Abstract:
A system simulating the reviewing process of breast imaging examination (i.e. mammographic) information provides a data file of breast imaging information including mammographic (and other breast imaging examinations) images that may be scored by a trainee on computerized worksheet and expert assessments of those images. Quantitative data in the expert assessment may be compared to trainee-entered data recovered from the worksheet to provide an assessment of trainee proficiency in interpreting breast imaging studies. The data file of breast imaging information may be derived from actual clinical data anonymized and selected to provide for realistic yet demanding simulation.

Description:
BACKGROUND OF THE INVENTION 
     The present invention relates to breast imaging and in particular to a computerized simulator for training and testing of individuals performing analysis of screening and diagnostic breast images. 
     Breast cancer is the second leading cause of death in women and the most common cancer in women in the United States. Screening mammography is the only current exam that increases the detection of early clinically occult breast cancers in women of average risk. 
     Mammography uses low energy x-rays to provide a radiographic image of breast tissue typically under mild compression. The radiographic image can reveal masses, asymmetries, architectural distortion or microcalcifications associated with breast cancer when reviewed by a trained professional. 
     The results of mammographic imaging are typically provided in the form of a prose clinical report describing features of the mammogram and clinical impressions. Included with the report will often be a ranking expressed in terms of a BI-RADS (Breast Imaging-Reporting and Data System) assessment category ranging from 0-6. In the BI-RADS ranking, categories 1 and 2 are normal or benign, category 3 is probably benign, categories 4 and 5 indicate a suspicion or likelihood of malignancy, and category 6 reflects imaging of a patient with a known diagnosis of breast cancer prior to definitive treatment. Normally a category 0 assessment during a screening mammogram will result in a recall for additional imaging. 
     The interpretation of mammograms is a complex process requiring a significant level of experience-honed judgment. For this reason, training in the interpretation of mammograms is normally done by parallel assessments of current cases by an experienced clinician working together in a one-on-one session with the individual to be trained. Although such training is extremely valuable in obtaining competency, the need for and format of one-on-one training sessions of this type understandably limits the opportunity for such training. 
     SUMMARY OF THE INVENTION 
     The present inventor has recognized that valuable ancillary training can be obtained on an automated basis by using a set of mammographic images that have been pre-characterized by an expert. For this purpose, much of the complexity of a clinical evaluation can be usefully simplified to a trainee&#39;s assessment of the BI-RADS or similar categories reflecting whether a recall for additional imaging would be required. A similar simplification may be used to obtain a more nuanced assessment of the trainee&#39;s understanding by soliciting a prediction of the likely BI-RADS results of that follow-up imaging. Unlike in an actual screening where an expert does not know the outcome of subsequent screening or a biopsy, the expert&#39;s characterization of the mammographic images in the simulation may optionally be informed by after-the-fact knowledge about the case (e.g. subsequent screening and/or biopsy) to provide an extremely high level of effective expertise. 
     Specifically, the present invention provides a simulator for breast imaging (mammograms, ultrasound, etc.) including a medical grade computer display and a data input device communicating with an electronic computer. The computer memory may hold a data structure having multiple records linking breast imaging studies to expert recall instructions, the latter indicating whether a patient associated with each mammographic image should be recalled for additional imaging based on the mammographic image. During operation, the computer executes a stored program to successively read the records of the data structure and, for each record, to display at least one mammographic image of the record and receive input through the trainee input device indicating a trainee recommended recall instruction. After reviewing of the records is complete, the program outputs a report indicating divergence between the trainee recommended recall instructions and the expert recall instructions. 
     It is thus a feature of at least one embodiment of the invention to provide an automatable method of training and assessing the interpretation of mammographic images. By identifying quantitative, computer-readable features of the diagnosis (for example BI-RADS categories) meaningful automated evaluation can be performed while maintaining a realistic clinical setting. 
     The records of the data structure may provide expert recall instructions that are predominantly instructions for no recall. 
     It is thus a feature of at least one embodiment of the invention to provide a simulation experience that is roughly analogous to real world practice in which most screenings do not result in a recall. 
     Nevertheless, the records of the data structure may provide expert recall instructions instructing recall of the patient greater than a normal expected percentage of five to twelve percent. For example, the expert recall instructions may instruct recall of the patient for greater than 20 percent and as high as 25 percent of the records, to enhance the learning experience and provide appropriate feedback where user has difficulty. 
     It is thus a feature of at least one embodiment of the invention to provide a more engaging and better training experience by increasing the recall rate above that found in a typical population. 
     The after-recall assessments may be encoded as numeric BI-RAD scores. 
     It is thus a feature of at least one embodiment of the invention to make use of a familiar and well-established scoring technique for automatic skill comparison and assessment. 
     The simulator may further receive input through the data input device indicating a trainee predicted “after-recall” assessment predicting an assessment after the patient of the record is recalled for further imaging and analysis and the records of the data structure may further provide an expert “after-recall” assessment indicating the ultimate assessment of the patient. In this case, the output report may indicate a divergence between trainee predicted “after-recall” assessment and expert “after-recall” assessment. This prediction may further include a numeric likelihood of biopsy and a type of biopsy. 
     It is thus a feature of at least one embodiment of the invention to derive greater insight into the understanding of the trainee beyond the readily quantified recall recommendation, by having the trainee classify the findings, predict the likely results of that recall and optionally whether a biopsy will be required and the type of biopsy. As before, this prediction may be captured by machine-readable numeric scores. 
     The simulator may further receive input through the data input device indicating a trainee-determined location and description of the suspicious imaging finding on the mammographic image of each record and the records of the data structure may provide expert-determined location of suspicious imaging findings (masses, calcifications, etc) on the mammographic image of each record. In this case, the output report may indicate a divergence between trainee-determined locations of suspicious imaging finding and expert-determined locations of suspicious imaging findings informed by positional error between the trainee-determined location of suspicious imaging finding and the expert-determined location of suspicious imaging findings. 
     It is thus a feature of at least one embodiment of the invention to provide an automatic analysis of a trainee&#39;s identification of one or more suspicious imaging findings. 
     The monitor may provide a patient clinical history associated with each record. 
     It is thus a feature of at least one embodiment of the invention to allow the trainee to take into account extrinsic information about the patient such as may assist in the analysis of the breast imaging study. 
     The records of the data structure may provide records reflecting a normal age distribution. Alternatively or in addition, the records may reflect a normal distribution of breast tissue density types. 
     It is thus a feature of at least one embodiment of the invention to provide a simulation that to the extent practical reflects a typical population of patients. 
     The data structure may include no less than 50 records. 
     It is thus a feature of at least one embodiment of the invention to provide reports that have statistical significance and that provide suitable training opportunity. 
     The simulator may provide a time limit for performing a given number of reviews and may permit a given review to be postponed while ultimately requiring completion of any postponed reviews by including them in the report regardless of whether they are subsequently completed. 
     It is thus a feature of at least one embodiment of the invention to provide a natural working environment where reviews may be made out of order while preventing subversion of the evaluative aspects of the simulation that might occur if different individuals reviewed self-selected different cases. 
     These particular objects and advantages may apply to only some embodiments falling within the claims and thus do not define the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of a simulation system according to the present invention providing a display of a data file of breast imaging (mammograms, for example) information by a computer for evaluation by a trainee and an input monitor for a worksheet to be completed by the trainee for evaluating that breast imaging information; 
         FIG. 2  is a logical diagram of the data file of breast imaging information of  FIG. 1  and a similar data file for receiving input from the trainee evaluating the breast imaging information; 
         FIG. 3  is an example worksheet presented on the input monitor for use by the trainee in entering data related to evaluations of the breast imaging (i.e. mammographic) information; 
         FIG. 4  is a flowchart of the program executed by the computer of  FIG. 1  for implementing a simulation session; 
         FIG. 5  is a simplified perspective view of a technique for comparing expert- and trainee-identified breast imaging finding locations for quantitative scoring; and 
         FIG. 6  is an example output report from the screening process. Similar outputs would also be made for diagnostic and other high order breast imaging examinations. 
     
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT 
     Referring now to  FIG. 1 , a simulator  10  for mammographic image viewing may provide mammography display monitors  12   a  and  12   b  for displaying mammographic images  14  of the type acquired with an x-ray mammography unit (not shown). The display monitors  12  will generally provide for at least five megapixel nominal resolution (for example, ten bit pixel resolution and 2048 by 2056 pixels) and will meet the standards of DICOM GSMF Part 14. 
     A work list monitor  16  may also be provided to display a list of cases in which mammograms or other breast imaging studies need to be reviewed and a report input monitor  18  into which clinical report data may be entered by a trainee. The work list monitor  16  and report input monitor  18  may be standard computer monitors. 
     Each of the monitors  12 ,  16 , and  18  may connect to a simulation computer  20  also connecting to data input devices  21  including, for example, a mouse  22  and keyboard  24  according to techniques and standards well understood in the art. The simulation computer  20  may be a standard desktop computer, for example, capable of running the Windows operating system. The simulation computer  20  may include one or more processors  26  communicating with an electronic memory  28 . The electronic memory  28  may be a logical combination of multiple memory devices including random access memory, disk drive memory and the like. 
     The electronic memory  28  may hold a commercially available operating system  30  as well as a simulation program  32  of the present invention as will be described below. In addition, the electronic memory  28  may hold a simulation data file  34  providing data for simulated patient cases that will be reviewed by the trainee and a trainee data file  36  holding information entered by the trainee during the simulation process. 
     Referring now to  FIG. 2 , the simulation data file  34  may be represented as a logical table having records  38  (represented by rows), each corresponding to a different patient, holding data associated with a breast imaging exam in various attributes  40  (represented by columns). A first attribute  40   a  may provide a record number, for example, an integer from one to the total number of records in the simulation data file  34 . In a preferred embodiment, the simulation data file  34  will include at least 100 records and typically more than 50 records. This record number will be used to match records  38  in the simulation data file  34  with corresponding records in the trainee data file  36 . 
     A second attribute  40   b  may provide for a patient identifier that has been anonymized so as not to identify an actual patient. This patient identifier may be a number or a pseudonym. 
     A third attribute  40   c  provides a breast imaging exam file  45  including one or more digitized mammographic images (or comparable other breast imaging study; for example ultrasound, MR, tomography)  14  associated with the patient of records  38 . Generally the mammographic images  14  will include left and right mediolateral oblique images and left and right craniocaudal images meeting DICOM standards. In one embodiment, the mammographic images  14  may include some text providing imaging parameters such as: laterality, image view, and radiation dose parameters (kVp, milliamps, compression thickness target and filter) but are otherwise free of any identification of an actual patient or any data indicating diagnostic conclusions. 
     A fourth attribute  40   d  may provide for an abbreviated patient history  42  in text form, for example, noting previous breast imaging studies in their dates, family history of breast cancer, patient identified concerns, history of pregnancies if any and the like for the patient of the records  38 . 
     A fifth attribute  40   e  provides an expert recall instruction  44  prepared by an expert having reviewed the data of attributes  40   c  and  40   d  and optionally having after-the-fact knowledge of the results of any later history on the patient including a recall examination, biopsy, and the like. The expert recall instruction  44  thus provides, to the greatest practical extent, a correct diagnosis based on the breast imaging exam information informed not only by experience but also by history on the patient. Generally the expert recall instruction  44  will be in the form of a BI-RADS rating of 0, 1, or 2 being the range typically provided after reviewing a screening mammogram according the following table: 
     
       
         
               
               
             
           
               
                 TABLE I 
               
               
                   
               
               
                 BI-RADS Assessment 
                   
               
               
                 Category 
                 Category Title 
               
               
                   
               
             
             
               
                 0 
                 Need additional 
               
               
                   
                 evaluation 
               
               
                 1 
                 Normal 
               
               
                 2 
                 Benign 
               
               
                 3 
                 Probably benign 
               
               
                 4 
                 Suspicion of 
               
               
                   
                 malignancy 
               
               
                 5 
                 Highly suggestive of 
               
               
                   
                 malignancy 
               
               
                 6 
                 Known biopsy-proven 
               
               
                   
                 malignancy 
               
               
                   
               
             
          
         
       
     
     Note that while the expert recall instruction  44  is informed by the ultimate history of the patient, for example the results of a follow-up biopsy, the recommendation is made from the point of view of an expert viewing the mammographic images  14  and reviewing the patient history  42  only, and thus may indicate a BI-RADS category of 4 or 5, for example, in cases both where history proves a known malignancy or absence of a malignancy. That is, the expert recall instruction  44  represents a nearly infallible expert without knowledge of the future. 
     In addition, an attribute  40   f  is provided for those records  38  having an expert recall instruction  44  of BI-RADS 4 or 5. This attribute  40   f  provides one or more expert predictions  46  including a likelihood that a recall for a second imaging session will lead to a requirement of biopsy (after-recall), a conditional prediction of a type of biopsy that will be required if biopsy is indicated, and a conditional prediction if a biopsy is required as to whether cancer will be detected (an expert predicted “after-recall” assessment). Again these predictions are informed by the patient history and the information in the simulation data file  34  but represent the viewpoint of the nearly infallible expert without knowledge of the future. 
     Finally, attributes  40   g  may store the coordinates  47  of any suspicious breast imaging finding identifiable from the breast imaging images  14  as informed by subsequent patient history but from the viewpoint of an expert without knowledge of the future. 
     The records  38  each may represent actual patients whose data has been anonymized. Desirably the set of records  38  is selected from a larger set of actual patient records to provide a distribution of different ages that represents a realistic population. For example, the age distribution of the records  38  may desirably conform approximately to the following table: 
     
       
         
               
               
               
               
               
             
           
               
                 TABLE II 
               
               
                   
               
               
                 &lt;39 years 
                 40-49 years 
                 50-59 years 
                 60-69 years 
                 &gt;70 years 
               
               
                   
               
             
             
               
                 2% 
                 23% 
                 33% 
                 28% 
                 14% 
               
               
                   
               
             
          
         
       
     
     In addition, the records  38  may be selected among actual records of patients to provide a distribution of breast tissue types (breast fiboglandular tissue density), for example, corresponding proximately to the following table: 
     
       
         
               
               
               
               
             
           
               
                 TABLE III 
               
               
                   
               
               
                   
                   
                 Heterogeneous 
                 Extremely dense 
               
               
                 fatty (&lt;25% 
                 Scattered (25-50% 
                 (51-75% 
                 (&lt;75% 
               
               
                 glandular) 
                 glandular) 
                 glandular) 
                 glandular) 
               
               
                   
               
             
             
               
                 8% 
                 49% 
                 39% 
                 4% 
               
               
                   
               
             
          
         
       
     
     The number of recall instructions typical in mammographic screening of a general population will vary from 5 to 12 percent; however, the simulation data file  34  is adjusted to provide for more recalls in order to maintain the interest level of the trainee and to provide for a more intense training experience. In one embodiment, the records  38  of simulation data file  34  will provide for 75% expert recall instructions  44  of BI-RAD categories 1 or 2 (no recall) and 25% expert recall instructions  44  of BI-RADS category 0 (recall). 
     The records  38  are selected so that within the 25% associated with expert recall instructions  44  of BI-RADS 0.17% will ultimately be classified as BI-RADS 1 or 2; 7% will ultimately be classified as BI-RADS 4 and 1% will ultimately be classified as BI-RADS 5. These percentages are based on the total number of records  38 . 
     Of the 25% of records  38  having expert recall instructions  44  of BI-RADS 0 that would, upon recall, then be classified as classified BI-RADS 4, 4% will have an expert BI-RADS subcategory of 4a (low suspicion of malignancy), 2% will have an expert BI-RADS subcategory of 4b (intermediate suspicion of malignancy), and 1% will have an expert BI-RADS subcategory of 4c (the moderate concern, but not classic for malignancy). 
     Referring now to  FIGS. 1 and 2 , the trainee in reviewing the information from each record  38  on the monitors  12 ,  16  and  18 , may enter his or her assessment on a worksheet  50  displayed on the report input monitor  18 . Generally, the worksheet  50  will provide for a number of data entry points  52  such as text record boxes, pulldown menus, and the like that may receive data entered by the trainee to be recorded in a trainee data file  36  in machine-readable form. In one embodiment, the worksheet  50  may include data entry points  52  mimicking those in commercial worksheets, such as the PenRad MIS system commercially available from PenRad™ Technologies Inc. of Buffalo, Minn., USA. A representation of portions of the PenRad MIS worksheet will be used in part in the following description of the invention; however, it should be appreciated that the invention is not limited to a particular commercial system or format. 
     Referring in particular to  FIG. 2 , data from the data entry points  52  on the worksheet  50  may be stored in records  53  of trainee data file  36 , the former records  53  corresponding individually and in number to each of the records  38  in the simulation data file  34 . In this respect, for example, each record  53  may include attribute  54   a  providing a record number matching one of the record numbers of attribute  40   a  of the simulation data file  34 . 
     Generally, it will be of interest to capture all data entered by the trainee at data entry points  52  in the worksheet  50  even if they are not used in the assessment provided by the present invention. Attribute  54   b  will hold this data including, for example, text descriptions of the trainee&#39;s assessment of the mammogram. All data collected could be used to provide insight into the trainee&#39;s evaluation process for breast imaging studies and thus reveal areas needing remediation. 
     Data specifically associated with a trainee recall instruction  56  (corresponding to the expert recall instruction  44  although not necessarily in value) will be captured in attribute  54   e  described above. This trainee recall instruction  56  provides the trainee&#39;s best diagnosis based on the collaboration of information from the mammographic images  14  and patient history  42  expressed in the form of a BI-RADS assessment number (e.g. 0 1, one, or 2). 
     Attribute  54   d  may store multiple trainee predictions  58  (corresponding generally to the expert predictions  46  although not necessarily in value) described above. 
     Finally, the data file  36  may also include attribute  54   e  capturing locations for one or more suspicious imaging findings that may be identified by the trainee to be compared against the data of attribute  40   g  for the corresponding record  38 . 
     Referring now to  FIG. 3 , more specifically, the worksheet  50  may provide a text box  70  into which the BI-RAD category of the trainee recall instruction  56  selected by the trainee may be entered after reviewing the mammographic information of a given record  53 . This data may be typed into the text box  70  or entered by means of a pulldown menu or the like. 
     The worksheet  50  may present simplified depictions  72  of mammographic images  14  on which the trainee may locate the positions of any suspicious imaging finding evident in the mammographic images  14  by means of position markers  74 . The position markers  74  may be located on both medio-lateral oblique images and cranio caudal images to provide three dimensions of a Cartesian coordinate for the suspicious imaging finding (mass, calcifications, etc). The positional accuracy of this coordinate may be augmented by clock faces  76  showing approximate angular location of the imaging finding in the anterior-posterior direction. As noted above, the coordinates of one or inure located imaging findings may be stored in attribute  54   e.    
     In one embodiment of the invention, the worksheet  50  may include a prediction section  77  allowing the trainee to enter, for example, trainee predictions  58  including a trainee predicted “after-recall” assessment indicating a predicted finding after a recall of the patient for additional imaging and diagnosis. This predicted finding after-recall will be in the form of a BI-RADS category, for example, 2, 4, or 5 per Table I above. Category 4 may be broken out into subcategories 4a, 4b, and 4c. Predicted finding after-recall may be entered by marking one checkbox  78  in a table  81  providing for possible BI-RAD values. The checkbox  78  provides data entry points  52 . 
     A similar table  83  with checkboxes  85  providing data entry points  52  may allow the trainee to predict the likelihood that a biopsy is required and checkboxes  87  of table  89  may allow the entry by the trainee of a predicted type of biopsy that would be required. These data entry points  52  provide numeric values that match the categories of the expert predictions  46  discussed above but may differ in values from the expert predictions  46  of corresponding records. 
     Referring now to  FIGS. 1 and 4 , in a simulation, program  32  may be started by trainee as indicated at process block  80  at a given time allocated for the simulation. 
     At the start of the simulation and during the simulation, the work monitor  16  provides a list of patient identifiers  41  each representing a case (and underlying records  38 ) that can be reviewed identified by the trainee. With some exceptions noted below, the trainee will typically move through the list in the order in which they are listed on the work monitor  16 . 
     At process block  82 , a current record  38  is identified and the mammographic images  14  for that record  38  are displayed on monitors  12   a  and  12   b  per process block  84 . Optional clinical data  43  may be also displayed, for example, on either work monitor  16  and  18 . 
     The trainee may postpone analysis of any given case, for example, as indicated by decision block  86 , in which case it is put back into a queue represented by the list of patient identifiers  41  and marked as incomplete. This postponed case must be completed before conclusion of the simulation session or counted against the trainee in an ultimate report which will be described. This postponing procedure prevents the trainees from cherry picking what may be perceived as easier cases but allows some flexibility in revisiting conclusions. 
     For cases that are not postponed, the trainee enters the assessments into the worksheet  50  as indicated by process block  88 . 
     If at decision block  90 , the trainee has assessed all of the cases in the simulation data file  34  or a predetermined simulation time has expired, an output report is generated as indicated by process block  92  as will be described below. At early stages of the simulation, however, at decision block  90 , the program will then go back to process block  82  and the next record  38  will be presented to the trainee. 
     Referring now to  FIG. 6 , the report  94  generated at process block  92  in its simplest form may provide a table indicating in a first column all possible values of BI-RAD categories for the trainee recall instruction  56 . In this case BI-RADS category is 1 and 2 may be grouped together in one row and BI-RADS category 0 separated on a second row. A second column may then indicate the number of times that the trainee recall instructions  56  deviated from the expert recall instructions  44 . Cases that were not fully reviewed by the trainee will count as category errors as well as cases with substantive differences in diagnosis. Thus, for example, a trainee recall instruction  56  having a BI-RADS value of zero (recall) will count as an error when compared to an expert recall instruction  44  having a BI-RADS value of 1 or 2. On the other hand, a trainee recall instruction  56  having a BI-RADS value of 1 (normal) will not count as an error when compared to an expert recall instruction  44  having a BI-RADS value of 1 or 2. 
     Provision may be made to allow the trainee to review those cases where deviations occurred and compare them against the expert values. Notably, however, the total number of records, in any given category is not revealed to prevent possible future case counting by individuals in the simulation. 
     In addition, a third column may indicate prediction accuracy by the trainee with respect to a comparison of trainee predictions  58  and corresponding expert predictions  46 . In one embodiment, this prediction accuracy number may be calculated by assigning every correct prediction a value of 100 and for cases where the trainee predictions  58  and expert predictions  46  deviated, subtracting from this 100 value 25 times the difference between the trainee predictions  58  and the expert predictions  46 . These weighted accuracy values may then be totaled and divided by the number of records  38  times 100 to obtain the prediction accuracy. Separate prediction accuracy may be provided for each of the different types of predictions including BI-RADS predictions, biopsy predictions and biopsy type predictions. Again, the provisions may be made to allow the trainee to review those cases where deviations occur and to compare them against the expert values. 
     Referring now to  FIGS. 3, 5, 6 , as noted above, the markers  74  entered by the trainee in the worksheet  50  report input monitor  18 , and stored at attribute  40   g , may be used to establish a three dimensional location  100  within a Cartesian coordinate system defined by the simplified images  72  of the suspicious mass identified by the trainee. Similar information stored at attribute  54   e  of the trainee data file  36  provides a second location  102  for an expert-located suspicious mass. The vector difference between these two locations  100  and  102  may be compared against a predetermined accuracy threshold to see if a suspicious mass has been properly located. 
     If a suspicious mass is identified by the trainee and located within the predetermined accuracy threshold with respect to each location  102 , no error is indicated. If a suspicious mass is identified outside of the predetermined accuracy threshold or no suspicious mass is identified corresponding to a mass at location  102 , a false negative or missed mass is identified in the fourth column of report  94  (with a predicate −). If a suspicious mass is identified having no corresponding location  102 , a false positive is indicated at report  94  (with a predicate +). 
     In the event that multiple masses are present, each location  100  is compared to one closest location  102  for this purpose. 
     Generally, the output report may satisfy′ part of an audit under the Mammographic Quality Standards Act (MQSA). In this regard, the trainee may obtain all or part of the output report and may provide information such as recall rate, PPV, cancer detection rate and more. 
     Certain terminology is used herein for purposes of reference only, and thus is not intended to be limiting. For example, terms such as “upper”, “lower”, “above”, and “below” refer to directions in the drawings to which reference is made. Terms such as “front”, “back”, “rear”, “bottom” and “side”, describe the orientation of portions of the component within a consistent but arbitrary frame of reference which is made clear by reference to the text and the associated drawings describing the component under discussion. Such terminology may include the words specifically mentioned above, derivatives thereof, and words of similar import. Similarly, the terms “first”, “second” and other such numerical terms referring to structures do not imply a sequence or order unless clearly indicated by the context. 
     When introducing elements or features of the present disclosure and the exemplary embodiments, the articles “a”, “an”, “the” and “said” are intended to mean that there are one or more of such elements or features. The terms “comprising”, “including” and “having” are intended to be inclusive and mean that there may be additional elements or features other than those specifically noted. It is further to be understood that the method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed or illustrated, unless specifically identified as an order of performance. It is also to be understood that additional or alternative steps may be employed. 
     References to “a microprocessor” and “a processor” or “the microprocessor” and “the processor,” can be understood to include one or more microprocessors that can communicate in a stand-alone and/or a distributed environment(s), and can thus be configured to communicate via wired or wireless communications with other processors, where such one or more processor can be configured to operate on one or more processor-controlled devices that can be similar or different devices. Furthermore, references to memory, unless otherwise specified, can include one or more processor-readable and accessible memory elements and/or components that can be internal to the processor-controlled device, external to the processor-controlled device, and can be accessed via a wired or wireless network. 
     It is specifically intended that the present invention not be limited to the embodiments and illustrations contained herein and the claims should be understood to include modified forms of those embodiments including portions of the embodiments and combinations of elements of different embodiments as come within the scope of the following claims. All of the publications described herein, including patents and non-patent publications, are hereby incorporated herein by reference in their entireties.