO) via a double balanced wideband mixer. The upconverted signal is amplified and filtered. Finally, the signal is transmitted via an omnidirectional antenna. The UWB signal travels through an indoor channel where significant multipath and pathloss effects cause noticeable signal degradation.

Referencing <FIG>, the UWB receiver <NUM> architecture is shown, where the signal is received via a directional UWB antenna and is filtered, amplified, downconverted, and low-pass filtered. Next, a sub-sampling mixer triggered by a second low noise system clock is used to time extend the pulse by <NUM>-<NUM>,000x. This effectively reduces the bandwidth of the UWB pulse and allows sampling by a conventional analog-to-digital converter (ADC).

Each UWB transmitter <NUM> is in communication with the computer <NUM>, as are the UWB receivers <NUM>. Accordingly, the computer <NUM> is aware each time the UWB transmitter transmits a UWB signal, as well as the time that the UWB transmitter transmits the UWB signal. Similarly, the computer <NUM> is aware of the position of each of the UWB receivers in a 3D coordinate system, as well as the time during which each of the UWB receivers receives the UWB signal from the UWB transmitter. By knowing the position of each UWB receiver, the time when each UWB receiver receives the UWB signal from the UWB transmitter, and the time that the UWB transmitter transmitted the UWB signal, the computer <NUM> uses custom digital signal processing algorithms to accurately locate the leading-edge of the received UWB pulse to within sub-sample resolution. The final time-difference-of-arrival (TDOA) calculation (see <FIG>) as well as additional filtering and averaging of data is also carried out by the computer <NUM>.

At least four base stations (receivers) <NUM> are needed to localize the 3D position of the UWB transmitter <NUM>. The geometry of the receivers <NUM> has important ramifications on the achievable 3D accuracy through what is known as geometric position dilution of precision (PDOP). A combination of novel filtering techniques, high sample rates, robustness to multipath interference, accurate digital ranging algorithms, low phase noise local oscillators, and high integrity microwave hardware are needed to achieve millimeter range accuracy (e.g. <NUM>-<NUM> 3D realtime). The analogy of the UWB positioning system to a GPS system is shown in <FIG>. Finally, <FIG> shows actual experimental errors in x,y,z coordinates for detecting the 3D position of the UWB transmitter <NUM> in real-time over <NUM> samples while the transmitter is moving freely within the designated view volume.

Because the A-mode ultrasound transducer(s) <NUM> and UWB transmitter(s) <NUM> are fixedly mounted to the wand <NUM>, the position of the ultrasound transducer(s) <NUM> with respect to the position of the UWB transmitter(s) <NUM> is known. Similarly, because the ultrasound transducers <NUM> are generating signals representative of a straight line distance from the transducer to the surface of the bone, and the position of the transducer(s) <NUM> with respect to the UWB transmitter(s) <NUM> is known, the position of the bone with respect to the UWB transmitter(s) <NUM> can be easily calculated. In other words, as the wand <NUM> is repositioned, the UWB transmitter(s) <NUM> transmit UWB signals, which are correspondingly received by the UWB receivers. This information is processed by the computer <NUM> in order to discern whether the relative position of the UWB transmitter(s) has changed in the 3D coordinate system, as well as the extent of such a change. This change in 3D position of the UWB transmitter(s) <NUM> can be easily correlated to the position of the bone in three dimensions because the position of the bone relative to the ultrasound transducer is known, as is the position of the UWB transmitter(s) with respect to the ultrasound transducers. Accordingly, the UWB transmitter(s) 3D position data is used in combination with the fixed position data (distance data for the position of the ultrasound transducer(s) with respect to the UWB transmitter(s)) for the ultrasound transducers <NUM> in combination with the distance data generated responsive to the signals received from the ultrasound transducers to generate composite data that is used to create a plurality of 3D points representing a plurality of distinct points on the surface of the bone. As will be discussed in more detail below, these 3D points are utilized in conjunction with a default bone model to generate a virtual, 3D representation of the patient's bone.

Regardless of the positioning devices <NUM> utilized with the ultrasound creation and positioning submodule <NUM>, the wand <NUM> is repositioned over the skin of a patient, proximate the knee joint, while the knee joint is bent in order to individually, and successively map (creation of 3D points corresponding to points on the surface of the patient's bone) the three bones of the knee joint (distal femur, proximal tibia, and patella). As the wand <NUM> is repositioned, the data from the transducer(s) <NUM> is transmitted to a wireless transmitter <NUM> mounted to the wand <NUM>. When the wireless transmitter receives the data from the transducer(s) <NUM>, the transmitter transmits the data via a wireless link to the computer <NUM>.

In order to power the devices on-board the wand <NUM>, an internal power supply (not shown) is provided. In exemplary form, the internal power supply comprises one or more rechargeable batteries.

Referring to <FIG>, before patient data is taken, the computer <NUM> software requests a series of inputs to adapt the system to equipment specific devices and the particular bone being modeled. For example, a dropdown menu on the user interface allows the user to input precisely what type of digitizer will be utilized, which may include, without limitation, ultrasound. After the type of digitizer is selected, the user may actuate buttons to connect to or disconnect from the digitizer. Before, during, or after the ultrasound transducer data is acquired, the software provides various dropdown menus allowing the software to load a bone model that roughly is the same shape as the patient's bone.

After the computer <NUM> receives the ultrasound transducer <NUM> data, the computer <NUM> includes software that interprets the A-mode ultrasound transducer data and is operative to construct a 3D map having discrete 3D points that correspond to points on the surface of the bone in question. Consequently, the wand <NUM> (see <FIG>) is repositioned over the bones (distal femur, patella, proximal tibia) for approximately <NUM> second so that the discrete points typify the topography of the bone. Consequently, repositioning the wand <NUM> over the bone in question for a longer duration results in more 3D points being generated by the computer <NUM>, which consequently helps ensure a more accurate patient-specific bone model, such as that shown in <FIG>. A partial range of motion of the knee joint while repositioning the wand <NUM> over the joint can help the wand view new portions of the bone in question for new 3D points that may have been obscured by other bones in another range of motion position.

After each of the bones has been mapped, the computer <NUM> then uses a default bone model as a starting point to construction of the ultimate patient specific, virtual bone model. The default bone model may be a generalized average, as the morphing algorithms use statistical knowledge of a wide database population of bones for a very accurate model. However, for expedited computation, a more generalized default bone model may be selected based upon the patient's gender, race, height, age, for example, as a starting point. For example, in the case of generating a patient-specific model of the femur where the patient is a <NUM> year old, Caucasian male, who is <NUM> (six feet) tall, a default femoral bone model is selected based upon the classification of Caucasian males having an age between <NUM>-<NUM>, and a height between <NUM> and <NUM> (<NUM>'<NUM>" and <NUM>'<NUM>"). In this manner, selection of the appropriate default bone model more quickly achieves an accurate patient specific, virtual bone model because the iterations between the patient's actual bone (typified by the 3D map of bone points) and the default bone model are reduced. After the appropriate default bone model is selected, the computer superimposes the 3D map of actual bone points onto the default bone model and thereafter carries out a deformation process so that the bone model exhibits the actual bone points detected during the wanding. The deformation process also makes use of statistical knowledge of the bone shape based upon reference bones of a wide population. After the deformation process is carried out, the resulting bone model is a patient-specific, virtual 3D model of the patient's actual bone. The foregoing process is carried out for each of the three bones of the knee joint to create patient-specific, virtual 3D models of the patient's proximal tibia, distal femur, and patella.

Referring back to <FIG>, <FIG>, and <FIG> the ultrasound registration submodule <NUM> and the ultrasound dynamic movement submodule <NUM> are mounted to a knee brace <NUM>. The data output from these submodules <NUM>, <NUM> is utilized to generate dynamic 3D models of the patient's own bones through a range of motion. In other words, a visual terminal associated with the computer <NUM> can display the patient's virtual own bone models moving in 3D that match the same movement of the patient's own bones. As will be discussed later, this dynamic 3D model taken through a range of motion is part of what the neural network analyzed to determine if an injury exists and the extent of the injury.

In exemplary form, the ultrasound registration submodule <NUM> comprises a plurality of A-mode ultrasound transducers <NUM> fixedly mounted to the knee brace <NUM>. Specifically, in the context of a knee joint, there are at least two A-mode ultrasound transducers <NUM> (i.e., a transducer group) fixedly mounted to the knee brace <NUM> for tracking of the tibia and femur. In other words, the knee brace <NUM> includes at least six ultrasound transducers in order to track the two primary bones of the knee joint. Each transducer group <NUM> includes a rigid mechanical connection linking the transducers <NUM>, the positioning devices <NUM>, and a connection the knee brace <NUM> which may or may not be rigid. Each transducer group <NUM> includes a rigid mechanical connection linking the transducers <NUM>, the positioning devices <NUM>, and the knee brace <NUM>. In this manner, the relative positions of the transducers with respect to one another do not change. In exemplary form, a first transducer group 158A at least partially circumscribes a distal portion of the femur, while a second transducer group 158B at least partially circumscribes a proximal portion of the tibia, while an optional third transducer group (not shown) overlies the patella if patella kinematics are to be tracked. The ultrasound registration submodule <NUM> is accordingly operative to provide a plurality of static reference points for each bone as the bone is moved through a range of motion.

Referring back to <FIG>, the ultrasound dynamic movement submodule <NUM> comprises a plurality of positioning devices <NUM> that are operative to feed information to the computer <NUM> regarding the 3D position of each transducer group 158A, 158B of the ultrasound registration submodule <NUM>. In a purely exemplary form, though the invention of claim <NUM> uses inertial measurement, the positioning devices <NUM> may comprise light detectors operative to detect pulses of light emitted from light emitters having known positions. The light detectors <NUM> detect the light and send representative signals to control circuitry associated with the knee brace <NUM>. The knee brace <NUM> transmits this information to the computer <NUM> (see <FIG>), which also knows when the light pulses were emitted as a function of time and position. In this manner, the computer can determine the position of the transducers <NUM> in a 3D coordinate system. Because the A-mode ultrasound transducer(s) <NUM> and optical devices <NUM> are fixedly mounted to the knee brace <NUM>, the position of the ultrasound transducer(s) <NUM> with respect to the position of the optical devices <NUM> is known. Similarly, because the ultrasound transducers <NUM> are generating signals representative of a straight line distance from the transducer to a surface of the bone, and the position of the transducer(s) <NUM> with respect to the optical devices <NUM> is known, the position of the bone with respect to the optical devices <NUM> can be easily calculated. In other words, as the knee is repositioned, and correspondingly so too is the knee brace <NUM>, the optical devices <NUM> generate data reflecting that the relative position of the optical devices has changed in the 3D coordinate system. This change in 3D position of the optical devices <NUM> can be easily correlated to the position of the bone in question (femur, patella, or tibia) in three dimensions because the position of the bone relative to the ultrasound transducer groups 158A, 158B is known, as is the position of the optical devices with respect to the ultrasound transducer groups. Accordingly, the optical devices generate data that is used in combination with the fixed position data (distance data for the position of the ultrasound transducer(s) with respect to the optical devices) for the ultrasound transducers <NUM> in combination with the distance data generated responsive to the signals received from the ultrasound transducers to generate composite data that is used to create dynamic movement map of the bone in question. By way of example, because the transducers <NUM> do not move with respect to the positioning devices <NUM>, any movement of the transducers <NUM> in space means that the positioning devices <NUM> have moved in 3D space, and by continuing to track the distance data provided by each transducer <NUM>, the movement of the bone in question can be correspondingly tracked.

Alternatively, the positioning devices <NUM> may comprise one or more inertial measurement units (IMUs). IMUs are known to those skilled in the art and include a combination of accelerometers, gyroscopes, and magnetometers that work together to determine the position of the IMUs in a 3D coordinate system. IMUs are known to those skilled in the art and include accelerometers, gyroscopes, and magnetometers that work together to determine the position of the IMUs in a 3D coordinate system. Because the A-mode ultrasound transducer(s) <NUM> are fixedly mounted to the IMUs <NUM>, the position of the ultrasound transducer(s) <NUM> with respect to the position of the IMUs <NUM> is known. Similarly, because the ultrasound transducers <NUM> are generating signals representative of a straight line distance from the transducer to the surface of the bone, and the position of the transducer(s) <NUM> with respect to the IMUs <NUM> is known, the position of the bone with respect to the IMUs <NUM> can be easily calculated. In other words, as the brace <NUM> is repositioned, the IMUs <NUM> generate data indicating that the relative position of the IMUs has changed in the 3D coordinate system. This change in 3D position of the IMUs <NUM> can be easily correlated to the position of the bone in 3D because the position of the bone relative to the ultrasound transducer is known, as is the position of the IMUs with respect to the ultrasound transducers. By way of example, because the transducers <NUM> do not move with respect to the brace <NUM>, any movement of the transducers <NUM> in space means that the brace has moved in 3D space, and by continuing to track the distance data provided by each transducer <NUM>, the movement of the bone in question can be correspondingly tracked. Such an IMU <NUM> allows relative tracking of the bone movements and requires a static registration between the multiple IMU units with an initial known position (such as standing). Thus the IMU enables measurement of the relative motion between different bones via their corresponding ultrasound transducer array data <NUM> and IMUs <NUM> data. The IMU <NUM> may be used alone or in conjunction with another positioning device <NUM> such as those described in paragraphs <NUM> and <NUM>. The IMU <NUM> may also be used in conjunction with another positioning devices <NUM> such as those described in paragraphs <NUM> and <NUM>. In this scenario, the IMUs <NUM> position is updated at a certain interval with the absolute position from the other positioning system <NUM> as a reference to minimize errors, so the two positioning systems <NUM> act together as one positioning system <NUM>.

Referring to <FIG>, the positioning devices <NUM> may alternatively comprise one or more ultra wide band (UWB) transmitters. UWB transmitters are known to those skilled in the art, but the use of UWB transmitters and receivers for 3D positioning is believed to be novel. One or more UWB transmitters <NUM> is fixedly mounted to the brace <NUM> and is operative to transmit sequential UWB signals to a three or more UWB receivers (having known positions in a 3D coordinate system). Each UWB transmitter <NUM> is in communication with the computer <NUM>, as are a plurality of UWB receivers <NUM>. Accordingly, the computer <NUM> is aware each time the UWB transmitter transmits a UWB signal, as well as the time that the UWB transmitter transmits the UWB signal. Similarly, the computer <NUM> is aware of the position of each of the UWB receivers in a 3D coordinate system, as well as the time during which each of the UWB receivers receives the UWB signal from the UWB transmitter. By knowing the position of each UWB receiver, the time when each UWB receiver receives the UWB signal from the UWB transmitter, and the time that the UWB transmitter transmitted the UWB signal, the computer <NUM> uses custom digital signal processing algorithms to accurately locate the leading-edge of the received UWB pulse to within sub-sample resolution. The final time-difference-of-arrival (TDOA) calculation (see <FIG>) as well as additional filtering and averaging of data is also carried out by the computer <NUM>. Again, because the transducers <NUM> do not move with respect to the brace <NUM>, any movement of the transducers <NUM> in space means that the brace has moved in 3D space. Yet the movement of the brace is tracked using the computer <NUM> in combination with the UWB transmitter and UWB receivers. Similarly, because the fixed orientation of the UWB transmitter and the ultrasound transducers <NUM>, changes in position in a 3D coordinate system of the UWB transmitter <NUM> can correspondingly be used to track movement of the bone in question.

In order to communicate information from the submodules <NUM>, <NUM> to the computer <NUM>, the brace <NUM> includes an ultra wide band (UWB) transmitter <NUM> in communication with the ultrasound transducers <NUM> to facilitate wireless communication of data to the computer <NUM>. It should be noted that if UWB transmitters are utilized as the positioning devices <NUM>, a dedicated UWB transmitter is unnecessary as the UWB transmitters <NUM> could function to send ultrasound sensor data directly to the computer <NUM> over a wireless link.

It should be understood that the use of wireless transmitters and a field programmable gate array design enables the computations to be carried out on a realtime basis, with final processing and display carried out on the computer <NUM>. It should be understood that the use of wireless transmitters and a computer <NUM> incorporating a field programmable gate array design enables the computations to be carried out on a real-time basis. For example, as the wand <NUM> is utilized to go across the epidermis covering the knee joint, the ultrasound transducer data is immediately transmitted to the computer, which in real time calculates the position of the bone in a 3D coordinate system and likewise displays the 3D points on a visual terminal again in real time. Similarly, when the knee brace <NUM> is utilized, the ultrasound transducer data and positioning device data is transmitted to the computer and evaluated in real time to provide motion to the static, 3D patient-specific bone models previously generated. Again, when the computer includes a visual terminal, the dynamic motion imparted to the 3D patient-specific bone models tracks in real time the actual motion of the patient's bones.

As discussed above, an exemplary knee brace <NUM> includes a plurality of A-mode ultrasound transducers <NUM> for transcutaneous detection of the bone's surface and positioning devices <NUM> to track the motion of the ultrasound transducers <NUM>, which in turn, track motion of the knee joint bones. The brace <NUM> is wirelessly connected to a computer <NUM> operative to perform computations and visualization in real-time showing movements of the patient-specific 3D bone models paralleling movements of the patient's actual knee joint in a time synchronized manner. The exemplary brace <NUM> includes a rigid or semi-rigid body having a plurality positioning devices <NUM> attached thereto. An even further alternate positioning device <NUM> includes a plurality of accelerometers in this case at least four accelerometers. The homogenous transformation between an accelerometer's reference coordinate frame and the world coordinate frame is calculated using the positions of the four accelerometers:.

where s(n+<NUM>) is position at the current state, s(n) is the position from previous state, v(n+<NUM>) is instantaneous velocity of the current state, v(n) is the velocity from previous state, a(n) is the acceleration from the accelerometer and dt is the sampling time interval. The previous equations describe the dynamic motion and positioning of a point in 3D Euclidean space. Additional information is needed to describe a 3D body orientation and motion.

The orientation of the transducer can be described by using a gravity based accelerometer (example: ADXL-<NUM>, analog device) by extracting the tilting information from each pair of orthogonal axis The acceleration output on x ,y, or z due to gravity is equal to the following: Ai = (Voutx - Voft) / S, where Ai is the acceleration at the x, y, or z axis, Voutx is the voltage output from the x, y, or z axis, Voff is the offset voltage, and S is the sensitivity of the accelerometer the yaw, pitch and roll can be calculated as shown in the following: <MAT> <MAT> <MAT> where pitch is ρ, which is the x-axis relative to the ground, roll is Ø, which is the y-axis relative to the ground, and roll is θ, which is the z-axis relative to the ground. Since the accelerometer is gravity based, the orientation does not require information from the previous state once the sensor is calibrated. The static calibration requires the resultant sum of accelerations from the three axis equal to1g (the nominal acceleration due to gravity at the Earth's surface at sea level, defined to be precisely <NUM>/s<NUM> (approximately, <NUM> ft/s<NUM>)). Alternatively, an orientation sensor that provides yaw, pitch, and roll information of the bodily tissue in question are also commercially available (e.g., IDG-<NUM>, available from Invensense). The orientation of the transducer can then be resolved by using direction cosine matrix transformation: <MAT> where C represents cosine and S represents sine.

The interpretation of the vibration and kinematic data is a complicated task involving an in-depth understanding of data acquisition, training data sets and signal analysis, as well as the mechanical system characteristics. Vibrations generated through the implant components, bones, and/or soft tissues interaction result from a forced vibration induced by driving force leading to a dynamic response. The driving force can be associated with the impact following knee ligament instability, bone properties, and conditions. A normal, intact knee will have a distinct pattern of motion, coupled with distinct vibrational characteristics. Once degeneration or damage occurs to the knee joint, both the kinematic patterns and vibrational characteristics become altered. This altering, for each type of injury or degeneration, leads to distinct changes that can be captured using both kinematic and vibration determination.

Referencing <FIG>, the exemplary diagnostic system <NUM> includes an intelligent diagnosis module <NUM> operative to diagnose ligament, other soft tissue, and bone injuries. From previous studies, normal and anterior cruciate ligament deficient (ACLD) knee subjects exhibit a similar pattern of posterior femoral translation during progressive knee flexion, but the subjects exhibit different axial rotation patterns of <NUM> degrees of knee flexion. Accordingly, the diagnosis module <NUM> includes three stages: (<NUM>) a first stage that involves data gathering and analysis; (<NUM>) detection of an injury by sending the data to a neural network; and (<NUM>) another stage of a neural network that classifies or determines the severity of any injury that is detected.

This first stage includes acquisition of kinematic feature vectors using multiple physiological measurements taken from the patient while the patient moves the joint in question through a range of motion. Exemplary measurements include, without limitation, medial condyle anteroposterior motion (MAP) and lateral condyle anteroposterior (LAP), with the latter pertaining to the anterior-posterior A/P distance of the medial and lateral condyle points relative to the tibia geometric center. Other exemplary measurements include LSI (distance between the lateral femoral condyle and the lateral tibial plateau) and MSI (distance between the medial femoral condyle and the medial tibial plateau) which are S/I (superior/inferior) distance of the lateral and medial condyle points to the tibial plane. Further exemplary measurements include condyle separation, which is the horizontal (x-y plane) distance between the two minimum condyle points to the tibia. Feature vectors also include the femoral position with respect to the tibia which is defined by three Euler angles and three translation components in addition to the vibration signal, and force data.

Referring to <FIG>, the motion features vectors, extracted from the kinematic and vibration analyses, are output to a multilayer back propagation neural network for determining the injured ligament.

Referencing <FIG>, an exemplary neural network classifier has multiple binary outputs. Each output is either a one or zero, with one corresponding to yes and zero corresponding to no. In this exemplary neural network classifier, each output represents the response of the neural network to a particular injury type; for example one output will represent the response for anterior cruciate ligament deficiency (ACLD), its state will be one if an ACL injury is detected, and zero otherwise. Obviously, the neural network may be significantly more sophisticated or less sophisticated, depending upon the underlying model of the joint in question.

Referring to <FIG>, construction of the exemplary neural network includes formulating a supervised classifier using a training set of the kinematic and vibration data corresponding to normal and injured knee joint. The NN is trained with a set of vectors. Each vector consists of data (kinematics, vibrations and forces) collected from one joint. Fluoroscopy data can be used to calculate the kinematics. Once the NN is trained, it can be used to classify new cases and categorize the injury type using these kinematics, vibration and forces data. Those skilled in the art will readily understand that the types and classifications desired to be accommodated by the neural network necessarily include training the neural network on these very types and classifications. Exemplary types and classifications of injuries to a mammalian knee joint include, without limitation, osteoarthritic conditions, soft tissue damage, and abnormal growths. Likewise, the neural network also needs to be trained as to indicators of normal knee function. In this manner, once the neural network is trained, it has the capability to differentiate between and output diagnosis data concerning normal and abnormal knee conditions.

Referencing <FIG>, the vibration, kinematics, forces, and other features of a person's knee joint are compiled and fed to the trained neural network. The trained neural network then diagnoses the condition of the patient's knee joint, identifying any degeneration by type and severity.

Exemplary embodiments may be adapted to collect data outside of a clinical setting. For example, an exemplary embodiment may be worn by a patient for an extended period of time while performing normal activities. For example, a patient may wear vibration sensors and/or a kinematics tracking brace during activities that are not reproducible in the office (for example, weight lifting, racquet ball etc.) that elicit the pain or symptom. In some embodiments, the patient may turn the device on immediately prior to the activity and/or the patient may mark the event when it occurs. This enables analysis of the data just a few seconds before the marked time to see what abnormal sounds or joint kinematic were occurring. Data may be stored on a portable hard drive (or any other portable storage device) and then may be downloaded to exemplary systems for analysis. The data can be transmitted and stored in a computer wirelessly. It can also be stored with a miniature memory drive if field data is desired. If the occurrence of the pain was more random, exemplary devices allow continuous gathering of data. In embodiments, the patient may mark the event. Devices capable of continuous monitoring may require a larger data storage capacity.

It should also be noted that electromagnetic tracking could be used as one of the positioning device <NUM>, <NUM> alternatives.

It should further be noted that EMG electrodes may also be utilized as a data input for the computer <NUM> and neural network <NUM>. In this fashion, one or more EMG electrodes are mounted to the surface of the skin proximate the muscles adjacent the knee joint to monitor the electrical signal transmitted to the muscles in order to provide relevant data of a muscle injury or disorder.

Claim 1:
A method of evaluating a physiological condition of bodily tissue, the method comprising:
repositioning one or more A-mode ultrasound transducers (<NUM>) over a patient's epidermis to generate signals representative of a straight line distance from the one or more ultrasound transducers (<NUM>) to a bone surface of the patient;
tracking the repositioning of the one or more ultrasound transducers in three dimensions using at least one inertial measurement unit (<NUM>) to generate 3D position data of a bone;
correlating the signals representative of the straight line distance from the one or more ultrasound transducers (<NUM>) to the bone surface of the patient and the change in 3D position data to generate a 3D map having a plurality of 3D points corresponding to points on the bone surface;
superimposing the 3D map onto a default bone model and carrying out a deformation process resulting in a patient-specific, virtual 3D model of the bone;
visually displaying the virtual 3D model of the bone.