RESPIRATION MOTION STABILIZATION FOR LUNG MAGNETIC NAVIGATION SYSTEM

A system for stabilization based on respiratory movement includes a medical device configured to navigate inside of a patient, a tracking sensor affixed on the medical device and configured to track a location of the medical device, at least one motion sensor located on the patient and configured to sense respiratory movements of the patient, a computer configured to generate a respiratory model based on the respiratory movements sensed by the at least one motion sensor for a predetermined period and to stabilize a location of the medical device based on the respiratory model after the predetermined period, and a display configured to display a graphical representation of the medical device based on the stabilized location on a pre-procedure two-dimensional (2D) image or three-dimensional (3D) model.

BACKGROUND

1. Technical Field

The present disclosure provides systems and methods for correcting the detected location of a sensor associated with a medical device in an electromagnetic field by manually or automatically stabilizing a location of the medical device caused by respiration and displaying the stabilized location of the medical device on a display. More particularly, the present disclosure relates to systems and methods for displaying the location of a medical device in a static image or 3D model based on the determined stabilized position of the medical device during medical procedures.

2. Discussion of Related Art

When performing a medical procedure, clinicians often rely on patient data including X-ray data, computed tomography (CT) scan data, magnetic resonance imaging (MRI) data, or other imaging data that allows the clinician to view the internal anatomy of a patient. These image data are also utilized to identify targets of interest and to develop strategies for accessing the targets of interest for the surgical treatment. Further, these image data have been used to create a three-dimensional (3D) model of the patient's body to help navigation of the medical device to a target of interest within a patient's body.

Since it is important to treat a target at an exact location from a planned direction, even a small discrepancy between the actual location and an estimated location of the medical device may cause undesired consequences in the medical procedure. Thus, precision in estimating the actual location of the medical device with sufficient level of accuracy is highly desirable during medical procedures.

Further, when the medical device approaches the target following the 3D model, patient's inhaling and exhaling causes medical device to appear to swing in (and possibly out) of the 3D model even though the medical device is stably positioned with respect to internal organs surrounding the target within the patient's body. Thus, stabilizing the respiratory movements for the medical device is also beneficial in properly displaying the location of the medical device during medical procedures.

SUMMARY

The present disclosure is directed to systems and methods for stabilizing respiratory movements of a medical device so that the medical device is displayed sufficiently stationary with respect to a static image or model while the patient continuously breathes and the medical device is positioned near a target of interest inside the patient's body.

According to an embodiment of the present disclosure, a system for stabilization based on respiratory movement includes a medical device configured to navigate inside of a patient, a tracking sensor affixed on the medical device and configured to track a location of the medical device, at least one motion sensor located on the patient and configured to sense respiratory movements of the patient, a computer configured to generate a respiratory model based on the respiratory movements sensed by the at least one motion sensor for a predetermined period and to stabilize a location of the medical device based on the respiratory model after the predetermined period, and a display configured to display a graphical representation of the medical device based on the stabilized location on a pre-procedure two-dimensional (2D) image or three-dimensional (3D) model.

In an aspect, the computer is further configured to receive an instruction to start sampling outputs of the at least one motion sensor and outputs of the tracking sensor for a predetermined period. The computer is further configured to calculate a weight based on the respiratory model and the tracked locations of the medical device, which have been sampled for the predetermined period. A new location of the medical device and new respiratory movement from the at least one motion sensor are sampled at each sampling time for stabilization after the predetermined period. The computer is further configured to multiply the new respiratory movement from the at least one motion sensor with the weight to obtain a reference stabilization signal. The stabilized location of the medical device is obtained by subtracting the reference stabilization signal from the new location of the medical device.

In another aspect, the predetermined period is at least two consecutive respiratory cycles.

In yet another aspect, the respiratory model is based on mean subtracted sampled outputs of the at least one motion sensor for the predetermined period. The respiratory model is generated in matrix representation by performing singular value decomposition on the mean subtracted sampled outputs of the at least one motion sensor for the predetermined period.

In yet another aspect, the computer is further configured to check a correlation of the tracked locations of the medical device during the predetermined period. The computer is further configured to restart the predetermined period if the correlation is greater than a threshold, or the tracked locations of the medical device are correlated when a periodic movement exists in the tracked locations of the medical device, which have been sampled during the predetermined period. The computer is further configured to generate a weight based on the respiratory model and the tracked locations of the medical device if the correlation is less than or equal to the threshold. A new location of the medical device and a new respiratory movement from the at least one motion sensor are sampled at each sampling time for stabilization after the predetermined period. The computer is further configured to multiply the new respiratory movement from the at least one motion sensor with the weight to obtain a reference stabilization signal for the medical device. The stabilized location of the medical device is obtained by subtracting the reference stabilization signal from the new location of the medical device.

In yet another aspect, the respiratory model is generated by performing principal components analysis (PCA). Three principal components are used in the PCA.

In still another aspect, the tracking sensor tracks a location of the distal portion of the medical device. The at least one motion sensor is located on a chest over a lung of the patient.

Any of the above aspects and embodiments of the present disclosure may be combined without departing from the scope of the present disclosure.

DETAILED DESCRIPTION

The present disclosure provides systems and methods for detecting the location of a sensor associated with a medical device in an electromagnetic field, depicting the location in one or more pre-procedure images or 3D models derived from the pre-procedure images on a display, and manually or automatically stabilizing the location of the medical device by reducing or eliminating movement caused by respiration and displaying the stabilized location of the medical device on a display.

The medical procedures of the present disclosure are generally divided into two phases: (1) a planning phase, and (2) a procedure phase. The planning and treatment phases for medical treatment (e.g., microwave ablation) are more fully described in U.S. Published Patent Application Nos. 2014/028196113, entitled PATHWAY PLANNING SYSTEM AND METHOD, filed on Mar. 15, 2013, by Baker and U.S. patent application Ser. No. 14/753,288 entitled SYSTEM AND METHOD FOR NAVIGATING WITHIN THE LUNG filed on Jun. 29, 2015, by Brown et al., the contents of which is hereby incorporated by reference in its entirety.

As described in the applications incorporated by reference above, the use of pre-procedure images, along with 3D models, particularly where the location if medical device is detected and displayed with reference to these images and models enhances clinicians' understanding about locations of the medical device with respect to internal organs of a patient. While these image displaying modalities are quite useful to show the real time location of the medical device with respect to the internal organs during the navigation within the patient, they are not perfect. One source of error is caused by the respiration of the patient. The physical movement of the lungs can cause movement and changes in the physiology of the patient as the lungs inflate and deflate. As can be appreciated, the pre-procedure images are taken at one point in the respiration cycle, often full inspiration while the patient holds their breath. As a result, at all times other than full inspiration (an occurrence which typically does not happen during procedures where the patient is intubated and sedated), there is some error in the registration of the location of the medical device with the pre-procedure images and its actual location within the physiology of the patient. The detected location of the medical device can appear to be moving in the static image or 3D model, and in some cases the detected and displayed movement of the medical device may cause it to appear to be outside a known channel (e.g. lung airway or blood vessel) which the clinician knows is not correct. In accordance with one aspect of the present disclosure, motion sensors which detect the physical movement of the patient are used to stabilize the respiratory-induced movement of the medical device to more accurately reflect the location of the medical device within the patient on the pre-procedure images or 3D model.

In one embodiment, motion sensors may be placed on the chest of the patient and capture respiratory movement. The medical device also includes a sensor, whose motion can be detected. By subtracting the movement of the motion sensors from the sensed movement of the medical device, the apparent movement of the medical device can be greatly reduced, and as a result the movement shown in the images or 3D model is greatly reduced and more accurately reflects the position of the medical device to the patient's physiology.

In a further embodiment, a respiration model may be generated to model the effect of a patient's respiration from the data captured by the motion sensors. Since any internal organ, for example, a portion of the lungs inside the chest moves differently from the respiratory movement of the chest, the respiration model allows for further refinement of the motion to be subtracted from the medical device movement to more accurately identify the position of the medical device with respect to the patient's physiology at locations remote from the locations of the motion sensors.

In the model embodiment, a weight is used to adjust the respiration model to accommodate for distances between the location of the motion sensors and the location of the medical device. The weight is also used to calculate a reference stabilization signal of the medical device, which models changes in location of the medical device due to the respiratory movement. Thus, by subtracting the reference stabilization signal from the detected changes in location of the medical device, which is sensed by a tracking sensor, the discrepancy, which caused by the respiratory movement, between the detected location of the medical device and the location of the medical device with respect to internal organs can be substantially removed and the medical device can be accurately displayed with respect to pre-procedure images or 3D model generated from the pre-procedure images.

Although the present disclosure will be described in terms of specific illustrative embodiments, it will be readily apparent to those skilled in this art that various modifications, rearrangements and substitutions may be made without departing from the spirit of the present disclosure. The scope of the present disclosure is defined by the claims appended hereto.

FIG. 1illustrates an electromagnetic navigation (EMN) system100using an electromagnetic field for identifying a real-time location of a medical device within a patient body. The EMN system100is configured to augment CT, MRI, or fluoroscopic images, with ultrasound image data assisting in navigation through a luminal network of a patient's lung to a target. One such EMN system100may be the ELECTROMAGNETIC NAVIGATION BRONCHOSCOPY® system currently sold by Covidien LP. The EMN system100includes a catheter guide assembly110, a bronchoscope111, a computing device120, a monitoring device130, an EM board140, an EM tracking system160, and motion sensor170. The bronchoscope111is operatively coupled to the computing device120and the monitoring device130via wired connection (as shown inFIG. 1) or wireless connection (not shown).

The computing device120, such as, a laptop, desktop, tablet, or other similar computing device, includes a display122, one or more processors124, memory126, a network card128, and an input device129. The EMN system100may also include multiple computing devices, wherein multiple computing devices120are employed for planning, treatment, visualization, or helping clinicians in a manner suitable for medical procedures. The display122may be touch-sensitive and/or voice-activated, enabling the display122to serve as both an input and output device. The display122may display a two dimensional (2D) images or 3D models of a chest of the patient to locate and identify a portion of the lung that displays symptoms of lung diseases. The display122may further display options to select, add, and remove a target to be treated and settable items for the visualization of the lung. In an aspect, the display122may also display the location of the catheter guide assembly110in the luminal network of the lung based on the 2D images or 3D model of the chest.

The one or more processors124execute computer-executable instructions. The processors124may perform image-processing functions so that the 3D model of the lung can be displayed on the display122. In embodiments, the computing device120may further include a separate graphic accelerator (not shown) that performs only the image-processing functions so that the one or more processors124may be available for other programs.

The memory126stores data and programs. For example, data may be image data for the 3D model or any other related data such as patients' medical records, prescriptions and/or history of the patient's diseases. One type of programs stored in the memory126is a 3D model and pathway planning software module (planning software). An example of the 3D model generation and pathway planning software may be the ILOGIC® planning suite currently sold by Medtronic PLC.

The memory126may store navigation and procedure software which interfaces with the EMN system100to provide guidance to the clinician and provide a representation of the planned pathway on the 3D model and 2D images derived from the 3D model. An example of such navigation software may be the ILOGIC® navigation and procedure suite sold by Covidien LP. In practice, the location of the patient150in the EM field generated by the EM field generating device145must be registered to the 3D model and the 2D images derived from the model.

The bronchoscope111is inserted into the mouth of the patient150and captures images of the luminal network of the lung using a video capturing device (not shown). In the EMN system100, inserted into the bronchoscope111is a catheter guide assembly110for achieving access to the periphery of the luminal network of the patient150. The catheter guide assembly110may include an extended working channel (EWC)112into which a locatable guide catheter (LG)113with a tracking sensor115, which is positioned or integrated near the distal portion of the LG113, is inserted. The EWC112, the LG113, and the tracking sensor115are used to navigate through the luminal network of the lung. Though described here with respect to the tracking sensor115being included in the LG113, those of skill in the art will recognize that the tracking sensor115may be formed integrally with the EWC112, or in another component insertable through the EWC112, such as an ablation catheter, biopsy tool, aspiration needle, tissue piercing and tunneling instrument, and others known to those of skill in the art.

The EM board140is configured to provide a flat surface for the patient to lie down and includes an EM field generating device145. When the patient150lies down on the EM board140, the EM field generating device145generates an EM field surrounding a portion of the patient150. The tracking sensor115at the distal portion of the LG113is used to determine the location of the EWC112in the EM field generated by the EM field generating device145.

The EM board140may be configured to be operatively coupled with the motion sensors170which are located around the chest of the patient150. The motion sensors170capture respiratory movement of the chest while the patient150is inhaling and exhaling. In an aspect, the motion sensor170may be EM sensors configured to sense the strength and changes to the strength of the EM field generated by the EM field generating device145. Based on the sensed results, locations of the motion sensor170may be calculated and thus the patient's respiratory movements are identified.

The tracking sensor115and the motion sensors170may each be capable of sensing 3 degrees of freedom (DOF) including translational movements along X, Y, and Z axes in the Cartesian coordinate system. The coordinate system may be the polar, spherical, or any suitable coordinate system to represent the EM field space. The tracking sensor115and the motion sensor170may also be capable of sensing 5 or 6 DOF including the three translational directions and three rotational movements (pitch, yaw, and roll) within the EM field.

While navigating toward a target of interest, movement of the LG113(specifically the tracking sensor115) due to respiration may not manifest itself as significantly on the displayed image of the LG113on the pre-procedure images or 3D models as it is initially navigated through the patient. The movement of the LG113due to the respiration, however, may affect accuracy, effectiveness, and reliability of the medical procedures including ablation or biopsy when the distal portion of the LG113or the EWC112approaches a target located closer to the pleura boundaries of the lungs. To address the potential accuracy issues, the EM tracking system160receives data representing respiratory movement of the patient's chest as sensed by the motion sensor170. In an aspect, a respiratory model may be generated by using singular value decomposition or principal component analysis (PCA), as will be described in greater detail below. This respiratory model may refine the data received from the motion sensor170to remove portions of a signal received from the motion sensor170that is attributable to noise.

By subtracting a reference stabilization signal from the sensed location of the LG113, a more accurate location (the stabilized location) of the LG113with respect to the physiology of a patient, and specifically the internal organs as they move through the respiration process may be obtained. In this way, the sensed location of the LG113may be stabilized so that the stabilized location of the LG113is synchronized with the respiration cycle of the physiology and the position of the LG113is accurately and stably displayed on pre-procedure 2D images or the 3D model.

In an aspect of the present disclosure, a special computer program or software module associated with the EM tracking system160may perform procedures and calculations for stabilization based on the respiratory movements. The positioning of the motion sensor170on a patient and the number of the motion sensor170affect calculation of a weighting factor and are important in considerations of the present disclosure. As an example, in accordance with aspects of the present disclosure two, three, or more motion sensors170may be employed. In at least one embodiment, as shown inFIG. 1, three motion sensors170are employed. These three motion sensors170are referred to herein as a patient sensor triplet (“PST”). The following description is based on the motion sensors170of the PST but the scope of this disclosure is not limited to the three motion sensors. Whether one or more sensor is employed, the sensed movement of the sensors in the EM field is output to generate a respiratory model.

One of the motion sensors170of the PST may be placed on the sternum of the patient, specifically about two fingers below the sternal notch. The other two motion sensors170of the PST may be placed along left and right sides of the chest, specifically the midaxillary line at the eighth rib on each side. In still another aspect, the placement of the motion sensors170of the PST may be determined based on the location of the target of interest so that movements of the LG113caused by respiration may be better stabilized with respect to the target.

FIG. 2Aillustrates graphical representations of sampled movements of the LG113(more specifically the tracking sensor115) due to respiratory movements of the patient. As described above, the tracking sensor115located at the distal portion of the LG113can sense strength of the EM field in at least three different directions X, Y, and Z. InFIG. 2A, the horizontal axis represents the number of samples taken over time and the vertical axis represents displacement in millimeters (mm) in each of the X, Y and Z directions. In one embodiment of the disclosure, analog-to-digital converters (ADCs), which are not shown inFIG. 1, may capture 30 samples per second in the three directions and a special program or software module installed on the EM tracking system160may identify and track the location of the LG113in three different directions X, Y, and Z axes over time during the respiration cycle.

Three curves210a-210cshow movement of the tracking sensor115at the distal portion of the LG113caused by respiration along three different axes (X, Y, and Z). As shown inFIG. 2A, non-periodic displacement of the tracking sensor115until time TAor after TBrepresent instances where the tracking sensor115of the LG113is moved by the clinician. Particularly, non-periodic displacements of the tracking sensor115until time TAmay be sampled during navigation toward a target of interest and non-periodic displacements of the tracking sensor115until after time TBmay be removal of the LG113or re-navigation toward a new target of interest. In contrast, instances the movement is periodically consistent, for example during a period from time TAto time TB, are likely caused by respiration without movement of the tracking sensor115caused by the clinician. This data is the raw movement data of the tracking sensor115.

In addition to respiratory movement of the lungs, the other organs, such as the heart, or patient's voluntary or involuntary muscle contractions can be detected by the tracking sensor115. For example, in a case where the LG113is placed in proximity to the heart, the position of the LG113and the tracking sensor115therein will be affected by the movement caused by the heart beating. However, in some instances, while these movements can be detected, their magnitude is sufficiently small that it is desirable to filter these from the detected movement data. Because the frequency of contractions of the heart is higher than respiratory movements and can be easily detected and filtered from the movement data detected by the tracking sensors115.

When the LG113approaches within close proximity to a target or region of interest and an accurate location of the LG113is needed, the EM tracking system160may manually or automatically start sampling respiratory movements of the chest through the motion sensors170of the PST.FIG. 2Billustrates a portion of signals sampled by the ADCs of the EM tracking system160from the motion sensors170of the PST. The top three curves220a-220care movements in the three directions sensed by one motion sensor170of the PST and the bottom curves230a-230care movements in the three directions sensed by another motion sensor170of the PST. Similar signals may be received from the third sensor of the three motion sensors170of the PST and additional or fewer motion sensors170may be used without departing from the scope of the present disclosure.

In one embodiment, the EM tracking system160identifies the respiratory movement of the patient (e.g., the patient's chest) via the motion sensors170of the PST for a predetermined period, for example 12-15 seconds, which is sufficient to capture sufficient data for the creation of a respiratory model. The sensed results from the motion sensors170of the PST are analyzed to create a respiratory model, which may mimic the patient's respiratory movements but eliminate noise and reduces the number of computations and time necessary to calculate the weights. The respiratory model is derived by performing singular value decomposition or principal component analysis (PCA) on the signals received from the motion sensors170of the PST and may be used to reduce the number of parameters for the computations. For example, the ADCs of the EM tracking system160may sample 9 signals, 3 signals from each motion sensor170. A sampling frequency by the ADCs of the EM tracking system160may be 30 per second. Thus, for a 15 second sample period, the total number of samples sampled by the ADCs of the EM tracking system160is 4050 (9 samples*30*15). In an aspect, by reducing the number of parameters calculation power, time, and resources for performing stabilization calculations based on respiration may be reduced.

FIG. 2Cshows principal components of the signals from the motion sensors170using the PCA. The period240, which is bound by the two vertical lines, is the predetermined sampling period (e.g., 15 seconds). In one example, the predetermined period is longer than or equal to a time required for two consecutive respiration cycles. Other periods and number of respiration cycles may also be utilized without departing from the scope of the present disclosure. For example, the predetermined period may be dependent on requirements of the ADCs of the EM tracking system160, the motion sensors170of the PST, the tracking sensor115, and others.

InFIG. 2C, curves250a-250fare illustrative examples of the result of a principal component analysis (PCA) of the signals shown inFIG. 2B. The first principal component250amay represent the respiratory movements. The second principal component250bmay represent movement based on the heartbeats, and have the second most weight but also includes some noise. The sixth principal component250fmay essentially all noise. The PCA is described in greater detail below.

FIG. 3Ashows a simplified block diagram illustrating the process of generating weight factor, which will be used to generate a stabilized location signal for the LG113using PCA as shown inFIG. 3B. When the LG113is placed in close proximity to a target or region of interest, movement of the LG113by the clinician is stopped for a predetermined time. During this predetermined time, the ADCs of the EM tracking system160sample the respiratory movements of the chest sensed by the motion sensors170of the PST and movements of the LG113sensed by the tracking sensor115. During the predetermined period, the clinician does not move the LG113and thus the LG113maintains its position with respect to the surrounding physiology of the patient. After the predetermined period has passed, a weight factor may be calculated from the samples of the motion sensors170of the PST and the LG113based on the following equation:

where L is an N by 3 matrix having (Xj, Yj, Zj) as a row vector sampled by the tracking sensor115of the LG113; A is an N by 9 matrix having (X1j, Y1j, Z1j, X2j, Y2j, Z2j, X3j, Y3j, Z3j) as a row vector sampled from motion sensors170of the PST, where (X1j, Y1j, Z1j), (X2j, Y2j, Z2j), and (X3j, Y3j, Z3j) are the j-th location sampled from the first, second, and third motion sensors170of the PST, respectively, along the X, Y, and Z axes; W1is an 9 by 3 matrix representing a weight for stabilization based on respiratory movements; and N is the total number of samples collected by each sensor over the predetermined period. The weight W1may be used to generate a reference stabilization signal for the LG113based on sensed movements of the motion sensors170of the PST. The reference stabilization signal represents a predicted displacement in the location of the LG113due to the respiratory movements and is subtracted from detected LG113position to determine a stabilized LG position signal.

As is apparent, A is not a square matrix and its inverse cannot be obtained to calculate the weight W1. In this regard, the EM tracking system160may employ PCA, which utilizes the singular value decomposition, to create a respiratory model in matrix representation for A, as will now be described in detail. Referring again toFIG. 3A, upon receipt of the sampled outputs from the motion sensors170of the PST, a program or software module installed in the EM tracking system160performs PCA by first subtracting the mean of these signals from the signals as follows:

where i=1, 2, and 3. Now, a singular value decomposition is applied to the matrix having the mean subtracted for each motion sensor170of the PST, as follows:

where M is an N by 9 matrix, rows of M include 9 signals from the motion sensors170of the PST, columns of U are orthonormal eigenvectors of MMT, columns of V are orthonormal eigenvectors of MTM, and S is an N by 9 matrix containing the squared roots of eigenvalues in the diagonal from U and V in descending order. U is an N by N square matrix and V is a 9 by 9 square matrix. Each entry in the diagonal of S is called a singular value or a principal component. Since diagonal entries of S are in the descending order, the first principal component has the largest value and has the most weight, meaning that the first diagonal entry or the first singular value has the largest effect on M and the other diagonal entries have less effect on M than the first singular value. All entries of S other than the entries in the diagonal are zeros. Now, a respiratory model M is created in matrix representation as USVT, which can be used to calculate the weight W1as follows:

where S−1includes entries in the diagonal, which are reciprocals of the non-zero entries in the diagonal of S, and zeros for all other entries.

As noted above, the size of the each matrix defines a large data set meaning that without some reduction in the volume of data the calculation power, processing resources, and time required to calculate the weight W1would potentially render the methods describe herein too costly or too time consuming to be effective. To address the volume of data issue, the number of principal components can be reduced by removing insignificant principal components so that the number of necessary computations is correspondingly reduced. The present disclosure, however, is not limited to the use of the PCA, and other methodologies for reduction of the dataset or computations may be understood and employed by those of ordinary skill in the art.

By selecting one or more of the nine principal components and zeroing out the rest or replacing them with zeros, time, resources, and power for calculating the weight W1can be further reduced. For example, the first three principal components, which are the largest of the principal components, may be selected from S to form a new matrix {tilde over (S)}, which includes all zeros other than the three selected singular values. With this new matrix {tilde over (S)}, a respiratory model {tilde over (M)} can be constructed as follows:

where {tilde over (S)} includes the selected principal components in the diagonal and zeros for the other entries. This respiratory model {tilde over (M)} better represents the respiratory movements of the chest due to the removal of noise-related principal components. With this respiratory model {tilde over (M)}, a weight W2may be calculated as follows:

where {tilde over (S)}−1includes entries in the diagonal, which are reciprocals of the non-zero entries, the selected principal components, in the diagonal of {tilde over (S)}, and zeros for all other entries. Since most of entries of {tilde over (S)}−1are zero except the number of the selected principal components, calculations for the weight W2are simpler than calculations of the above equation (6) W1=VS−1UTL. In this way, the weight W2is calculated by the program or software module of the EM tracking system160. The weight W2is a 9 by 3 matrix.

After calculating the weight W1or W2(hereinafter the weight W), stabilization of the location of the LG113with respect to the respiratory movement is performed as shown inFIG. 3B. The ADCs of the EM tracking system160sample outputs of the motion sensors170of the PST and outputs of the tracking sensor115of the LG113, respectively, at every sampling time for stabilization. The means of the 9 locations from the motion sensors170of the PST are subtracted from the 9 location values from the motion sensors170of the PST and are multiplied by the weight W to obtain a reference stabilization signal. Specifically, the reference stabilization signal is calculated using the following equation:

where (X1, Y1, Z1), (X2, Y2, Z2), and (X3, Y3, Z3) are sampled locations from the first, second, and third motion sensors170of the PST, respectively, along the X, Y, and Z axes; and R is the reference stabilization signal and a 1 by 3 matrix, which represents a predicted displacement for the LG113based on the respiratory model. The reference stabilization signal R is subtracted from the tracking sensor115signal, and results in a stabilized location of the LG113. As a result, a graphical representation of the LG113at the stabilized location, which is displayed over the pre-procedure 2D images or 3D model on the display, will not noticeably move with respect to the pre-procedure 2D images or 3D model on the display122.

In an embodiment, a weight may be calculated for each motion sensor170of the PST satisfying the following formula:

where Miis a N by 3 matrix or each row [xi, yi, zi] of Ai, which is a mean subtracted signal from the i-th motion sensor170of the PST, Wiis the weight corresponding to the i-th motion sensor170, and i is 1, 2, and 3. The weight Wican be calculated by performing PCA, during which a respiratory model may be generated by selecting a portion of the principal components. Descriptions for detailed procedures for performing PCA have been described above and are omitted here. The reference stabilization signal R may be calculated as follows:

The stabilized location of the LG113is calculated by subtracting the reference stabilization signal R from the sensed location of the tracking sensor115.

In another embodiment, differences between samples of the motion sensors170of the PST may be used to generate the weight. Patients under medical procedures can move voluntarily or involuntarily. Such movements may be shown in the samples collected by the motion sensors170of the PST and by the tracking sensor115as common-mode shifts. By taking differences between samples, the common-mode shifts may be removed. The singular value decomposition is applied to a difference matrix D as follows:

where xij, yij, and zijare mean subtracted signals from the motion sensors170of the PST, and UD, SD, VDare corresponding matrixes to the difference matrix based on the singular value decomposition. UDSDVDTis a respiratory model for the difference matrix D and is used to calculate a weight WDby the following equation:

After the predetermined period, a reference stabilization signal RDfor the LG113based on the difference matrix D is calculated as follows:

Since the total number of singular values is six when using the difference matrix D, calculation power, time, and resources may also be reduced. In the same way described above, a portion of the principal components of SDmay be selected for constructing a respiratory model and calculating the weight WDwith the respiratory model so as to further reduce calculation power, time, and resources.

FIG. 4shows locations of the LG113before and after the stabilization based on the respiratory movements. Curve410illustrates movement (i.e., location over time) of the LG113in one axis (e.g., the X-axis) before the stabilization. As shown in the curve410, even though the LG113is not moved to navigate toward a target, the effects of respiratory movements are apparent. Displacement of the location of the LG113may be as much as 4 centimeters in one direction (X, Y. or Z axis) and is also shown in the curve210bofFIG. 2Aduring the period from time TAto time TB.

Curve420illustrates the stabilized movement of the LG113. As compared to the displacement of the LG113before stabilization, the maximum displacement in the stabilized movement signal is less than about 1 centimeter even at instances of maximum displacement.

FIGS. 5A and 5Bare flow charts illustrating a method500for manually stabilizing the respiratory movements for the LG113in accordance with embodiments of the present disclosure. When a patient is placed on the EM board140, the method500is started by generating an EM field at step505, for example using the EM field generating device145.

At step510, a clinician follows a pathway plan for navigation within the luminal structure of the patient (e.g. the airways of the lungs) so that the LG113navigates toward a target. At this stage, displacement of locations of the LG113caused by respiratory movements may be minimal in comparison to the movement caused by advancement of the LG113by the clinician, and thus respiration-induced movements can be ignored. At step515, it is determined whether an instruction for stabilization is received. In at least one embodiment, this may be instituted by the clinician by clicking of a button on a user interface of a procedure software application presented on display122. If not, the clinician continues navigation of the LG113.

When it is determined that the instruction is received in step515, the EM tracking system160then displays a message on the display122, warning that the LG113should not be moved for a predetermined period at step520. In some, though not necessarily all instances, it will be understood that receipt of the instruction to initiate stabilization based on respiration occurs when the LG113is in close proximity to the target where displacement of the location of the LG113caused by the respiratory movement may have significant effect on the following steps of a medical procedure. In an aspect, the predetermined period may be greater than or equal to a period for at least two consecutive respiration cycles. The warning message may be a textual message displayed via a user interface on the display122or an audio message.

During the predetermined period, the EM tracking system160obtains samples from the tracking sensor115for locations of the LG113, and samples from the motion sensors170of the PST at step525.

At step530, a determination must be made whether it is determined whether a correlation function is turned on. Correlation of the sampled data may be used to determine whether the obtained samples show periodic displacements in any direction. In other words, the correlation can be another safety feature ensuring that displacements identified from the obtained samples are caused mainly by the respiratory movements and can be used for stabilization.

When it is determined that the correlation is turned on, an auto-correlation measure is computed in step535. As described above, during the predetermined period, the LG113is not to be moved by the clinician. This auto-correlation measure is used to check whether periodic displacements are shown in the obtained samples during the predetermined period by the tracking sensor115of the LG113. For example, referring back toFIG. 2A, samples obtained during a period from time TAto time TBshow periodic displacements, which can be identified by the auto-correlation measure. In contrast, samples obtained during a period until time TAor after time TBdo not show periodic displacements for the predetermined time, which can be also identified by the auto-correlation measure. Thus, the auto-correlation measure is used in step540to check whether stabilization based on the respiratory movement can be started. If it is determined that the auto-correlation measure is less than or equal to a threshold or the auto-correlation measure indicates that periodic movement exists in the obtained samples, the method500goes back to step510.

When it is determined that the correlation is not turned on in step530or when it is determined that all signals are correlated at step540, a further step545is to determine whether sampling has been cancelled. This may be performed by selection of a stop sampling button on a user interface by a clinician or automatically when it is determined that the sensed motion of the LG113are not caused by respiration but by other causes, such as further navigation of the LG113within the patient. If the sampling is canceled, stabilization based on the respiratory movements cannot be performed and the method500goes back to step510.

When it is determined that sampling has not been canceled in step545, the method progresses to step550where it is determined whether a weight factor has already been calculated. If the weight factor has been already calculated, then no new weight needs to be calculated and the method500proceeds to step565. If no weight factor has been calculated, the EM tracking system160generates a respiratory model of the chest in step555based on the samples obtained by the motion sensors170of the PST.

As described above inFIG. 3A, a few principal components may be selected for the respiratory model based on PCA or other suitable methodologies to reduce computational power, time, and resources and to better represent the respiration-induced movements by removing other periodic or non-periodic movements. In an aspect, the number of selected principal components may be dependent upon a threshold. For example, if the threshold is 90 percent, the largest principal component is selected until the sum of the selected principal components is greater than or equal to 90 percentage of the total sum of all the principal components. The respiratory model Ã may be obtained after selecting the largest principal components from the following equation:

where {tilde over (S)} only includes the selected principal components in the diagonal and all zeros for the other entries. Detailed descriptions for the singular value decomposition have been described with respect toFIG. 3Aabove and are omitted here.

In step560, the weight W is calculated based on the respiratory model and the samples obtained from the tracking sensor115for the locations of the LG113, based on equation (6), (8), (10), or (13) above. Once the weight W is calculated after the predetermined period, stabilization can be initiated. At step565, new samples obtained from the motion sensors170of the PST at every sampling time for stabilization after the predetermined period are multiplied by the weight W to generate a reference stabilization signal for the LG113. The reference stabilization signal is subtracted from new location data of the tracking sensor115at the same sampling time to generate a stabilized location of the LG113. Once the weight W is calculated for the target of interest, the same weight is used to stabilize the location of the LG113during a medical operation for the same target. Thus, calculations for the stabilized location of the LG113are simple and thus can be performed real-time.

In an aspect, at step565, the weight may be updated based on changes to the locations of the LG113with respect to the motion sensors170of the PST. For example, a new weight may be calculated for every predetermined period (e.g., two consecutive respiration cycles) and a weighted average between the previous weight and the new weight may be calculated as the updated weight. By updating the weight, abrupt changes in samples from the motion sensors170of the PST or from the tracking sensor115may be subdued.

At step570, the EM tracking system160then displays a graphical representation of the LG113on the display122based on the stabilized location with reference to the pre-procedure 2D images or the 3D model. The displayed stabilized location minimizes the effect of breathing on the display of the detected location of the LG113, and the clinician is provided greater accuracy with respect to the actual physiology proximate the LG113being shown in the display122and the medical procedures may be performed with greater accuracy than without stabilization based on the respiratory movements.

It is determined whether the medical procedure is completed for the target of interest at step575. If it is determined that the procedure is not completed, the stabilization based on the respiratory movements continues until the medical procedure for the target of interest is determined complete by reiterating steps550-575. When it is determined that the medical procedure is completed, the method500ends for the target of interest. In an aspect, if there is another target of interest for medical procedures, method500is restarted and performed until the medical procedure for the new target is completed.

FIG. 6shows a flow chart illustrating a method600for automatically stabilization based on the respiratory movements for the LG113in accordance with an embodiment of the present disclosure. As inFIG. 5A, this method also starts with generating an EM field at step605. At step610, a clinician follows a pathway plan so that the LG113navigates toward a target of interest without stabilization based on the respiratory movements.

At step615, the ADCs of the EM tracking system160samples data from the tracking sensor115for the LG113and from the motion sensors170of the PST. At step620, it is determined whether the correlation is turned on. The method600goes back to step610when the correlation is determined not being turned on.

When it is determined that the correlation is turned on, the EM tracking system160calculates an auto-correlation measure based on the samples from the motion sensors170of the PST and the tracking sensor115at step625. The auto correlation measure has been described inFIG. 5Aand thus descriptions thereof are omitted here. It is also determined whether all samples are correlated based on the auto-correlation measure at step630. If it is determined that the all samples are not correlated, the method600goes back to step610. When it is determined that the all samples are correlated or periodic movements are detected in the obtained samples, the method600follows the steps550-575ofFIG. 5Buntil medical procedure is completed.

Although embodiments have been described in detail with reference to the accompanying drawings for the purpose of illustration and description, it is to be understood that the inventive processes and apparatus are not to be construed as limited thereby. It will be apparent to those of ordinary skill in the art that various modifications to the foregoing embodiments may be made without departing from the scope of the disclosure.