Patent ID: 12254404

DETAILED DESCRIPTION OF THE DRAWINGS

The present invention is preferably implemented in a device for treating a patient with ionising radiation.FIG.1depicts a radiotherapy device suitable for delivering, and configured to deliver, a beam of radiation to a patient during radiotherapy treatment. The device and its constituent components will be described generally for the purpose of providing useful accompanying information for the present invention. The device depicted inFIG.1is in accordance with the present disclosure and is suitable for use with the disclosed systems and apparatuses. While the device inFIG.1is an MR-linac, the implementations of the present disclosure may be any radiotherapy device, for example a linac device.

The device10depicted inFIG.1is an MR-linac. The device10comprises both MR imaging apparatus12and radiotherapy (RT) apparatus which may comprise a linac device. The MR imaging apparatus12is shown in cross-section in the diagram. In operation, the MR scanner produces MR images of the patient, and the linac device produces and shapes a beam of radiation and directs it toward a target region within a patient's body in accordance with a radiotherapy treatment plan. The depicted device does not have the usual ‘housing’ which would cover the MR imaging apparatus12and RT apparatus in a commercial setting such as a hospital.

The MR-linac device depicted inFIG.1comprises a source of radiofrequency waves11, a waveguide13, a source of electrons14, a source of radiation14, a collimator15such as a multi-leaf collimator configured to collimate and shape the beam, MR imaging apparatus12, and a patient support surface16. In use, the device would also comprise a housing (not shown) which, together with the ring-shaped gantry, defines a bore. The moveable support surface16can be used to move a patient, or other subject, into the bore when an MR scan and/or when radiotherapy is to commence. The MR imaging apparatus112, RT apparatus, and a subject support surface actuator are communicatively coupled to a controller or processor. The controller is also communicatively coupled to a memory device comprising computer-executable instructions which may be executed by the controller.

The RT apparatus comprises a source of radiation and a radiation detector (not shown). Typically, the radiation detector is positioned diametrically opposed to the radiation source. The radiation detector is suitable for, and configured to, produce radiation intensity data. In particular, the radiation detector is positioned and configured to detect the intensity of radiation which has passed through the subject. The radiation detector may also be described as radiation detecting means, and may form part of a portal imaging system.

The radiation source may comprise a beam generation system. For a linac, the beam generation system may comprise a source of RF energy11, an electron gun14, and a waveguide13. The radiation source is attached to the rotatable gantry17so as to rotate with the gantry17. In this way, the radiation source is rotatable around the patient so that the treatment beam18can be applied from different angles around the gantry17. In a preferred implementation, the gantry is continuously rotatable. In other words, the gantry can be rotated by 360 degrees around the patient, and in fact can continue to be rotated past 360 degrees. The gantry may be ring-shaped. In other words, the gantry may be a ring-gantry.

The source11of radiofrequency waves, such as a magnetron, is configured to produce radiofrequency waves. The source11of radiofrequency waves is coupled to the waveguide13via circulator19, and is configured to pulse radiofrequency waves into the waveguide13. Radiofrequency waves may pass from the source11of radiofrequency waves through an RF input window and into an RF input connecting pipe or tube. A source of electrons14, such as an electron gun, is also coupled to the waveguide13and is configured to inject electrons into the waveguide13. In the electron gun14, electrons are thermionically emitted from a cathode filament as the filament is heated. The temperature of the filament controls the number of electrons injected. The injection of electrons into the waveguide13is synchronised with the pumping of the radiofrequency waves into the waveguide13. The design and operation of the radiofrequency wave source11, electron source and the waveguide13is such that the radiofrequency waves accelerate the electrons to very high energies as the electrons propagate through the waveguide13.

The design of the waveguide13depends on whether the linac accelerates the electrons using a standing wave or travelling wave, though the waveguide typically comprises a series of cells or cavities, each cavity connected by a hole or ‘iris’ through which the electron beam may pass. The cavities are coupled in order that a suitable electric field pattern is produced which accelerates electrons propagating through the waveguide13. As the electrons are accelerated in the waveguide13, the electron beam path is controlled by a suitable arrangement of steering magnets, or steering coils, which surround the waveguide13. The arrangement of steering magnets may comprise, for example, two sets of quadrupole magnets.

Once the electrons have been accelerated, they may pass into a flight tube. The flight tube may be connected to the waveguide by a connecting tube. This connecting tube or connecting structure may be called a drift tube. The electrons travel toward a heavy metal target which may comprise, for example, tungsten. Whilst the electrons travel through the flight tube, an arrangement of focusing magnets act to direct and focus the beam on the target.

To ensure that propagation of the electrons is not impeded as the electron beam travels toward the target, the waveguide13is evacuated using a vacuum system comprising a vacuum pump or an arrangement of vacuum pumps. The pump system is capable of producing ultra-high vacuum (UHV) conditions in the waveguide13and in the flight tube. The vacuum system also ensures UHV conditions in the electron gun. Electrons can be accelerated to speeds approaching the speed of light in the evacuated waveguide13.

The source of radiation is configured to direct a beam18of therapeutic radiation toward a patient positioned on the patient support surface16. The source of radiation may comprise a heavy metal target toward which the high energy electrons exiting the waveguide are directed. When the electrons strike the target, X-rays are produced in a variety of directions. A primary collimator may block X-rays travelling in certain directions and pass only forward travelling X-rays to produce a treatment beam18. The X-rays may be filtered and may pass through one or more ion chambers for dose measuring. The beam can be shaped in various ways by beam-shaping apparatus, for example by using a multi-leaf collimator15, before it passes into the patient as part of radiotherapy treatment.

In some implementations, the source of radiation is configured to emit either an X-ray beam or an electron particle beam. Such implementations allow the device to provide electron beam therapy, i.e. a type of external beam therapy where electrons, rather than X-rays, are directed toward the target region. It is possible to ‘swap’ between a first mode in which X-rays are emitted and a second mode in which electrons are emitted by adjusting the components of the linac. In essence, it is possible to swap between the first and second mode by moving the heavy metal target in or out of the electron beam path and replacing it with a so-called ‘electron window’. The electron window is substantially transparent to electrons and allows electrons to exit the flight tube.

The subject or patient support surface16is configured to move between a first position substantially outside the bore, and a second position substantially inside the bore. In the first position, a patient or subject can mount the patient support surface. The support surface16, and patient, can then be moved inside the bore, to the second position, in order for the patient to be imaged by the MR imaging apparatus12and/or imaged or treated using the RT apparatus. The movement of the patient support surface is effected and controlled by a subject support surface actuator, which may be described as an actuation mechanism. The actuation mechanism is configured to move the subject support surface in a direction parallel to, and defined by, the central axis of the bore. The terms subject and patient are used interchangeably herein such that the subject support surface can also be described as a patient support surface. The subject support surface may also be referred to as a moveable or adjustable couch or table.

The radiotherapy apparatus/device depicted inFIG.1also comprises MR imaging apparatus12. The MR imaging apparatus12is configured to obtain images of a subject positioned, i.e. located, on the subject support surface16. The MR imaging apparatus12may also be referred to as the MR imager. The MR imaging apparatus12may be a conventional MR imaging apparatus operating in a known manner to obtain MR data, for example MR images. The skilled person will appreciate that such a MR imaging apparatus12may comprise a primary magnet, one or more gradient coils, one or more receive coils, and an RF pulse applicator. The operation of the MR imaging apparatus is controlled by the controller.

The controller is a computer, processor, or other processing apparatus. The controller may be formed by several discrete processors; for example, the controller may comprise an MR imaging apparatus processor, which controls the MR imaging apparatus18; an RT apparatus processor, which controls the operation of the RT apparatus; and a subject support surface processor which controls the operation and actuation of the subject support surface. The controller is communicatively coupled to a memory, e.g. a computer readable medium.

The linac device also comprises several other components and systems as will be understood by the skilled person. For example, in order to ensure the linac does not leak radiation, appropriate shielding is also provided.

With reference now toFIG.2, a general method20according to the present invention will be described. At step21, sets of medical image data are received, for example, a stream of medical images of a patient including a target to be treated with radiation therapy. A pre-processing step can be implemented, for example, some form of dimensionality reduction such as PCA, t-SNE, autoencoder; or some classical computer vision method, e.g. resizing, de-texturize, de-colorize, edge enhancement, salient edge map, local phase, flip, rotate, crop, image registration; or some machine learning method, e.g. a convolutional neural network. Further, it may be some classical preprocessing method for that medical imaging modality: bias-field correction, intensity normalization, distortion correction, denoising.

In step22, tracking regions in an image are selected. The selection of regions may be based on a distance based procedure, a segmentation mask procedure, an edge-based procedure, a superpixel procedure, a good features to track procedure, a randomized procedure, or a gridded procedure. InFIG.3, the tracking region selection30is described in more detail. A pre-process is performed in step31, in step32the tracking region selection is started. Preferably, a first set of tracking regions is selected. In step33, it is checked whether the performance is satisfactory or not. If yes, that is the selected set of tracking regions fulfil predetermined conditions, for example, no outliers found in the set, the selected regions are not too many, a score is above a predetermined level, the procedure proceeds to step35where the selection of tracking regions is terminated. On the other hand, if not, then in step33, the procedure proceeds to step34where regions are removed and/or added. For example, regions are removed if they have a low score, are considered as outliers by joint estimation, there are too many regions according to a predetermined criteria, or if the method is running too slow. Regions are added when: estimates are too noisy, too low score, or score maps are not sharply peaked (in which one may sample new regions based on the score).

In step23, the tracking of selected tracking regions is initiated, for example, using a feature extractor. For example, a discriminative correlation filter that optimizes a correlation filter for a given region may be used. By using a discriminate correlation filter as tracking algorithm, a modality agnostic model can be created. Since the filter is trained and learn from its initial image and it's updated online no modality assumptions have to be made. Other choices of tracking methods are of course possible, e.g. the more traditional Lucas-Kanade (1981) method. The tracking may use old images, or intermediate results (e.g. confidence maps) and/or vector fields as additional inputs. Update of the feature extractor can be done at fixed interval or dynamically (when some criteria is met), in either case it need not happen on every iteration. In the preferred embodiment, the feature extractor is a discriminative correlation filter (DCF), e.g. MOSSE, ECO, C-COT, DiMP, ATOM, but other types of correlation/convolution filters are also possible as well as different kinds of handcrafted feature transforms SIFT, RIFT, G-RIF, SURF, GLOH, HOG. Joint estimation could use information from external sources such as ultrasound, respirometry, surface tracking, planar X-ray, CT, 1D MRI, 2D MRI, 3D MRI, camera. Joint estimation could use some form of regularization that could, for example, be handcrafted, a statistical prior, or a learned regularization. The feature extractor could be updated based on the result of joint estimation.

InFIG.4, the tracking procedure40is described in more detail. In step41, a pre-processing of image data captured at one instant or from several different times is performed. In step42, the feature extractor is run to extract matching tracking targets in consecutive images. In step44, matching candidates are provided. The pre-processed image data is also used as training data for the feature extractor in step45, as mentioned above, and to update the feature extractor in step46. A refining process of the matching candidates may be performed in step47and in step48, a conversion to a sparse motion field is performed and in step49the sparse motion field is computed. In a preferred embodiment, the feature tracker uses a discriminative correlation filter (Daneilljan, Martin et al “ECO: Efficient Convolution Operators for Tracking”.Proceeding if the IEEE Conference on Computer Vision and Pattern Recogintion,2017).

Returning now toFIG.2, the procedure continues to step24where the interpolation is performed. With reference toFIG.4, the interpolation is described in more detail. The sparse motion field is input in the interpolation. In step51, it is checked whether image data is also to be used in the interpolation. If no, in step51, the procedure proceeds to step53where a dense motion field or displacement field is estimated, and in step54the dense motion field is computed. On the other hand, if image data is also to be used, the procedure proceeds to step52, where the image data is pre-processed and then forwarded to step53, where the estimation of the dense motion field is performed.

As mentioned above, the interpolation is performed using normalized convolution. The confidence could be binary (1 if tracked, 0 otherwise), or at same locations but real-valued (preferably a value between, or equal to, 0 and 1). Further, it is also possible to use predictions from previous iterations as a prior. Normalized convolution is an example from a more general class of filtering methods, referred to as moving least-squares (Milanfar 2013) although it is known under other names as well. Mathematically, it's a class of non-parametric estimators where the value of an underlying signal z(x) at position x is estimated from the weighted least-squares problem:
{circumflex over (z)}(xj)=arg minz(xj)Σi=1n(yi−z(xi))2K(xi,xj,yi,yj).

Where the kernel (weight) K is a non-negative, unimodal function that is symmetric with respect to i and j. K measures the “similarity” between the samples yiand yj, at respective positions xiand xj. If the kernel function is restricted to be only a function of the spatial locations xiand xj, then the resulting formulation is what is known as (classical, or not data-adaptive) kernel regression in the nonparametric statistics literature. Perhaps more importantly, the key difference between local and nonlocal patch-based methods lies essentially in the definition of the range, n, of the sum above. Specifically, indices covering a small spatial region around a pixel of interest define local methods, and vice versa.

The interpolation step could use a (finite-dimensional) parameterized representation or an infinite-dimensional representation. Results could be predicted directly or could be found by an iterative optimization algorithm. Most conventional optimization methods would apply to the parameterized case while the infinite-dimensional case could be approached using e.g. a variational framework (solving the Euler-Lagrange equation). The interpolation could use any scattered interpolation method, for instance nearest-neighbor interpolation, triangulated irregular network-based natural neighbor, triangulated irregular network-based linear interpolation (a type of piecewise linear function), inverse distance weighting, kriging, gradient-enhanced kriging (GEK), thin plate spline, polyharmonic spline (the thin-plate-spline is a special case of a polyharmonic spline), radial basis function (polyharmonic splines are a special case of radial basis functions with low degree polynomial terms), least-squares spline, or natural neighbor interpolation.

With reference now toFIG.6, a training phase in accordance with the present invention will be described. In accordance with the present invention, a trainable deep learning method using normalized convolutions with constant basis functions is preferably used in the interpolation step as mentioned above. Normalized convolutions extend ordinary convolutions by using a confidence map to improve interpolation of irregularly sampled and/or noisy data. The architecture of the network uses a coarse-to-fine schema which down-sample the features to lower resolutions and propagates the result by up-sampling and concatenating with the finer resolution. Inputs to the network is the sparse displacement field and the confidence map. The network uses a coarse-to-fine schema and down-sample the features using max-pooling based on the confidence feature map.

In the training of the deep learning model, deformation fields or displacements fields are synthesized, giving the deformation sampled at a set of sparse and irregular locations as input, and asking the deep learning model to estimate the dense deformation field. Since most physiological deformations are diffeomorphic, such deformations are synthesized using a geodesic shooting method. The sampling points can, for example, be selected using a randomized procedure. As a pre-processing step in the training procedure, noise can also be added to the synthetic displacement field which are reflected in the confidence map. When estimating the dense deformation field, the uncertainty from the discriminative correlation filter can be used in the confidence map during the interpolation.

In step61, synthetic image data is gathered or generated. In step62, a parameterized model for the deep learning model is created or specified and in step63, a loss function is created or specified. The loss function can be based on the motion field or image similarity, or some property derived from those. Examples include End-point error (EPE), mean-squared error, Huber norm, normalized cross-correlation, mutual information, SSIM, accuracy of propagated segmentation maps ((soft) Dice, cross-entropy). The loss function could itself be part of the training, as in adversarial training.

In step64, the deep learning model is trained and in step65a trained model is provided. In the preferred embodiment the machine learning algorithm is based on supervising learning, but it would be possibly to use any method in the spectrum from supervised to unsupervised learning. This includes semi-supervised learning, weakly supervised learning, transfer learning, adversarial learning, multi-task learning, domain adaptation, meta-learning. It could use reinforcement learning to e.g. select neural network architecture or, more generally, to combine, mix, rearrange the pipeline and its constituents.

FIG.7is a flow chart describing the generation of synthetic data. In step71, a basic or ground dense motion field or displacement field is produced or created describing displacement vectors for a number of points in a data set, each displacement vector specifying a position of a point in reference to a previous position. At step72, a number of positions are sampled and at step73, a dense motion field is extracted based on the sampled positions as input data. At step74, a selection is made whether to include medical image data of a patient into the generation of synthetic data. If yes, the procedure moves to step75, where the displacement filed is applied to the medical image data and further to step76where the result from step75is appended to the input data, i.e. the extracted displacement filed at the sampled positions. Then, at step77, the training data is generated. If no at step74, the procedure proceeds directly to step77. Data can be generated and stored in advance, or it can be generated on-the-fly. Mathematical models for motion fields include LDDMM, Gaussian Processes (GP), physical model (e.g. biomechanical model), physics simulation, video game engine. Realistic motion field can be learned via a learned generative model, e.g. GAN, normalizing flow. Motion fields can be “borrowed” from other applications, where data is more plentiful, e.g. 4D CT, ultrasound; further, such motion fields could be artificially distorted to increase the number of examples, using for instance data augmentation methods.

Noise could be added to any part of the data generation, including the ground truth motion field, the sampled coordinates, and the confidence

With reference toFIG.8, the training of the machine learning model will be described. First, at step81, optimization settings are selected which may include choosing an optimizer, for example: a least-squares method; a derivative-free method such as Nelder-Mead, genetic algorithms, Bayesian optimization; a stochastic gradient descent method such as Adam, RMSProp, AdaGrad; conjugate gradient descent; a quasi-Newton method such as BFGS or L-BFGS; a method for constrained optimization such as SQP or an interior point method; a linear programming method such as a simplex method; a global optimization method such as a branch-and-bound method. The step of selecting optimization settings may further include selecting the loss function (objective function), constraints, batch size, learning rate, initial guess, weight initialization and specifying optimizer hyperparameters such as step-length, trust-region size, various tolerances. At step82, the optimizer is initalized and in step83, sets of training data is collected. In step84, the loss function is estimated and/or the gradient of the loss function is estimated based on the training data. In step85, the parameters of the parameterized model is updated based on the estimated loss function and/or its gradient. At step86, it is checked whether a convergence criterion is met, e.g. maximum number of iterations reached, maximum number of function evaluations reached, maximum number of epochs reached, duality gap small enough, step size small enough, loss function improvement small enough, norm of gradient small enough, running time long enough If no, the procedure returns to step83, where further training data may be collected. If yes in step86, the procedure proceeds to step87where trained parameters are provided.

Turning now toFIG.9, a computer structure or software202in which the methods according to the present invention may be implemented will be described. The computer structure or software202may be included in a radiation therapy system200as shown inFIG.9. As shown inFIG.9, radiation therapy system200may include a computer structure202, a database220, a radiation therapy device130such as a MR linac radiation therapy device, for example, an Elekta Unity® described above with reference toFIG.1. The computer structure202may include hardware and software components to control radiation therapy device130including an image acquisition device140and/or to perform functions or operations such as treatment planning using a treatment planning software and dose planning using computer structure or software202, treatment execution, image acquisition, image processing, motion tracking, motion management, or other tasks involved in a radiation therapy process.

The hardware components of computer structure202may include one or more computers (e.g., general purpose computers, workstations, servers, terminals, portable/mobile devices, etc.); processor devices (e.g., central processing units (CPUs), graphics processing units (GPUs), microprocessors, digital signal processors (DSPs), field programmable gate arrays (FPGAs), special-purpose or specially-designed processors, etc.); memory/storage devices (e.g., read-only memories (ROMs), random access memories (RAMs), flash memories, hard drives, optical disks, solid-state drives (SSDs), etc.); input devices (e.g., keyboards, mice, touch screens, mics, buttons, knobs, trackballs, levers, handles, joysticks, etc.); output devices (e.g., displays, printers, speakers, vibration devices, etc.); or other suitable hardware. The software components of computer structure202may include operation system software, application software, etc. For example, as shown inFIG.9, computer structure202includes an image processing module216, for example, executing pre-processing of image data, a tracking module214including for example, the feature extractor, and an interpolation module212including a deep learning module performing normalized convolutions as described above.

Software212,214and216may include computer readable and executable codes or instructions for performing the processes described in detail in this application. For example, a processor device of computer structure202may be communicatively connected to a memory/storage device storing software212,214, and216to access and execute the codes or instructions. The execution of the codes or instructions may cause the processor device to perform operations to achieve one or more functions consistent with the disclosed embodiments.

The software221,214, and216be configured to execute the methods described herein, for example, the methods described with reference toFIGS.2-8.

Further, computer structure202may be communicatively connected to a database220to access data. In some embodiments, database220may be implemented using local hardware devices, such as one or more hard drives, optical disks, and/or servers that are in the proximity of computer structure202. In some embodiments, database220may be implemented in a data center or a server located remotely with respect to computer structure202. Computer structure202may access data stored in database220through wired or wireless communication.

Database220may include patient data232. Patient data may include information such as (1) imaging data associated with a patient anatomical region, organ, or volume of interest segmentation data (e.g., MRI, CT, X-ray, PET, SPECT, and the like); (2) functional organ modeling data (e.g., serial versus parallel organs, and appropriate dose response models); (3) radiation dosage data (e.g., may include dose-volume histogram (DVH) information); or (4) other clinical information about the patient or course of treatment.

Database220may include machine data224. Machine data224may include information associated with radiation therapy device130, image acquisition device140, or other machines relevant to radiation therapy, such as radiation beam size, arc placement, on/off time duration, radiation treatment plan data, multi-leaf collimator (MLC) configuration, MRI pulse sequence, and the like.

Image acquisition device140may provide medical images of a patient. For example, image acquisition device140may provide one or more of MRI images (e.g., 2D MRI, 3D MRI, 2D streaming MRI, 4D volumetric MRI, 4D cine MRI); Computed Tomography (CT) images; Cone-Beam CT images; Positron Emission Tomography (PET) images; functional MRI images (e.g., fMRI, DCE-MRI, diffusion MRI); X-ray images; fluoroscopic images; ultrasound images; radiotherapy portal images; Single-Photo Emission Computed Tomography (SPECT) images; and the like. Accordingly, image acquisition device140may include an MRI imaging device, a CT imaging device, a PET imaging device, an ultrasound imaging device, a fluoroscopic device, a SPECT imaging device, or other medical imaging devices for obtaining the medical images of the patient.

Various operations or functions are described herein, which may be implemented or defined as software code or instructions. Such content may be directly executable (“object” or “executable” form), source code, or difference code (“delta” or “patch” code). Software implementations of the embodiments described herein may be provided via an article of manufacture with the code or instructions stored thereon, or via a method of operating a communication interface to send data via the communication interface. A machine or computer readable storage medium may cause a machine to perform the functions or operations described, and includes any mechanism that stores information in a form accessible by a machine (e.g., computing device, electronic system, and the like), such as recordable/non-recordable media (e.g., read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory devices, and the like). A communication interface includes any mechanism that interfaces to any of a hardwired, wireless, optical, and the like, medium to communicate to another device, such as a memory bus interface, a processor bus interface, an Internet connection, a disk controller, and the like. The communication interface can be configured by providing configuration parameters and/or sending signals to prepare the communication interface to provide a data signal describing the software content. The communication interface can be accessed via one or more commands or signals sent to the communication interface.

The present disclosure also relates to a system for performing the operations herein. This system may be specially constructed for the required purposes, or it may comprise a general-purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CDROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMS, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions, each coupled to a computer system bus.

The order of execution or performance of the operations in embodiments of the present disclosure illustrated and described herein is not essential, unless otherwise specified. That is, the operations may be performed in any order, unless otherwise specified, and embodiments of the present disclosure may include additional or fewer operations than those disclosed herein. For example, it is contemplated that executing or performing a particular operation before, contemporaneously with, or after another operation is within the scope of aspects of the present disclosure.

Embodiments of the present disclosure may be implemented with computer-executable instructions. The computer-executable instructions may be organized into one or more computer-executable components or modules. Aspects of the present disclosure may be implemented with any number and organization of such components or modules. For example, aspects of the present disclosure are not limited to the specific computer-executable instructions or the specific components or modules illustrated in the figures and described herein. Other embodiments of the present disclosure may include different computer-executable instructions or components having more or less functionality than illustrated and described herein.

When introducing elements of aspects of the present disclosure or the embodiments thereof, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements.

Having described aspects of the present disclosure in detail, it will be apparent that modifications and variations are possible without departing from the scope of aspects of the present disclosure as defined in the appended claims. As various changes could be made in the above constructions, products, and methods without departing from the scope of aspects of the present disclosure, it is intended that all matter contained in the above description and shown in the accompanying drawings shall be interpreted as illustrative and not in a limiting sense.