Patent Publication Number: US-11391803-B2

Title: Multi-shot echo planar imaging through machine learning

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application represents the national stage entry of PCT International Application No. PCT/US2019/020569 filed Mar. 4, 2019, which claims the benefit of U.S. Provisional Patent Application Ser. No. 62/637,655 filed on Mar. 2, 2018, and entitled “Improved Multi-Shot Echo Planar Imaging through Machine Learning,” which is herein incorporated by reference in its entirety. 
    
    
     STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH 
     This invention was made with government support under EB02061302 and EB01733704 awarded by the National Institutes of Health. The government has certain rights in the invention. 
    
    
     BACKGROUND 
     MRI has demonstrated ability to provide exquisite contrast for non-invasive imaging. Slow data acquisition has always been a critical limiting factor in MRI. Slow acquisition increases sensitivity to patient motion induced image artifacts and reduces patient throughput and compliance. To limit acquisition time of each imaging scan, 2-dimensional imaging is used with thick slices and slice gaps. These gaps can result in missed information while thick slices necessitate additional scans if viewing in a different plane is desired, resulting in a lengthened MRI exam. 
     SUMMARY OF THE DISCLOSURE 
     The present disclosure addresses the aforementioned drawbacks by providing systems and methods using machine learning for improving MRI data acquisition efficiency while providing more detailed information with high and isotropic resolution without gaps. The systems and methods described in the present disclosure utilize a machine learning algorithm implemented with a hardware processor and a memory to estimate imperfections in fast imaging sequences and remove physiological artifacts caused by patient motion, respiration, cardiac pulsation, and other physiological noise sources. 
     In one configuration, a method is provided for reducing image artifacts using a magnetic resonance imaging system. The method includes acquiring image data of a subject using a magnetic resonance imaging (MRI) system implementing a rapid imaging sequence. The method also includes providing to a computer system a trained machine learning algorithm that has been trained on training data that includes corrupted image data and physiological artifacts in order to reconstruct images from image data such that the reconstructed images depict reduced physiological artifacts. The method also includes applying the acquired image data to the trained machine learning algorithm, and generating an output that includes one or more images reconstructed from the image data, where the one or more images depict reduced physiological artifacts. 
     In one configuration, a system is provided for reducing image artifacts using a magnetic resonance imaging system. The system includes a computer system configured to: i) acquire image data of a subject using a magnetic resonance imaging (MRI) system implementing a rapid imaging sequence; ii) implement a trained machine learning algorithm that has been trained on training data that includes corrupted image data and physiological artifacts in order to reconstruct images from image data such that the reconstructed images depict reduced physiological artifacts; and iii) apply the acquired image data to the trained machine learning algorithm to generate an output that includes one or more images reconstructed from the image data, and where the one or more images depict reduced physiological artifacts 
     In one configuration, a non-transitory computer-readable medium storing instructions is provided. The instructions include a method for reducing image artifacts using a magnetic resonance imaging system. One or more instructions are included that, when executed by one or more processors, cause the one or more processors to: i) acquire image data of a subject using a magnetic resonance imaging (MRI) system implementing a rapid imaging sequence; ii) implement a trained machine learning algorithm that has been trained on training data that includes corrupted image data and physiological artifacts in order to reconstruct images from image data such that the reconstructed images depict reduced physiological artifacts; and iii) apply the acquired image data to the trained machine learning algorithm to generate an output that comprises one or more images reconstructed from the image data, and where the one or more images depict reduced physiological artifacts. 
     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. 1A  is a block diagram of one non-limiting example system in accordance with the present disclosure. 
         FIG. 1B  is a block diagram showing further details of one, non-limiting example of the system of  FIG. 1 . 
         FIG. 1C  is a block diagram of an example computer system that can implement the methods described in the present disclosure. 
         FIG. 2  is a block diagram of an example MRI system that can implement the methods described in the present disclosure. 
         FIG. 3  is a flowchart setting forth some non-limiting examples of steps for one configuration of removing physiological image artifacts using a machine learning routine in accordance with the present disclosure. 
         FIG. 4  illustrates an example of a convolutional neural network for improving multi-shot echo planar imaging. 
     
    
    
     DETAILED DESCRIPTION 
     Described here are systems and methods for improving MRI data acquisition efficiency while providing more detailed information with high and isotropic resolution without gaps. Using the systems and methods described in the present disclosure, the diagnostic power of MRI can be improved, motion sensitivity can be reduced, and patient throughput can be improved to make MRI more cost effective and more widely used in the clinic, particularly in time-sensitive situations. 
     The systems and methods described in the present disclosure utilize a machine learning algorithm implemented with a hardware processor and a memory to estimate imperfections in fast imaging sequences that are difficult to model or otherwise estimate using standard physics-based reconstructions. Such imperfections can include patient motion, physiological noise, phase variations, and the like. The systems and methods described in the present disclosure also provide physics-based image reconstruction with better initialization points, thus allowing synergistic combination with existing techniques. 
     One particular rapid imaging sequence that can be implemented with the systems and methods described in the present disclosure is multi-shot echo planar imaging (MS-EPI). EPI is a fast acquisition technique that can encode large volume (e.g., whole-brain) information in as short as two seconds, but does not lend itself to high-resolution structural imaging due to severe image distortion artifacts. These artifacts stem from the inhomogeneity of the MRI scanner&#39;s magnetic field, and can be mitigated using a multi-shot approach. MS-EPI encodes the image in several shorter segments, thus reducing the exposure to field inhomogeneity during the smaller acquisition windows. Although this mitigates image distortion, MS-EPI has been elusive because combining the multiple shots into a single image can be prohibitively difficult. For instance, the mismatch between the shots caused by physiological motion (respiration, cardiac pulsation, and patient motion) is too complicated to be modeled, and can often lead to severe image artifacts that preclude the usefulness of this method. 
     These physiological variations may be mitigated using additional “navigator” echoes, but at the cost of imaging efficiency and more complex and time consuming reconstruction, and in many cases significant remaining artifacts. Navigators provide 2-dimensional information on the shot-to-shot image phase variations, which are taken into account during conventional image reconstruction. 
     Navigation techniques have so far been applied to spin-echo (SE) acquisitions, where the physiological motion is largely compensated by the imaging sequence. But gradient-echo (GRE) applications have proved elusive since shot-to-shot phase variations can be much more severe. Alternatively, individual images from each shot can be reconstructed using parallel imaging, and shot-to-shot phase variations can be estimated based on the reconstructed images. This entails complicated post-processing and limits the maximum number of shots to around four. 
     Using the systems and methods described in the present disclosure, the need for additional navigator scans that reduce the scan efficiency—while not successfully removing artifacts in many cases—can be obviated. Moreover, using the systems and methods described in the present disclosure MS-EPI with GRE contrast can be achieved. GRE-based imaging sequences form the basis of the important susceptibility weighted imaging and functional MRI acquisitions, where the physiological motion is not compensated by the imaging sequence and is more severe than in SE-based imaging sequences. 
     A machine learning algorithm implemented with a hardware processor and memory may be used to learn and remove physiological artifacts. Many types of machine learning algorithms could be utilized to learn the shot-to-shot motion artifacts. As one non-limiting example, the machine learning algorithm can be a Residual Convolution Neural Network (CNN). These types of networks can be particularly useful for analyzing imaging data that have much fewer parameters that need to be trained than dense networks as a result of parameter sharing. 
     In some implementations, the machine learning algorithm can implement deep learning to add more layers. These additional layers can allow the network to describe complex interactions across many voxels due to the increase in receptive field (e.g., the number of input voxels that contribute to each output). A residual machine learning algorithm, such as a residual CNN, can be used such that rather than estimating the artifact-free image, the mapping between the corrupted images and motion artifacts can be learned instead. The optimization for such a residual mapping is easier than the original mapping between the corrupted and clean images, and deeper networks with improved accuracy can be trained this way. 
     As one non-limiting example, a Residual CNN with three types of layers can be constructed and implemented. The first layer can include convolution plus a rectified linear unit (“RELU”). As an example, this layer can include 64 filters with 3×3 convolutional kernels followed by RELU nonlinearity. The second layer can include convolution plus batch normalization and a RELU. As one example, the batch normalization can convert the samples to zero mean and unit variance and can provide higher learning rates. The last layer can include convolution, and as one example can include a single filter using a 3×3 convolutional kernel. An example of such a Residual CNN is shown in  FIG. 4 . 
     For training, pairs of corrupted MS-EPI data and the physiological motion artifacts can be created, and a patch-based approach can be implemented. A sliding window of size 51×51 (or other suitable size) can be used to extract training patches, and a CNN with a desired number of layers (e.g., 25 layers) can be trained. After estimating and removing the artifacts in each patch, an average over patches in a sliding window fashion can be computed to obtain the final reconstruction. 
     In some instances, training data may include simulated data. Corrupted MS-EPI data and physiological artifacts can be simulated in various ways. One approach can include acquiring in vivo single-shot EPI data time-series, during which physiological changes will naturally occur. After this, k-space data from different time points can be combined to synthesize a corrupted multi-shot acquisition, which will exhibit physiological artifacts. Because clean, single-shot data are also available, the artifact images could be obtained by subtracting the corrupted synthetic MS-EPI data from the clean single-shot images. 
     Referring to  FIG. 1A , an example of a system  100  in accordance with some aspects of the disclosed subject matter is provided. As shown in  FIG. 1 , a computing device  110  can receive multiple types of image data from an image source  102 . In some configurations, the computing device  110  can execute at least a portion of a system for removing or reducing physiological artifacts from medical images  104 . That is, as described above, medical imaging data, such as acquired from an MRI system, may be processed by a machine learning algorithm to remove image artifacts created by physiological motion. 
     Additionally or alternatively, in some configurations, the computing device  110  can communicate information about image data received from the image source  102  to a server  120  over a communication network  108 , which can also include a version of a system for removing or reducing physiological artifacts from medical images  104 . 
     In some configurations, the computing device  110  and/or server  120  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, etc. 
     In some configurations, the image source  102  can be any suitable source of medical image data, such as an MRI, CT, ultrasound, PET, SPECT, x-ray, or another computing device (e.g., a server storing image data), and the like. In some configurations, the image source  102  can be local to the computing device  110 . For example, the image source  102  can be incorporated with the computing device  110  (e.g., the computing device  110  can be configured as part of a device for capturing and/or storing images). As another example, the image source  102  can be connected to the computing device  110  by a cable, a direct wireless link, or the like. Additionally or alternatively, in some configurations, the image source  102  can be located locally and/or remotely from the computing device  110 , and can communicate image data to the computing device  110  (and/or server  120 ) via a communication network (e.g., the communication network  108 ). 
     In some configurations, the communication network  108  can be any suitable communication network or combination of communication networks. For example, the communication network  108  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, etc. In some configurations, the communication network  108  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), other suitable type of network, or any suitable combination of networks. Communications links shown in  FIG. 1  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, etc. 
       FIG. 1B  shows an example of hardware  200  that can be used to implement the image source  102 , computing device  110 , and/or server  120  in accordance with some aspects of the disclosed subject matter. As shown in  FIG. 2 , in some configurations, the computing device  110  can include a processor  202 , a display  204 , one or more inputs  206 , one or more communication systems  208 , memory  210 . In some configurations, the processor  202  can be any suitable hardware processor or combination of processors, such as a central processing unit (CPU), a graphics processing unit (GPU), and the like. In some configurations, the display  204  can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc. In some configurations, the inputs  206  can include any of a variety of 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 the like. 
     In some configurations, the communications systems  208  can include a variety of suitable hardware, firmware, and/or software for communicating information over the communication network  108  and/or any other suitable communication networks. For example, the communications systems  208  can include one or more transceivers, one or more communication chips and/or chip sets, etc. In a more particular example, the communications systems  208  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, etc. 
     In some configurations, the memory  210  can include any suitable storage device or devices that can be used to store instructions, values, etc., that can be used, for example, by the processor  202  to present content using the display  204 , to communicate with the server  120  via the communications system(s)  208 , and the like. The memory  210  can include any of a variety of suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, the memory  210  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, etc. In some configurations, the memory  210  can have encoded thereon a computer program for controlling operation of the computing device  110 . In such configurations, the processor  202  can execute at least a portion of the computer program to present content (e.g., MRI images, user interfaces, graphics, tables, and the like), receive content from the server  120 , transmit information to the server  120 , and the like. 
     In some configurations, the server  120  can include a processor  212 , a display  214 , one or more inputs  216 , one or more communications systems  218 , and/or memory  220 . In some configurations, the processor  212  can be a suitable hardware processor or combination of processors, such as a CPU, GPU, and the like. In some configurations, the display  214  can include a suitable display devices, such as a computer monitor, a touchscreen, a television, and the like. In some configurations, the inputs  216  can include a 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 the like. 
     In some configurations, the communications systems  218  can include a suitable hardware, firmware, and/or software for communicating information over the communication network  108  and/or any other suitable communication networks. For example, the communications systems  218  can include one or more transceivers, one or more communication chips and/or chip sets, and the like. In a more particular example, the communications systems  218  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 the like. 
     In some configurations, the memory  220  can include any suitable storage device or devices that can be used to store instructions, values, and the like, that can be used, for example, by the processor  212  to present content using the display  214 , to communicate with one or more computing devices  110 , and the like. The memory  220  can include any of a variety of suitable volatile memory, non-volatile memory, storage, or any suitable combination thereof. For example, the memory  220  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 the like. In some configurations, the memory  220  can have encoded thereon a server program for controlling operation of the server  120 . In such configurations, the processor  212  can execute at least a portion of the server program to transmit information and/or content (e.g., MRI data, results of image artifact removal, a user interface, and the like) to one or more computing devices  110 , receive information and/or content from one or more computing devices  110 , receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, and the like). 
     In some configurations, the image source  102  can include a processor  222 , imaging components  224 , one or more communications systems  226 , and/or memory  228 . In some embodiments, processor  222  can be any suitable hardware processor or combination of processors, such as a CPU, GPU, and the like. In some configurations, the imaging components  224  can be any suitable components to generate image data corresponding to one or more imaging modes (e.g., T1 imaging, T2 imaging, fMRI, and the like). An example of an imaging machine that can be used to implement the image source  102  can include a conventional MRI scanner (e.g., a 1.5 T scanner, a 3 T scanner), a high field MRI scanner (e.g., a 7 T scanner), an open bore MRI scanner, and the like. 
     Note that, although not shown, the image source  102  can include any suitable inputs and/or outputs. For example, the image source  102  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, hardware buttons, software buttons, and the like. As another example, the image source  102  can include any suitable display devices, such as a computer monitor, a touchscreen, a television, etc., one or more speakers, and the like. 
     In some configurations, the communications systems  226  can include any suitable hardware, firmware, and/or software for communicating information to the computing device  110  (and, in some embodiments, over the communication network  108  and/or any other suitable communication networks). For example, the communications systems  226  can include one or more transceivers, one or more communication chips and/or chip sets, and the like. In a more particular example, the communications systems  226  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, and the like), Wi-Fi connection, a Bluetooth connection, a cellular connection, an Ethernet connection, and the like. 
     In some configurations, the memory  228  can include any suitable storage device or devices that can be used to store instructions, values, image data, and the like, that can be used, for example, by the processor  222  to: control the imaging components  224 , and/or receive image data from the imaging components  224 ; generate images; present content (e.g., MRI images, a user interface, and the like) using a display; communicate with one or more computing devices  110 ; and the like. The memory  228  can include any suitable volatile memory, non-volatile memory, storage, or any of a variety of other suitable combination thereof. For example, the memory  228  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 the like. In some configurations, the memory  228  can have encoded thereon a program for controlling operation of the image source  102 . In such configurations, the processor  222  can execute at least a portion of the program to generate images, transmit information and/or content (e.g., MRI image data) to one or more the computing devices  110 , receive information and/or content from one or more computing devices  110 , receive instructions from one or more devices (e.g., a personal computer, a laptop computer, a tablet computer, a smartphone, and the like). 
     In some configurations, image source  102  may generate any format of medical image data, such as an MRI, CT, ultrasound, PET, SPECT, x-ray, and the like. Medical image data includes not only data for reconstructing the image itself, which may be compressed or not, but also contains patient identification and demographic information and technical information about the exam, including image series data, acquisition or protocol information, and other details. Medical image data may also be in the form of complex image series information, such as time-resolved 2D image series, 3D volumes, and may include additional information, such as elastography data on tissue stiffness or other diagnostic notations. 
     Referring now to  FIG. 1C , a block diagram of an example of a computer system  1200  that can perform the methods described in the present disclosure is shown. The computer system  1200  generally includes an input  1202 , at least one hardware processor  1204 , a memory  1206 , and an output  1208 . Thus, the computer system  1200  is generally implemented with a hardware processor  1204  and a memory  1206 . 
     In some embodiments, the computer system  1200  can be a workstation, a notebook computer, a tablet device, a mobile device, a multimedia device, a network server, a mainframe, one or more controllers, one or more microcontrollers, or any other general-purpose or application-specific computing device. 
     The computer system  1200  may operate autonomously or semi-autonomously, or may read executable software instructions from the memory  1206  or a computer-readable medium (e.g., a hard drive, a CD-ROM, flash memory), or may receive instructions via the input  1202  from a user, or any another source logically connected to a computer or device, such as another networked computer or server. Thus, in some embodiments, the computer system  1200  can also include any suitable device for reading computer-readable storage media. 
     In general, the computer system  1200  is programmed or otherwise configured to implement the methods and algorithms described in the present disclosure. For instance, the computer system  1200  can be programmed to implement the methods described in the present disclosure. 
     The input  1202  may take any suitable shape or form, as desired, for operation of the computer system  1200 , including the ability for selecting, entering, or otherwise specifying parameters consistent with performing tasks, processing data, or operating the computer system  1200 . In some aspects, the input  1202  may be configured to receive data, such as data acquired with an MRI system. Such data may be processed as described above. In addition, the input  1202  may also be configured to receive any other data or information considered useful for implementing the methods described above. 
     Among the processing tasks for operating the computer system  1200 , the one or more hardware processors  1204  may also be configured to carry out any number of post-processing steps on data received by way of the input  1202 . 
     The memory  1206  may contain software  1210  and data  1212 , such as data acquired with an MRI system, and may be configured for storage and retrieval of processed information, instructions, and data to be processed by the one or more hardware processors  1204 . In some aspects, the software  1210  may contain instructions directed to implementing the methods described in the present disclosure. 
     In addition, the output  1208  may take any shape or form, as desired, and may be configured for displaying images, such as images reconstructed or otherwise generated using the methods described in the present disclosure, in addition to other desired information. 
     Referring particularly now to  FIG. 2 , 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     (1);
 
     and the phase of the received magnetic resonance signal may also be determined according to the following relationship: 
     
       
         
           
             
               
                 
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     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&#39;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 particularly to  FIG. 3 , a flowchart setting forth some non-limiting examples of steps for training and implementing a machine learning algorithm to generate images in which physiological artifacts are reduced or otherwise removed. The method includes training and then applying the machine learning algorithm. Training the machine learning algorithm can include acquiring or otherwise accessing previously acquired medical image data, as indicated at step  391 . In one configuration, medical image data includes MR image data acquired with a rapid imaging sequence, such as an MS-EPI sequence. A machine learning algorithm is then trained on these medical image data to learn and reduce or remove the physiological artifacts in the medical image data, as indicated at step  392 . As one example, the machine learning algorithm may be trained as discussed above. The trained machine learning algorithm is then stored for later use, as indicated at step  393 . Storing the machine learning algorithm may include storing weights, biases, or both, which have been computed or otherwise estimated by training the machine learning algorithm on the training data. When the machine learning algorithm implements a neural network, storing the trained machine learning algorithm may include storing the particular neural network architecture to be implemented. For instance, data pertaining to the layers in the neural network architecture (e.g., number of layers, type of layers, ordering of layers, connections between layers) may be stored. 
     The trained machine learning algorithm is then applied to generate images in which physiological artifacts are reduced or otherwise removed. Medical image data are acquiring, or previously acquired medical image data are accessed, as indicated at step  394 . As an example, the medical image data can include MR image data acquired with a rapid imaging sequence, such as an MS-EPI sequence. These medical image data are then input to the trained machine learning algorithm, as indicated at step  395 . Inputting the medical image data to the trained machine learning algorithm generated output, which includes an image in which physiological artifacts have been reduced or otherwise removed, as indicated at step  396 . This resulting image may be displayed for a user or stored for later use, as indicated at step  397 . 
     Referring particularly to  FIG. 4 , a non-limiting example of a convolutional neural network (CNN)  400  for improving multi-shot echo planar imaging is shown. Corrupted MS-EPI image  410  may include real and imaginary channels. Corrupted image patch  415  may be selected to be a size that optimizes the CNN learning rate. The first layer of the CNN may be a convolution and rectified linear unit (RELU) layer  420 . The second layer of the CNN may be a convolution, batch norm, and rectified linear unit (RELU) layer  430 . Any number of layers  440  may be used, and the number of layers may be selected based upon the complexity of the physiological artifacts being removed. A fourth layer of the CNN may be a convolution, batch norm, and rectified linear unit (RELU) layer  450 . A final layer of the CNN may be a convolution layer  460 . An artifact-only image  470  may include real and imaginary channels and may be generated and assessed with a patch  475  for comparison to the original corrupted MS-EPI image  410 . 
     In one non-limiting example, the corrupted MS-EPI image patch  415  was selected to be 51×51×2. 25 layers were used with each of the layers being 3×3×64 and the final layer  460  being 3×3×1. The artifact-only image patch was also selected to be 51×51×2 and included both real and imaginary channels. 
     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.