Apparatus and method for magnetic detection of tissue motion - Patents.com

Abstract

Systems, devices and methods for positioning magnets are useful in biological tissue analysis and other applications. A tissue analysis system for use with a tissue analyzer can include a storage medium having computer readable instructions stored thereon that cause the magnet positioning method to be performed. The system can include a tissue analyzer that includes a magnet and a magnetometer array positioned adjacent to the magnet. When executed by a processor, the instructions cause operations including recording the magnetic field of the magnet with the magnetometer array, simulating a simulated magnetic field at each magnetometer of the magnetometer array, and estimating the position information of the magnet by repeating the simulated position information of the simulated magnet.

Description

[Technical field]

[0001] (CROSS REFERENCE TO RELATED APPLICATIONS) This application claims the benefit of U.S. Provisional Patent Application No. 63 / 308,474, filed February 9, 2022, the entire contents of which are incorporated herein by reference. The present disclosure relates to devices and methods for detecting motion, structure or volumetric changes in biological tissue. [Background technology]

[0002] Measurement of migration of in vitro cultured biological tissues can be a useful indicator of the health and functionality of said tissues. Such tissues can be spontaneously migrating or can be induced to migrate by electrical stimulation or other means.

[0003] US20190029549A1 discloses a method for detecting tissue movement using a magnet embedded in a holder surrounded by tissue. In theory, changes in the magnetic field at a single magnetic field sensor adjacent to the magnet can be related to the movement of the magnet caused by the tissue. This allows tissue movement to be measured with higher throughput and sensitivity than can be achieved by conventional methods using, for example, optical microscopes.

[0004] However, there are drawbacks associated with known devices and methods that are based on the assumption that a magnet is positioned in a particular initial position, oriented in a particular direction, and effectively moves in a one-dimensional path aligned with the particular orientation direction of the magnet, such that the position of each moving magnet in a set of magnets can be identified by multiplying the magnetic field values ​​from any underlying magnetic sensor by a single numerical constant.

[0005] However, because the distribution of the magnetic field around a typical permanent magnet changes in a highly nonlinear manner, if the magnet is moved far away from the sensor, the relationship between the measured magnetic field and the magnet position can become nonlinear. Such nonlinearities can cause further inaccuracies because it cannot be assumed that the changes in the magnetic field recorded by the magnetic sensor as the magnet moves are linearly correlated with the physical movement of the magnet.

[0006] WO2021071954A1 attempts to solve this problem by converting magnetic field readings into changes in magnet position by utilizing empirically derived nonlinear relationships rather than constants. However, such methods also fail in practical environments where high accuracy is required, as the starting position and orientation of the magnet are precisely known and the magnet moves in a predictable manner. For all magnets operably attached to tissue in a given device, such assumptions are unlikely to be accurate. Inaccuracies may result from deviations of magnet orientation, position and / or movement from their assumed values.

[0007] Moreover, a set of multiple magnets attached to tissue and moving simultaneously can result in cross-talk phenomena, where magnetic fields resulting from moving tissue adjacent to the tissue of interest interfere with measurements obtained from the tissue of interest. All these sources of error, individually or together, can lead to significant inaccuracies and inconsistencies in the data obtained using such simplified methods of the prior art, which can limit their effectiveness in assessing tissue behavior, especially when compared to more conventional methods (e.g., optical microscope-based analysis).

[0008] Other magnetic sensing methods employed in endoscope (Su 2017) or prosthesis (Taylor 2019) tracking applications are customized to track several large magnets through large volumes. Such methods are second-best when tracking sub-millimeter-scale movements (e.g., on the order of millimeters) of magnets moving in close proximity to corresponding magnetic sensors. Summary of the Invention [Problem to be solved by the invention]

[0009] Thus, there is a need for improved magnetic positioning devices and methods to overcome the difficulties in tracking magnets operably attached to in vitro cultured tissue. [Means for solving the problem]

[0010] The devices and methods described herein overcome the shortcomings of the prior art by allowing for accurate, parallel measurement of magnet movement in a high throughput manner. For example, the devices and methods disclosed herein overcome limitations of prior art systems by providing accurate, simultaneous tracking of any number of magnets, and further enable parallel measurement of changes in the motion of multiple tissues.

[0011] The present disclosure provides systems, devices and methods for positioning a magnet, including tissue analysis systems, tissue analysis devices, magnet positioning methods, and computer program products storing instructions causing the operation of the magnet positioning methods.

[0012] In one aspect, the present disclosure provides an apparatus and method (positioning method) for detecting motion, structure, or volumetric changes in three-dimensional biological tissue by tracking the position of magnets embedded therein using a magnetic sensor array and a magnetic model that characterizes and iteratively simulates multi-parameter position information of a given set of moving magnets embedded in the tissue.

[0013] In one aspect, the present disclosure provides tissue analysis systems, including a tissue analysis device and a non-transitory machine-readable storage medium (computer program product) storing instructions. The tissue analysis device includes at least one magnet and a plurality of magnetometers (magnetometer arrays) adjacent each of the at least one magnet. For example, the tissue analysis device can include a plurality of magnets and a different magnetometer array disposed adjacent each magnet. The storage medium stores instructions that, when executed by a processor, cause the tissue analysis system to perform operations including, for each magnet, recording the magnetic field of the magnet with each magnetometer of the plurality of magnetometers, simulating a simulated magnetic field of a simulated magnet with each magnetometer of the plurality of magnetometers, the simulated magnet having simulated position information, and iterating the simulated position information of the simulated magnet to estimate the position information of the magnet.

[0014] In another aspect, the present disclosure provides a tissue analysis device as described above.

[0015] In another aspect, the present disclosure provides a tissue analysis system for use with a tissue analyzer including at least one magnet and a plurality of magnetometers (magnetometer array) adjacent each of the at least one magnet. The tissue analysis system includes a non-transitory machine-readable storage medium (computer program product) storing instructions that, when executed by a processor, cause performance of operations including, for each magnet, recording the magnetic field of the magnet with each magnetometer of the plurality of magnetometers, simulating a simulated magnetic field of the simulated magnet with each magnetometer of the plurality of magnetometers, the simulated magnet having simulated position information, and estimating position information of the magnet by iterating the simulated position information of the simulated magnet.

[0016] In another aspect, the present disclosure provides magnet positioning methods that include recording a magnetic field of a magnet at a plurality of positions (e.g., at one or more positions of each magnetometer of a magnetometer array), simulating a simulated magnetic field of a simulated magnet at the plurality of positions (e.g., at one or more positions of each magnetometer of a magnetometer array), the simulated magnet having simulated position information in a theoretical space, and estimating position information of the magnet by iterating the simulated position information.

[0017] In another aspect, the present disclosure provides magnet positioning methods that include recording a magnetic field of each magnet of a plurality of magnets at a nearest magnetometer array of the plurality of magnetometer arrays, simulating a simulated magnetic field of a simulation magnet of the plurality of simulation magnets at the location of each magnetometer array, each simulation magnet having position information, and estimating position information of each magnet by repeating the simulated position information of the simulation magnet at the nearest magnetometer array.

[0018] Any one of the above systems, devices and methods may include any one or more of the following optional features.

[0019] In any embodiment, iterating the simulated position information of the simulated magnet may include iterating at least one of an x ​​position, a y position, a z position, a roll value, a pitch value, and a yaw value.

[0020] In any embodiment, iterating the simulated position information of the simulated magnet may include iterating each of an x ​​position, a y position, a z position, a roll value, a pitch value, and a yaw value.

[0021] In any embodiment, estimating the position information of the magnet may be based on a cost function that compares the magnetic field to a simulated magnetic field.

[0022] In an optional embodiment, iterating the simulated position information of the simulated magnet may include determining whether the value of the minimized cost function is from the first iteration of the cost minimization algorithm.

[0023] In any embodiment, iterating the simulated position information of the simulated magnet can include iterating based on the simulated position information from a previous iteration at a time point.

[0024] In any embodiment, repeating the simulated position information of the simulated magnet may include repeating based on simulated position information associated with a previous time point or based on nominal position information.

[0025] In any embodiment, iterating through the simulated position information of the simulated magnet may include determining whether a value of the cost function satisfies a termination criterion.

[0026] In any embodiment, estimating the position information of the magnet may include comparing the magnetic field to a simulated magnetic field.

[0027] In any embodiment, each magnetometer of the plurality of magnetometers may include a plurality of magnetic sensing elements, and recording the magnetic field may include sensing a magnetic flux density of the magnet with the plurality of magnetic sensing elements of each magnetometer.

[0028] In any embodiment, for each magnetometer, each magnetic sensing element can sense a different axis (eg, the x, y or z axis) of the magnetic field.

[0029] In any embodiment, a magnet can be mounted to the artificial tissue assembly and / or tissue structure, and multiple magnetometers can be mounted to a printed circuit board positioned adjacent to the artificial tissue assembly.

[0030] In an optional embodiment, the magnet is one of a plurality of magnets in a tissue analyzer, and the recordings, simulations and estimations are performed for all magnets of the plurality of magnets.

[0031] In any embodiment, the instructions can be configured to record the magnetic field of each magnet at a different time than each of the other magnets.

[0032] In any embodiment, estimating the position information for each magnet may be based on simulated position information for each magnet.

[0033] In any embodiment, estimating position information of the magnet is performed at multiple time points, and the operations further include determining a position change of the magnet between the multiple time points.

[0034] In any embodiment, the operating can further include determining a characteristic of tissue operably attached to the magnet based on the position change.

[0035] Any embodiment may include a stimulation lid arranged to move the magnet, and operation may record the magnetic field of the magnet after or simultaneously with the movement of the magnet.

[0036] In any embodiment, estimating position information for each magnet may include iteratively varying the simulated position information of all of the simulated magnets.

[0037] In any embodiment, the instructions or methods can further include moving a plurality of tissues, each tissue can be operably attached to one magnet of the plurality of magnets.

[0038] In any embodiment, moving the multiple tissues can include moving the multiple tissues with contractions that overlap in time.

[0039] In any embodiment, recording the magnetic field of each magnet can occur at a different time than each of the other magnets.

[0040] In any embodiment, recording the magnetic field of each magnet may include recording magnetic field data associated with different points in space.

[0041] In any embodiment, the instructions or methods may further include identifying an empirical relationship between the estimated position information and the recorded magnetic data, and estimating position information of the second plurality of magnets using the empirical relationship.

[0042] In any embodiment, estimating position information for each magnet can include adjusting for crosstalk based on the recorded magnetic fields of every other magnet in the plurality of magnets. Exemplary embodiments are described with reference to the following drawings, in which like reference numerals refer to like parts in different drawings unless otherwise specified. [Brief description of the drawings]

[0043] [Figure 1A] 1 illustrates a partially exploded view of an example tissue analysis system according to the present disclosure. [Figure 1B] A first cross-sectional view in the xz plane is shown. [Figure 1C] FIG. 1C shows a detailed view of the first cross-sectional view of FIG. 1B. [Figure 1D] FIG. 1B shows a second cross-sectional view in the xz plane of the tissue analysis system of FIG. 1A. [Figure 1E] FIG. 1D shows a detailed view of the second cross-sectional view. [Figure 1F] FIG. 1B shows a third cross-sectional view in the xy plane of the tissue analysis system of FIG. 1A. [Figure 1G] FIG. 1C shows a detailed view of the third cross-sectional view of FIG. 1F. [Figure 2A] 1 illustrates a schematic diagram of a magnetometer array and magnet of an example tissue analysis system according to the present disclosure. [Figure 2B] Its geometric parameters are shown. [Figure 2C]The additional parameters are shown below. [Diagram 3] 13A-13C are schematic diagrams illustrating a magnet positioning method according to an embodiment of the present disclosure; [Figure 4] FIG. 2 is a diagram showing a configuration of a representative program code according to an embodiment of the present disclosure. [Diagram 5] 1 is a plot of magnet position information estimated by the magnet positioning method of the present disclosure as a function of magnetic flux density measured from a single axis of a single underlying magnetometer. [Figure 6] 1 is a plot of magnet position information comparing optically tracked position information with a) position information estimated by the magnet positioning method of the present disclosure and b) position information estimated by a single axis sensing method of magnets displaced in discrete steps. The data represents a set of six magnets. [Figure 7] 7 is a plot comparing the accuracy of the two methods compared in FIG. 6. [Figure 8] FIG. 13 shows a schematic diagram of a 24-well diagram validating the effectiveness of an embodiment of the magnet positioning method according to the present disclosure. [Figure 9] 1 shows the well-to-well "crosstalk" of the magnetometer's single axis measurements. [Figure 10] 1 illustrates a representative computing device configured to perform the methods described herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0044] The disclosed systems, devices and magnet positioning methods accurately track magnet movement, especially when relatively small magnets (single tens of millimeters scale) move relatively close to their corresponding magnetometers in a multi-magnet system, such as a multi-well tissue analysis system. In particular, the systems and methods take into account the different components of the magnetic field at different positions of each magnet, which more accurately reflects the actual characteristics of the magnetic field being measured, rather than assuming that all components are measured at a common position. Furthermore, the systems and methods described herein take into account differences in the position and movement of each magnet, and eliminate the need to calibrate the measurement process for each magnet. These advantages result in higher accuracy and faster measurements.

[0045] The disclosed embodiments are generally described in the context of a tissue analysis system having multiple magnets, each of which is operatively attached to a biological sample, such as tissue, for example, in vitro cultured tissue structures, such as artificial muscles, including cardiac and skeletal EMT. Such tissue analysis is particularly valuable for pharmaceutical research and disease modeling. However, the systems and methods are limited to tissue analysis systems.

[0046] 1A-1G show various embodiments of a tissue analysis system 100 of the present disclosure. The tissue analysis system 100 includes a magnetometer array and a magnet array, with the magnetometer array located below each magnet. In the application shown, each magnet is embedded in a pillar of a two-pillar system, and a biological sample such as a tissue structure can be cast or grown to be attached between a pair of pillars. Thus, the magnets are operably attached to the tissue. In other embodiments, the magnets can be operably attached to the tissue in other ways, for example mechanically secured or attached. The tissue analysis system 100 includes a plurality of such pillar pairs and an equivalent number of magnets and magnetometer arrays.

[0047] Each magnetometer array of tissue analysis system 100 is positioned to record, i.e., sense or detect, a magnetic field emanating from a magnet located in its immediate vicinity (particularly a magnet located directly above the magnetometer array). Furthermore, each magnetometer array is positioned to sense the magnetic field at different positions and along different axes as the magnet(s) move. Furthermore, tissue analysis system 100 includes software and / or firmware that includes machine-readable logic or instructions that perform the magnet positioning methods described herein based on the recorded magnetic fields, which methods identify the location of the magnet in three-dimensional space.

[0048] Referring first to FIG. 1A, a tissue analysis system 100 includes a tissue analyzer 102 having at least one magnet. In this example, the tissue analyzer 102 includes a multi-well cartridge 104 having 24 wells, each well containing a magnet therein. The cartridge 104 is mounted on top of a magnetometer plate 106 (e.g., a printed circuit board on which are provided a number of magnetometer arrays (e.g., 118a, 118b), described below). As clearly shown in FIGS. 1B-1F, each magnet in the cartridge 104 is positioned adjacent to one of the magnetometer arrays 118a. Each magnetometer of each magnetometer array 118a is positioned to record the magnetic field emitted by the closest magnet. Because the magnetic ancillary components of the tissue analysis system 100 may alter or interfere with the magnetic field from the magnet being analyzed, the magnetometers may be supported by passive components that do not include magnetic material (e.g., non-magnetic capacitors or resistors).

[0049] An optional magnetometer shield 108 is placed between the magnet and the magnetometer on magnetometer plate 106. The magnetometer shield 108 may be formed of non-ferromagnetic metals including aluminum or brass, polymers such as acetal, poly-methyl-methacrylate and polycarbonate, or other materials except those having ferromagnetic components (i.e., materials containing iron, nickel, or cobalt).

[0050] The tissue analysis system 100 may include or form part of a computing device 110 as described with reference to Fig. 10. The computing device 110 may include a processor 112 and one or more modular non-transitory machine-readable storage media 114 storing computer readable logic or instructions 116 that, when executed by the processor 112, cause the processor 112 to perform operations, including any one of the magnet positioning and tissue analysis methods described herein. For example, the tissue analyzer 102 may be an input device operably connected to the processor 112 and the instructions 116. More specifically, an output from the tissue analyzer 102 (e.g., a signal from a magnetometer) may be an input to the instructions 116 of the methods described herein.

[0051] The tissue analysis system 100 may be packaged within the housing of a larger device having a power supply operably connected to a motherboard, magnetometer board 106, a display and input devices. Such a device may include an electrical stimulation lid and / or an electrical stimulation board for applying electrical stimulation to the tissue structures in the cartridge 104.

[0052] FIG. 1B shows a first cross-sectional view in the x-z plane of the tissue analyzer 102 of FIG. 1A. The cartridge 104 includes a number of wells (e.g., 120a, 120b) formed in a microplate 122, each well having a number of posts attached thereto. In the embodiment shown, the cartridge 104 includes 24 wells. However, other embodiments may have more or fewer wells. As described below, each magnetometer array of the magnetometer plate 106 is located directly below one of the wells in the z-direction. Because each well, magnetometer array, and post pair may be the same for all wells, a detailed view showing a representative well 120a is provided in FIG. 1C. In some embodiments, the microplate 122 can be used as a stimulation plate for applying electrical stimulation to tissue structures.

[0053] FIG. 1C shows a detailed view of the cross-sectional view of FIG. 1B, thereby illustrating the relationship between one representative well 120a of the tissue analyzer 102 and the magnetometer array 118a located below it.

[0054] The well 120a is formed as a recess in the microplate 122 and has a cylindrical or frusto-conical inner wall 140. A bottom surface 134 surrounds the end of the well 120a closest to the magnetometer plate 106 and magnetometer shield 108.

[0055] The artificial tissue assembly 126 is attached to the upper surface of the microplate 122 and includes a mounting cover 128 of a plurality of post assemblies 130 coupled thereto. Each post assembly 130 extends from its base into at least one post in the well. In particular, at least two posts extend from one or more post assemblies into each well, thereby forming a post pair. For example, posts 124a, 124b form a post pair in well 120a.

[0056] In each well, one of the pillars is a flexible pillar (e.g., an elastically flexible pillar) and the other pillar is relatively rigid (i.e., has a stiffness at least an order of magnitude greater than the flexible pillar). In well 120a, pillar 124a is a flexible pillar and pillar 124b is a rigid pillar.

[0057] A removable lid 138 covers the well and post assembly. The lid 138 may be a stimulation lid arranged to apply an electrical stimulation to a tissue structure 142 (e.g., an artificial cardiac tissue structure) grown between the two posts 124a, 124b. The electrical stimulation causes the tissue structure 142 to move (e.g., contract), which bends the flexible post 124a, as shown.

[0058] The magnet 132 is embedded within or glued to the outer surface of the distal end of the resilient flexible post 124a. The magnet may be a neodymium magnet, a ferrite magnet, or other suitable magnet type. In the embodiment shown, the magnet 132 is oriented to have a magnetic dipole moment vector along the x-direction.

[0059] To implement the magnet positioning methods described herein, the magnetometer plate 106 is positioned and positioned such that one magnetometer array is adjacent to each magnet in each well of the cartridge 104. For example, the magnetometer array 118a is located directly below the magnet 132 in the z-direction and is within a range of about 1 mm to about 15 mm, such as within a range of about 2 mm to about 10 mm. In some embodiments, the magnetometer array (i.e., the magnetometers in the array) are positioned around their associated magnets in the x-y plane (as shown in FIG. 1G).

[0060] Each magnetometer array may include two, three, four or more magnetometers, each of which may include a plurality of magnetic sensing elements, as described below with reference to FIG. 2A. For example, magnetometer array 118a includes magnetometers 136a, 136b and 136c (see FIG. 1G), each positioned about 1 mm to about 15 mm, e.g., about 2 mm to about 10 mm, from magnet 132. In particular, each magnetometer of each magnetometer array is positioned and arranged to detect the magnetic field of the nearest magnet. For example, magnetometers 136a, 136b are positioned at least partially opposite magnet 132 in the x-direction and positioned about 1 mm to about 15 mm, e.g., about 2 mm to about 10 mm, from magnet 132 in the z-dimension of the x-z plane. In the illustrated embodiment, at least two magnetometers of magnetometer array 118a are aligned along the direction of the moment vector of magnet 132 (in this case, along the x-direction).

[0061] Figure ID shows a second cross-sectional view in the x-z plane of the tissue analyzer 102 of Figure IA. Relative to the cross-section of Figure IB, the cross-section of Figure ID is translated a few millimeters in the positive y direction. Figure IE shows another detailed view of well 120a to illustrate additional features thereof.

[0062] Figure 1E shows a detailed view of the cross-section of Figure 1D, further illustrating the relationship between well 120a and magnetometer array 118a disposed therebelow, including a third magnetometer 136c, which is also disposed below well 120a and magnet 132 in the z-direction.

[0063] 1C and 1E together, it is apparent that all of the magnetometers 136a, 136b, 136c of magnetometer array 118a are positioned below magnet 132 in the z-direction.

[0064] Figure IF shows a third cross-sectional view in the xy plane of the tissue analysis device 102 of Figure IA. Similar to Figures 1C and IE, Figure IG shows another detailed view of well 120a to show additional features thereof.

[0065] FIG. 1G shows another detailed view of the cross-section of FIG. 1F, with the microplate 122 shown transparent to show the magnetometer array 118a and its spatial relationship to the wells 120a and magnets 132.

[0066] As shown, magnetometer array 118a includes a magnetometer cluster including magnetometers 136a, 136b, 136c positioned around flexible post 124a (and magnet 132) in the x-y plane. The centers of magnetometers 136a and 136b are offset by about 3 mm to about 5 mm and are aligned with the dipole moment vector of magnet 132 along the x-direction.

[0067] The center of magnetometer 136c is offset in the y direction, ie, in a direction perpendicular to the magnetic moment vector of magnet 132, from the centers of magnetometers 136a, 136b by about 3 mm to about 5 mm.

[0068] During use, the tissue structures grown between the posts 124a, 124b move (e.g., contract), causing the magnet 132 to move about + / - 5 mm (e.g., about + / - 3 mm) in the x direction and within about + / - 3 mm (e.g., about + / - 1 mm) in the y direction. Application of a current causes the tissue to move, or the tissue can move spontaneously. Magnetometers 136a, 136b, 136c sense the change in the magnetic field of magnet 132 caused by this movement at their corresponding locations, as described below.

[0069] Figure 2A shows a three-dimensional layout of magnetometer array 218, which includes a cluster of magnetometers 236a, 236b, 236c positioned around magnet 232. Magnetometer array 218 is similar to magnetometer array 118a of Figures 1A-1G.

[0070] Magnetometer 236c is shown with its exterior transparent to reveal magnetic sensing elements 242a, 242b, 242c. As used herein, a magnetic sensing element is an element (e.g., a ferromagnetic element) that can measurably change one or more properties when subjected to one or more components of the magnetic flux density of an applied magnetic field. Magnetometer 236c measures relevant properties of magnetic sensing elements 242a, 242b, 242c to identify magnetic field components (of magnet 232) over a particular region or volume, e.g., average magnetic field components, and outputs a signal conveying magnetic field data based on the recorded magnetic field.

[0071] For example, each of the magnetic sensing elements 242a, 242b, 242c is positioned to record the flux density of the magnetic field emanating from the magnet 232 along the x, y, or z axis. Magnetometers 236a and 236b are similar to magnetometer 236c. Thus, each of the multiple magnetometers includes multiple individual magnetic sensing elements. Thus, in the illustrated embodiment, the magnetometer array 218 senses the magnetic field from the magnet 132 at nine different points in space. Exemplary magnetometers include anisotropic magnetoresistive sensors (e.g., type MMC5983MA manufactured by Memsic Semiconductor Co., Ltd. of Tianjin, China), fluxgates, Hall sensors, giant magnetoresistance, or tunnel magnetoresistance sensors.

[0072] It is particularly advantageous to include multiple magnetic sensing elements at different locations within each magnetometer. Typically, it is assumed that the magnetic sensing elements in a magnetometer completely overlap one another and that all components of the magnetic field are sampled at the same location. However, when a magnet is positioned in close proximity to the magnetometer, as in the tissue analysis system and magnet positioning method described herein, such an assumption does not hold because the distance between the magnetic sensing elements is sufficiently large relative to the distance between the magnetic sensing elements and the magnet that the magnetic sensing elements experience a significantly different magnetic field than if they were completely overlapping. The magnet positioning method of the present disclosure derives from the scalar product of the simulated magnetic field a unit vector bounded by the directional sensitivity of the magnetic sensing elements to that simulated magnetic field. We mathematically take into account this feature of the magnetometer array by deriving each component of the magnetic field based on TIFF2025507355000002.tif127.

[0073] Magnetometers 236a, 236b, 236c are positioned around magnet 232 (e.g., within about 1 mm to about 15 mm or about 2 mm to about 10 mm) to record the magnetic field of magnet 232, for example, when magnet 232 is positioned at a particular position relative to the magnetometers.

[0074] The magnetometer array 218 is positioned to optimize magnet positioning accuracy and signal-to-noise performance of the magnetic field recording. In addition to the magnetic sensing elements, each magnetometer may have additional components to filter, amplify or otherwise process and / or digitize the output of the magnetic sensing elements therein. The output of each magnetometer may be selectably communicated to additional elements in a signal chain. The signal chain is terminated when the magnetic field data reaches a state that can be suitably incorporated into any of the magnet positioning methods described herein, thereby allowing further processing by computational algorithms to extract desired information about the magnets in the system.

[0075] The above arrangement of a single magnetometer array 218 and magnet 232 may be repeated for each magnet of a multi-magnet system (eg, tissue analysis system 100 of FIG. 1A).

[0076] 2B defines the geometric parameters of the magnet positioning method with respect to the magnetometer array 218. Double-headed arrows indicate scalar distances and single-headed arrows indicate vector numbers. Subscripts refer to specific magnetic sensing elements unless the subscript has the letter "M" in which case the subscript numbers relative to the magnetometer.

[0077] The magnet positioning method utilizes a computational model of magnetic behavior coupled with magnetic field data acquired from magnetometers (e.g., magnetometers 236a, 236b, 236c) to quantify the following position information of one or more adjacent permanent magnets (e.g., magnet 232) relative to an arbitrary reference point in three-dimensional space, as shown in FIG. 2B: ·x position (a) ·y position (b) ·z position (c) Roll value, or rotation around the y-axis relative to the magnet's centerline Pitch value (θ), or rotation around the x-axis relative to the centerline of the magnet Yaw value (Φ), or rotation about the z-axis, perpendicular to the x-axis and the centerline of the magnet Residual magnetic flux density TIFF2025507355000003.tif129

[0078] The above-mentioned parameters can also characterize the positional information of one or more simulated magnets, and these parameters are input into the equations described below.

[0079] FIG. 2C shows the rotation of magnet 232 in the xy and xz planes, referred to as pitch (θ) or yaw (Φ), respectively. TIFF2025507355000004.tif1014 shows the projected magnetic moment of a magnet onto the x-y plane, and TIFF2025507355000005.tif913 shows the projection of the magnetic rectangle onto the xz plane.

[0080] Before describing representative methods when limiting the above parameters, the principles of magnet positioning methods will be introduced.

[0081] Magnet positioning methods typically involve calculating and simulating the magnetic field at the location of at least one simulated magnet, e.g., one or more magnetometers. The simulated magnet can be modeled as a magnetic dipole, which has been empirically proven to be accurate when estimating the magnetic field at a distance from the magnet on the order of several hundred percent of its characteristic dimension (as described for the tissue analysis system 100 of Figures 1A-1G). Other embodiments can combine other analytical or numerically approximated models of magnetic behavior, e.g., models for cylindrical, spherical or cubic models. For ease of understanding, the magnetic dipole model is described herein as one suitable representative and non-limiting model.

[0082] In the magnetic dipole model, the magnetization vector TIFF2025507355000006.tif78 surrounding the dipole Distance at points in TIFF2025507355000007.tif99 Magnetic field in TIFF2025507355000008.tif88 (e.g. magnetic flux density of a simulated magnet) TIFF2025507355000009.tif1446), where μ0 represents the magnetic permeability of a vacuum.

number

[0083] The Cartesian coordinates corresponding to the simulated magnet's position are used to estimate the real world position and / or column deflection of magnet 232. If a, b, and c correspond respectively to the x, y, and z positions of the simulated magnet relative to a global reference point (0,0,0), then the magnetic field B from the simulated magnet at point (a,b,c) relative to this same reference point at (x,y,z) is:

number

[0084] position(a i ,b i ,c i ), pitch and yaw (θ i ,Φ i ) and residual magnetic flux density B r,iIf we use x to represent each simulated magnet i among the n simulated magnets in the system in the theoretical space, then s ,y s ,z s ) unit vector For the magnetic field at the magnetic sensing element Bs measuring the magnetic field component along TIFF2025507355000015.tif1222, it is given as follows:

number

[0085] For example, compared to known methods that assume three components of the magnetic field are sensed at a single location, equation (3) considers each component (e.g., x, y, and z) at a different location of the magnetic field. Advantageously, this takes into account the failure of assumptions in prior art methods when the sensed magnet is located relatively close to the magnetometer.

[0086] As mentioned above, TIFF2025507355000017.tif127 is a vector of unit length, bounded according to the directional sensitivity of the real-world magnetic sensing element in which the magnetic field is simulated. As shown in Figure 2B, TIFF2025507355000018.tif138 points, This forms the basis of TIFF2025507355000019.tif89.

[0087] A vector of simulated magnetic field values ​​at all magnetometer locations in the system Similarly, readings from real-world magnetometers (e.g., magnetometers 236a, 236b, 236c) in space of the same magnetic field components generated by a real-world magnet (e.g., magnet 232) can be represented as vectors It can be placed in TIFF2025507355000021.tif1130.

[0088] Because the magnets in the tissue analysis systems described herein can be operated in close proximity (on the order of millimeters) to the corresponding magnetometers, the magnetometers can be made physically large enough so that the magnetic field on the magnetometer and its magnetic sensing elements is effectively constant or does not vary linearly in space. In this case, TIFF2025507355000022.tif1122 and The values ​​in TIFF2025507355000023.tif1130 can give the average magnetic field within the area of ​​the magnetometer or a model measurement volume.

[0089] A mathematical function can be used to compare the two vectors, i.e., to compare the simulated magnetic field with the real-world magnetic field. For example, one representative function is TIFF2025507355000024.tif1127 is as follows,

number

[0090] The function may be iteratively calculated based on a minimization algorithm to identify an "optimum" or sufficient solution for the magnet parameters of interest. For example, one or more parameters of the simulated position information may be iteratively varied until the value of the function is minimized or otherwise meets certain termination criteria.

[0091] For example, if the minimization algorithm is compatible with the least squares method, then this function TIFF2025507355000026.tif1531 (or alternatively called a possible cost function for the problem) 2 By minimizing the norm, the simulation position information, e.g., the a of all magnets in the system, is obtained. i , b i , c i , θ i , Φ i and B. r,iBy adjusting the θ, real-world position information, i.e., the position and orientation of each real-world magnet in the system, can be estimated.

[0092] Although the least squares method is described as an exemplary cost minimization algorithm to compare the simulated magnetic field to the real-world magnetic field, other suitable minimization methods include methods for performing such non-linear regression. Minimization can be performed by algorithms such as Levenberg-Marquardt and trust-region reflection algorithms.

[0093] Having explained the principles of these methods, the details of the magnet positioning methods will now be described.

[0094] Unless otherwise indicated, any step described below is optional, and embodiments of the present disclosure may include any combination of the following steps. Additionally, any step or combination of steps described below may be implemented as instructions on a non-transitory machine-readable storage medium. For example, the methods described below may be implemented as one or more software or firmware instruction modules executed by a processor coupled to a tissue analysis system.

[0095] 3 illustrates an exemplary magnet positioning method 300 whereby iterative magnetic modeling is used to accurately estimate position information of one or more real-world magnets in a system. Generally, for each magnet in a system, the magnet positioning method 300 records the magnetic field of that magnet and simulates the magnetic field of the simulated magnet with the simulated position information. The method compares the recorded magnetic field of the real-world magnet with the simulated magnetic field of the simulated magnet and iterates the position information of the simulated magnet until the comparison between the real-world magnetic field and the simulated magnetic field meets a termination criterion, whereupon the simulated position information of the simulated magnet is an accurate estimate of the position information of the real-world magnet.

[0096] The following describes an exemplary method shown in Figure 3. Any of the following steps may be calculated by a special purpose computing device (eg, a processor of the tissue analysis system described herein).

[0097] For ease of understanding, the magnet positioning method 300 will be described with reference to the tissue analysis system described in Figures 1A-2C. Accordingly, the terms used to describe the following method have the same meaning as previously introduced. In any embodiment, the steps of the following method may be performed in a tissue analysis system having any one of the physical characteristics described above, such as the number of magnetometers, magnetometer spacing, magnetometer alignment, etc. However, the following method is not limited to this application and may be performed independently of a tissue analysis system.

[0098] The magnet positioning method 300 applies to single magnet systems and multi-magnet systems at multiple times. In a multi-magnet system, the following steps may be performed, e.g., simultaneously or sequentially, for each magnet in the system or a subset of magnets in the system, so that the recording and simulating steps described below accurately take into account all magnets (or relevant subsets) of the underlying system. With this understanding, for clarity, the following steps are generally described for a single magnet.

[0099] If desired, first a system, such as a tissue analysis system, can be provided that includes a plurality of magnets and a plurality of magnetometer arrays.

[0100] In step 350, as part of a selectable calibration sequence, local magnetic field data is recorded, for example, when no magnet is present. The local magnetic field data may be affected by the Earth's geomagnetic field, the magnetometer array and / or magnets and other elements of the underlying system. For example, in a tissue analysis system, local magnetic field data may be recorded at each magnetometer of the magnetometer array 118a when the magnet 132 is not present, such as when the cartridge 104 containing the magnet 132 is removed from the magnetometer plate 106 or the magnet 132 is otherwise removed from the immediate vicinity of the magnetometer array 118a.

[0101] In step 352, a magnet is placed adjacent to the magnetometer array. The magnet can be positioned such that the magnetometers of the adjacent magnetometer array are located around and / or within about 1 mm to about 15 mm or about 2 mm to about 10 mm of the magnet. The magnet can be positioned such that its magnetic moment vector is axially aligned with two or more magnetometers of the magnetometer array. For example, a cartridge 104 containing multiple magnets can be placed adjacent to a magnetometer plate 106 having multiple magnetometer arrays 118a thereon, such that each magnet is placed adjacent to one of the magnetometer arrays, for example, within about 1 mm to about 15 mm or about 2 mm to about 10 mm.

[0102] In step 354, device magnetic data (e.g., magnetic field data) is recorded for the magnet. For example, the magnetic field of the magnet 132 of the tissue analysis system 100 can be recorded simultaneously with multiple magnetometers 136a, 136b, 136c of the magnetometer array 118a. The device magnetic data can include the magnetic flux density of the magnetic field of the magnet along the x-, y-, and / or z-axis at different positions, for example, as sensed by magnetic sensing elements of the magnetometers of the magnetometer array. Each of the x-, y-, and / or z-axis components of the magnetic field can be sensed at multiple positions, for example, using different magnetometers of the magnetometer array to record the magnetic field at different points in space. In a multi-magnet system, the device magnetic data of each magnet can be recorded at a different time with respect to each of the other magnets.

[0103] Step 354 can include displacing at least one tissue (e.g., tissue structure) attached to the magnet prior to or simultaneously with recording the magnet device magnetic data. Each tissue structure can be displaced, e.g., contracted, by electrical stimulation applied to the tissue structure or the surrounding medium. In a multi-magnet system, displacing at least one tissue can include displacing multiple tissues with contractions that overlap in time.

[0104] The device magnetic data for the magnet or for all magnets can be compiled into a first vector.

[0105] In step 356, as another step in the selectable calibration sequence, the local magnetic field data recorded in step 350 is subtracted from the device magnetic data, e.g., subtracted element-wise according to x, y and z components. This is adjusted for magnetic fields or currents caused by the Earth or adjacent permanent magnets that are not considered in the magnet positioning method below. Subtracting the local magnetic field data may include subtracting from the device magnetic data a time average of the local magnetic field data at one or more points in time or at different points in time. For example, in some embodiments, the first time series of the local magnetic field data recorded in step 350 is subtracted from the second time series (of equal length) of the device magnetic data recorded in step 354 to obtain an adjusted magnetic field for each point in time. In other embodiments, the average of the time series of the local magnetic field data is subtracted from the device magnetic data recorded in step 354. As used herein, device magnetic data includes both adjusted and unadjusted device magnetic data.

[0106] In step 358, a magnet is simulated at a point in theoretical space on the magnetometer array by assuming simulated position information including, for example, at least one of a simulated position (Cartesian coordinates), a simulated orientation, a magnetic flux density, and / or a simulated magnet volume. Such a magnet in theoretical space is referred to herein as a "simulated magnet." The point in space may be positioned on the magnetometer array along at least one of an x, y, and / or z direction. The point in space may be an arbitrary, best guess based on simulated position information from a previous time point and / or a different magnet. The simulated position may be represented by an x ​​position, a y position, and / or a z position, and the orientation may be represented by a pitch value and / or a yaw value.

[0107] Step 360 simulates a simulated magnetic field of the simulated magnet based on the simulation position information, as described with reference to Figures 2A-2C, thereby preparing simulated magnetic field data. The simulated magnetic field may include directional components and may be simulated at a position corresponding to each magnetometer of the multiple magnetometers (or each of its magnetic sensing elements) of the nearest magnetometer array. The simulated magnetic field may be simulated at one or more additional magnetometer array positions (e.g., all magnetometer positions of all magnetometer arrays).

[0108] For example, the simulated magnetic field can be calculated using equations such as equations (1) to (3) above. Simulating the simulated magnetic field may be based on a magnetic dipole model, a cylindrical magnetic model, a spherical magnetic model, a cubic magnetic model, or other magnetic models. Simulating the simulated magnetic field may include simulating the magnetic flux density of the simulated magnet at a point in the space around the simulated magnet at a certain distance away, for example using a magnetization vector. Simulating the simulated magnetic field may include taking a scalar product of the simulated magnetic field and a unit vector defined according to the directional sensitivity of the magnetic sensing element at the location where the magnetic field is simulated.

[0109] The simulated magnetic field data for the simulated magnet, or for all of the simulated magnets, may be compiled into a second vector.

[0110] Following step 360, the magnet positioning method 300 includes estimating position information of the real-world magnet including at least one of its position, orientation or magnetization based on the simulated position information of the corresponding simulated magnet (or optionally based on all simulated magnets). Estimating the position information compares the device magnetic data with the simulated field data and is based on one or more functions described below. The estimating step may include any one or more of the following steps:

[0111] In step 362, a solution or cost function is calculated for one or more time points of the device magnetic data to compare the device magnetic data to the simulated magnetic field data. The cost function compares a first vector (which is based on the device magnetic data) to a second vector (which is based on the simulated magnetic field data). The cost function is a one-dimensional mathematical function. One exemplary cost function was described above with respect to equation (4). Differential entities of the cost function (e.g., its Jacobian matrix) are calculated to assist in subsequent steps of the process.

[0112] To accurately estimate the position information of the magnet(s), a cost function is iteratively calculated (e.g., a least-squares method implemented by a minimization algorithm, e.g., the Levenberg-Marquardt algorithm or a trust-region reflection algorithm) by iterating through the simulated position information of the simulated magnets until its value is minimized or a termination criterion is met. Thus, these methods provide an iterative solution procedure:

[0113] Step 364 involves determining whether the cost function solution of step 362 is the solution from the first iteration of the cost minimization algorithm.

[0114] If the determination at step 364 is negative, the magnet positioning method 300 proceeds to step 366, whereby the minimization algorithm iterates based on at least one parameter of the simulated position information from a previous iteration of the device magnetic data at the same time point to again minimize the cost function at the same time point. The iteration may include updating at least one or all of the x, y, z positions, roll, pitch and yaw values ​​of the simulated position information from the previous iteration of the same time point.

[0115] Alternatively, if the determination at step 364 is affirmative, the magnet positioning method 300 proceeds to step 368 where it determines whether the cost function solution of step 362 is a solution for the first point in time in the device magnetic data.

[0116] If the determination at step 368 is negative, the magnet positioning method 300 proceeds to step 370, whereby the minimization algorithm iterates to solve the cost function based on at least one parameter of the simulated position information associated with a previous point in time in the device magnetic data. The iterations may include iterating over at least one or all of the x, y, z positions, roll, pitch and yaw values ​​of the simulated position information of the previous point in time (e.g., a final or optimal estimate of any of the above parameters from the previous point in time). This improves the speed of processing and also improves the likelihood that the numerical methods used will converge to a relevant solution.

[0117] Alternatively, if the determination at step 368 is affirmative, the magnet positioning method 300 proceeds to step 372 whereby the minimization algorithm iterates based on nominal position information, thereby minimizing the cost function. The nominal position information may be, for example, arbitrary or random position information, a best guess, or simulated position information for the same point in time of another magnet. The iterations may include iterating through at least one or all of the x-position, y-position, z-position, roll value, pitch value, and yaw value of the simulated position information.

[0118] After performing steps 366, 370 or 372, the method determines in step 374 whether the calculated cost function satisfies a termination criterion that terminates the magnet positioning method 300. The termination criterion may include a threshold on the cost function solution. For example, if the minimization algorithm includes a least squares analysis, the termination criterion may include an absolute threshold on the sum of squares and / or a relative threshold, such as whether the sum for the current iteration is less than one or more previous sums. If the cost function exceeds the threshold (e.g., is less than the threshold), the termination criterion is met.

[0119] If the determination at step 374 is positive, i.e., the termination criteria are met, then it is assumed that the simulated position information of the simulated magnet in the most recent iteration of the minimization algorithm is a close enough estimate of the real world magnet position information at the particular time point at which the cost function was minimized. Thus, the magnet positioning method 300 has completed estimating the magnet position information at this time point and ends at end 376 for that time point.

[0120] Alternatively, if the determination at step 374 is negative, i.e., the termination criteria are not met, the magnet positioning method 300 returns to step 358 and repeats the process by iterating through the simulated position information until the termination criteria are met for that point in time.

[0121] The above steps are typically described for a single time point and a single magnet in the device magnetic data. In a multi-magnet system, the magnet positioning method 300 can perform the above process simultaneously for all magnets at a given time point, and then perform it again for all remaining time points in the device magnetic data until position information for all magnets in the system has been determined for all time points.

[0122] For ease of understanding, a representative illustration of the above method is described with reference to Figure 1C for a magnet 132 in a tissue analyzer 102. When a cartridge 104 containing a magnet 132 is positioned adjacent to the magnetometer plate 106, local magnetic field data is recorded by each of the magnetic sensing elements of the magnetometer array 118a, i.e., magnetometers 136a to 136c (step 350). The cartridge 104 is then positioned adjacent to the magnetometer plate 106 such that the magnet 132 is in a starting position or orientation adjacent to the magnetometer array 118a (step 352).

[0123] The magnet 132 is then moved from its starting position to a first position by the tissue structure 142 contracting at a first time point. In this first position, the magnet 132 is characterized by position information including its x-position, y-position, z-position, roll, pitch and yaw (x0, y0, z0, roll0, θ0, Φ0).

[0124] As the magnet moves away from its starting position, each magnetic sensing element of each of the magnetometers 136a-136c changes its characteristics in response to the changing magnetic field of the magnet 132. Thus, when the magnet 132 is in a first position at a first time, the magnetometers 136a-136c output corresponding signals, and these signals are processed and recorded as device magnetic data (particularly components of the magnetic field) at the positions corresponding to each of the magnetic sensing elements of the magnet 132 (step 354). Optionally, the local magnetic field data is subtracted from the device magnetic data (step 356).

[0125] For example, a simulated magnet is simulated (step 358) by assuming a model for magnetic behavior (e.g., a dipole model) and position information in theoretical space of the simulated magnet. The simulated position information may include the x position, y position, z position, roll, pitch, and / or yaw (x s , y s , z s , roll s , θ s , Φ s ), and then, based on iterative steps 366-370, calculate simulated magnetic field data for the simulated magnet at a first time point at positions in theoretical space that correspond to the positions of each of the magnetic sensing elements of the magnetometer array 118a (step 360).

[0126] A cost function is then calculated by comparing the device magnetic data at the first time point with the simulated magnetic field data at the first time point (step 362). The result of the cost function is used for the first iteration and for the first time point in the series, so the cost function is calculated using the nominal position information (e.g., the simulated position information (xs , y s , z s , roll s , θ s , Φ s ) based on a nominal value of the position information of the magnet 132. The nominal value may be a best estimate (step 372). If the comparison between the simulated magnetic field data based on the device magnet and the nominal position information meets the termination criteria, the nominal position information is assumed to be a close enough estimate of the position information of the magnet 132 (step end 376). However, if the comparison does not meet the termination criteria, the process is repeated at step 358. In particular, since the next iteration is not the first iteration for the first time point, the cost function iterates based on the previous iteration (step 366), i.e., iterates one or more simulated position information values ​​based on those position information values ​​assumed in the previous iteration (step 374), until the termination criteria is met.

[0127] Adapting the above example to a multi-magnet method, steps 358-376 can be performed simultaneously for each magnet. Furthermore, for each magnet, simulation steps 366-372 can be repeated based on simulated position information of one or more other magnets (e.g., all other magnets) in the system. In this way, the simulated position information of each simulated magnet (and by extension, the estimation of each magnet's position information) can take into account the positions of all magnets in the system. Furthermore, the estimation of each magnet's position information is based on simulated magnetic field data at multiple positions of each magnet (i.e., positions corresponding to each magnetic sensing element of each magnetometer array). These advantages result in greater accuracy.

[0128] The multi-parameter magnet positioning method 300 described above can be time intensive when processing large amounts of data, possibly including processing hundreds of parameters to track multiple magnets, for example in a tissue array in a tissue analysis system. As a solution to this potential drawback, the magnet positioning method 300 can include performing steps 350-376 described above for a subset of magnets and / or time points in the device magnetic data of the system, identifying an empirical relationship between the estimated position information of the magnet subset and the device magnetic data recorded by the corresponding magnetometer, and optionally using the empirical relationship to estimate position information for subsequent time points without performing steps 350-376 for all time points. For example, the empirical relationship can include a polynomial function of the device magnetic data that can be evaluated to estimate position information.

[0129] Another potential drawback of the above magnet positioning method is the increased position noise due to the large number of magnetic sensing elements in the underlying system and the relatively close positioning of the magnet relative to the magnetic sensing elements. When calculating TIFF2025507355000027.tif1022, x s , y s or z s If θ is large enough, noise on a given magnetic sensing element can translate into a relatively large movement of the magnet due to a relatively large gradient in the magnetic field and magnet geometry at a physical point distant from the magnet.

[0130] The estimated position information generated by magnet positioning method 300 can be used for additional analysis. For example, magnet positioning method 300 can include comparing estimated position information of one or more magnets at different times to identify position changes of the magnet(s) over time, and optionally, identifying one or more characteristics of tissue to which the magnet(s) are operably attached based on the identified position changes.

[0131] As mentioned above, the methods consider different components of the magnetic field at different positions of each magnet, which more accurately reflects the actual characteristics of the magnetic field being measured, rather than assuming that all components are measured at a common position. Furthermore, the robust methods described herein take into account differences in the positions and movements of different magnets in a multi-magnet system. For example, these methods do not require that all magnets have the same starting position or orientation of their corresponding magnetometer arrays. This reduces the time and importance of experimental setup and calibration, thereby improving throughput.

[0132] The method shown in Figure 3 can be modified to provide further variations of the disclosed embodiments using various concepts of iterative modeling. Thus, the particular order of steps shown in Figure 3 is representative and should not be construed to limit the disclosure to the particular embodiments disclosed herein. On the contrary, the magnet positioning method shown in Figure 3 should be construed to include all possible embodiments and the full range of equivalents of those steps or processes.

[0133] Any of the methods described herein may be embodied as computer readable program code, and thus, Figure 4 illustrates an exemplary program code 402 configuration according to an embodiment of the present disclosure.

[0134] The recording module 404 may include computer readable instructions related to recording magnetic fields from one or more magnets of a system (e.g., a tissue analysis system). For example, the recording module 404 may include instructions corresponding to steps 350, 354, and 356 described with reference to Figure 3 and may perform calculations based on input received from a magnetometer of the system. Output from the recording module 404 forms input to the simulation module 406 and / or the estimation module 408.

[0135] The simulation module 406 may include computer readable instructions for simulating the magnet and its magnetic field. For example, the simulation module 406 may include instructions corresponding to steps 358 and 360 described with reference to Figure 3. Output from the simulation module 406 forms an input to the estimation module 408.

[0136] The estimation module 408 may include computer readable instructions for estimating position information for one or more magnets in the system, which are calculated based on inputs from the recording module 404 and the simulation module 406. For example, the estimation module 408 may include computer readable instructions corresponding to steps 362 through 376 described with reference to FIG.

[0137] The above program code configurations are exemplary and other embodiments may include additional or different modules that implement the methods described herein.

[0138] FIG. 5 shows the trajectory of the estimated position information of a magnet as a function of the magnetic flux density of the z coordinate measured from a single axis of a single underlying magnetometer (solid) and its corresponding best fitting line (dashed) of the magnet positioning method of the present disclosure. As shown, for a magnet of interest, the correlation between its estimated position and the recorded magnetic field can be highly linear. This proves the accuracy of the magnet positioning method when each magnet of the system moves along a predictable trajectory, and its position information varies one-dimensionally in time, for example, due to the mechanical or structural constraints that the system design requires. However, the method is not limited to the case of the constraints.

[0139] FIG. 5 provides an illustration of an empirical relationship between estimated magnet position information and device magnetic data recorded by the corresponding magnetometer. Based on such observations in FIG. 5, the output of a single axis of a single magnetometer can be multiplied by the slope of the best fitting line, thereby identifying magnet position changes. However, in some multi-magnet systems, such as multi-well tissue analysis systems, this slope may vary between wells containing tissue. The combined method of magnet positioning methods described herein can estimate the slope of these changes, thereby allowing for much greater accuracy without appreciably sacrificing processing speed.

[0140] The embodiments of the present disclosure are not limited to linear multiplication with constants, but rather can achieve greater accuracy with polynomials or other mathematical functions that can use such magnetic position information to more accurately relate it. However, to maintain high linearity, the tissue analysis system can position the magnet during the measurement so that there is a high degree of linearity between its change in x-position and the change in magnetic field it produces. For example, a magnet with a magnetic dipole moment vector aligned along the x-axis direction, positioned in the center of the magnetometer cluster as described above, can be moved within + / - 3 mm in the x-direction and within + / - 1 mm in the y-direction of the magnetic sensing element that detects the z-component of the magnetic field.

[0141] 6 is a plot comparing magnet position information estimated by the magnet positioning method of the present invention with a single axis sensing method for magnets displaced in discrete steps and optically tracked. Specifically, the plot tracks six different magnets as they move and compares their optically measured positions along the x-axis with a) positions along the x-axis estimated by an embodiment of the magnet positioning method described herein (indicated by diamonds) where the estimated positions are based on device magnetic data from nine magnetic sensing elements (three in each of the three magnetometers of the magnetometer array, resulting in nine axes), and b) positions along the x-axis estimated by a known method (indicated by x's) based on a polynomial function of a single axis. Data points for each method are taken at the same point in time.

[0142] As shown, the magnet positioning method of the present disclosure tracks the ideal line with a deviation that is clearly less than that of the single axis method, indicating improved accuracy over known methods.

[0143] Figure 7 is a plot comparing the accuracy of the two methods plotted in Figure 6. Specifically, the plot shows the percentage difference between the optically measured position along the x-axis and the position estimated using each of the two methods. As shown, the magnet positioning method described herein (represented by diamonds) has a percentage difference ranging from about -1% to about 5%, whereas the known method (represented by x's) has a significantly larger difference, ranging from + / -15%. Thus, the method described herein estimates position information with greater accuracy and precision.

[0144] FIG. 8 shows results from one magnet positioning method of the present disclosure, where each square of the grid corresponds to a particular well in a 24-well microplate (e.g., microplate 122), where each well contains a magnet having a moving (contracting or beating) tissue structure operably connected to it, as described above with reference to FIGS. 1A-1G.

[0145] The squares represent the change slope (in mm / mT) on each magnet of 24 tissues moving (or pulsating) within the plate. The high number in each square represents an empirical relationship, i.e., a slope determined as described with reference to FIG. 5, by relating the estimated position information of the magnet in a given well (determined by the magnet positioning method described herein) to the recorded magnetic field of one or more magnetometers of a magnetometer array positioned adjacent to that magnet. For each well, the slope can be multiplied with the magnetic field recorded by the corresponding magnetometer at different times to produce the position change of the magnet.

[0146] The second row in each square represents the fitness (R 2 A fit coefficient of less than 0.95 (as shown for wells D5 through A6) may indicate that the magnet is not moving (pulsating) enough to be of interest or relevance.

[0147] The third row shows the predicted percentage difference in tissue contraction amplitude prediction. Smaller absolute values ​​indicate greater accuracy. As shown, wells D1 through A4 all have absolute values ​​below 2.82. Additionally, wells D5 through A6 are not moving (beating) enough to be of interest or relevance.

[0148] The present disclosure further provides a method that can solve the problem of "crosstalk" that afflicts known methods and systems. As shown in Figure 9, multiple tissue contractions in a tissue analysis system having multiple magnets each operably connected to a tissue structure can add errors to the magnetic field data recorded by a particular magnetometer, especially if the magnets are close to each other and have contractions that overlap in time. This situation occurs because each magnetometer measures magnetic field changes caused by the magnet moving above it and other nearby magnets.

[0149] Mathematically, due to the additive nature of magnetic fields, the magnetic field recorded by a particular magnetometer can be considered to be a superposition of the magnetic field from the magnet of interest and the crosstalk (or interference) magnetic fields from magnets adjacent to the magnet of interest. For a given sensor i, such a relationship can be expressed as:

number

[0150] In equation (5), TIFF2025507355000029.tif810 is the magnetic field value measured by magnetometer i, B i is the magnetic field generated at magnetic sensor i by a magnet in the absence of crosstalk, and f i,i+1 is the magnetic field of the adjacent magnet in the absence of crosstalk, and B i+1 and which is a function of the interference field induced at magnetic sensor i. Equations of this form can be prepared and used for each magnetometer and placed in a set of equations that will represent each magnetic field (B i ,B i+1 ,...,B n ) can be solved for

number

[0151] To generate this set of equations accurately, we define the function f i,i+n , and thus how the magnetic field generated by a magnet moving directly above a given magnetometer affects neighboring magnetometers.

[0152] f i,i+nrepresents the "gradient" or other correspondence between the estimated position change of the magnet and the magnetic field recorded by the underlying magnetometers of the magnetometer array. It can be derived by tracking the trajectory of each magnet independently using the magnet positioning methods described herein and simulating the magnetic field at each magnet's neighboring sensors when the other magnet is not present.

[0153] A solution to this family of equations in Equation 6 can be rapidly identified and, in combination with the single sensor methodology described above, can greatly accelerate data analysis. In another embodiment, such a slope or corresponding empirical relationship can be identified by timely controlling the movement of the tissue, and thereby the magnet. The tissue structures operably attached to the magnets are electrogenic, so that one tissue structure can be electrically stimulated to move (or contract) at a different time than other tissue structures, thereby allowing the f of the magnet attached to a particular tissue structure to be determined without interference from the other tissue structures. i,i+n This allows the identification of

[0154] The systems and devices described herein, in particular the tissue analysis system, may include or form part of a computing device having one or more processors and a non-transitory machine-readable storage medium storing instructions, as described below, which when executed by the processor perform the magnet positioning methods and other methods described herein.

[0155] Figure 10 is a block diagram illustrating components of computing device 700. The reference numbers used to illustrate Figure 10 do not necessarily correspond to other figures described herein.

[0156] The computing device 700 reads instructions 724 from a non-transitory machine-readable storage medium (e.g., a hard drive storage system) and performs all or part of any one or more of the methods discussed herein. In particular, FIG. 10 illustrates a computing device 700 in the form of an exemplary computer system in which the computing device 700 may perform all or part of the instructions 724 (e.g., software, programs, applications, applets, apps, or other executable code) of any one or more of the methods discussed herein. For example, the computing device 700 may effectively perform all or part of the methods described above with reference to FIG. 3. Furthermore, in some embodiments, the computing device may perform some or all of the functions of the tissue analysis system described above with reference to FIGS. 1A-1F.

[0157] In some embodiments, computing device 700 can operate as a standalone device or can be connected (e.g., via a network) to other computing devices. In a networked arrangement, computing device 700 can operate as a server computing device or a client computing device in a server-client network environment, or as a peer computing device in a distributed (e.g., peer-to-peer) network environment. Computing device 700 can be embodied as any one or more of a number of electronic devices, and can be, for example, a server computer, a client computer, a personal computer (PC), a tablet computer, a laptop computer, a netbook, a mobile phone, a smart phone, a web appliance, a network router, a network switch, a network bridge, or any computing device capable of executing instructions 724, sequentially or otherwise, that specify operations to be performed by the computing device. Additionally, although only a single computing device 700 is shown, the term "computing device" should be considered to include any combination of computing devices that, alone or in concert, execute instructions 724 to perform all or a portion of any one or more of the methods discussed herein.

[0158] The computing device 700 includes a processor 702 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a radio frequency integrated circuit (RFIC), or any suitable combination thereof), a main memory 704, and a static memory 706, which are arranged to communicate with each other via a bus 708. The processor 702 may include microcircuits that are temporarily or permanently configured with some or all of the instructions 724, such that the processor 702 may be configured to execute, in whole or in part, any one or more of the methods described herein. For example, a set of one or more microcircuits of the processor 702 may be configured to execute one or more modules (e.g., software modules) described herein. The computing device 700 may further include a display component 710, such as one or more devices, such as a light emitting diode (LED) display, a liquid crystal display (LCD) screen, a gas plasma-based flat panel display, an LCD projector, or other type of display device.

[0159] Computing device 700 may include one or more input devices 712 operable to receive input from a user. The input devices 712 may include, for example, buttons, touch pads, touch screens, wheels, joysticks, keyboards, mice, track balls, keypads, accelerometers, light guns, game controllers, or any other such devices or elements by which a user can provide input to computing device 700. These input devices 712 may be physically incorporated into computing device 700 or may be operably coupled to computing device 700 via a wired or wireless interface. In the case of a computing device having a touch screen display, the input devices 712 may include touch sensors that operate in conjunction with display component 710 to allow a user to interact with images displayed by display component 710 using touch input (e.g., using a finger or stylus).

[0160] Computing device 700 may further include at least one communication interface 720, which includes one or more wireless components that operate to communicate with one or more remote devices within communication range of a particular wireless protocol. The wireless protocol may be any suitable protocol that allows the device to wirelessly communicate, such as Bluetooth, cellular (including 3GPP or ETSI standards), IEEE 802.11, or an infrared communication protocol, such as a protocol compliant with lrDA. It should be understood that communication interface 720 may additionally or alternatively include one or more wired communication interfaces for coupling to and communicating with other devices.

[0161] Computing device 700 may further include a power source 728, such as a rechargeable battery that is operated and charged via normal plug-in methods or via other methods of capacitive charging. Alternatively, power source 728 may include a power supply unit that converts AC power from a power grid to regulated DC power used by the internal components of device 700.

[0162] Computing device 700 may further include a memory element 716. The memory element 716 may include a machine-readable medium on which instructions 724 for any one or more of the methods or functions described herein are stored. Before or during execution of the instructions 724 by computing device 700, the instructions may reside, completely or at least partially, in main memory 704, in processor 702 (e.g., in a processor cache memory), or both. The instructions 724 may also reside in static memory 706.

[0163] As such, memory 704 and processor 702 may be considered a machine-readable medium (e.g., a tangible and non-transitory machine-readable medium). Instructions 724 may be transmitted or received over network 202 via communication interface 720. For example, communication interface 720 may communicate instructions 724 using any one or more transmission protocols (e.g., HTTP).

[0164] In some embodiments, computing device 700 is a distributed computing device, e.g., a cloud computing device distributed across multiple different servers. In some exemplary embodiments, computing device 700 may have one or more additional input components (e.g., sensors or gauges) (not shown). Examples of such input components include an electrical stimulation input (e.g., a stimulation cap or stimulation board), an image input component (e.g., one or more cameras), an audio input component (e.g., a microphone), a direction input component (e.g., a compass), a position input component (e.g., a GPS receiver), an orientation component (e.g., a gyroscope), a motion detection component (e.g., one or more accelerometers), an altitude detection component (e.g., an altimeter), and a gas detection component (e.g., a gas sensor). Input obtained by any one or more of these input components may be accessed and used by any one of the modules described herein.

[0165] As used herein, the term "memory" refers to a non-transitory machine-readable medium capable of storing data temporarily or permanently, and may be considered to include, but is not limited to, random access memory (RAM), read-only memory (ROM), buffer memory, flash memory, and cache memory. Machine-readable media are non-transitory because they do not include a propagating signal. Although the machine-readable medium is described in the exemplary embodiment as a single medium, the term "machine-readable medium" should be considered to include a single medium or multiple media capable of storing instructions 724 (e.g., a centralized or distributed database, or associated caches and servers). The term "machine-readable medium" should be considered to include any medium or combination of multiple media capable of storing instructions 724 executable by computing device 700, such that, when instructions 724 are executed by one or more processors (e.g., processor 702) of computing device 700, computing device 700 performs, in whole or in part, any one or more of the methods described herein. Accordingly, a "machine-readable medium" refers to a cloud-based storage system or storage network, including a single storage device or device as well as multiple storage devices or devices. The term "machine-readable medium" should therefore be considered to include, but is not limited to, one or more tangible (e.g., non-transitory) data repositories in the form of solid-state memory, optical media, magnetic media, or any suitable combination thereof.

[0166] Although the present invention has been described with reference to particular embodiments and illustrative drawings, those skilled in the art will recognize that the present invention is not limited to the described embodiments or drawings.

[0167] The detailed description set forth above with reference to the drawings (like reference numerals refer to like elements) is intended as a description of representative embodiments of the present disclosure, and is not intended to represent an embodiment. Each embodiment described in this disclosure is provided as an example or illustration, and should not be construed as preferred or advantageous over other embodiments. The exemplary embodiments provided herein are not intended to be exhaustive or to limit the present disclosure to the precise form disclosed. Similarly, any step described herein may be interchangeable with other steps or combinations of steps to achieve the same or substantially similar results. Also, in addition, one or more of the features of any embodiment may be combined with one or more features of one or more embodiments, thereby forming additional embodiments, which are within the scope of the present disclosure.

[0168] In general, the embodiments disclosed herein are non-limiting, and the inventors contemplate that other embodiments within the scope of the present disclosure may include structure and function from one or more of the specific embodiments shown in the drawings and described in the specification. Changes and modifications may be made, and equivalents may be employed, without departing from the spirit of the present disclosure. Accordingly, all such changes, modifications, and equivalents are expressly intended to be within the spirit and scope of the present disclosure as claimed. For example, the present disclosure includes additional embodiments having any one or more combinations of features described with respect to the representative embodiments.

[0169] In the above description, specific details are given to provide a thorough understanding of representative embodiments of the present disclosure. However, it will be apparent to one of ordinary skill in the art that the embodiments disclosed herein may be practiced without embodying all of the specific details. In some instances, well-known process steps have not been described in detail in order to not unnecessarily obscure various aspects of the present disclosure.

[0170] This application may include references to directions such as "first," "second," "vertical," "horizontal," "front," "back," "left," "right," "top" and "bottom," "lower," "around," etc. These references, and other similar references in this application, are intended to aid in describing and understanding particular embodiments (such as when the embodiments are positioned for use) and are not intended to limit the disclosure to those orientations or locations.

[0171] The application may also refer to amounts and numbers. Unless otherwise stated, such amounts and numbers should not be considered limiting, but rather exemplary of possible amounts or numbers associated with the application. In this regard, the application may also use the term "plurality" to refer to an amount or number. In this regard, the term "plurality" means any number greater than 1, e.g., 2, 3, 4, 5, etc. The terms "about," "approximately," and the like, mean ±5% of the stated value. The term "based on" means "based at least in part on." The term "between" includes the value stated in relation thereto. "At least one of A, B, or C," "at least one of A, B, and C," and "at least one of A, B, and / or C" have the same meaning, i.e., any one of the following conditions satisfies all of the above expressions, A, B, C, AB, AC, BC, ABC.

Claims

1. 1. A tissue analysis system comprising: a tissue analyzer and a non-transitory machine-readable storage medium storing instructions; The tissue analysis device includes a magnet and a plurality of magnetometers disposed adjacent to the magnet; The instructions, when executed by a processor, cause the tissue analysis system to perform operations: The operation is recording the magnetic field of the magnet with each magnetometer of the plurality of magnetometers; simulating a simulation magnetic field of a simulation magnet having simulation position information at each magnetometer of the plurality of magnetometers; and estimating position information of the magnet by iterating the simulated position information of the simulated magnet.

2. 2. The system of claim 1, wherein iterating the simulated position information of the simulated magnet includes iterating at least one of an x ​​position, a y position, a z position, a roll value, a pitch value, and a yaw value.

3. 3. The system of claim 2, wherein iterating the simulated position information of the simulated magnet includes iterating each of the x position, the y position, the z position, the roll value, the pitch value, and the yaw value.

4. 2. The system of claim 1, wherein estimating the position information of the magnet is based on a cost function that compares the magnetic field to the simulated magnetic field.

5. 5. The system of claim 4, wherein iterating the simulated position information of the simulated magnet includes determining whether the value of the cost function is from a first iteration of a cost minimization algorithm.

6. 6. The system of claim 5, wherein iterating the simulated position information of the simulated magnet comprises iterating based on simulated position information from a previous iteration one time point.

7. 6. The system of claim 5, wherein repeating the simulated position information of the simulated magnet comprises repeating based on simulated position information associated with a previous time point or based on nominal position information.

8. 6. The system of claim 5, wherein iterating through the simulated position information of the simulated magnet includes determining whether a value of the cost function meets a termination criterion.

9. The system of claim 1 , wherein estimating the position information of the magnet comprises comparing the magnetic field to a simulated magnetic field.

10. 2. The system of claim 1 , wherein each magnetometer of the plurality of magnetometers includes a plurality of magnetic sensing elements, and wherein recording the magnetic field includes sensing a magnetic flux density of the magnet with the plurality of magnetic sensing elements of each magnetometer.

11. The system of claim 10 , wherein for each magnetometer, each magnetic sensing element senses a different axis of the magnetic field.

12. The system of claim 1, wherein the magnet is mounted within an artificial tissue assembly and the plurality of magnetometers are mounted on a printed circuit board arranged adjacent to the artificial tissue assembly.

13. 2. The system of claim 1, wherein the magnet is one of a plurality of magnets of the tissue analyzer, and recording, simulation and estimation are performed for all magnets of the plurality of magnets.

14. 14. The system of claim 13, wherein estimating the position information for each magnet is based on the simulated position information for each magnet.

15. 2. The system of claim 1, wherein estimating the position information of the magnet is performed at multiple time points, and the operation further comprises determining a change in position of the magnet between the multiple time points.

16. 10. The system of claim 1, wherein said operating further comprises identifying a characteristic of tissue operably attached to said magnet based on said change in position.

17. 2. The system of claim 1, further comprising a stimulation lid arranged to move the magnet, and the operation includes recording the magnetic field of the magnet after or simultaneously with the movement of the magnet.

18. 1. A tissue analysis system for use with a tissue analyzer, comprising: The tissue analysis device includes a magnet and a plurality of magnetometers disposed adjacent to the magnet; The tissue analysis system includes a non-transitory machine-readable storage medium storing instructions that, when executed by a processor, perform operations, the operations including: recording the magnetic field of the magnet with each magnetometer of the plurality of magnetometers; simulating a simulation magnetic field of a simulation magnet having simulation position information at each magnetometer of the plurality of magnetometers; and estimating position information of the magnet by repeating the simulated position information of the simulated magnet.

19. 20. The tissue analysis system of claim 18, wherein iterating the simulated position information of the simulated magnet includes iterating at least one of an x ​​position, a y position, a z position, a roll value, a pitch value, and a yaw value.

20. 1. A magnet positioning method, comprising: recording the magnetic field of the magnet at a plurality of positions; Simulating a simulation magnetic field of a simulation magnet having simulation position information in theoretical space at the plurality of positions; and estimating position information of the magnet by iterating the simulated position information.

21. 21. The method of claim 20, wherein iterating the simulated position information of the simulated magnet includes iterating at least one of an x ​​position, a y position, a z position, a roll value, a pitch value, and a yaw value.

22. 1. A magnet positioning method, comprising: recording a magnetic field of each magnet of the plurality of magnets with a nearest magnetometer array of the plurality of magnetometer arrays; simulating a simulated magnetic field of a simulation magnet of the plurality of simulation magnets at each magnetometer array location, each simulation magnet having position information; and estimating position information for each magnet by repeating the simulated position information of the simulated magnet in the nearest magnetometer array.

23. 23. The method of claim 22, wherein estimating the position information for each magnet comprises iteratively varying the simulated position information for all of the simulated magnets.

24. further comprising transferring a plurality of tissues; 23. The method of claim 22, wherein each tissue is operably attached to one of said plurality of magnets.

25. 25. The method of claim 24, wherein moving the plurality of tissues comprises moving the plurality of tissues with contractions that overlap in time.

26. 23. A method as claimed in claim 22, wherein recording the magnetic field of each magnet comprises recording magnetic field data associated with different points in space.

27. 23. The method of claim 22, further comprising identifying an empirical relationship between estimated position information and recorded magnetic data, and estimating position information for the second plurality of magnets using the empirical relationship.

28. 23. The method of claim 22, wherein estimating position information for each magnet includes adjusting for crosstalk based on the recorded magnetic fields of every other magnet of the plurality of magnets.

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