Patent Publication Number: US-2021181246-A1

Title: Electromagnetic interference pattern recognition tomography

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application is a U.S. continuation patent application of, and claims priority under 35 U.S.C. § 120 to U.S. patent application Ser. No. 15/953,694 to Semenov, filed Apr. 16, 2018 and previously published on Aug. 16, 2018 as U.S. Patent Application Publication No. 2018/0231594 A1 and issued as U.S. Pat. No. 10,921,361 on Feb. 16, 2021, which &#39;694 application, the application publication thereof, and the patent issuing therefrom are each incorporated herein by reference in their entirety, and which &#39;694 application is a U.S. continuation patent application of, and claims priority under 35 U.S.C. § 120 to, International Application No. PCT/US2016/057254, filed Oct. 16, 2016, designating the U.S., and entitled “ELECTROMAGNETIC INTERFERENCE PATTERN RECOGNITION TOMOGRAPHY,” which &#39;254 application published as WO 2017/066731 A1 on Apr. 20, 2017, which &#39;254 application and the application publication thereof are each expressly incorporated herein by reference in their entirety, and which &#39;254 application, for purposes of the United States, is a U.S. nonprovisional patent application of, and claims priority under 35 U.S.C. § 119(e) to, U.S. provisional patent application Ser. No. 62/242,915, filed Oct. 16, 2015 and entitled “ELECTROMAGNETIC INTERFERENCE PATTERN RECOGNITION TOMOGRAPHY,” which &#39;915 application is expressly incorporated herein by reference in its entirety. In addition, each of the following patents, patent applications and patent application publications is incorporated by reference herein in its entirety:
         (a) U.S. Pat. No. 9,414,749 to Semenov, issued Aug. 16, 2016 and previously published on Jun. 5, 2014 as U.S. Patent Application Publication No. 2014/0155740 A1, which is intended, at least, to provide background and technical information with regard to the systems and environments of the inventions of the current patent application; and   (b) U.S. Patent Application Publication No. 2012/0010493 A1, which was published Jan. 12, 2012 based on U.S. patent application Ser. No. 13/173,078 to Semenov, filed Jun. 30, 2011 and entitled “SYSTEMS AND METHODS OF ELECTROMAGNETIC TOMOGRAPHY (EMT) DIFFERENTIAL (DYNAMIC) FUSED IMAGING,” which is intended to provide background and technical information with regard to 4D EMT imaging.       

    
    
     COPYRIGHT STATEMENT 
     All of the material in this patent document is subject to copyright protection under the copyright laws of the United States and other countries. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in official governmental records but, otherwise, all other copyright rights whatsoever are reserved. 
     BACKGROUND OF THE PRESENT INVENTION 
     Field of the Present Invention 
     The present invention relates generally to electromagnetic tomography, and, in particular, to the use of electromagnetic interference pattern recognition strategies to remove amplified distortions caused by repeated interference patterns. 
     Background 
     Electromagnetic tomography (EMT) is a relatively recent imaging modality with great potential for both biomedical and industrial applications. Biomedical applications include but are not limited to the non-invasive assessment of functional and pathological conditions of biological tissues. Industrial applications include but are not limited to oil and gas exploration, mine search and assessment, and flow assessment within non-metallic pipes. Using EMT, objects such as biological tissues are differentiated and, consequentially, can be imaged based on the differences in the dielectric properties of such objects. EMT is believed to have high potential for biomedical applications based on the recent demonstration of the dependency of tissue dielectric properties on the tissue&#39;s various functional and pathological conditions, such as blood and oxygen contents, ischemia and infarction malignancies, edema and others. 
     Two-dimensional (2D), three-dimensional (3D) and even “four-dimensional” (4D) EMT systems and methods of image reconstruction have been developed over the last decade or more. Feasibility of the technology for various biomedical and industrial applications has been demonstrated, for example, for cardiac imaging and extremities imaging. 
     As in any tomographic imaging, the classical EMT imaging scenario consists of cycles of measurements of complex signals, as affected by the presence of an object under study located within a so-called imaging domain, as produced by a plurality of transmitters located at various points around the object and measured on a plurality of receivers located at various points around the object. This is illustrated in  FIG. 1 . The locations of the transmitters and receivers may be within the imaging domain, on the boundary of the imaging domain, or outside the imaging domain. As recounted elsewhere herein, the measured matrix of EM signals may then be used in image reconstruction methods in order to reconstruct 3D distribution of dielectric properties of the object  19 , i.e., to construct a 3D image of the object. Hardware components disclosed and described in the aforementioned U.S. Pat. No. 9,414,749, can be utilized to generate the necessary EM signals to reconstruct a 3D image of the object  19 . 
     Generally, it is very important for image reconstruction to precisely describe a distribution of an EM field with an imaging domain  21 . The distribution of an EM field within an imaging chamber is a very complex phenomenon, even when there is no object of interest inside. 
     This invention relates to the use of electromagnetic (EM) fields for imaging of the structure of an object  19 . The object  19  may be a human body or part of a human body, such as a head, a torso, an arm or the like, but may also be any object without metal shielding. The use of EM fields for imaging inside of a strongly shielded object (but not metallically shielded) is a problem of high complexity. One example of such an application is imaging of the human brain. However, it should be appreciated that other such applications might include imaging of any human tissue that is shielded by a bony structure. The EM imaging of the brain or other tissue surrounded by bone presents a very complicated, high dielectric contrast problem. The challenge is to reconstruct hidden properties of deep brain tissues which are effectively shielded by a high dielectric contrast shield, comprising the skull (with dielectric properties in a range of 16+j5) and the cerebral spinal fluid (with dielectric properties in a range of 60+j30). While this invention, as stated above, is applicable for the imaging of any objects, it is believed to be especially applicable for imaging inside of strongly shielded objects. 
     EMT imaging of high dielectric contrast objects, including biological objects, possesses the very complicated problem of so-called “diffraction tomography.” Devaney A. J. “Current research topics in diffraction tomography”, in  Inverse Problems in Scattering and Imaging , M. BNertero and E. R. Pike, Eds, New York: Adam Hilger, 1992, pp. 47-58. A high dielectric contrast between tissues with high water content, such as but not limited to muscle tissue, and low water content, such as but not limited to bone, presents an additional complication when using EM fields for imaging. Various approaches in 2D and 3D geometries, using scalar and vector approximations, have been developed in an attempt to solve the problem of diffraction tomography. See H. Harada, D. Wall, T. Takenaka, and T. Tanaka, “Conjugate gradient method applied to inverse scattering problem”,  IEEE Trans. Antennas and Propagations , vol. 43, 784-792, August 1995; R. E. Kleinman, and P. M. van den Berg, “A modified gradient method for two-dimensional problems in tomography”,  J. Comput. Appl. Math ., vol. 42, pp. 17-35, January 1992; A. Abubakar, P. M. van den Berg, and J. J. Mallorqui, “Imaging of Biomedical Data Using A Multiplicative Regularized Source Inversion Method,”  IEEE Trans. Microwave Theory and Techniques , v. 50, pp. 1761-1771, July 2002; N. Joachimowicz, J. J. Mallorqui, J. Ch. Bolomey, and A. Brouguetas, “Convergence and stability assessment of Newton-Kantorovich reconstruction algorithms for microwave tomography”,  IEEE Trans. Medical Imaging , vol. 17, pp. 562-570, August 1998; P. Lobel, R. Kleinman, Ch. Pichot, L. Blanc-FHraud, and M. Barlaud “Conjugate Gradient Method for Solving Inverse Scattering with Experimental Data”,  IEEE Antennas  &amp;  Propagation Magazine , Vol. 38, pp. 48-51, June 1996; W. C. Chew, and Y. M. Wang, “Reconstruction of two-dimensional permittivity distribution using the distorted Born iterative method”,  IEEE Trans. Medical Imaging , vol. 9, p. 218-225, June 1990; P. M. Meaney, K. D. Paulsen, A. Hartov, and R. K. Crane, “Microwave imaging for tissue assessment: Initial evaluation in multitarget tissue equivalent phantoms”,  IEEE Trans. Biomedical Engineering , vol. 43, pp. 878-890, September 1996. 
     In previous works, mathematical algorithms and their respective systems and software implementations have been developed that proved to be very reliable and delivered images of objects of different sizes from a few centimeters in the excised canine heart up to a full-size body in 2D, 3D and 3D vector cases. See A. E. Souvorov, A. E. Bulyshev, S. Y. Semenov, R. H. Svenson, A. G. Nazarov, Y. E. Sizov, and G. P. Tatsis, “Microwave tomography: A two-dimensional Newton iterative scheme”,  IEEE Trans. Microwave Theory and Techniques , vol. 46, pp. 1654-1659, November 1998; A. E. Bulyshev, A. E. Souvorov, S. Y. Semenov, R. H. Svenson, A. G. Nazarov, Y. E. Sizov, and G. P. Tatsis, “Three-dimensional microwave tomography. Theory and computer experiments in scalar approximation”,  Inverse Problems , vol. 16, pp. 863-875, June 2000; Bulyshev A. E, Souvorov A. E., Semenov S. Y., Posukh V. G., Sizov Y. E. “Three-dimensional Vector Microwave Tomography. Theory and Computational experiments”,  Inverse Problems,  2004, 20, 4, 1239-1259; Semenov S. Y., Bulyshev A. E., Souvorov A. E., Svenson R. H., Sizov Y. E., Borisov V. Y., Posukh V. G., Kozlov I. M., Nazarov A. G., Tatsis G. P. “Microwave Tomography: Theoretical and Experimental Investigation of the Iteration Reconstruction Algorithm”,  IEEE Trans MTT,  1998, 46, 2, 133-141; Semenov S. Y., Bulyshev A. E., Abubakar A., Posukh V. G., Sizov Y. E., Souvorov A. E., Van den Berg P., Williams T. “Microwave tomographic imaging of the high dielectric contrast objects using different imaging approaches”,  IEEE Trans. MTT , v. 53, No 7, pp 2284-2294, 2005; Semenov S. Y., Kellam J. F., Althausen P., Williams T. C., Abubakar A., Bulyshev A., Sizov Y. “Microwave tomography for functional imaging of extremity soft tissues. Feasibility assessment”,  Phys. Med. Biol.,  2007, 52, 5705-5719; Semenov S. Y., Bulyshev A. E., Posukh V. G., Sizov Y. E., Williams T. C., Souvorov A. E. “Microwave tomography for detection/imaging of myocardial infarction. 1. Excised canine hearts”,  Annals of Biomedical Engineering,  2003, 31, 262-270. However, none of above methods cited in these previous works have proven to be effective when imaging inside of strongly shielded objects. A new approach is needed for the accurate representation of EMT imaging of objects that have a high dielectric contrast shield, such as but not limited to the human brain. 
     SUMMARY OF THE PRESENT INVENTION 
     Some exemplary embodiments of the present invention may overcome one or more of the above disadvantages and other disadvantages not described above, but the present invention is not required to overcome any particular disadvantage described above, and some exemplary embodiments of the present invention may not overcome any of the disadvantages described above. 
     Broadly defined, the present invention according to one aspect is a method of the use of electromagnetic field in such a manner that an electromagnetic interference picture is generated within an imaging domain, revealing the superposition of 3D dielectric structure of an object together with electromagnetic interference pattern, while electromagnetic interference pattern was further recognized and applied to a 3D electromagnetic superposition, nullifying or diminishing electromagnetic interference pattern and revealing 3D dielectric structure of an object. 
     Broadly defined, the present invention according to another aspect is a method of 4D dynamic fused electromagnetic pattern recognition tomography. 
     Broadly defined, the present invention according to another aspect is a method of monitoring of viability and functional conditions of biological tissue utilizing 4D dynamic fused electromagnetic recognition tomography. 
     Broadly defined, the present invention according to another aspect is an electromagnetic Interference Pattern Recognition Tomography (EMIPRT) method for use in an image reconstruction system, as shown and described. 
     Broadly defined, the present invention according to another aspect is an Electromagnetic Interference Pattern Recognition Tomography (EMIPRT) method for use in an image reconstruction system, comprising: via an electromagnetic tomography system, generating electromagnetic field data corresponding to an object in an imaging domain, wherein the electromagnetic field data is measured at a plurality of receivers after being produced at a plurality of transmitters and interacting with the object; and using the generated electromagnetic field data, repeatedly, in a recursive manner: forming an undisturbed electromagnetic interference image, forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image, recognizing electromagnetic interference patterns in the repeatedly formed disturbed electromagnetic interference images, and forming a superposition image by nullifying or diminishing the recognized electromagnetic interference patterns from the disturbed electromagnetic interference image. 
     In a feature of this aspect, the step of forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image includes forming a disturbed electromagnetic interference image based at least in part on determination of an object factor that is a function of the differences between experimentally electromagnetic fields and electromagnetic fields calculated during the step of forming an undisturbed electromagnetic interference image. In further features, the object factor is determined as 
     
       
         
           
             
               
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     where for simplicity the frequency terms are omitted, where  E i= (x,y,z)  and  E j= (x,y,z)  are 3D electromagnetic fields (x,y,z) distribution from electromagnetic sources located at the positions of physical sources (from 1 to N) in the electromagnetic tomography system and at the position of the physical receivers (from 1 to M) correspondingly, taken as conjugate values, and wherein Object Factor i,j  is the “ij” th  component of the object factor, from transmitter i to receiver j. 
     In another feature of this aspect, the method further includes a step, carried out after each repeated step of forming a superposition image, of determining whether a convergence objective has been reached. 
     In another feature of this aspect, the method is used as part of a method of generating 4D differential (dynamic) fused images. In further features, generating 4D differential (dynamic) fused images includes combining at least one successively-formed images indicating relative physiological change with a baseline anatomical image for display as a single unified image; and/or the method of generating 4D differential (dynamic) fused images is used as part of a method of monitoring viability and/or functional conditions of biological tissue utilizing 4D dynamic fused electromagnetic pattern recognition tomography. 
     In another feature of this aspect, the steps of forming an undisturbed electromagnetic interference image, forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image, recognizing electromagnetic interference patterns in the repeatedly formed disturbed electromagnetic interference images, and forming a superposition image by nullifying or diminishing the recognized electromagnetic interference patterns from the disturbed electromagnetic interference image are carried out sequentially. 
     In another feature of this aspect, the method further includes a step of displaying the superposition image via a display unit. 
     Broadly defined, the present invention according to another aspect is a method of reconstructing an image using electromagnetic interference pattern recognition tomography, including: via an electromagnetic tomography system, generating electromagnetic field data corresponding to an object in an imaging domain, wherein the electromagnetic field data is measured at a plurality of receivers after being produced at a plurality of transmitters and interacting with the object; and using the generated electromagnetic field data, repeatedly, in a recursive manner: forming an undisturbed electromagnetic interference image, forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image, recognizing electromagnetic interference patterns in the repeatedly formed disturbed electromagnetic interference images, and forming a superposition image by nullifying or diminishing the recognized electromagnetic interference patterns from the disturbed electromagnetic interference image. 
     In a feature of this aspect, the step of forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image includes forming a disturbed electromagnetic interference image based at least in part on determination of an object factor that is a function of the differences between experimentally electromagnetic fields and electromagnetic fields calculated during the step of forming an undisturbed electromagnetic interference image. 
     In another feature of this aspect, the method further includes a step, carried out after each repeated step of forming a superposition image, of determining whether a convergence objective has been reached. 
     In another feature of this aspect, the method is used as part of a method of generating 4D differential (dynamic) fused images. In further features, generating 4D differential (dynamic) fused images includes combining at least one successively-formed images indicating relative physiological change with a baseline anatomical image for display as a single unified image; and/or the method of generating 4D differential (dynamic) fused images is used as part of a method of monitoring viability and/or functional conditions of biological tissue utilizing 4D dynamic fused electromagnetic pattern recognition tomography. 
     In another feature of this aspect, the steps of forming an undisturbed electromagnetic interference image, forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image, recognizing electromagnetic interference patterns in the repeatedly formed disturbed electromagnetic interference images, and forming a superposition image by nullifying or diminishing the recognized electromagnetic interference patterns from the disturbed electromagnetic interference image are carried out sequentially. 
     In another feature of this aspect, the method further includes a step of displaying the superposition image via a display unit. 
     Broadly defined, the present invention according to another aspect is an image reconstruction system using electromagnetic interference pattern recognition tomography, including: an electromagnetic tomography system that generates electromagnetic field data corresponding to an object in an imaging domain, the electromagnetic tomography system having a plurality of electromagnetic transmitters, a plurality of receivers that measure the electromagnetic data after being produced at the plurality of transmitters and interacting with the object, and a boundary apparatus; and a processing center that, using the generated electromagnetic field data, repeatedly, in a recursive manner: carries out steps of forming an undisturbed electromagnetic interference image, forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image, recognizing electromagnetic interference patterns in the repeatedly formed disturbed electromagnetic interference images, and forming a superposition image by nullifying or diminishing the recognized electromagnetic interference patterns from the disturbed electromagnetic interference image. 
     In a feature of this aspect, the step of forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image includes forming a disturbed electromagnetic interference image based at least in part on determination of an object factor that is a function of the differences between experimentally electromagnetic fields and electromagnetic fields calculated during the step of forming an undisturbed electromagnetic interference image. 
     In another feature of this aspect, the processing center further carries out a step, carried out after each repeated step of forming a superposition image, of determining whether a convergence objective has been reached. 
     In another feature of this aspect, the steps carried out by the processing center are used as part of a method of generating 4D differential (dynamic) fused images. In further features, generating 4D differential (dynamic) fused images includes combining at least one successively-formed images indicating relative physiological change with a baseline anatomical image for display as a single unified image; and/or the method of generating 4D differential (dynamic) fused images is used as part of a method of monitoring viability and/or functional conditions of biological tissue utilizing 4D dynamic fused electromagnetic pattern recognition tomography. 
     In another feature of this aspect, the steps of forming an undisturbed electromagnetic interference image, forming a disturbed electromagnetic interference image based at least in part on the undisturbed electromagnetic interference image, recognizing electromagnetic interference patterns in the repeatedly formed disturbed electromagnetic interference images, and forming a superposition image by nullifying or diminishing the recognized electromagnetic interference patterns from the disturbed electromagnetic interference image are carried out sequentially. 
     In another feature of this aspect, the method further includes a display unit that displays the superposition image. 
     Further areas of applicability of the present invention will become apparent from the detailed description provided hereinafter. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Further features, embodiments, and advantages of the present invention will become apparent from the following detailed description with reference to the drawings, wherein: 
         FIG. 1  is a simplified schematic illustration of portions of an electromagnetic tomography (EMT) system; 
         FIG. 2  is a flow diagram of an EM Interference Pattern Recognition Tomography (EMIPRT) method for use in an image reconstruction system in accordance with one or more preferred embodiments of the present invention; 
         FIG. 3  is an exemplary undisturbed EM interference image for an imaging domain designed for human head imaging; 
         FIGS. 4A and 4B  are exemplary disturbed EM interference images for two human head cases; 
         FIG. 5  is a flow diagram of one pattern recognition strategy and set of equations for the pattern recognition block shown in  FIG. 2 ; 
         FIG. 6  is an image illustrating the subdivision of the imaging domain into sub-domains; 
         FIG. 7  is a flow diagram of one possible pattern correction strategy that may be carried out in the superposition image formation block for each of a plurality of sub-domains; 
         FIG. 8A  is an exemplary output image of a virtual stroke victim&#39;s head from an iterative imaging method similar to the method of  FIG. 2 , but where the pattern recognition block and its output information are not used during the image formation; and 
         FIG. 8B  is an exemplary output image of the same virtual stroke victim&#39;s head as that of  FIG. 8A , but where the pattern recognition block and its output information are used during the superposition image formation block. 
     
    
    
     DETAILED DESCRIPTION 
     As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art (“Ordinary Artisan”) that the present invention has broad utility and application. Furthermore, any embodiment discussed and identified as being “preferred” is considered to be part of a best mode contemplated for carrying out the present invention. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure of the present invention. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the invention and may further incorporate only one or a plurality of the above-disclosed features. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present invention. 
     Accordingly, while the present invention is described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present invention, and is made merely for the purposes of providing a full and enabling disclosure of the present invention. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded the present invention, which scope is to be defined by the claims and the equivalents thereof. It is not intended that the scope of patent protection afforded the present invention be defined by reading into any claim a limitation found herein that does not explicitly appear in the claim itself. 
     Thus, for example, any sequence(s) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order, absent an indication otherwise. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present invention. Accordingly, it is intended that the scope of patent protection afforded the present invention is to be defined by the appended claims rather than the description set forth herein. 
     Additionally, it is important to note that each term used herein refers to that which the Ordinary Artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein—as understood by the Ordinary Artisan based on the contextual use of such term-differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the Ordinary Artisan should prevail. 
     Regarding applicability of 35 U.S.C. § 112, ¶6, no claim element is intended to be read in accordance with this statutory provision unless the explicit phrase “means for” or “step for” is actually used in such claim element, whereupon this statutory provision is intended to apply in the interpretation of such claim element. 
     Furthermore, it is important to note that, as used herein, “a” and “an” each generally denotes “at least one,” but does not exclude a plurality unless the contextual use dictates otherwise. Thus, reference to “a picnic basket having an apple” describes “a picnic basket having at least one apple” as well as “a picnic basket having apples.” In contrast, reference to “a picnic basket having a single apple” describes “a picnic basket having only one apple.” 
     When used herein to join a list of items, “or” denotes “at least one of the items,” but does not exclude a plurality of items of the list. Thus, reference to “a picnic basket having cheese or crackers” describes “a picnic basket having cheese without crackers,” “a picnic basket having crackers without cheese,” and “a picnic basket having both cheese and crackers.” Finally, when used herein to join a list of items, “and” denotes “all of the items of the list.” Thus, reference to “a picnic basket having cheese and crackers” describes “a picnic basket having cheese, wherein the picnic basket further has crackers,” as well as describes “a picnic basket having crackers, wherein the picnic basket further has cheese.” 
     Referring now to the drawings, in which like numerals represent like components throughout the several views, one or more preferred embodiments of the present invention are next described. The following description of one or more preferred embodiment(s) is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses. 
       FIG. 1  is a simplified schematic illustration of portions of an electromagnetic tomography (EMT) system  10 . In the system  10 , a boundary apparatus  12  surrounds an imaging domain  21 . An object  19  (in this example, a human head) is placed in the imaging domain  21 . A plurality of EM hardware devices  30 , usually but not necessarily always disposed on the boundary apparatus  12 , act as transmitters (sources) and/or receivers. (As described herein, the hardware devices  30  are generally considered to act as both transmitters and receivers, but it will be appreciated that transmitters and receivers that are separate from each other may additionally or alternatively be utilized.) A computer system  28  serves as a processing center where initial data is pre-processed and images are reconstructed and post-processed. In at least some embodiments, the EMT system  10  further includes a background or matching fluid or other media. The matching media is a solution or gel that is needed or useful inside the imaging domain when the object  19  is being imaged in order to address electromagnetic body-matching problems and/or other issues. In at least some embodiments, the computer system  28  and its data processing functionality and imaging software is directly connected to the EM field transmitting/receiving hardware devices  30 , while in other embodiments some or all of the computer system  28  is remotely connected through wireless technology and/or high speed wire connections. Functionally, much of the operation of the EMT system  10  may be similar to that described in the aforementioned U.S. Pat. No. 9,414,749 but various particular embodiments and features may be described herein. 
     As described above, EMT imaging of high dielectric contrast objects, including biological objects, involves the very complicated problem of so-called “diffraction tomography.” A high dielectric contrast between tissues with high water content, such as but not limited to muscle tissue, and low water content, such as but not limited to bone, presents an additional complication when using EM fields for imaging.  FIG. 2  is a flow diagram of an EM Interference Pattern Recognition Tomography (EMIPRT) method  100  for use in an image reconstruction system in accordance with one or more preferred embodiments of the present invention. The EMIPRT method  100  may be carried out using EM fields data generated using some or all of the elements of the EMT system  10  described briefly with regard to  FIG. 1 . 
     The EMIPRT method  100  is an iterative process where a convergence check (shown at step  180 ) occurs after each iteration through the various image formation processes until suitable results are obtained and provided as the image output  200 . Primary inputs to the EMIPRT method  100  are imaging domain data  110  and experimentally measured EM data  120 . The imaging domain data  110  includes the spatial location of the source EM field hardware devices  30  at a plurality of spatial locations and at k th  frequencies (k from 1 to K) (E i=1 to N (f k=1 to K )) and the receiver EM field hardware devices  30  at a plurality of spatial locations and at k th  frequencies (k from 1 to K) (E j=1 to M (f k=1 to K )) with respect to the imaging domain  21 , where N is the number of source EM field devices  30  and M is the number of receiver EM field devices  30 . The imaging domain data  110  also includes the dielectric properties of a matching media (ε 0 ) within an imaging domain  21 , and may also include other environmental or physical information/data. The experimentally measured EM data  120  is a matrix containing experimentally measured EM fields (for example as in amplitude, phase and polarization form or as real, imaginary and polarization form) on receivers&#39; points, represented as {Ē ij   Exp (f k )}. EM fields receivers&#39; points may be associated with any or all of the EM field devices  30 . 
     At block  130 , an “undisturbed” EM interference image is formed. In the initial pass, formation of the undisturbed EM interference image (shown as an output  135  of the undisturbed image formation block  130 ) is obtained based only on the imaging domain data  110  of the EMIPRT method  100 . In subsequent iterations, formation of the undisturbed interference image preferably also uses a spatial distribution of dielectric properties within the imaging domain  21 . Depending on the application, the spatial distribution may be a 2D spatial distribution or a 3D spatial distribution. In at least some embodiments, the 3D spatial distribution of dielectric properties is described as ε*(x,y,z) in the Cartesian coordinate system and is obtained during a previous iteration in a superposition image formation block  170 , described below. The 3D spatial distribution of dielectric properties ε*(x,y,z) may be characterized as an image of the study object  19  in dielectric scale. 
     The task of the undisturbed EM interference image formation block  130  is to synthesize a 2D or 3D interference image (picture)  135  from the plurality of EM sources/receivers  30  located within or on the boundary of an imaging domain  21 . During the image formation process, all EM field devices  30  (i=1 to N and j=1 to M) are considered as sources of the EM field. The determination of the (3D) undisturbed EM interference image  135  is generated from the following function: 
     
       
         
           
             
               
                 
                   
                     ∑ 
                     
                       
                         k 
                         = 
                         1 
                       
                       , 
                       N 
                     
                   
                    
                   
                     
                       W 
                       k 
                     
                      
                     
                       
                         ∑ 
                         
                           
                             i 
                             = 
                             1 
                           
                           , 
                           
                             N 
                             ; 
                             
                               j 
                               = 
                               1 
                             
                           
                           , 
                           M 
                         
                       
                        
                       
                         
                           
                             E 
                             i 
                           
                            
                           
                             ( 
                             
                               
                                 f 
                                 k 
                               
                               , 
                               x 
                               , 
                               y 
                               , 
                               z 
                             
                             ) 
                           
                         
                         * 
                         
                           
                             E 
                             j 
                           
                            
                           
                             ( 
                             
                               
                                 f 
                                 k 
                               
                               , 
                               x 
                               , 
                               y 
                               , 
                               z 
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
     where E i=1 to N (f k ,x,y,z) and E j=1 to M (f k ,x,y,z) are the 3D EM fields (x,y,z) distribution within the imaging domain  21  from the EM field devices  30  of probing frequency f k  located at the position of the physical sources (from 1 to N) and at the position of the physical receivers (from 1 to M) correspondingly, and where W k  is the frequency weight function, which accounts for the different input of frequency dependent EM interference images into the function (1).  FIG. 3  is an exemplary undisturbed EM interference image  135  for an imaging domain  21  designed for human head imaging. The image  135  was obtained at a specific frequency, which in this case was 1 GHz. The image is a 2D cross-sectional view in the X-Y plane wherein the X and Y scales are in centimeters. 
     Returning to  FIG. 2 , the next major operation in the EMIPRT method  100  is the determination of an object factor, as shown at block  145 , to be applied to the EM interference image. The object factor is preferably a function of the differences between measured (from block  120 ) and simulated (from block  130 ) EM fields for each pair of transmitting (i=1 to N) to receiving (j=1 to M) devices  30  at a certain probing frequency. Thus, the primary inputs to the object factor determination block  145  are the second of the two main inputs to the method  100  (experimentally measured EM fields on receivers&#39; points  120 , described previously) and input data  140  (calculated or simulated EM Fields on the receivers&#39; points) generated from the undisturbed EM interference image formation block  130 . These two main inputs into the object factor determination block  145  are matrices of data. The first matrix contains experimentally measured EM fields, represented as {Ē ij   Exp (f k )}, and the second matrix contains simulated EM fields, represented as {Ē ij   Sim (f k )}, where in both cases E is a complex vector value representing electrical (E) and/or magnetic (H) components of the EM field. These values might be presented as a complex (real and imaginary parts) value of each vector component of E and/or H field or scalar value of E and/or H field. They also might be presented as amplitude and/or phase of each vector component of E and/or H field or scalar value of E and/or H field. In at least one embodiment of the present invention, the object factor might be presented in general terms as follows: 
     
       
         
           
             
               
                 
                   
                     ( 
                     
                       
                         
                           E 
                           _ 
                         
                         ij 
                         Sim 
                       
                       - 
                       
                         
                           E 
                           ¯ 
                         
                         ij 
                         Exp 
                       
                     
                     ) 
                   
                   
                     
                       M 
                       _ 
                     
                     ij 
                   
                 
               
               
                 
                   ( 
                   2 
                   ) 
                 
               
             
           
         
       
     
     where Ez ij   Sim or Exp  is the experimentally simulated or measured value, respectively, of the z-component of the EM field measured by receiver j when transmitter i is the source of the EM field, where  M ij    is presented in a general form as: 
       α* f ( E   ij   Exp )+β*(Σ ij   Ē   ij *Σ ij   E   ij )+γ*Ω  (3)
 
     where α, β and γ are coefficients of real non-zero or zero values, Ω is a regularization operator, and where f(E ij   Exp ) is a function of its argument. 
     In other embodiments of the present invention, the object factor may have a different composition. This will affect the EM interference image and its pattern. One example of an alternative object factor is presented below for α=1, β=0 and γ=0: 
     
       
         
           
             
               
                 
                   
                     ( 
                     
                       
                         Ez 
                         ij 
                         Sim 
                       
                       - 
                       
                         Ez 
                         ij 
                         Exp 
                       
                     
                     ) 
                   
                   
                     max 
                      
                     
                        
                       
                         ( 
                         
                           Ez 
                           ij 
                           Exp 
                         
                         ) 
                       
                        
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
     where Ez ij   Sim or Exp  is the experimentally simulated or measured value, respectively, of the z-component of the EM field measured by receiver j when transmitter i is the source of the EM field, and where max∥Ez ij   Exp ∥ is the maximal norm of the experimentally measured z-component of the EM field. Another example of an alternative object factor  145  is presented below for α=1, β=0 and γ=0: 
     
       
         
           
             
               
                 
                   
                     ( 
                     
                       
                         Ez 
                         ij 
                         Sim 
                       
                       - 
                       
                         Ez 
                         ij 
                         Exp 
                       
                     
                     ) 
                   
                   
                     
                        
                       
                         ( 
                         
                           Ez 
                           ij 
                           Exp 
                         
                         ) 
                       
                        
                     
                     θ 
                   
                 
               
               
                 
                   ( 
                   5 
                   ) 
                 
               
             
           
         
       
     
     where Ez ij   Sim or Exp  is the experimentally simulated or measured value, respectively, of the z-component of the EM field measured by receiver j when transmitter i is the source of the EM field, and where ∥Ez ij   Exp ∥ θ  is the norm of the experimentally measured z-component of the EM field measured by receiver j when transmitter i is the source of the EM field in power of θ. The power of θ might, for example, have value of 1 or 2 or 3 or ½ or even some other value. 
     Output from the object factor determination block  145  and output from the undisturbed EM interference image formation block  130  (i.e., the 2D or 3D undisturbed EM interference image  135 ) are used in the formation of the disturbed EM interference image, as shown at block  150 . The image generated in block  150  is a 2D or 3D image, depending on the application. Formation of the (3D) disturbed EM interference image in the imaging domain  21 , as shown at block  150 , includes the calculation of sums as shown, for example, in equation (6): 
     
       
         
           
             
               
                 
                   
                     ∑ 
                     
                       
                         k 
                         = 
                         1 
                       
                       , 
                       N 
                     
                   
                    
                   
                     
                       W 
                       k 
                     
                      
                     
                       
                         ∑ 
                         
                           
                             i 
                             = 
                             1 
                           
                           , 
                           
                             N 
                             ; 
                             
                               j 
                               = 
                               
                                 1 
                                  
                                 M 
                               
                             
                           
                         
                       
                        
                       
                         
                           E 
                           i 
                         
                          
                         
                           
                             ( 
                             
                               
                                 f 
                                 k 
                               
                               , 
                               x 
                               , 
                               y 
                               , 
                               z 
                             
                             ) 
                           
                           _ 
                         
                         * 
                         
                           E 
                           j 
                         
                          
                         
                           
                             ( 
                             
                               
                                 f 
                                 k 
                               
                               , 
                               x 
                               , 
                               y 
                               , 
                               z 
                             
                             ) 
                           
                           _ 
                         
                         * 
                         Object 
                          
                         
                             
                         
                          
                         
                           
                             Factor 
                             
                               i 
                               , 
                               j 
                             
                           
                            
                           
                             ( 
                             
                               f 
                               k 
                             
                             ) 
                           
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
     where  E i=1 to N (f k ,x,y,z)  and  E j=1 to M (f k ,x,y,z)  are 3D EM fields (x,y,z) distribution from EM sources  30  of frequency f k  located at the positions of the physical sources (from 1 to N) and at the position of the physical receivers (from 1 to M) correspondingly, taken as conjugate values, and Object Factor i,j  is “ij” th  component of the chosen object factor, from transmitter i to receiver j, as determined at block  145 . 
     The output of the disturbed EM interference image formation block  150  is a disturbed EM interference image  155  (or data representative thereof). In this regard,  FIGS. 4A and 4B  are exemplary disturbed EM interference images  155  for two human head cases. Each image  155  is a 2D cross-sectional view in the X-Y plane wherein the X and Y scales are in centimeters. (It will be appreciated that although  FIGS. 4A and 4B  are 2D illustrations, 3D images may be achieved through further application of the same process.) The alternative object factor shown at (4) was used for these two examples. When comparing the undisturbed EM image  135  of  FIG. 3  and the disturbed EM images  155  of  FIGS. 4A and 4B , the superposition of a dielectric structure of an object  410 , 420  (X-Y cross-section of a human head) together with an EM interference pattern  415 , 425  is clearly seen in  FIGS. 4A and 4B , wherein the outermost dark ring in each illustration represents the human skull, and the topologically similar patterns inside such dark ring are remaining EM interference patterns. 
     After the formation of the disturbed EM interference image in block  150 , the image (data)  155  is sent both to a pattern recognition block  160  and to the superposition image formation block  170 . The interference pattern recognition and its application work as a two-stage process, wherein stage  1  is the pattern recognition itself and stage  2  is the application of a recognized pattern to a 2D or 3D EM superposition image, nullifying or diminishing the EM interference pattern and revealing a 2D or 3D dielectric structure of the object  19 . Those two stages are represented in  FIG. 2  by the pattern recognition block  160  and the superposition image formation block  170 . 
     Various pattern recognition approaches may be utilized. For example,  FIG. 5  is a flow diagram of one pattern recognition strategy  500  and set of equations for the pattern recognition block  160  shown in  FIG. 2 . A 2D example is used in  FIG. 5 , but it will be appreciated that such pattern recognition may be applied across multiple X-Y cross-sections to create a 3D image. As shown in  FIG. 5 , 
     An alternative pattern recognition strategy, sometimes referred to hereinafter as “2ndT optimization,” advantageously allows for the development of much sharper patterns in the initial or first iteration of the superposition image formation block  170  and, as a consequence, makes pattern recognition easier on subsequent iterations. The key feature of 2ndT optimization is to use the generalization of (6) at iteration i&gt;1 for the formation of the superposition image (where for simplicity the frequency terms are omitted): 
     
       
         
           
             
               
                 
                   
                     
                       ∑ 
                       
                         
                           i 
                           = 
                           1 
                         
                         , 
                         
                           N 
                           ; 
                           
                             j 
                             = 
                             1 
                           
                         
                         , 
                         M 
                       
                       
                         at 
                          
                         
                             
                         
                          
                         iteration 
                          
                         
                             
                         
                          
                         i 
                       
                     
                      
                     
                       
                         E 
                         i 
                       
                        
                       
                         
                           ( 
                           
                             x 
                             , 
                             y 
                             , 
                             z 
                           
                           ) 
                         
                         _ 
                       
                       * 
                       
                         E 
                         j 
                       
                        
                       
                         
                           ( 
                           
                             x 
                             , 
                             y 
                             , 
                             z 
                           
                           ) 
                         
                         _ 
                       
                       * 
                       Object 
                        
                       
                           
                       
                        
                       
                         Factor 
                         
                           i 
                           , 
                           j 
                         
                       
                     
                   
                   - 
                   
                     α 
                     * 
                     
                       ( 
                       
                         
                           
                             ɛ 
                             
                               i 
                               - 
                               1 
                             
                           
                            
                           
                             ( 
                             
                               r 
                               ¯ 
                             
                             ) 
                           
                         
                         - 
                         
                           ɛ 
                           bkgd 
                         
                       
                       ) 
                     
                     * 
                     
                       
                         ∑ 
                         
                           
                             i 
                             = 
                             1 
                           
                           , 
                           
                             N 
                             ; 
                             
                               j 
                               = 
                               1 
                             
                           
                           , 
                           M 
                         
                         
                           
                             at 
                              
                             
                                 
                             
                              
                             iteration 
                              
                             
                                 
                             
                              
                             i 
                           
                           - 
                           1 
                         
                       
                        
                       
                         
                           E 
                           i 
                         
                          
                         
                           
                             ( 
                             
                               x 
                               , 
                               y 
                               , 
                               z 
                             
                             ) 
                           
                           _ 
                         
                         * 
                         
                           E 
                           j 
                         
                          
                         
                           
                             ( 
                             
                               x 
                               , 
                               y 
                               , 
                               z 
                             
                             ) 
                           
                           _ 
                         
                         * 
                         Object 
                          
                         
                             
                         
                          
                         
                           Factor 
                           
                             i 
                             , 
                             j 
                           
                         
                       
                     
                   
                   - 
                   
                     ( 
                     
                       
                         
                           ɛ 
                           
                             i 
                             - 
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                           ( 
                           
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                         ɛ 
                         bkgd 
                       
                     
                     ) 
                   
                 
               
               
                 
                   ( 
                   7 
                   ) 
                 
               
             
           
         
       
     
     In some embodiments, the coefficient α may be independent from the iteration number (and might be found by trial methods), while in other embodiments it may have an iteration-dependent value which helps to speed up the convergence process. In the latter case, one possible strategy is to have a value of coefficient α at iteration i+1 as the maximal absolute difference between the image at iteration i and the image at iteration i−1: 
       max∥ε( x,y,z ) i −ε( x,y,z ) i-1 ∥.  (8)
 
     In other embodiments of the present invention, other pattern recognition strategies and/or algorithm to find optimal coefficient α might be used. 
     In some embodiments, the same pattern recognition strategy/procedure may be performed during every iteration of the method  100 , while in other embodiments, the pattern recognition strategy/procedure may be dynamically updated after each successive iteration. 
     The output from the pattern recognition block  160  and the output from the disturbed EM interference image formation block  150  (i.e., the disturbed EM interference image  155 ) are provided to the superposition image formation block  170 . Superposition image formation  170  may be done by recognizing the patterns via block  160 , comparing them to the disturbed EM interference images  155 , and then doing pattern corrections. In block  170  the data is analyzed to diminish or nullify the EM interference pattern and reveal the true 2D or 3D structure of an object  19 , represented as the image output  200 . 
     Pattern correction may be carried out as follows. First, in at least some embodiments of the method  100 , the whole imaging domain  21  may subdivided into a number of characteristic imaging sub-domains. In this regard,  FIG. 6  is an image  600  illustrating the subdivision of the imaging domain  21  into sub-domains. More particularly, using the example of a human head as the object  19 , the imaging domain  21  may subdivided into an outer (external) domain  605 , a skull domain  610 , a cerebrospinal fluid (CSF) domain  615 , a gray matter domain  620 , and a white matter domain  625 .  FIG. 7  is a flow diagram of one possible pattern correction strategy  700  that may be carried out in the superposition image formation block  170  for each sub-domain. At step  705 , a normalized ∫ V  {value of (6) or (7)} is calculated, where V is a volume of the specific sub-domain. At step  710 , a normalized ∫ L-pattern  {value of (6) or (7)} is calculated, where L is the length of the “ip” th  pattern. The logic of step  710  might be easy understood with reference  FIG. 3 . It can be appreciated that there are clear circular patterns. The number of those circular patterns Np is equal to the number of grids over a radius. Thus, for simple circular patterns like those in  FIG. 3 , the integration at step  710  is conducted over each pattern (“ip” th  pattern) from 1 to Np over the length of 2πR Np . Similarly, for more complex object patterns (for example, the somewhat annular patterns presented in  FIGS. 4A and 4B ), step  710  involves the calculation of a normalized ∫ L-pattern  {value of (6) or (7)}, where L is the length of the “ip” th  pattern. Then at step  715 , the image over the ip th  pattern is corrected by setting the new value of (6) or (7) equal to the sum of the old value and {∫ V {(6) or (7)}−∫ L-pattern  {(6) or (7)}. Steps  710  and  715  are then repeated for each ip th  pattern. 
     It will be appreciated that the pattern correction strategy shown in  FIG. 7  is not the only pattern correction strategy that may be utilized; other pattern correction strategies may additionally or alternatively be utilized without departing from the scope of the present invention. 
     After each iteration through block  170 , the output is evaluated against a “convergence objective” at step  180 . When the convergence objective, the final output image  200  is generated. 
     An important benefit of the incorporation of the pattern recognition technology presented herein may be understood as follows. As the iterative process presented in  FIG. 2  progresses, the influence of EM interference patterns tends to significantly distort the resulting images of the target 2D or 3D dielectric structure. This is because of the amplification of such a pattern by similar EM interference patterns obtained on the following iterations and the resulting distortion in images produced thereby. In this regard,  FIG. 8A  is an exemplary output image of a virtual stroke victim&#39;s head  19  from an iterative imaging method similar to the method  100  of  FIG. 2 , but where the pattern recognition block  160  and its output information are not used during the image formation, while  FIG. 8B  is an exemplary output image  200  of the same virtual stroke victim&#39;s head  19  as that of  FIG. 8A , but where the pattern recognition block  160  and its output information are used during the superposition image formation block  170 .  FIG. 8B  clearly reveals the virtual stroke area  820  at right bottom of the image. The image in  FIG. 8A , obtained without the interference pattern recognition block  160  and its application in block  170 , reveals some image distortion within the virtual stroke area  810 , but without greater clarity it can only be speculated that the distortion indicates an area of stroke. 
     It will also be appreciated that although the methodology (including variations and permutations) described herein has been described mostly in terms of 2D and 3D imagery, it is believed equally applicable to 4D EMT technology (3D in space plus 1D in time), wherein 4D dynamic fused electromagnetic tomographic images are generated as described, for example, in the U.S. patent application Ser. No. 13/173,078, the entirety of which has been incorporated herein by reference. 
     Based on the foregoing information, it will be readily understood by those persons skilled in the art that the present invention is susceptible of broad utility and application. Many embodiments and adaptations of the present invention other than those specifically described herein, as well as many variations, modifications, and equivalent arrangements, will be apparent from or reasonably suggested by the present invention and the foregoing descriptions thereof, without departing from the substance or scope of the present invention. 
     Accordingly, while the present invention has been described herein in detail in relation to one or more preferred embodiments, it is to be understood that this disclosure is only illustrative and exemplary of the present invention and is made merely for the purpose of providing a full and enabling disclosure of the invention. The foregoing disclosure is not intended to be construed to limit the present invention or otherwise exclude any such other embodiments, adaptations, variations, modifications or equivalent arrangements; the present invention being limited only by the claims appended hereto and the equivalents thereof.