Patent Publication Number: US-2016220844-A1

Title: Radiography imaging parameter selection based on extant patient information

Description:
TECHNICAL FIELD 
     Embodiments of the present disclosure relate generally to imaging devices, and more particularly, to radiographic imaging devices. 
     BACKGROUND 
     Radiation therapy (RT) is a popular and efficient method for cancer treatment, where ionizing radiation is used in an attempt to kill malignant tumor cells or to slow down their growth. RT is often combined with surgery, chemotherapy, or hormone therapy, but may also be used as a primary therapy mode. Radiation therapy may be administered as internal RT or brachytherapy or, more commonly, external beam RT. 
     Internal RT treatment typically includes placing one or more radioactive sources near a designated treatment area, either permanently or temporarily. Conversely, external beam RT typically involves directing radiation beams produced by sources located externally with respect to the patient or radiation subject to the afflicted treatment area. The beam can consist of photons, electrons, protons or other heavy ions; photons being (at present) the most commonly used particle type. Malignant cells are damaged by the ionizing radiation used during the RT. However, the damage from the radiation is not limited to malignant cells and thus, the dosage of radiation to healthy tissues outside the treatment volume is ideally minimized to avoid those tissues being similarly damaged. 
     The purpose of traditional RT treatment planning methodologies is to devise a treatment regimen that produces as uniform a dose distribution as possible to the target volumes whilst minimizing the dosage outside this volume. Often, an imaging device such as an X-ray device, Computer Tomography (CT), cone-beam computed tomography (CBCT), or Magnetic Resonance Imaging (MRI) device is used to generate one or more initial scans or images of the area of interest (e.g., pre-treatment planning images). Typically, once an image has been acquired, critical structures (e.g., regions or organs) disposed in the target area are specifically identified so that treatment may be optimally directed. 
     Typically, immediately prior to a treatment session-but following a pre-treatment planning imaging session-a patient is imaged to confirm patient positioning and provide verification of the location of regions-of-interest for treatment. It is crucial to successful radiation therapy that the discrepancies between dose distributions calculated at the treatment planning stage and those delivered to the patient are minimized. In particular, CBCT integrated with a radiotherapy device can be used for patient positioning and verification in IGRT, especially immediately prior to performing radiation therapy session. 
     Conventionally, a practitioner must select appropriate source parameters (e.g., source voltage and current, scan trajectory) and image capture parameters (e.g., frame rate, dynamic gain) based on the practitioner&#39;s experience and knowledge of the particular patient history. While leveraging human expertise, such an approach is also prone to human error, inconsistency between different practitioners, and a failure to utilize all available information regarding the patient. 
     SUMMARY 
     Imager source settings (e.g., radiographic device settings) and image capture settings are ideally optimized for the patient positioning procedure, in order to both acquire a high quality image and at the same time minimize the radiation dose to the patient. Accordingly, embodiments of the present disclosure utilize extant patient-specific information—for example, patient imaging derived from a pre-treatment planning CT scan—in order to automatically generate one or more imaging parameters to be used for imaging the patient just prior to a radiation treatment, e.g., with a kV/MV x-ray imaging system. 
     This Summary is provided to introduce a selection of concepts in a simplified form that is further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. 
     According to embodiments of the present disclosure, a system and method are provided for automatic radiography imaging parameter generation by use of extant patient information. The patient information is categorized, and one or more radiography imaging parameters of a radiographic device are selected and processed according to a knowledge-based model applied to a selection of at least one patient information category. A value for the selected imaging parameter(s) is output. Values for several imaging parameters may be automatically generated according to the processing and optimization. Alternatively, a value, or a range of values, may be suggested for presentation to a clinician. The processing and optimization of the radiography imaging parameters may be based on a subset of the patient information. 
     More specifically, an aspect of the present disclosure includes a method of generating one or more parameters for a radiography image of a patient. The method includes a step of receiving patient information pertaining to a radiography image for a patient. At least one imaging category is determined, based upon the received patient information. From a number of knowledge-based models, one or more knowledge-based models are selected, the selection made according to the imaging category. From a number of radiography imaging parameters, one or more radiography imaging parameters are selected, the selection made according to the imaging category. The selected radiography imaging parameters are processed according to one or more knowledge-based models for optimization of the selected radiography imaging parameters, and a value of the selected imaging parameter(s) is generated as an output. 
     The foregoing is a summary and thus contains, by necessity, simplifications, generalizations and omissions of detail; consequently, those skilled in the art will appreciate that the summary is illustrative only and is not intended to be in any way limiting. Other aspects, inventive features, and advantages of the present invention, as defined solely by the claims, will become apparent in the non-limiting detailed description set forth below. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
       The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the invention: 
         FIG. 1  depicts an exemplary radiation system, in accordance with an embodiment of the present disclosure. 
         FIG. 2  depicts a functional block diagram illustrating an exemplary configuration of a radiography imaging parameter generation system in accordance with an embodiment of the present disclosure. 
         FIG. 3  depicts a flowchart of a method of generating an imaging parameter for a radiography imaging device, in accordance with an embodiment of the present disclosure. 
         FIG. 4  depicts a flowchart of a method of generating an imaging parameter for a radiography imaging device, including optional consideration of a subset of patient data and/or manual selection of a knowledge-based model, in accordance with an embodiment of the present disclosure. 
         FIG. 5  depicts an exemplary computing environment, in accordance with embodiments of the present disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made in detail to several embodiments. While the subject matter will be described in conjunction with the alternative embodiments, it will be understood that they are not intended to limit the claimed subject matter to these embodiments. On the contrary, the claimed subject matter is intended to cover alternative, modifications, and equivalents, which may be included within the spirit and scope of the claimed subject matter as defined by the appended claims. 
     Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. However, it will be recognized by one skilled in the art that embodiments may be practiced without these specific details or with equivalents thereof. In other instances, well-known methods, procedures, and components, have not been described in detail as not to unnecessarily obscure aspects and features of the subject matter. 
     Portions of the detailed description that follows are presented and discussed in terms of a method. Although steps and sequencing thereof are disclosed in figures herein (e.g.,  FIGS. 3 and 4 ) describing the operations of this method, such steps and sequencing are exemplary. Embodiments are well suited to performing various other steps or variations of the steps recited in the flowchart of the figure herein, and in a sequence other than that depicted and described herein. 
     Embodiments described herein may be discussed in the general context of computer-executable instructions residing on some form of computer-usable medium, such as program modules, executed by one or more computers or other computing devices. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or distributed as desired in various embodiments. 
     By way of example, and not limitation, computer-usable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read only memory (ROM), electrically erasable programmable ROM (EEPROM), flash memory or other memory technology, compact disk ROM (CD-ROM), digital versatile disks (DVDs) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information. 
     Communication media can embody computer-readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer-readable media. 
     As used herein, the term “image” or “medical image” may be used interchangeably and refers to an image created by an imaging system, which includes but is not limited to x-ray radiography, X-ray computed tomography (CT) imaging, cone-beam computed tomography (CBCT) imaging, magnetic resonance imaging (MRI), positron emission tomography (PET) imaging, single photon emission computed tomography (SPECT) imaging, and ultrasound (US) imaging. A medical image can be either a 2 D image or a 3D image. 
     In the following embodiments, a technique to automatically generate radiography imaging parameters for a patient is described. Embodiments include a method for generating one or more radiography imaging parameters, based on extant patient information and in accordance with a knowledge-based model. 
     Exemplary Radiation Treatment and Imaging Machine 
     With reference now to  FIG. 1 , an illustration of an exemplary radiation therapy and imaging device  100  is depicted, in accordance with one embodiment of the present disclosure. In one configuration, radiation therapy and imaging device  100  includes a support structure (e.g., gantry  101 ), and a radiation source (e.g., a medical or clinical linear accelerator) including a treatment head  103 . In one embodiment, the treatment head  103  is adapted to receive a primary electron beam (e.g., from an electron gun), which upon incidence with a target material (commonly consisting of a high-Z metal) will produce what are referred to as “bremsstrahlung photons,” and thereby generate a photon beam, ultimately emanating from a treatment source  105 . 
     In further embodiments, a plurality of robotic arms (e.g., robotic arms  107 ,  108  and  109 ) may be attached to the gantry and coupled to a second (diagnostic) radiation source  111 , and an area detector panel  113  (typically comprising a grid of single point detectors). According to some embodiments, an additional arm may be attached to a portal area detector panel  118  for receiving radiation from the treatment source  105 . In still further embodiments, the device  100  may also include a patient couch  115 . The couch  115  in an embodiment is a robotic couch, able to move in order to change a position of a patient on the couch. The radiation therapy and imaging device  100  may further include a communicatively coupled computing device  117  for processing images and/or controlling and manipulating the device  100 . According to some embodiments, radiation emanating from the treatment source  105  and received in the portal area detector panel  118  may operate in the range of MV radiation (mega-voltage radiation), whereas radiation distributed from the second radiation source  111  and received in the area detector panel  113  may operate in the range of kV radiation (kilo-voltage). 
     In one embodiment, the end of gantry  101  positioned above patient couch  115  is attached to a treatment source  105  used to generate radiation (e.g., for therapeutic or imaging purposes). A patient is positioned (typically supine) on patient couch  115 . A target volume (generally disposed within or about the patient subject) is irradiated by transmitting a radiation beam (e.g., X-ray photon beam) through the target volume and receiving the beam in the area detector panel  113  from source  111  or detector panel  118  from source  105 . 
     According to one embodiment, an image of the target volume can be acquired using either the diagnostic source  111  or therapeutic radiation source  105  in conjunction with either the area detector  113  or area detector  118 , respectively. In alternate embodiments, x-ray detectors and radiation sources may be used for diagnostic and other applications that are not related to the radiotherapy field. In an embodiment, data corresponding to radiation energy received for each radiographic image are stored by computing device  117 . In a further embodiment a memory comprised by computing device  117  is substantially radiation-hardened, using substrates and/or logical methods known to those skilled in the art. 
     In other embodiments, the system may not include a radiation therapy device. Instead, the system may consist of a radiation source and an imager. For example, in other embodiments, the system may include an x-ray source and a flat panel detector. In further embodiments, the source and imager may or may not be mounted on a rotational gantry (e.g., rotational gantry for CBCT). While embodiments are described herein to comprise the generation and usage of X-ray (photon) beams, it is to be understood that alternate embodiments are well suited for the generation and use of other particles and corresponding particle beams. These alternate embodiments may include, but are not limited to, electrons and electron beams; protons and proton beams; and ions and ion beams. 
     According to embodiments of the present disclosure, radiography imaging parameters are generated for imaging that is performed for registration (that is, positioning) of the patient, just prior to a treatment session. Imaging parameters may be suggested, or preselected, according to a determined optimization. Patient information may be categorized in order to direct the selection of radiography imaging parameters for optimization. For example, patient information about the weight of the patient, materials present (such as metal), what is to be optimized—e.g., optimization of dose for a child, where induced cancer is a danger, versus image contrast for soft tissue (which requires greater radiation dose)—may all be factored into imaging parameter generation. The registration of the patient may be established according to optimization of regions of interest—e.g., if a target area is of a bony structure, imaging performed may be optimized for locating bony structures only (lower radiation dose required, since higher contrast for bony structures). Or, if the target area is of soft tissue, greater radiation dose may be necessary. 
     According to embodiments of the present disclosure, certain radiography imaging parameters of the device  100  may be automatically calibrated (or alternatively, suggested) to produce or emphasize certain qualities of generated images. These parameters may include, for example, modifying the intensity (e.g., source voltage and/or source current) or geometry (e.g., angle or trajectory) of the photon beam, and/or position of area detector  113  or area detector  118 . A geometric image acquisition parameter may include a source and/or detector position, orientation, and/or trajectory, a scan trajectory, a fan type selection (e.g., half- or full-fan), and a position of couch  115 . Additionally, a beam pulse length of the radiation source may be controlled, as well as a focus mode. In one aspect, an imaging and/or matching mode can be automatically selected, or alternatively, suggested. Imaging parameters may encompass, for example: determination of capturing a static volume or a sequence of multiple volumes (e.g., 4D-CBCT); a Digital tomosynthesis (DTS) setting; a CBCT setting; determination of a gating setting (e.g., synchronization of imaging with a phase of patient anatomy movement); and an image reconstruction parameter (e.g., image slice thickness), as a non-exhaustive list. 
     Image acquisition parameters may include, for example: detector frame rate; filter and bowtie selection; settings for collimator blades, and/or a multi-leaf collimator (MLC); detector settings such as gain, dark field, and binning. Other image processing parameters known to those of skill in the art are consistent with the spirit and scope of the present disclosure. Further, according to embodiments of the present disclosure, control of one or more functions of an external device (or devices) in communication with the imaging device. Exemplary external device functionality includes: gating; a surrogate; a motion management device; and an external positioning device. The manner of automatic generation for these and other radiography imaging parameters is described further herein, especially in the description of  FIGS. 2-5 . 
     In one aspect, embodiments of the present disclosure employ all of the patient data from pre-treatment steps in order to optimize radiography imaging parameters for a patient positioning image capture. The radiography imaging parameters may include both source parameters (e.g., source voltage) and image capture parameters (e.g., frame rate). To “optimize” may entail optimization of one or more imaging parameters, depending on the nature of the patient case and what the desired outcome may be. 
     Referring now to  FIG. 2 , a functional block diagram depicts an exemplary configuration of a radiography imaging parameter generation system  200  in accordance with an embodiment of the present disclosure. The radiography imaging parameter generation system  200  includes a data processing component  210  that implements one or more knowledge-based model(s)  220 , a selection module  225 , and an optimization module  230 . The radiography imaging parameter generation system  200  may optionally further include an input interface  235  and output interface  240 . The system  200  in whole or in part may be implemented as a software program, hardware logic, or a combination thereof. 
     Knowledge-based models  220  are able to include expert systems and/or physical models. An expert system has knowledge of the particular patient case, and depending on the case, the system proposes pre-set parameters. For example, an expert system can be implemented as a database of different cases, and for each case, unique parameters are specified. Each case in the database can included image pixel data, the image pixel data including anatomical information such as contours of anatomical structures, bone diameters, an amount of water present, fat tissue present, size of lung tissue, a tumor contour, etc. Typically an expert system exists at the subsystem level—that is, a metal expert for a particular metal exists (e.g., parameters chosen to optimize imaging of metal), and for a patient having a metal implant the metal expert for the corresponding metal implant material may be used. Likewise, experts exist for varying types of soft-tissue, for bony tissue, etc. Different combinations of expert models can together select the imaging parameters for a present case. 
     In contrast, a physical model possesses knowledge about the particular materials present in imaging volume (e.g., an amount of bone, an amount of metal, an amount of soft-tissue identified in the image pixel data of the case). Based on the information regarding the materials in the imaging volume, one or more physical models can be applied in order to determine a radiography imaging parameter (e.g., a source voltage). 
     Employing a combination of expert and physical model protocols is possible. In operation, extant patient data  205  (that is, patient data that exists prior to the positioning imaging session) is provided to the radiography imaging parameter generation system  200 , preferably via the input interface  235 . The input patient data  205  may contain any combination of parameters that can practically affect the radiography imaging and may be organized as a vector or other data structure. The patient data  205  is able to include various kinds of information, such as: a patient record (e.g., age, disease type); patient measurements (e.g., weight, height, body outline from an optical system measurement); a radiation therapy plan; pre-treatment planning CT data; a body region for treatment (e.g., head, thorax, pelvis); an image purpose and/or application, to name a few. Other patient-specific information known to those of skill in the art is consistent with the spirit and scope of the present disclosure. Based on the patient data  205 , one or more imaging categories for the radiography image are determined. The one or more imaging categories can include a type of patient to be imaged (e.g., a pediatric patient, an obese patient), as well as an anatomical region to be imaged (e.g., head, thorax, pelvis). In general, an imaging category is able to be specified such that it is discriminative with regard to the radiography imaging parameters that are optimally generated for the patient. That is, a pediatric patient should have a different imaging protocol than an adult, and likewise an image of the thorax (which is likely moving during imaging) may require a different protocol than an image of the head. 
     Determination of the imaging category (or categories) enables selection of one or more radiography imaging parameters to be optimized for the image, the selection performed by selection module  225 . According to an embodiment of the present disclosure, the selection module  225  can automatically select the one or more radiography imaging parameters. The selection module  225  is further configured to identify and select one or more knowledge-based models, based on the patient data  205  and the determined imaging category, the selected knowledge-based model for determining an imaging protocol suitable for the present patient. The selected one or more radiography imaging parameters are processed according to the selected knowledge-based model (e.g., an expert model), in order to generate a value for the selected radiography imaging parameters, which is output as imaging parameter(s)  215 . 
     The selection of the radiography imaging parameters, and of the knowledge-based model, may be transparent to a user, e.g., a therapy planner or practitioner, such that manual selection is unnecessary. Alternatively, as described herein (e.g., at  FIGS. 3 and 4 ), manual selection of certain radiography imaging parameters and knowledge-based models for processing is consistent with the spirit and scope of the present disclosure. In some embodiments, the system may comprise a user interface (e.g., input interface  235  and output interface  240 ) that allows a user to narrow down one or more of the radiography imaging parameters, the patient data considered, and the knowledge-based models used, by user-defined constraints. Alternatively, the system may restrict parameters, so that user may only select values within a certain range (determined by system according to planning knowledge). Alternatively, the system may suggest parameters (e.g., based on planning data showing a head scan, the system may generate radiography imaging parameters corresponding to a head protocol). In an embodiment, the system  200  may be implemented “offline,” and determination of the proper imaging parameters is able to be made prior to the patient treatment. 
     As will be appreciated by those skilled in the art, the present disclosure is not limited to any mechanism or criteria of determining an imagine category and/or suitable knowledge-based model based on patient data. The selection could be based on a similarity between the patient data  205  and an expert system and/or physical model included in knowledge-based models  220 . The selection could be based on maximum similarity, e.g., only one imaging category (or knowledge-based model) is selected, the one with the highest similarity according to a chosen similarity metric with the patient data  205 . Alternatively, the selection could also be based on certain acceptable similarity level, e.g., the number of selected imaging categories (or knowledge-based models) could differ, where all imaging categories (or knowledge-based models) with sufficiently high similarity are selected. For example, bony matches are often performed for patient positioning imaging in pre-treatment. For a bony match soft tissue contrast is not particularly important, and therefore a knowledge-based model selection that is based on only one expert model (or physical model) can be made, that knowledge-based model including a protocol that reduces the radiation dose substantially in order to obtain only the image of the bone. 
     The individual knowledge-based models  220  may originate from a clinic having several models to cover different regions, or be developed by a radiation equipment provider, or are shared among several clinics. The models may be derived from published literature data or clinical data as submitted by clinic practitioners. 
     Radiography Imaging Parameter Generation 
     Referring now to  FIG. 3 , a flowchart  300  is depicted of one embodiment of a method for generating one or more radiography imaging parameters for a radiography image of a patient, based on extant patient information. Method  300  can be implemented as a system, for example, the system  200  illustrated in  FIG. 2 . Steps  305 - 330  describe exemplary steps comprising the process depicted in flowchart  300  in accordance with the various embodiments herein described. In one embodiment, the flowchart  300  is implemented as computer-executable instructions stored in a computer-readable medium. 
     At step  305 , patient information (e.g., patient data  205 ) is received at a system configured to generate radiography imaging parameters to be used for imaging the patient by a radiographic device. The patient data is able to include various kinds of information, such as: a patient record (e.g., age, disease type); patient measurements (e.g., weight, height, body outline from an optical system measurement); a radiation therapy plan; pre-treatment planning CT data; a body region for treatment (e.g., head, thorax, pelvis); an image purpose and/or application, to name a few. Other patient-specific information known to those of skill in the art is consistent with the spirit and scope of the present disclosure. 
     Once the patient data is received, a determination of one or more imaging categories to which the received patient information belongs is made at step  310 . As described herein, an imaging category can include a type of patient to be imaged (e.g., a pediatric patient, an obese patient), as well as an anatomical region to be imaged (e.g., head, thorax, pelvis). In general, an imaging category is able to be specified such that it is discriminative with regard to the radiography imaging parameters that are optimally generated for the patient. That is, a pediatric patient has a different imaging protocol than an adult, and likewise an image of the thorax (which is likely moving during imaging) may require a different protocol than an image of the head. 
     At step  315  a selection is made of a knowledge-based model (e.g., knowledge-based model  220 ), the selection based on the imaging category determined at step  310 . As described herein, the knowledge-based model can include expert systems and/or physical models. The knowledge-based model(s) may be automatically selected, and can be an expert model for a particular subsystem—e.g., radiography imaging parameters optimized for imaging soft-tissue, or for bony tissue, etc. Alternatively or additionally, the knowledge-based system is based on physical model, where knowledge exists about the materials present in imaging volume (e.g., an amount of bone, an amount of metal, an amount of soft-tissue, etc.). A plurality of expert models and physical models are able to be selected, and combinations of expert and physical models are also consistent with the spirit and scope of the present disclosure. According to embodiments of the present disclosure, the selection of the knowledge-based model can be automatic, or alternatively, a user of the system can perform manual selection (e.g., via input interface  235 ). 
     At step  320 , a selection is made of one or more radiography imaging parameters, where the radiography imaging parameter(s) are selected using the determined imaging category. There are several factors that affect both the imaging quality and the imaging parameters to be used. These include the structures (like bone, or a high amount of soft tissue, e.g., pelvis region) surrounding the target image volume. Additionally, anticipated motion of the subject (due to, for example, breathing) affects image quality and acquisition, as well as the acquisition geometry. For certain target areas, selection of an imaging parameter is made to extend a scan radius—e.g., for head, an imaging modality with a smaller radius is used (e.g., full-fan mode), whereas for pelvis, a larger field of view is desired (e.g., half-fan mode). 
     According to an embodiment of the present disclosure, selecting a radiography imaging parameter includes a user interaction (e.g., via input interface  235 ), enabling user-specified values to be used for generating imaging parameters. For example, a user is able to specify a specific value or range of values for one or more imaging parameters, with other imaging parameters being generated while taking the user-specified parameter value(s) into account. 
     At step  325 , the selected radiography imaging parameters are processed in accordance with one or more knowledge-based models for optimization of the radiography imaging parameter(s) selected at step  320 . The knowledge-based model functions to determine a value for the one or more radiography imaging parameters, specifically the radiography imaging parameters from step  320 . For example, for an image of thorax, motion of the thorax is assumed, and therefore an expert system will determine radiography imaging parameters that include the use of 4D-CBCT (which considers breathing motion), and/or gating. That is, in order to minimize effect of patient motion, a knowledge-based model will process the patient data in order to generate an imaging protocol which is very short. Conversely, the knowledge-based model can indicate the use of gating, acquiring an image only if patient is in maximum inhale phase, for example. If the image were of a patient head, a different protocol would be selected/suggested by the knowledge-based model. 
     In the case of the physical model, radiography imaging parameters affected may be an imager frame rate, filter/bowtie selection, a source current/voltage, among others. In some embodiments, an expert system is able to determine certain classes of parameters (e.g., gating, 4D-CBCT, etc.), while a physical model influences frame rate, etc., as above. A combination of the expert system and the physical model may be used to determine a suite of parameter values that are generated. 
     At step  330 , an output is made of a generated value for the selected radiography imaging parameter(s). In an embodiment, one value is generated for each radiography imaging parameter selected in step  315 . In an embodiment, a range of values are generated for each radiography imaging parameter, or for a select number of a plurality of radiography imaging parameters. According to embodiments of the present disclosure, the value (or range of values) may be suggested values, or they may be automatically applied to the imaging system (e.g., a radiographic device and imaging system). According to embodiment, the values may be excluded values. 
     Referring now to  FIG. 4 , a flowchart  400  depicts one embodiment of a method of generating one or more radiography imaging parameters for a radiography image of a patient, including optional selection of a subset of extant patient information for processing by select knowledge-based models. The process  400  includes optional user selection of certain imaging parameter values, with other imaging parameters being generated with respect to the user-specified values. Steps  405 - 455  describe exemplary steps comprising the process depicted in flowchart  400  in accordance with the various embodiments herein described. In one embodiment, the flowchart  400  is stored in a computer-readable medium. 
     At step  405  extant patient data is received. The patient data is able to include various kinds of information, such as: a patient record (e.g., age, disease type); patient measurements (e.g., weight, height, body outline from an optical system measurement); a radiation therapy plan; pre-treatment planning CT data; a body region for treatment (e.g., head, thorax, pelvis); an image purpose and/or application, to name a few. Other patient-specific information known to those of skill in the art is consistent with the spirit and scope of the present disclosure. 
     At step  410  a determination is made of whether only a subset of the patient data received at step  405  will be considered. If NO, the process continues to step  420  and considers all of the patient data received at step  405 . If YES, the process continues to step  415 , where the subset of patient data to be considered by process  400  is selected. The selection may be made by an operator of a system performing the process  400 , via input interface  235  for example. Alternatively, the selection may be predetermined, and the predetermined subset of patient data may be provided. For example, one or more categories of patient data may be identified for subset selection (e.g., patient record and radiation therapy plan) while others are identified to be ignored (e.g., image purpose and patient measurements). In this manner a relative importance of different aspects (e.g., categories) of the patient data can be indicated by a practitioner, or any user of a system implementing process  400 . 
     Once the patient data for consideration is determined, a determination of one or more imaging categories is made at step  425 . As described herein, an imaging category can include a type of patient to be imaged (e.g., a pediatric patient, and obese patient), as well as an anatomical region to be imaged (e.g., head, thorax, pelvis). In general, an imaging category is able to be specified such that it is discriminative with regard to the radiography imaging parameters that are optimally generated for the patient. 
     At step  430  a determination is made of whether a knowledge-based model (or models) is to be selected manually. If NO, the process continues to step  440 , where automatic selection of one or more knowledge-based models occurs. If YES, the process continues at step  435 , where a selection is made of the particular knowledge-based model (or models) that will be used for processing the patient data from step  415  or  420 . For example, the knowledge-based model can be an expert model for a particular subsystem—e.g., radiography imaging parameters optimized for imaging soft-tissue, or for bony tissue, etc. Alternatively or additionally, the knowledge-based system is based on physical model, where knowledge exists about the materials present in imaging volume (e.g., an amount of bone, an amount of metal, an amount of soft-tissue, etc.). A plurality of expert models and physical models are able to be selected, and combinations of expert and physical models are also consistent with the spirit and scope of the present disclosure. In an embodiment, the selection may be made by an operator of a system performing the process  400 , via input interface  235  for example. Alternatively, the selection may be predetermined based upon, for example, the knowledge of a practitioner regarding the specific history and disease of a patient for whom a positioning image is being planned. 
     An optional step  445  includes a user selection of one or more imaging parameters (e.g., via input interface  235 ), enabling user-specified values to be used. In one respect, user-specified values of given imaging parameters cause the process  400  to generate other imaging parameters (in step  455 ) with respect to the user-specified imaging parameters. As a non-limiting example, a user specification of a full-fan image capture mode (due to, for example, a scan of the neck) can cause the process  400  to generate other imaging parameters around the full-fan constraint, leading to specified source/detector orientations and scan trajectories. 
     Following the selection of the particular knowledge-based model(s), at step  450  the patient data is processed in accordance with the selected knowledge-based model(s). At step  455 , one or more radiography imaging parameters are generated. Different combinations of expert systems can together select the radiography imaging parameters for the patient. According to embodiments of the present disclosure, the generated imaging parameter(s) from step  455  are suggestions presented to a user (e.g., via output interface  240 ). The suggested imaging parameter(s) are able to be confirmed by a user, or alternatively, the suggested imaging parameter(s) can be changed from the suggested value to a user-specified value. The suggested imaging parameter(s) can be presented as a specified value (e.g., source oriented at 30 degrees), or as a range of values (e.g., source current between 60-80 mA). 
     Embodiments of the present disclosure may be appreciated by the use of exemplary imaging cases. In one example, for a young person (e.g., a pediatric case) it is appropriate to optimize radiography imaging parameters such that a reduced radiation dose is delivered. Imaging parameters that are appropriate to select in this case are a setting of a lower source voltage, as well as a lower source current. Additionally, a shorter beam pulse length may automatically be selected (or suggested, in some embodiments). If higher soft-tissue contrast is indicated (e.g., by a practitioner) the optimization of these radiography imaging parameters could include an increase in one or more of these parameter values. 
     In one example, for an obese patient image parameter selection and optimization can be made to avoid image truncation. Truncation occurs when less than 100% of a target imaging volume is successfully imaged. From patient data  205  including a planning CT of the patient, the weight of the patient, body outline, etc. are known. The patient data  205  includes the body weight stated in patient record, as well as information about the body part that is going to be imaged. These can be used to estimate the necessary dose to image the body part. It is possible to avoid truncation by selecting and optimizing parameters that include moving the couch (e.g., couch  115 ), for example. Alternatively or additionally, the detector (e.g., detector  113  and/or detector  118 ) may be moved in order to change the imaging volume. Reduced detector motion can also lead to acquiring an image more quickly, as the imaged volume may be reduced. Conversely, moving the detector allows imaging of a larger volume, at the cost of taking more time. Additionally, for a couch  115  that is robotic, the patient can be moved around to be placed in an optimal position. Therefore the volume imaged may be changed via imaging parameters including detector movement, couch movement, and a combination of both detector and couch movement. 
     Another exemplary case involves a treatment plan (e.g., from patient data  205 ) that indicates fractionated treatments. Often, a treatment is fractionated into a number of fractions. For example, a full treatment regimen may be broken down into 30 fractions so that, for a total dose of 30 units, there is one (1) dose unit per fraction. Each fraction may be performed on a given day—so, for 30 fractions, the treatment is completed in 30 days. For a fractionated treatment plan, the required accuracy of dosage delivery is dependent to some degree on the number of fractions. For a large number of fractions (highly fractionated), since there will be more occurrences of radiation delivery, each individual radiation dose is reduced and the concomitant need for accuracy in delivery is lessened. Conversely, if few fractions are used, this is greater dose per fraction and a concomitant greater accuracy in dose delivery to the target area is needed. This means that the pre-treatment imaging is affected, in the sense that more time and/or a greater dose may be used for placement image when lower fractionation is used, and less time and/or a lower dose may be used for placement image when greater fractionation is used. According to an embodiment, an input parameter to the system  200  includes what the tolerance for the imaging dose may be. 
     A function of the system and methods of the present disclosure is to provide prevention of an image fault, for example, an image overexposure due to operator error. If an operator applies too much dosage (e.g., dosage applied for a maximum volume when a maximum volume is not present), the image will be overexposed. Knowledge of the volume being imaged, from patient data  205 , enables guidelines on a particular dosage range for that image. Embodiments according to the present disclosure are able to place a limit on applied dose in order to prevent overexposure/underexposure. 
     Exemplary Computing Device 
     As presented in  FIG. 5 , an exemplary system upon which embodiments of the present disclosure may be implemented includes a general purpose computing system environment, such as computing system  500 . In its most basic configuration, computing system  500  typically includes at least one processing unit  501  and memory, and an address/data bus  509  (or other interface) for communicating information. Depending on the exact configuration and type of computing system environment, memory may be volatile (such as RAM  502 ), non-volatile (such as ROM  503 , flash memory, etc.) or some combination of the two. In some embodiments the memory is substantially radiation-hardened, using substrates and/or logical methods known to those skilled in the art. 
     Computer system  500  may also comprise an optional graphics subsystem  505  for presenting information to the computer user, e.g., by displaying information on an attached display device  510 , connected by a video cable  511 . According to embodiments of the present disclosure, the graphics subsystem  505  may be coupled directly to the display device  510  through the video cable  511 . A graphical user interface of an application for controlling a medical linear accelerator executing in the computer system  500  may be generated in the graphics subsystem  505 , for example, and displayed to the user in the display device  510 . In alternate embodiments, display device  510  may be integrated into the computing system (e.g., a laptop or netbook display panel) and will not require a video cable  511 . 
     Additionally, computing system  500  may also have additional features/functionality. For example, computing system  500  may also include additional storage (removable and/or non-removable) including, but not limited to, magnetic or optical disks or tape. Such additional storage is illustrated in  FIG. 5  by data storage device  507 . Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. RAM  502 , ROM  503 , and data storage device  507  are all examples of computer storage media. 
     In an embodiment, computer system  500  comprises instructions for performing processes according to aspects of the present disclosure, where the instructions may be stored on RAM  502 , ROM  503 , and/or data storage  504 . For example, the computer system  500  may comprise radiography imaging parameter generation instructions  513 , where radiography imaging parameter generation instructions  513  contain instructions causing computer system  500  to perform a process of generating a value for one or more radiographic imaging parameters automatically from extant patient information, according to embodiments of the present disclosure (e.g., processes  300 ,  400 ). 
     Computer system  500  also comprises an optional alphanumeric input device  506 , an optional cursor control or directing device  507 , and one or more signal communication interfaces (input/output devices, e.g., a network interface card)  509 . Optional alphanumeric input device  506  can communicate information and command selections to central processor  501 . Optional cursor control or directing device  507  is coupled to bus  509  for communicating user input information and command selections to central processor  501 . Signal communication interface (input/output device)  509 , also coupled to bus  509 , can be a serial port. Communication interface  509  may also include wireless communication mechanisms. Using communication interface  509 , computer system  500  can be communicatively coupled to other computer systems over a communication network such as the Internet or an intranet (e.g., a local area network), or can receive data (e.g., a digital television signal). 
     Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.