Adaptive tracking and MRI-guided catheter and stent placement

Abstract

A method of automatically adjusting at least one MR image parameter for an interventional procedure includes adaptively tracking an MR micro-coil catheter and automatically updating an imaging scan plane's position and orientation, as well as other features including, but not limited to, field-of-view, resolution, temporal resolution, slice thickness, tip angle, and TE. The disclosed system provides a more natural interface for a physician operating the MR scanner during an interventional procedure. The scanner can react to changes in the clinical environment and automatically adjust a number of image parameters. For example, during catheter insertion, images are acquired at lower resolutions, and possibly larger fields of view, to help facilitate faster updates and tracking. Once the catheter reaches a target area in the tissue and its motion slows, an MR image of higher resolution, and possibly lower field of view, is acquired.

Description

BACKGROUND

In a typical magnetic resonance imaging (MRI) system, a subject such as a human body is placed in a static magnetic field that orients the proton magnetic dipoles. A field gradient is imposed along the z-axis in the direction of the main magnetic field such that a narrow plane of protons resonate within a band of frequencies. A phase encoding gradient along the x-axis is activated for a short time during which the dipoles acquire a different phase. A frequency encoding gradient along the y-axis is then activated to frequency encode the positions of the dipoles while a receiver coil is activated to record the signal. Typically, 128, 256, or 512 data points are recorded along the frequency encoded axis. As each recorded data point corresponds to a respective pixel in the image to be generated, the number of recorded data points determines the resolution of that image.

A Fourier Transform (FT) algorithm is used to decode frequency information contained in a proton signal at each location in the imaged plane to corresponding intensity levels. The resulting image is then displayed as shades of gray in a matrix arrangement of pixels. MRI may be used to reconstruct images for any standard orientation, such as transverse, coronal, or sagittal slices for example, or for any oblique orientation. MRI can also be used for interventional procedures, such as guiding medical devices through vessels and placing such devices inside vessels.

Interventionalists can perform procedures efficiently and safely if provided access to relevant image information. For example, an interventionalist may require information about a current position/orientation of a device, such as a catheter, in the body, which could be overlaid with a three-dimensional (3D) reference map showing the catheter's current position in real time. Real-time imaging can also overcome problems associated with patient movement.

In imaging most tissues with MRI, the hydrogen protons from water are preferably detected as most soft tissues are composed of greater than approximately eighty percent water. A device with a different resonance frequency can be attached to or incorporated in the catheter, with software in the scanner alternating between localizing the catheter and collecting image data.

Adjusting the MR image parameters (e.g., slice position, orientation, resolution, TE, TR, etc.) during an interventional catheter-based procedure can be a cumbersome process that may require the interventionalist or technologist to leave the magnet room and use a keyboard and a mouse in combination with a graphical user interface. Other conventional adaptive image parameter systems verify the feasibility of using feedback based on a catheter's insertion speed to adjust, in real-time, the value of specific adaptive image parameters. These systems, however, provide little clinical utility because they are neither sufficiently flexible nor robust for use in intravascular MR guided procedures.

It would therefore be desirable to provide a system and process that automatically and continuously adjusts specific image parameters in real time, based on a catheter's speed of insertion.

SUMMARY OF THE INVENTION

In one embodiment, the invention is directed to a system and method that, inter alia, incorporates real-time imaging sequences, flexible catheter tracking methods, adaptive parameter modes, and a user-friendly interface for the interventional physician.

This adaptive tracking system uses, in various embodiments, real-time tracking techniques to continually monitor a catheter tip's 3D position (including, for example, position relative to a target anatomy or position relative to the MR imager's receive coils), orientation, insertion speed, and a combination of physiological parameters, such as, without limitation, breathing rate, heart rate, etc. The device position and orientation information is used to automatically adjust the scan plane for real-time imaging. Insertion speed may be used to automatically adjust pre-specified adaptive image parameters in real-time. Image resolution, FOV, Bandwidth, TE, TR, and temporal resolution are employed as adaptive parameters; many other image parameters can also be used.

The systems and methods described herein represent a “hands free” interventional MRI systems and methods that automatically and adaptively adjust imaging parameters and react to changing clinical requirements in real-time. The systems and methods disclosed herein provide the interventional radiologist with a means to effect a change in specific image parameters, in real-time, using the catheter itself.

According to one embodiment, the invention is directed at a method of adaptively adjusting at least one MR imaging parameter for an interventional procedure. The method includes (a) adaptively tracking an MR-guided probe inserted into an object by (i) locating the probe by acquiring first probe coordinates with respect to a reference coordinate system, and (ii) calculating a velocity of the probe relative to the object by acquiring second probe coordinates with respect to the reference coordinate system (typically the MR scanner/imager's coordinate system, which may or may not be an orthogonal coordinate system), and (b) based at least partially on the calculated velocity, adjusting a subset of the at least one MR imaging parameter to adaptively track the probe, image a target region of the object, or both.

According to one practice, acquiring the first probe coordinates includes acquiring a plurality of one-dimensional frequency-encoded projections to determine at least one of a three-dimensional position of the probe and an orientation of the probe. Various imaging parameters that may be adjusted by the systems and methods disclosed herein include a subset of: field of view, image spatial resolution, image scan plane position, scan plane orientation, temporal resolution, bandwidth, slice thickness, imaging pulse sequence, image contrast, TR, TE, active receiver channels, k-space trajectory, excitation flip angle, MR scanner table position (e.g., to keep the probe proximal to an isocenter of the table).

According to one aspect, adjusting the one or more imaging parameters is at least partially based on an auxiliary parameter. The parameter includes an element belonging to a subset of: position of the probe relative to a target region of the object, position of the probe relative to the MR imager's receive coils, probe orientation, a physiological parameter associated with the object, and a combination these.

According to another embodiment, the invention is directed at A method of adaptively adjusting at least one MR imaging parameter for an interventional procedure. The method includes (a) locating an MR-guided probe inserted into an object by acquiring first probe coordinates with respect to a reference coordinate system, and (b) based at least partially on a parameter associated with the located probe, adjusting a subset of the at least one MR imaging parameter to adaptively to track the probe, to image a target region of the object, or both.

Further features and advantages of the present invention will be apparent from the following description of preferred embodiments and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The following figures depict certain illustrative embodiments of the invention in which like reference numerals refer to like elements. These depicted embodiments are to be understood as illustrative of the invention and not as limiting in any way.

FIG. 1A is a functional diagram of an exemplary adaptive tracking system architecture;

FIG. 1B is a functional diagram of an exemplary adaptive tracking system architecture, wherein a 3D MIP roadmap is acquired in addition to real-time 2D images of the catheter and the surrounding tissue;

FIG. 2 shows one embodiment of a tuned resonance circuit mounted on a catheter;

FIG. 3 shows a second embodiment with two tuned resonance circuits mounted on a catheter;

FIG. 4 shows an exemplary binary function for adjusting image parameters;

FIG. 5 shows an exemplary continuous function for adjusting image parameters;

FIG. 6 shows an exemplary user interface for real-time updating of parameters;

FIG. 7 shows a temporal sequence of image frames using a phantom experiment; and

FIG. 8 shows a temporal sequence of image frames using a porcine experiment.

DETAILED DESCRIPTION OF CERTAIN ILLUSTRATIVE EMBODIMENTS

The invention is directed to a system and method for real-time catheter tracking and adaptive imaging using MRI. In particular, the system and method described herein can be used to track and position catheters and stents in a human body.

Referring now to the figures, and in particular to FIG. 1, an adaptive parameter system software architecture is shown that was interfaced with a 1.5 Tesla Siemens Sonata scanner (Siemens Medical Solutions, Erlangen Germany) using the Siemens Integrated Development Environment for Applications (IDEA) and Image Calculation Environment (ICE) for pulse sequence design and image reconstruction. IDEA and ICE are built around the C++ programming language to afford the developer a sufficient degree of software flexibility. The software architecture has three main components: (1) a fast device localization method, (2) data processing software that performs velocity calculations and updates image parameter values, and (3) a real-time imaging technique that automatically incorporates the updated scan plane parameters, performs appropriate pulse sequence revision, acquires new image data, and reconstructs the image online. These components form a closed feedback loop system that, when continuously repeated, provides an interventional environment with real-time imaging and image parameters that adapt to the changing clinical circumstances.

As seen in FIG. 1A, the system alternates between acquiring (substantially in real-time) a two-dimensional (2D) catheter-selective image and an image of the surrounding tissue (e.g., a tissue map image). The system then localizes the catheter-based markers to localize the device, and updates the scan plane position based on the location, trajectory, and/or orientation of the tracking coil. The image parameter values are adjusted based on the velocity of the tracking coil calculated from two or more successive position measurements, using Eqs. [1] or [2] described below. The image resolution and/or the field of view (FOV) are adjusted using the adjusted image values and a new image is acquired reflecting the new image parameters.

FIG. 1B depicts an alternative embodiment of the systems and methods described herein. The process pictorially depicted by FIG. 1B includes acquiring a 3D roadmap based on maximum intensity projections (MIP), such as, without limitation, an angiogram roadmap, in addition to the 2D real-time images referred to in FIG. 1A.

Various device localization techniques can be used, with one exemplary technique incorporating a single coil tip tracking method depicted in FIG. 2, which can provide information about the catheter's three-dimensional position within the magnet. FIG. 2 depicts a tuned resonant circuit that is capacitively coupled to the MR system and is mounted on the tip of a catheter to provide information about the three-dimensional position of the device.

The second tracking method, depicted in FIG. 3, includes an analytic radial tracking method that uses two active regions or markers on a catheter and can provide information about the three-dimensional position and orientation of the catheter. In particular, FIG. 3 depicts a tuned resonant circuit with two active regions. Additional detail of the coil designs will be described later.

Both localization methods require the collection of a limited number of 1D projections (the first tracking method requires 3 projections for catheter position, whereas the second tracking method requires 8 projections for catheter position and orientation). These localization projections are collected prior to the acquisition of each set of image data. The raw k-space projection data is sent to the image reconstruction computer where the data processing software (written in C++ and developed in ICE) determines the location of the tracking markers. The software then computes six values: three positional values that define the 3D position of the catheter and three angles that define the 3D orientation of the catheter. When using the first localization method, the three angles are set to fixed values that define a standard transverse, sagittal or coronal plane. Values are defined within the scanner's X, Y, and Z coordinate space and the positions have units of pixels.

Using standard methods provided in ICE, a dedicated real-time link is established between the image reconstruction computer and the hardware control computer, which executes the pulse sequence software on the scanner. The six position and orientation values are sent via this real-time link, where the pulse sequence software (written in C++ and developed in IDEA) accepts and stores them for use. The position and orientation values are then converted from magnet XYZ coordinate space, to a coordinate system defined by the patient using Read, Phase, and Slice-Shift axis. The 3D positions are also converted from units of pixels, to millimeters. A real-time software kernel ensures that these computations are performed within a 20 ms pause located after the collection of the localization projections and before acquisition of the next set of rapid image data. The new catheter position and orientation information is then used to automatically define the new scan plane just prior to image data acquisition.

The pulse sequence software uses localization data from multiple time points to calculate the speed of the device. A variable-point finite difference digital filter is used for this calculation. The number of time points used in the digital filter is adjustable via the user interface, allowing the clinician to control the system's sensitivity to sudden changes in catheter speed. The catheter speed is then used to adjust the value of selected image acquisition parameters (e.g., image resolution, temporal resolution, bandwidth, field of view, slice thickness). A variable image parameter, P(V), is expressed as a function of the device speed, V, with limits for the device speed set, for example, before each procedure. The value of this function determines how each adaptive parameter is set, relative to its full range of acceptable values. P(V) need not be calculated more than once before all of the selected image parameters can be updated.

Referring now to FIGS. 4 and 5, the system has two types of functions describing the relationship of the catheter speed to the variable image parameter: a step function (e.g., a binary step function is shown in FIG. 4, but a multi-step function is also allowable), and a continuous sigmoidal function (FIG. 5). The binary set function uses a velocity threshold, which is adjustable via the user interface, to determine if the device is moving or stationary (Eq. [1]). When using this function, the adaptive image parameters will be set to one of two values: $\begin{array}{cc}P\left(V\right)=\{\begin{array}{cc}{P}_{\min }& \mathrm{if}V<V_{\mathrm{Thresh}}\\ P_{\max }& \mathrm{Otherwise}\end{array}& \left[1\right]\end{array}$

If the current speed of the catheter is found to be less than the designated user-defined threshold V_Thresh, then the selected image parameters will be set to one predetermined value P_min (which may be ideal for imaging with a stationary or slowly-moving catheter). Similarly, if the calculated catheter speed is larger than the designated threshold V_thresh, then the selected image parameters will be set to a different predetermined value P_max (which may be better suited for a faster moving catheter).

A binary function may be inadequate to adjust to a changing insertion speed. Accordingly, a continuous mode can be employed that uses a smoothly-varying function of catheter speed, such as a sigmoidal function, to adjust the adaptive image parameters (Eq. 2): $\begin{array}{cc}P\left(V\right)=P_{\min }+\frac{P_{\max }-P_{\min }}{1+e^{-S^{*}\left(V-V_o\right)}}& \left[2\right]\end{array}$

This function is depicted in FIG. 5. The upper and lower asymptotes prevent image acquisition parameters from being set in ranges that would cause the pulse sequence to exceed hardware limitations. This function's center velocity, V_o, and static sensitivity, S, are also adjustable via the user interface. Any image parameter can be automatically and separately adjusted by this system, and the system can incorporate virtually any MR imaging technique.

A user-defined range for each specific image parameter is set prior to the experiment. Subsequently, during the intervention, the binary and/or continuous set functions are used to set the values of the parameters relative to their specified respective ranges. The clinician is able to designate which image parameters to treat as adaptive parameters via the user interface. The parameter set function calculations and the image parameter update are performed by the real-time kernel immediately prior to the acquisition image data.

In the exemplary system and method, a variety of pulse sequences have been integrated into the adaptive tracking software. One pulse sequence is a True-FISP pulse sequence (TR=5 ms, TE=2.5 ms, FA=70°, matrix=[128×128 to 1024×1024], FOV=[150 mm to 300 mm]). Another pulse sequence is a FISP sequence (TR=5 ms, TE=2.5 ms, FA=70°, matrix=[128×128 to 1024×1024], FOV=[150 mm to 300 mm]). Another pulse sequence is a FLASH sequence (TR=8 ms, TE=4 ms, FA=150, matrix=[128×128 to 1024×1024], FOV=[150 mm to 300 mm]). Yet another pulse sequence is a Radial True-FISP sequence (TR=3.6 ms, TE=1.8 ms, FA=70 degrees, Matrix=128×128, FOV=[50 mm to 400 mm], Radial Lines=[64 to 512]).

FIG. 6 shows an exemplary user interface for the system with an online display that updates images in real-time as soon as new image data is reconstructed, or shortly thereafter. The interface also allows the clinician to interactively toggle and configure the device tracking and adaptive parameters; this can be done, for example, using a dialogue card.

To evaluate the system's feasibility and performance in vitro and in vivo, trials were conducted in two vessel phantoms and eight porcine imaging experiments, using methods approved by our Institutional Animal Care and Use Committee. To quantify the system's reliability and precision, 100 image frames were collected (in vivo) in which the system updated the slice position and orientation so that it was centered on the tip of a stationary catheter. This procedure was then repeated on a catheter that was moving at clinically relevant speeds within a vessel phantom. The experiments measured image data with two active receive channels: the imaging coil and the tracking coil. The data from these two channels was combined before displaying the image so that the tracking markers appeared in the image as areas of high signal amplitude. The distance between the position of the tracking markers within the image and the center of the image was used as a measure of the system's accuracy.

FIG. 7 shows results from in vivo porcine experiments in which the catheter was inserted throughout the length of the abdominal aorta. FIG. 8 shows image data collected in a vessel phantom experiment. In both the phantom and porcine trials, the resolution and FOV were automatically varied. Both sets of images illustrate a temporal sequence in which the catheter is slowed to a stop.

The system was able to accurately localize a motionless catheter 100% of the time and a moving catheter 98% of the time. The small error rate is substantially due to the system misidentifying flowing spins outside the imaging plane as the signal from the tracking markers. When using the single coil tracking method, the system collected all of the necessary tracking data within 15 ms, whereas the figure for the two-marker method was about 25 ms. An additional 20 ms was then required to perform the localization, velocity calculations, and updating of the image parameter values. The system ran continuously and responded in real-time to calculated changes in all eight in-vivo and both phantom trials. Following parameter determination, the system successfully responded to changes in device speed by dynamically adjusting specified image parameters. In all cases, the slice plane location and orientation was automatically placed at the catheter tip using information extracted during the localization phase.

The adaptive interface was further tested by performing MR imaging-guided renal artery stent placement, which was performed in two pigs using a catheter-based system that interactively adjusts the scan plane and automatically adjusts a number of imaging parameters in a manner described above.

Although renal MR angiograms have become state-of-the-art for the diagnosis of renovascular disease, conventional fluoroscopy remains the guidance method of choice for renovascular intervention. However, MRI-guided stent placement has recently become recognized as a feasible alternative.

Miniature radiofrequency coils were implemented into a catheter to determine the exact 3D position of the device in real time. As mentioned above, multiple coils can be implemented to derive information about the device orientation.

In the experiment, multiple MR image-guided procedures were performed in two fully anesthetized pigs using a 1.5 T scanner (Magnetom Sonata, Siemens Medical Solutions, Erlangen, Germany). Gadomer-17 (Schering AG, Berlin, Germany), an intravascular contrast agent for prolonged vascular enhancement, was used to acquire a gradient echo baseline 3D-MRA and to improve artery opacification during the intervention. A 5F C1-catheter equipped with two short single loop coils at the tip was then used to catheterize the renal arteries using a transfemoral approach.

The three-dimensional position and orientation of the catheter micro-coils were determined every 300 ms by means of an analytic radial tracking method using the two active catheter-based coils. The position data was used to define the MR scan plane position and orientation of a steady-state free-precession (SSFP) sequence acquired with a frame rate of three images per second. The field of view (FOV) of the images was adjusted according to the speed of the catheter movement: an FOV was enabled for a slowly-moving catheter and a larger FOV was used at higher catheter speeds. Using a balloon catheter equipped with one short single loop coil proximal to the balloon (e.g., immediately proximal thereto), MR guidance was then used to place a stent over a wire into the ostium of the renal arteries of both pigs.

The procedure time was measured, and the stent position was verified using conventional angiography. The high-amplitude signal from the coil of the instrumented balloon catheter was used to exactly position the stent at the level of the renal artery ostium in both pigs. The stent deviation as measured with conventional angiography was less than 3 mm. The procedure times were 14 and 19 minutes, including the MRI acquisition.

The systems and methods described herein demonstrate a method for intravascular imaging that significantly improves the performance of image-guided intravascular procedures. With this, the physician has means with which to control the scanner and dynamically alter specific image parameters during an MR procedure. Allowing the MR scanner to respond to a moving catheter by adjusting the value of imaging parameters creates a more natural interface with the MR scanner for the clinician during intravascular procedures by eliminating the need for manual adjustment of the scan plane position or specific image parameters during the intervention. These scan plane adjustments are applied to imaging slices that are also following the location and orientation of the catheter. Hence, advancing the catheter more slowly will automatically improve the resolution or SNR properties of the images, or can even effect a total change in tissue contrast to allow more accurate characterization of vessel wall pathology if the clinician wishes to see more detail in a certain region of interest.

The interventional systems and methods described herein can be incorporated into virtually any imaging protocol. The tuned coils operating as capacitively-coupled localization markers can be manufactured to be small enough to fit a variety of clinical catheters. Other fiducial marker designs may also be incorporated with ease into the systems and methods disclosed herein. For example, and without limitation, multiple inductor types can be used (e.g. single-loop, solenoid, and opposed solenoid inductors); capacitively-coupled tuned resonant circuit markers may be connected to a single receiver channel or multiple channels as phased-arrays; these phased-array markers may also be used for catheter-based vessel wall imaging). According to one practice, inductively-coupled tuned resonant circuit markers may be used. According to another practice, markers filled with a distinct signal source, such as a fluid with a large chemical shift relative to water, can also be used. An underlying theme is that a localized signal is available that may be readily segmented from a non-marker background signal.

The systems and methods according to the invention have been able to reliably (with accuracy of at least 98%) localize a catheter to within 2 mm and 1 degree of rotational error; these values are comparable to existing commercially-available MR-tracking technology which is unable to provide real-time tracking with adaptive imaging. The observed small localization error rate of at most about 2% occurs at least in part because the imaging plane contains the tracking markers and tends to saturate the spins surrounding the markers, making it possible for signals outside the imaging plane (and farther away from the tracking markers) to be mistakenly identified as the tracking markers.

The problem can be addressed by allowing more time for signal recovery, introducing a non-selective saturation between imaging and localization, or constraining the extent of rotational or positional shifts between sequential images. During normal use, the system minimizes localization errors by monitoring the distance that the catheter has moved between each image frame. The slice position and adaptive parameters are not updated if the detected change in catheter position distance is greater than a pre-determined value corresponding to the preset maximum clinically permissible insertion speed. In practice, this ensures that a catheter localization failure during a given frame will generally not cause the slice to be placed at an incorrect position and the adaptive image parameters to be set to incorrect values.

The systems and methods described herein use standard clinical hardware, with the exception of the small markers that were affixed to the catheter. The systems and methods also use standard clinical gradient and RF hardware, control computer hardware, and reconstruction computer.

The software interface was also merged into the standard clinical interface provided by the vendor. These features make the systems and methods according to the invention easy and inexpensive to implement and intuitive for most who are experienced with the operation of the MR imager.

The adaptive tracking system requires, at most, an additional 60 ms per image. This allows real-time imaging sequences to continue to operate in real time, while providing a great deal of increased functionality and flexibility. The ability to respond, in real time, to changes in device velocity allows the scanner to automatically adjust a number of image parameters. Without requiring the clinician to intervene, the scanner can automatically increase resolution and decrease frame rate as the catheter slows, or increase the field of view (FOV) as the catheter's speed increases.

Moreover, according to one practice an adaptive tracking system uses markers with a resonant frequency that is distinct from a resonant frequency of surrounding tissue; in this practice, the MR scanner employed has RF hardware configured to transmit and/or receive signals at this distinct frequency and a marker has an internal signal source characterized by a resonant frequency substantially equal to the distinct frequency at the scanner's field strength.

While the invention has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present invention is to be limited only by the following claims.

Claims

1. A method of adaptively adjusting at least one MR imaging parameter for an interventional procedure, comprising: a. adaptively tracking an MR-guided probe inserted into an object by i. locating the probe by acquiring first probe coordinates with respect to a reference coordinate system; and ii. calculating a velocity of the probe relative to the object by acquiring second probe coordinates with respect to the reference coordinate system; and b. based at least partially on the calculated velocity, adjusting a subset of the at least one MR imaging parameter to adaptively perform at least one of tracking of the probe and imaging of a target region of the object.

2. The method of claim 1, wherein acquiring the first probe coordinates includes acquiring a plurality of one-dimensional frequency-encoded projections to determine at least one of a three-dimensional position of the probe and an orientation of the probe.

3. The method of claim 1, wherein the at least one imaging parameter is selected from the group consisting of: field of view, image spatial resolution, image scan plane position, scan plane orientation, temporal resolution, bandwidth, slice thickness, imaging pulse sequence, image contrast, TR, TE, active receiver channels, k-space trajectory, excitation flip angle, MR scanner table position (e.g., to keep the probe proximal to an isocenter of the table), and a combination thereof.

4. The method of claim 1, wherein the adjusting is at least partially based on an auxiliary parameter.

5. The method of claim 4, wherein the parameter includes an element selected from the group consisting of: position of the probe relative to a target region of the object, position of the probe relative to the MR imager's receive coils, probe orientation, a physiological parameter associated with the object, and a combination thereof.

6. The method of claim 1, wherein adjusting the at least one parameter is at least partially based on a step function of the probe velocity.

7. The method of claim 6, wherein the step function includes a binary function of the probe velocity.

8. The method of claim 1, wherein adjusting the at least one parameter is at least partially based on a smoothly-varying function of the probe velocity.

9. The method of claim 8, wherein the at least one imaging parameter varies between an upper asymptotic value and a lower asymptotic value.

10. The method of claim 9, wherein at least one of the upper asymptotic value and the lower asymptotic value is at least partially defined based on a hardware constraint of an MRI machine.

11. The method of claim 1, wherein the probe includes a stent.

12. The method of claim 1, wherein the probe includes a catheter.

13. The method of claim 1, wherein the probe includes a tuned resonant circuit having a resonance frequency substantially the same as a resonance frequency of tissue surrounding the probe.

14. The method of claim 1, wherein the object includes an anatomic tissue.

15. The method of claim 14, including using the probe to treat at least a portion of the anatomic tissue as part of the interventional procedure.

16. The method of claim 1, including providing a user interface to a user so the user can adjust at least one of: the at least one imaging parameter, the lower asymptotic value, the upper asymptotic value, the binary function, the smoothly-varying function, and a combination thereof.

17. The method of claim 16, wherein the user interface includes a graphical display for showing the user at least one of the probe and the object.

18. A method of adaptively adjusting at least one MR imaging parameter for an interventional procedure, comprising: a. locating an MR-guided probe inserted into an object by acquiring first probe coordinates with respect to a reference coordinate system; and b. based at least partially on a parameter associated with the located probe, adjusting a subset of the at least one MR imaging parameter to adaptively perform at least one of tracking of the probe and imaging of a target region of the object.

19. The method of claim 18, wherein the adjusting is at least partially based on an auxiliary parameter.

20. The method of claim 19, wherein the auxiliary parameter includes an element selected from the group consisting of: position of the probe relative to a target region of the object, position of the probe relative to the MR imager's receive coils, probe orientation, a physiological parameter associated with the object, and a combination thereof.

21. The method of claim 18, including acquiring second probe coordinates with respect to the reference coordinate system to determine a velocity of the probe.

22. The method of claim 20, wherein the adjusting is at least partially based on the determined velocity of the probe.