Patent Publication Number: US-2023162861-A1

Title: Neuronal Activity Mapping Using Phase-Based Susceptibility-Enhanced Functional Magnetic Resonance Imaging

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Pat. Application Serial No. 63/015,145, filed on Apr. 24, 2020, and entitled “NEURONAL ACTIVITY MAPPING USING PHASE-BASED SUSCEPTIBILITY-ENHANCED FUNCTIONAL MAGNETIC RESONANCE IMAGING,” which is herein incorporated by reference in its entirety. 
    
    
     BACKGROUND 
     Blood oxygen level dependent (“BOLD”) functional magnetic resonance imaging (‘fMRI”) contrast is derived from changes in regional blood concentrations of oxyhemoglobin and deoxyhemoglobin in direct response to brain activity. Based on these changes, the local heterogeneity of the magnetic field causes dephasing of fMRI signal in the immediate vicinity of brain activation. Heavily T2*-weighted sequences are typically used to detect this change, which is on the order of 1-5%. Routine fMRI processing only utilizes magnitude information from the source images to create activation maps. All phase information is discarded and not utilized for routine fMRI processing. 
     SUMMARY OF THE DISCLOSURE 
     The present disclosure addresses the aforementioned drawbacks by providing a method for generating a functional activation map indicative of neuronal activity in a subject. Functional magnetic resonance imaging (“fMRI”) data are accessed with a computer system, where the fMRI data include a time-series of images containing magnitude information and phase information, and where the time-series of images was acquired from a subject using an MRI system. Phase images are extracted from the fMRI data using the computer system, where each phase image depicts the phase information in the fMRI data. Phase masks are generated from the phase images with the computer system, where each phase mask scales phase information in the phase image to a range of values. Susceptibility-enhanced images are then generated with the computer system by applying the phase masks to the magnitude information in the fMRI data, generating output as the susceptibility-enhanced images. A functional activation map is then generated from the susceptibility-enhanced images using the computer system, where the functional activation map depicts neuronal activity that occurred in the subject when the fMRI data were acquired. 
     The foregoing and other aspects and advantages of the present disclosure will appear from the following description. In the description, reference is made to the accompanying drawings that form a part hereof, and in which there is shown by way of illustration a preferred embodiment. This embodiment does not necessarily represent the full scope of the invention, however, and reference is therefore made to the claims and herein for interpreting the scope of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a workflow for generating susceptibility-enhanced functional magnetic resonance imaging (“fMRI”) data. 
         FIG.  2    is a flowchart setting forth the steps of an example method for generating functional activation maps from susceptibility-enhanced fMRI data. 
         FIG.  3    is a block diagram of an MRI system that can implement methods described in the present disclosure. 
         FIG.  4    is a block diagram of an example system for generating susceptibility-enhanced functional activation maps. 
         FIG.  5    is a block diagram of example components that can implement the system of  FIG.  4   . 
     
    
    
     DETAILED DESCRIPTION 
     Described here are systems and methods for functional magnetic resonance imaging (“fMRI”) that make use of both the magnitude and phase information contained in magnetic resonance signals in order to enhance the visualization of blood-oxygenation-level-dependent (“BOLD”) fMRI activation. As a result, the functional activation maps generated with these techniques are more sensitive to subtle neuronal activity than maps generated with conventional fMRI techniques, which utilize only magnitude information. 
     In general, post-processing techniques are implemented to preserve and utilize phase information to accentuate the detection of signal changes related to diamagnetic oxyhemoglobin versus paramagnetic deoxyhemoglobin. As a result of this post-processing, the sensitivity for detection of BOLD contrast changes is increased relative to existing fMRI techniques. 
     A general workflow showing an example method for generating functional activation maps using both the magnitude and phase information contained in magnetic resonance data is shown in  FIG.  1   . Data are acquired from a subject during an fMRI scan and from these data, which are complex-valued data, magnitude and phase images are reconstructed. The raw phase images are filtered to create filtered phase images, which are then processed together with the magnitude images in order to generate one or more phase masks. Using the phase mask(s), susceptibility-enhanced images are generated. These susceptibility-enhanced images are then processed using fMRI processing techniques to generate functional activity, or activation, maps. As compared to activation maps generated directly from the magnitude images, the activation maps created from the susceptibility-enhanced images show increased sensitivity to detecting neuronal activation patterns. 
     Referring now to  FIG.  2   , a flowchart is illustrated as setting forth the steps of an example method for generating susceptibility-enhanced fMRI images, from which functional activation maps are then also generated. 
     The method includes accessing magnetic resonance imaging data with a computer system, as indicated at step  202 . Accessing these data can include retrieving previously acquired data from a memory or other data storage device or medium. Additionally or alternatively, accessing these data can include acquiring the data with an MRI system and transferring or otherwise communicating the data to the computer system, which may be a part of the MRI system. 
     In general, the magnetic resonance imaging data may be fMRI data, which may include images and/or data acquired using fMRI techniques, such as those that acquire images that depict a BOLD contrast. Preferably, fMRI data are acquired by imaging the subject’s brain, but in some examples fMRI data may be acquired by imaging other portions of the subject’s central nervous system, such as the spinal cord. 
     The fMRI data can be acquired during a resting state (i.e., resting state fMRI data) or during the performance of one or more functional tasks (i.e., activation-based fMRI data). As a non-limiting example, functional tasks that can be performed by a subject during an fMRI scan can include motor tasks, language tasks, memory-based tasks, visual tasks, and so on. 
     As indicated at step  204 , phase data are extracted from the fMRI data. When multiple receive coils are used to acquire the magnetic resonance data, the phase data can be extracted on a coil-by-coil basis and then combined. Extracting the phase data can include extracting the phase information from k-space data, or the phase component of complex-valued images. In general, the phase data will include one or more phase images, which depict a spatial distribution of the phase information contained in the measured magnetic resonance signals represented in the fMRI data. 
     A filtered phase image is generated from the extracted phase data, as indicated at step  206 . For example, the filtered phase image can be generated by applying a high-pass filter to a phase image contained in the phase data. Using a high-pass filter reduces low-spatial frequency components from the background field. Additionally or alternatively, phase unwrapping can be applied in order to generate the filtered phase image. As is known in the art, phase unwrapping can be implemented so that the phase range of the phase images is 2π (e.g., 0 to 2π, or -π to π). 
     One or more phase masks are then generated, as indicated at step  208 . The phase masks generally scale data from the filtered image over a range of values, such as 0-1. The phase mask is designed to suppress signal intensities in areas where the phase information has certain values (e.g., diamagnetic and/or paramagnetic susceptibility changes), which may be selected by a user. As one non-limiting example, the phase mask can map values from the phase image that are in a first range of phase values to a first range of phase mask values, and values from the phase image that are in a second range of phase values to a second range of phase mask values. Different mappings of phase values can also be used in order to highlight different signals (e.g., diamagnetic or paramagnetic). 
     As one non-limiting example, the first range of phase values may be phase values greater than zero radians and the first range of phase mask values may be equal to 1, such that all phase values greater than zero radians are mapped to a value of 1 in the phase mask. In this same example, the second range of phase value may be ―π to 0 radians and the second range of phase mask values may be 0 up to 1 (i.e., [0,1) ). Alternatively, phase values of zero may be mapped to a value of 1 in the phase mask. This example may be referred to as a “diamagnetic” phase mask, since the phase values corresponding to a negative or no shift (diamagnetic effects) are enhanced by the phase mask positive shifts (paramagnetic effects) are unchanged. Alternatively, the second range of phase mask values may be 0, such that all phase values in the second range of phase values are mapped to a value of 0 in the phase mask. 
     For instance, the first range of phase values may be –π to 0 radians and the first range of phase mask values may be equal to 1, such that all phase values less than or equal to zero radians are mapped to a value of 1 in the phase mask. Then, the second range of phase values may be phase values greater than zero and the second range of phase mask values may be 0 up to 1 (i.e., [0, 1)). Alternatively, phase values of zero may be mapped to a value of 1 in the phase mask. This example may be referred to as a “paramagnetic” phase mask, since the phase values corresponding to a positive shift (paramagnetic effects) are enhanced by the phase mask while negative or no phase shifts (diamagnetic effects) are unchanged. Alternatively, the second range of phase mask values may be 0, such that all phase values in the second range of phase values are mapped to a value of 0 in the phase mask. 
     In still other examples, the first and/or second range of phase values may correspond to any arbitrary range of phase values, and the first and/or second range of phase mask values may correspond to any range of phase mask values. In any instance, the phase values in a particular range may be mapped to phase mask values using a linear mapping, a power function mapping, or other suitable mapping function. 
     In some instances, two phase masks are generated from the filtered phase image: a diamagnetic susceptibility phase mask and a paramagnetic susceptibility phase mask. These maps can be individually applied to the magnitude images in the fMRI data, or may be combined to form a combined phase mask that is applied to the magnitude images in the fMRI data. 
     Susceptibility-enhanced fMRI data are then generated from the magnitude information in the fMRI data using the phase mask (e.g., a diamagnetic susceptibility phase mask), as indicated at step  210 . For instance, a magnitude image in the fMRI data can be multiplied by the phase mask to generate a corresponding susceptibility-enhanced image in the susceptibility-enhanced fMRI data. The magnitude image may be repeatedly multiplied by the phase mask until a desired mix of phase information is applied to the magnitude image. For example, n =1, 2, 3, 4, ... N different multiplications can be performed. The number of multiplications can be optimized to achieve a desired contrast-to-noise ratio (“CNR”) in the susceptibility enhanced image(s). 
     Functional activation maps are then generated from the susceptibility-enhanced fMRI data, as indicated at step  212 . For instance, statistical-based functional activation maps can be created from the susceptibility-enhanced fMRI data using conventional fMRI processing tools. These functional activation maps can include activation-based maps, in which the neuronal activation is associated with neuronal activity induced in response to the performance of a functional task. The functional activation maps can also include resting-state maps, in which the neuronal activation is associated with neuronal activity occurring when the subject is resting or otherwise not performing a particular functional task. 
     The functional activation maps can be output to a user, as indicated at step  214 . For example, the maps can be displayed to a user. Additionally or alternatively, the maps can be stored for later use or processing. In addition to the functional activation maps, the fMRI data, phase image(s), filtered phase image(s), phase mask(s), and/or susceptibility-enhanced fMRI data can also be output to the user. 
     This post-processing described in the present disclosure substantially increases the measured fMRI BOLD activation signal, and permits creation of activation maps with higher statistical confidence. Susceptibility enhancement is typically performed using either paramagnetic or diamagnetic susceptibility enhancement, and can be applied to all data (e.g., control and data frames), or only to data frames. A combination diamagnetic-paramagnetic difference phase mask can also be generated for post-processing. The degree of filtering in the phase image and degree of exponential phase-mask processing are variables that can be optimized for the specific fMRI activation task being studied. 
     This post-processing technique may also be adapted to DICOM level processing if phase images are generated at the time of fMRI acquisition. The DICOM phase images would then then be unwrapped and filtered. Susceptibility maps would be created and applied to magnitude images to produce an enhanced fMRI dataset that could then be used for creation of fMRI activation maps using standard processing tools. 
     Referring particularly now to  FIG.  3   , an example of an MRI system  300  that can implement the methods described here is illustrated. The MRI system  300  includes an operator workstation  302  that may include a display  304 , one or more input devices  306  (e.g., a keyboard, a mouse), and a processor  308 . The processor  308  may include a commercially available programmable machine running a commercially available operating system. The operator workstation  302  provides an operator interface that facilitates entering scan parameters into the MRI system  300 . The operator workstation  302  may be coupled to different servers, including, for example, a pulse sequence server  310 , a data acquisition server  312 , a data processing server  314 , and a data store server  316 . The operator workstation  302  and the servers  310 ,  312 ,  314 , and  316  may be connected via a communication system  340 , which may include wired or wireless network connections. 
     The pulse sequence server  310  functions in response to instructions provided by the operator workstation  302  to operate a gradient system  318  and a radiofrequency (“RF”) system  320 . Gradient waveforms for performing a prescribed scan are produced and applied to the gradient system  318 , which then excites gradient coils in an assembly  322  to produce the magnetic field gradients G x , G y , and G z  that are used for spatially encoding magnetic resonance signals. The gradient coil assembly  322  forms part of a magnet assembly  324  that includes a polarizing magnet  326  and a whole-body RF coil  328 . 
     RF waveforms are applied by the RF system  320  to the RF coil  328 , or a separate local coil to perform the prescribed magnetic resonance pulse sequence. Responsive magnetic resonance signals detected by the RF coil  328 , or a separate local coil, are received by the RF system  320 . The responsive magnetic resonance signals may be amplified, demodulated, filtered, and digitized under direction of commands produced by the pulse sequence server  310 . The RF system  320  includes an RF transmitter for producing a wide variety of RF pulses used in MRI pulse sequences. The RF transmitter is responsive to the prescribed scan and direction from the pulse sequence server  310  to produce RF pulses of the desired frequency, phase, and pulse amplitude waveform. The generated RF pulses may be applied to the whole-body RF coil  328  or to one or more local coils or coil arrays. 
     The RF system  320  also includes one or more RF receiver channels. An RF receiver channel includes an RF preamplifier that amplifies the magnetic resonance signal received by the coil  328  to which it is connected, and a detector that detects and digitizes the I and Q quadrature components of the received magnetic resonance signal. The magnitude of the received magnetic resonance signal may, therefore, be determined at a sampled point by the square root of the sum of the squares of the I and Q components: 
     
       
         
           
             M 
             = 
             
               
                 
                   I 
                   2 
                 
                 + 
                 
                   Q 
                   2 
                 
               
             
           
         
       
     
      and the phase of the received magnetic resonance signal may also be determined according to the following relationship: 
     
       
         
           
             φ 
             = 
             
               
                 tan 
               
               
                 − 
                 1 
               
             
             
               
                 
                   Q 
                   I 
                 
               
             
           
         
       
     
     The pulse sequence server  310  may receive patient data from a physiological acquisition controller  330 . By way of example, the physiological acquisition controller  330  may receive signals from a number of different sensors connected to the patient, including electrocardiograph (“ECG”) signals from electrodes, or respiratory signals from a respiratory bellows or other respiratory monitoring devices. These signals may be used by the pulse sequence server  310  to synchronize, or “gate,” the performance of the scan with the subject’s heart beat or respiration. 
     The pulse sequence server  310  may also connect to a scan room interface circuit  332  that receives signals from various sensors associated with the condition of the patient and the magnet system. Through the scan room interface circuit  332 , a patient positioning system  334  can receive commands to move the patient to desired positions during the scan. 
     The digitized magnetic resonance signal samples produced by the RF system  320  are received by the data acquisition server  312 . The data acquisition server  312  operates in response to instructions downloaded from the operator workstation  302  to receive the real-time magnetic resonance data and provide buffer storage, so that data is not lost by data overrun. In some scans, the data acquisition server  312  passes the acquired magnetic resonance data to the data processor server  314 . In scans that require information derived from acquired magnetic resonance data to control the further performance of the scan, the data acquisition server  312  may be programmed to produce such information and convey it to the pulse sequence server  310 . For example, during pre-scans, magnetic resonance data may be acquired and used to calibrate the pulse sequence performed by the pulse sequence server  310 . As another example, navigator signals may be acquired and used to adjust the operating parameters of the RF system  320  or the gradient system  318 , or to control the view order in which k-space is sampled. In still another example, the data acquisition server  312  may also process magnetic resonance signals used to detect the arrival of a contrast agent in a magnetic resonance angiography (“MRA”) scan. For example, the data acquisition server  312  may acquire magnetic resonance data and processes it in real-time to produce information that is used to control the scan. 
     The data processing server  314  receives magnetic resonance data from the data acquisition server  312  and processes the magnetic resonance data in accordance with instructions provided by the operator workstation  302 . Such processing may include, for example, reconstructing two-dimensional or three-dimensional images by performing a Fourier transformation of raw k-space data, performing other image reconstruction algorithms (e.g., iterative or backprojection reconstruction algorithms), applying filters to raw k-space data or to reconstructed images, generating functional magnetic resonance images, or calculating motion or flow images. 
     Images reconstructed by the data processing server  314  are conveyed back to the operator workstation  302  for storage. Real-time images may be stored in a data base memory cache, from which they may be output to operator display  302  or a display  336 . Batch mode images or selected real time images may be stored in a host database on disc storage  338 . When such images have been reconstructed and transferred to storage, the data processing server  314  may notify the data store server  316  on the operator workstation  302 . The operator workstation  302  may be used by an operator to archive the images, produce films, or send the images via a network to other facilities. 
     The MRI system  300  may also include one or more networked workstations  342 . For example, a networked workstation  342  may include a display  344 , one or more input devices  346  (e.g., a keyboard, a mouse), and a processor  348 . The networked workstation  342  may be located within the same facility as the operator workstation  302 , or in a different facility, such as a different healthcare institution or clinic. 
     The networked workstation  342  may gain remote access to the data processing server  314  or data store server  316  via the communication system  340 . Accordingly, multiple networked workstations  342  may have access to the data processing server  314  and the data store server  316 . In this manner, magnetic resonance data, reconstructed images, or other data may be exchanged between the data processing server  314  or the data store server  316  and the networked workstations  342 , such that the data or images may be remotely processed by a networked workstation  342 . 
     Referring now to  FIG.  4   , an example of a system  400  for generating susceptibility-enhanced functional activation maps in accordance with some embodiments of the systems and methods described in the present disclosure is shown. As shown in  FIG.  4   , a computing device  450  can receive one or more types of data (e.g., fMRI data) from image source  402 , which may be a magnetic resonance image source. In some embodiments, computing device  450  can execute at least a portion of a susceptibility-enhanced functional activation map generation system  404  to generate susceptibility-enhanced fMRI data, from which functional activation maps are then generated, from data received from the image source  402 . 
     Additionally or alternatively, in some embodiments, the computing device  450  can communicate information about data received from the image source  402  to a server  452  over a communication network  454 , which can execute at least a portion of the susceptibility-enhanced functional activation map generation system  404 . In such embodiments, the server  452  can return information to the computing device  450  (and/or any other suitable computing device) indicative of an output of the susceptibility-enhanced functional activation map generation system  404 . 
     In some embodiments, computing device  450  and/or server  452  can be any suitable computing device or combination of devices, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable computer, a server computer, a virtual machine being executed by a physical computing device, and so on. The computing device  450  and/or server  452  can also reconstruct images from the data. 
     In some embodiments, image source  402  can be any suitable source of image data (e.g., measurement data, images reconstructed from measurement data), such as an MRI system, another computing device (e.g., a server storing image data), and so on. In some embodiments, image source  402  can be local to computing device  450 . For example, image source  402  can be incorporated with computing device  450  (e.g., computing device  450  can be configured as part of a device for capturing, scanning, and/or storing images). As another example, image source  402  can be connected to computing device  450  by a cable, a direct wireless link, and so on. Additionally or alternatively, in some embodiments, image source  402  can be located locally and/or remotely from computing device  450 , and can communicate data to computing device  450  (and/or server  452 ) via a communication network (e.g., communication network  454 ). 
     In some embodiments, communication network  454  can be any suitable communication network or combination of communication networks. For example, communication network  454  can include a Wi-Fi network (which can include one or more wireless routers, one or more switches, etc.), a peer-to-peer network (e.g., a Bluetooth network), a cellular network (e.g., a 3G network, a 4G network, etc., complying with any suitable standard, such as CDMA, GSM, LTE, LTE Advanced, WiMAX, etc.), a wired network, and so on. In some embodiments, communication network  454  can be a local area network, a wide area network, a public network (e.g., the Internet), a private or semi-private network (e.g., a corporate or university intranet), any other suitable type of network, or any suitable combination of networks. Communications links shown in  FIG.  4    can each be any suitable communications link or combination of communications links, such as wired links, fiber optic links, Wi-Fi links, Bluetooth links, cellular links, and so on. 
     Referring now to  FIG.  5   , an example of hardware  500  that can be used to implement image source  402 , computing device  450 , and server  452  in accordance with some embodiments of the systems and methods described in the present disclosure is shown. As shown in  FIG.  5   , in some embodiments, computing device  450  can include a processor  502 , a display  504 , one or more inputs  506 , one or more communication systems  508 , and/or memory  510 . In some embodiments, processor  502  can be any suitable hardware processor or combination of processors, such as a central processing unit (“CPU”), a graphics processing unit (“GPU”), and so on. In some embodiments, display  504  can include any suitable display devices, such as a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs  506  can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on. 
     In some embodiments, communications systems  508  can include any suitable hardware, firmware, and/or software for communicating information over communication network  454  and/or any other suitable communication networks. For example, communications systems  508  can include one or more transceivers, one or more communication chips and/or chip sets, and so on. In a more particular example, communications systems  508  can include hardware, firmware and/or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on. 
     In some embodiments, memory  510  can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor  502  to present content using display  504 , to communicate with server  452  via communications system(s)  508 , and so on. Memory  510  can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory  510  can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory  510  can have encoded thereon, or otherwise stored therein, a computer program for controlling operation of computing device  450 . In such embodiments, processor  502  can execute at least a portion of the computer program to present content (e.g., images, user interfaces, graphics, tables), receive content from server  452 , transmit information to server  452 , and so on. 
     In some embodiments, server  452  can include a processor  512 , a display  514 , one or more inputs  516 , one or more communications systems  518 , and/or memory  520 . In some embodiments, processor  512  can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, display  514  can include any suitable display devices, such as a computer monitor, a touchscreen, a television, and so on. In some embodiments, inputs  516  can include any suitable input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, and so on. 
     In some embodiments, communications systems  518  can include any suitable hardware, firmware, and/or software for communicating information over communication network  454  and/or any other suitable communication networks. For example, communications systems  518  can include one or more transceivers, one or more communication chips and/or chip sets, and so on. In a more particular example, communications systems  518  can include hardware, firmware and/or software that can be used to establish a Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on. 
     In some embodiments, memory  520  can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor  512  to present content using display  514 , to communicate with one or more computing devices  450 , and so on. Memory  520  can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory  520  can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory  520  can have encoded thereon a server program for controlling operation of server  452 . In such embodiments, processor  512  can execute at least a portion of the server program to transmit information and/or content (e.g., data, images, a user interface) to one or more computing devices  450 , receive information and/or content from one or more computing devices  450 , receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone), and so on. 
     In some embodiments, image source  402  can include a processor  522 , one or more image acquisition systems  524 , one or more communications systems  526 , and/or memory  528 . In some embodiments, processor  522  can be any suitable hardware processor or combination of processors, such as a CPU, a GPU, and so on. In some embodiments, the one or more image acquisition systems  524  are generally configured to acquire data, images, or both, and can include an MRI system. Additionally or alternatively, in some embodiments, one or more image acquisition systems  524  can include any suitable hardware, firmware, and/or software for coupling to and/or controlling operations of an MRI system. In some embodiments, one or more portions of the one or more image acquisition systems  524  can be removable and/or replaceable. 
     Note that, although not shown, image source  402  can include any suitable inputs and/or outputs. For example, image source  402  can include input devices and/or sensors that can be used to receive user input, such as a keyboard, a mouse, a touchscreen, a microphone, a trackpad, a trackball, and so on. As another example, image source  402  can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc., one or more speakers, and so on. 
     In some embodiments, communications systems  526  can include any suitable hardware, firmware, and/or software for communicating information to computing device  450  (and, in some embodiments, over communication network  454  and/or any other suitable communication networks). For example, communications systems  526  can include one or more transceivers, one or more communication chips and/or chip sets, and so on. In a more particular example, communications systems  526  can include hardware, firmware and/or software that can be used to establish a wired connection using any suitable port and/or communication standard (e.g., VGA, DVI video, USB, RS-232, etc.), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and so on. 
     In some embodiments, memory  528  can include any suitable storage device or devices that can be used to store instructions, values, data, or the like, that can be used, for example, by processor  522  to control the one or more image acquisition systems  524 , and/or receive data from the one or more image acquisition systems  524 ; to images from data; present content (e.g., images, a user interface) using a display; communicate with one or more computing devices  450 ; and so on. Memory  528  can include any suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, memory  528  can include RAM, ROM, EEPROM, one or more flash drives, one or more hard disks, one or more solid state drives, one or more optical drives, and so on. In some embodiments, memory  528  can have encoded thereon, or otherwise stored therein, a program for controlling operation of image source  402 . In such embodiments, processor  522  can execute at least a portion of the program to generate images, transmit information and/or content (e.g., data, images) to one or more computing devices  450 , receive information and/or content from one or more computing devices  450 , receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, etc.), and so on. 
     In some embodiments, any suitable computer readable media can be used for storing instructions for performing the functions and/or processes described herein. For example, in some embodiments, computer readable media can be transitory or non-transitory. For example, non-transitory computer readable media can include media such as magnetic media (e.g., hard disks, floppy disks), optical media (e.g., compact discs, digital video discs, Blu-ray discs), semiconductor media (e.g., random access memory (“RAM”), flash memory, electrically programmable read only memory (“EPROM”), electrically erasable programmable read only memory (“EEPROM”)), any suitable media that is not fleeting or devoid of any semblance of permanence during transmission, and/or any suitable tangible media. As another example, transitory computer readable media can include signals on networks, in wires, conductors, optical fibers, circuits, or any suitable media that is fleeting and devoid of any semblance of permanence during transmission, and/or any suitable intangible media. 
     The present disclosure has described one or more preferred embodiments, and it should be appreciated that many equivalents, alternatives, variations, and modifications, aside from those expressly stated, are possible and within the scope of the invention.