Ray opaque object positioning device and method

Abstract

A control circuit accesses information about at least one field of view of a particular patient, the field of view containing information about at least one radiopaque object. The control circuitry then processes the information using a plurality of different filters to produce a plurality of filtering fields of view that are different from each other. The filtered fields of view are then fused to produce fused information about the previously mentioned radioopaque object (or objects).

Description

Technical Field

[0001] These teachings generally relate to treating a planned target volume of a patient with energy according to an energy-based treatment plan, and more particularly to taking advantage of the presence of radiopaque objects. Background Art

[0002] The use of energy to treat medical conditions comprises a known area of ​​prior art endeavor. For example, radiation therapy comprises an important component of many treatment plans for reducing or eliminating unwanted tumors. Unfortunately, the energy applied by itself cannot distinguish between the unwanted material and adjacent tissues, organs, etc., which are necessary or even vital for the patient's continued survival. Therefore, energy, such as radiation, is typically applied in a cautious manner to at least attempt to confine the energy to a given target volume. So-called radiation treatment plans typically play a role in the above aspects.

[0003] A radiation treatment plan typically includes specified values ​​for each of a plurality of treatment platform parameters during each of a plurality of consecutive fields. Treatment plans for radiation treatment sessions are typically automatically generated through a so-called optimization process. "Optimization" as used herein should be understood as improving candidate treatment plans without necessarily ensuring that the optimized result is in fact a single best solution. Such optimization typically includes automatically adjusting one or more physical treatment parameters (typically while observing one or more corresponding constraints in these respects) and mathematically calculating possible corresponding treatment outcomes (such as dose levels) to identify a given set of treatment parameters that represents a good compromise between the desired treatment outcome and avoiding undesirable collateral effects.

[0004] Fiducials are sometimes used to help locate a given patient volume. A fiducial refers to a reference marker placed within the image field of view of an x-ray. Fiducials are often used to provide a scaled spatial reference within an image to aid in measurement, alignment, and / or calibration. For example, a fiducial can be a physical marker made of material visible on an x-ray image. Unfortunately, accurately locating a fiducial in a patient image can sometimes be challenging. BRIEF DESCRIPTION OF THE DRAWINGS

[0005] The above needs are at least partially met by providing a radiopaque object positioning apparatus and method as described in the following detailed description, particularly when studied in conjunction with the accompanying drawings, wherein:

[0006] Figure 1 Includes block diagrams configured according to various embodiments of these teachings;

[0007] Figure 2 Includes flow charts configured according to various embodiments of these teachings;

[0008] Figure 3 including an X-ray image configured according to various embodiments of these teachings;

[0009] Figure 4 including a plurality of filtered images configured according to various embodiments of these teachings;

[0010] Figure 5 including a plurality of images configured according to various embodiments of these teachings;

[0011] Figure 6 including two images configured according to various embodiments of these teachings;

[0012] Figure 7 including diagrams configured according to various embodiments of the present invention;

[0013] Figure 8 includes diagrams configured according to various embodiments of these teachings;

[0014] Fig. 9 including diagrams configured according to various embodiments of the present invention;

[0015] Fig.10 including an X-ray image configured according to various embodiments of these teachings;

[0016] Fig.11 including a plurality of filtered images configured according to various embodiments of these teachings;

[0017] Fig.12 including a plurality of images configured according to various embodiments of these teachings;

[0018] Fig.13 including a plurality of images configured according to various embodiments of these teachings;

[0019] Fig.14 includes diagrams configured according to various embodiments of these teachings;

[0020] Fig.15 includes diagrams configured according to various embodiments of these teachings;

[0021] Fig.16 Included are diagrams configured according to various embodiments of these teachings.

[0022] The elements illustrated in the accompanying drawings are illustrated for simplicity and clarity, and are not necessarily drawn to scale. For example, the size and / or relative positioning of some elements in the accompanying drawings may be exaggerated relative to other elements to help improve the understanding of the various embodiments of this teaching. In addition, common but easy-to-understand elements that are useful or necessary in commercially feasible embodiments are not usually depicted, so as to facilitate the observation of these various embodiments of this teaching with less obstruction. Some actions and / or steps can be described or depicted in a specific order of occurrence, and those skilled in the art will understand that this particularity about the order is not actually necessary. The terms and expressions used herein have common to technical meanings, such as those skilled in the art as described above to give these terms and expressions, unless otherwise different specific meanings are given in this article. Unless otherwise clearly stated, the word "or" used herein should be interpreted as having a separation structure rather than a connection structure. DETAILED DESCRIPTION

[0023] In general, in accordance with these various embodiments, control circuitry may access information about at least one field of view of a particular patient, the field of view containing information about at least one radiopaque object. The control circuitry may then process the information using a plurality of different filters to produce a plurality of filtered fields of view that are different from one another. The filtered fields of view may then be fused to produce fused information about the aforementioned radiopaque object (or objects).

[0024] By one approach, the aforementioned information about at least one field of view includes X-ray vision. However, these teachings will be readily adaptable to other imaging modalities.

[0025] By one approach, the aforementioned information about at least one field of view includes only a single image (eg, a single X-ray image). However, if desired, additional images (including images from dissimilar imaging modalities) may be accessed and processed.

[0026] By one approach, the aforementioned radiopaque objects include fiducials. Such fiducials may be implanted in a particular patient, although these teachings will readily adapt to other placement methods. By another approach, in lieu of or in combination with the aforementioned methods, the aforementioned radiopaque objects may include patient structures such as bones.

[0027] When the information about at least one field of view includes one or more images, the plurality of different filters mentioned above may include one or more image filters as desired. These teachings are highly flexible in practice and will accommodate the use of any of a variety of image filters. Some examples include, but are certainly not limited to, contour detection filters, bandpass filters, adaptive threshold filters, fiducial structure enhancement filters, maximum stable extrema filters, and / or homogeneity filters.

[0028] These teachings will also accommodate any of a variety of fusion methods.By one approach, the aforementioned fusion of multiple filtered fields produces a probability-based likelihood image that, for example, identifies the most likely location of at least one radio-opaque object.

[0029] This resulting information may then be used, for example, to facilitate accurate optimization of a radiation treatment plan for a specific patient and / or to set up a radiation treatment platform to administer a corresponding treatment plan, wherein the latter is based on accurate positioning of the aforementioned radiopaque object(s).

[0030] So configured, these teachings can improve the robustness of target detection. In particular, this can lead to more robust tracking of structures in X-ray imaging systems, such as kV or MV detector platforms.

[0031] These and other advantages will become more apparent upon a thorough review and examination of the following detailed description. Figure 1 , an illustrative device 100 compatible with many of these teachings will first be presented.

[0032] In this particular example, the enabling device 100 includes a control circuit 101. As a "circuit", the control circuit 101 thus includes a structure that includes at least one (typically multiple) conductive paths (such as paths composed of conductive metals (such as copper or silver)) that carry electrical power in an orderly manner, and the path(s) will typically also include corresponding electrical components (including passive components (such as resistors and capacitors) and active components (such as any of a variety of semiconductor-based devices) as appropriate) to permit the circuit to affect the control aspects of these teachings.

[0033] Such control circuit 101 may include a fixed-purpose hardwired hardware platform (including but not limited to an application-specific integrated circuit (ASIC) (which is an integrated circuit customized for a specific purpose rather than general purpose), a field programmable gate array (FPGA), etc.), or may include a partially or fully programmable hardware platform (including but not limited to a microcontroller, a microprocessor, etc.). The architectural options for these structures are well known and understood in the art and therefore do not need to be further described herein. The control circuit 101 is configured to perform one or more steps, actions, and / or functions described herein (e.g., by using corresponding programming that will be fully understood by those skilled in the art).

[0034] The control circuit 101 is operably coupled to a memory 102. The memory 102 may be integrated into the control circuit 101, or may be physically separate (in whole or in part) from the control circuit 101 as desired. The memory 102 may also be located locally relative to the control circuit 101 (e.g., where the two share a common circuit board, chassis, power supply, and / or housing), or may be remotely located in part or in whole relative to the control circuit 101 (e.g., where the memory 102 is physically located in another facility, metropolitan area, or even country than the control circuit 101).

[0035] In addition to information such as optimization information for a particular patient, information about a particular radiation treatment platform as described herein, information about one or more fields of view for a particular patient as described herein, and / or information about a plurality of different filters as described herein, the memory 102 may also be used to, for example, non-transitory store computer instructions that, when executed by the control circuit 101, cause the control circuit 101 to act as described herein. (As used herein, references to "non-transitory" should be understood to refer to the non-transitory state of the stored content (thus excluding the case where the stored content merely constitutes a signal or wave) rather than the volatility of the storage medium itself, thus including non-volatile memory (such as read-only memory (ROM)) as well as volatile memory (such as dynamic random access memory (DRAM).)

[0036] By an optional approach, the control circuit 101 is also operatively coupled to a user interface 103. The user interface 103 may include any of a variety of user input mechanisms (such as but not limited to a keyboard and keypad, a cursor control device, a touch display, a voice identification interface, a gesture identification interface, etc.) and / or user output mechanisms (such as but not limited to a visual display, an audio sensor, a printer, etc.) to receive information and / or instructions from a user and / or provide information to a user.

[0037] If desired, the control circuit 101 may also be operably coupled to a network interface (not shown). So configured, the control circuit 101 may communicate with other components (internal and external to the device 100) via the network interface. Network interfaces (including wireless and non-wireless platforms) are well known in the art and need not be specifically described here.

[0038] By one approach, a computed tomography device 106 and / or other imaging device 107 known in the art may acquire some or all of any desired patient-related imaging information.

[0039] In one illustrative example, the control circuit 101 is configured to ultimately output an energy-based optimized treatment plan (e.g., such as the optimized radiation treatment plan 113). The energy-based treatment plan generally includes specified values ​​for each of a variety of process platform parameters during each exposure field in a plurality of consecutive exposure fields. In this case, the energy-based treatment plan is generated by an optimization process, an example of which is further provided herein.

[0040] By one approach, the control circuit 101 may be operably coupled to an energy-based treatment platform 114 configured to deliver therapeutic energy 112 to a tissue having at least one treatment volume 105 and one or more organs at risk (at least one of the tissues) according to an energy-based optimized treatment plan 113. Figure 1 104) of a corresponding patient 104 (represented by the first to Nth organs at risk 108 and 109). These teachings are generally applicable to a variety of energy-based treatment platforms / devices. In a typical application setting, the energy-based treatment platform 114 will include an energy source, such as a radiation source 115 of ionizing radiation 116.

[0041] By one approach, the radiation source 115 can be selectively moved along an arcuate path via a gantry (wherein the path at least to some extent contains the patient itself during treatment). The arcuate path can include a complete or nearly complete circle as desired. By one approach, the control circuit 101 controls the movement of the radiation source 115 along the arcuate path and can accordingly control when the radiation source 115 starts moving, stops moving, speeds up, slows down, and / or the speed at which the radiation source 115 travels along the arcuate path.

[0042] As an illustrative example, radiation source 115 may include, for example, an X-ray source based on a radio frequency (RF) linear particle accelerator (linac-based). A linac is a particle accelerator that greatly increases the kinetic energy of charged subatomic particles or ions by subjecting the charged particles to a series of oscillating electric potentials along a linear beam line, which can be used to generate ionizing radiation (e.g., X-rays) 116 and high-energy electrons.

[0043] A typical energy-based treatment platform 114 may also include one or more support devices 110 (e.g., a couch) to support the patient 104 during a treatment session, one or more patient fixation devices 111, a gantry or other movable mechanism to permit selective movement of the radiation source 115, and one or more energy shaping devices (e.g., a beam shaping device 117, such as a secondary collimator (jaw), a multi-leaf collimator, and the like) to provide selective energy shaping and / or energy modulation as needed.

[0044] In a typical application setting, it is assumed herein that the patient support device 110 can be selectively controlled by the control circuit 101 to move in any direction (i.e., any X, Y, or Z direction) during an energy-based treatment session. Because the aforementioned elements and systems are well known in the art, further detailed description of these aspects is not provided herein unless relevant to the description.

[0045] Reference now Figure 2 , a process 200 will be described that can be performed, for example, in conjunction with the above-mentioned application settings (more specifically, via the aforementioned control circuit 101). In general, the process 200 can be used to facilitate the generation and / or delivery of an optimized radiation treatment plan 113, thereby facilitating the use of a specific radiation treatment platform to treat a specific patient with therapeutic shots according to the optimized radiation treatment plan and / or appropriately setting up such a platform during treatment to facilitate accurate implementation of such a plan.

[0046] At block 201 , the process 200 provides accessing information 202 about at least one field of view for a particular patient, the field of view containing information about at least one radiopaque object.

[0047] For many application settings, this information may include X-ray images. However, these teachings will be applicable to images produced by other imaging modalities.

[0048] In many cases, the foregoing information 202 may include only a single image, such as a single X-ray image. However, if desired, these teachings will accommodate access to multiple images (e.g., X-ray images of a patient's region of interest from different fields of view, such as 90 degrees apart from each other). When multiple images are used, those images may or may not (as desired) share a common image capture mode.

[0049] These teachings will accommodate any number of radio-opaque objects, ranging from one. That is, in many application settings, the number of fiducials will be relatively small, such as from one to five or so. When multiple radio-opaque objects are involved, the objects may be identical to one another, or at least some of the objects may be different from the other objects as desired.

[0050] These teachings will also accommodate a variety of radiopaque objects. By one approach, for example, a radiopaque object can include an anatomical structure that includes a portion of a particular patient, such as a bone. By another approach, in lieu of or in combination with the foregoing approach, a radiopaque object can include a fiducial. The present teachings will accommodate the use of implanted fiducials as well as non-implanted fiducials.

[0051] At block 203, control circuit 101 processes the aforementioned accessed information 202 about at least one field of view of a particular patient using a plurality of different filters. When information 202 includes one or more images, one or more (including all, if desired) of the filters may include image filters. These teachings will accommodate any of a variety of such filters. Some useful examples include, but are not limited to:

[0052] Contour detection filter;

[0053] Bandpass filter;

[0054] Adaptive threshold filter;

[0055] Fiducial point structure enhancement filter;

[0056] Maximum stable extremum region filter;

[0057] Homogeneity filter.

[0058] By one approach, the foregoing processing produces a plurality of filtered fields of view that are different from one another. For example, when information 202 includes a single X-ray image depicting three fiducials, the filtered fields of view will include different filtered versions of the single image depicting the three fiducials. In this case, each of these images, although they may differ slightly in appearance from one another, each share the same field of view.

[0059] At block 204, the control circuit 101 then fuses the aforementioned multiple filtered views to generate fused information, for example, regarding at least one radio-opaque object. By one approach, the fused information includes a probability-based likelihood image that, for example, identifies the most likely locations of one or more radio-opaque objects.

[0060] So configured, these teachings can provide a multi-cue probabilistic representation by fusing available target candidates from several different image processing filters into a common framework from which the final target location can be more easily extracted. The multi-cue probabilistic fusion can be additive in that the new information from the additional imaging filters can simply be integrated as new weighted evidence. Thus, the disclosed method is highly flexible. In at least some cases, adding additional imaging filters / cues can help make the overall detection estimate more robust by increasing the likelihood of detecting the correct target location while also tending to suppress false detections (such as false positive or false negative responses).

[0061] Now some more specific examples consistent with the present teaching will be presented. It should be understood that the specific details of these examples are intended to serve as an illustration and are not intended to impose any particular limitations on these teachings.

[0062] When formulating the sequential target tracking problem in a Bayesian framework, where the hidden (estimated) target state is [x t ; t∈l:T} and the observation set is {z t ; t∈l:T} (image frame), there are usually two steps performed recursively.

[0063] First, the prediction step

[0064] p(x t |z 1:t-1 )=∫p(x t |x t-1 )p(x t-1 |z 1：t-1 )dx

[0065] Where p(x t-1 |z 1:t-1 ) is the sum of the previous step t-1 and p(x t |x t-1 ) predicts the posterior probability estimate of the (motion) model estimate.

[0066] Next, the observation / correction step

[0067]

[0068] Where p(z t |x t ) denotes the observed model likelihood, and p(z t |z 1:t-1 ) is the normalization factor.

[0069] In the case where no motion model is available and the predicted object state at the current step t is uniform (object detection only), the current posterior estimate reduces to:

[0070] p(xt |z 1：t )=p(x t |z t )∝p(z t |x t )

[0071] Then, the state estimate can be found:

[0072]

[0073] When multiple image filters or cues are processed, the composite observation model likelihood can be described as:

[0074]

[0075] in is the weight of the kth clue, If there is no online adaptation, each image cue can also be assigned a constant

[0076] We will now present two examples of multi-cue fusion methods, specifically image processing and cue fusion to obtain the observation model p(z t |x t ). The two examples differ from each other in terms of patient anatomy and fiducial type, the number of image filtering cue types and their fusion, and the individual cue calculation method.

[0077] This first example involves a patient's prostate, arbitrarily shaped fiducials, six imaging filter cues, and a single cue likelihood calculation based on reference template cross-correlation scores.

[0078] Figure 3 An initial image comprising an X-ray kV projection image 300 of a pelvis case study including three fiducial point surrogates 301 is presented. Figure 4 Six filtered images 400 of the aforementioned image 300 are presented in sequence. The six filters employed in this example are a contour detection filter, a bandpass filter, an adaptive threshold filter, a fiducial structure enhancement filter, a maximum stable extrema filter, and a homogeneity filter (from top left to bottom right).

[0079] Figure 5 Six corresponding images 500 are presented, which depict the single-cue observation likelihood based on the cross-correlation score. The reference template of the upper left fiducial (the fiducial image extracted by the planning computed tomography scan) is cross-correlated with the output corresponding to the different imaging filters mentioned above. In this example, the corresponding cross-correlation score observation likelihood map is calculated for each individual fiducial separately.

[0080] Reference now Figure 6, the combined multi-cue observation likelihood based on the cross-correlation scores is presented in the left image 601. The likelihood is highest at the actual fiducial target location due to the individual cue voting contributions. Different image filters along the anatomical structure and background reduce spurious responses because spurious responses and votes along the same false structure are less likely to occur. Appearing on the right is image 602 depicting the final fiducial detection.

[0081] Figure 7-Figure 9 Figure 2. The confusion matrix performance corresponding to the previous example, where Figure 7 False positive rate information 700 is presented, Figure 8 False negative rate information 800 is presented, and Fig. 9 Accuracy information at different truth detection tolerances is presented 900. It can be seen that the overall detection performance / robustness is significantly higher when multiple image filters / cues (solid line) are fused instead of a single image filter / cue (bandpass filter) (dashed line).

[0082] The next example involves a patient's lungs, assuming the use of embolic coil fiducials, utilizing three imaging filter cues, and a single cue likelihood calculation employing bounding box occupancy likelihood.

[0083] Fig.10 An initial starting point X-ray kV projection image 1000 is presented, depicting the patient's lungs and four fiducials 1001 . Fig.11 The corresponding filtered images 1100 are presented in sequence, including, from left to right, a blob detection filtered image, a contour detection filtered image, and a homogeneity / morphology filtered image.

[0084] Fig.12 A single-cue observation likelihood image 1200 based on bounding box occupancy is presented. In some lung shadows, there may be isolated spurious (e.g., due to contour detection in the middle) or missing candidates due to noise or false detection of a single image processing cue (see homogeneity / morphology filter on the right). In addition, bounding box candidates may also have different sizes because they are extracted via different underlying image features.

[0085] Fig.13 The combined multi-cue observation likelihood image 1301 based on bounding box occupancy is presented. The likelihood is highest at the actual fiducial target location due to the individual cue voting contributions. Different image filter spurious responses along anatomical structures and background are reduced due to the smaller spurious responses and voting likelihood along the same false structure. The final fiducial detection is presented in the image 1302 on the right.

[0086] Figure 14-16 Confusion matrix performance information related to the preceding examples is presented. Fig.14Figure 1400 in Figure 1400 presents the false positive rate performance, Fig.15 Figure 1500 shows the false negative rate performance, while Fig.16 1600 in presents the accuracy performance at different truth detection tolerances. The overall detection performance / robustness is significantly higher when multiple image filters / cues (solid line) are fused compared to a single image filter / cue (blob detection) (dashed line).

[0087] Those skilled in the art will appreciate that various modifications, changes and combinations may be made to the above-described embodiments without departing from the scope of the present invention, and such modifications, changes and combinations should be considered to be within the scope of the inventive concept.

Claims

1. A method comprising: Through the control circuit: accessing information about at least one field of view for a specific patient, the field of view containing information about at least one radio-opaque object; processing the information about the at least one field of view using a plurality of different filters to produce a plurality of filtered fields of view that are different from one another; The plurality of filtered fields of view are fused to generate fused information about the at least one radio-opaque object. 2 . The method of claim 1 , wherein the information about the at least one field of view comprises an X-ray image.

3. The method of claim 2, wherein the information about the at least one field of view comprises only a single X-ray image. The method of claim 1 , wherein the at least one radiopaque object comprises a fiducial. The method of claim 4 , wherein the fiducials comprise implanted fiducials. The method of claim 1 , wherein the plurality of different filters comprises at least one image filter. The method of claim 6 , wherein each filter of the plurality of different filters comprises an image filter.

8. The method of claim 6, wherein the plurality of different filters comprises at least one of: Contour detection filter; Bandpass filter; Adaptive threshold filter; Fiducial point structure enhancement filter; Maximum stable extremum region filter; Homogeneity filter.

9. The method of claim 1, wherein the fused information regarding the at least one radio-opaque object comprises a probability-based likelihood image.

10. The method of claim 9, wherein the probability-based likelihood image identifies a most likely location of the at least one radio-opaque object.

11. An apparatus comprising: A control circuit, the control circuit being configured to: accessing information about at least one field of view for a specific patient, the field of view containing information about at least one radio-opaque object; processing the information about the at least one field of view using a plurality of different filters to produce a plurality of filtered fields of view that are different from one another; The plurality of filtered fields of view are fused to generate fused information about the at least one radio-opaque object.

12. The apparatus of claim 11, wherein the information about the at least one field of view comprises an X-ray image.

13. The apparatus of claim 12, wherein the information about the at least one field of view comprises only a single X-ray image.

14. The apparatus of claim 11, wherein the at least one radiopaque object comprises a fiducial.

15. The device of claim 14, wherein the fiducials comprise implanted fiducials.

16. The apparatus of claim 11, wherein the plurality of different filters comprises at least one image filter.

17. The apparatus of claim 16, wherein each filter of the plurality of different filters comprises an image filter.

18. The apparatus of claim 16, wherein the plurality of different filters comprises at least one of: Contour detection filter; Bandpass filter; Adaptive threshold filter; Fiducial point structure enhancement filter; Maximum stable extremum region filter; Homogeneity filter.

19. The apparatus of claim 11, wherein the fused information regarding the at least one radio-opaque object comprises a probability-based likelihood image.

20. The apparatus of claim 19, wherein the probability-based likelihood image identifies a most likely location of the at least one radio-opaque object.

Images