Method and system for generating a phase contrast image

A system and method for generating a phase contrast image based on an absorption contrast image are provided. The method may include obtaining an absorption contrast image of a first object. The method may further include obtaining a phase contrast image generation model, wherein the phase contrast image generation model may be associated with at least one sample absorption contrast image and at least one sample phase contrast image of a second object. The method may further include executing the phase contrast image generation model to generate a phase contrast image of the first object based on the absorption contrast image of the first object.

TECHNICAL FIELD

This disclosure generally relates to a method and system for X-ray imaging, and more particularly, to a method and system for generating a phase contrast image based on an absorption contrast image.

BACKGROUND

X-ray imaging is commonly used in medical diagnosis for revealing conditions of organs or tissues of a patient. Absorption contrast imaging is a simple and common X-ray imaging technique, and it normally produces high-quality images of hard tissues and low-quality images of soft tissues. A different X-ray imaging technique, phase contrast imaging, may produce images of soft tissues with improved quality. At present, phase contrast imaging may be conducted using a phase contrast imaging device, which is expensive and inconvenient. Moreover, the current phase contrast imaging device usually has a small field of view (FOV) compared to a normal X-ray imaging device, and thus produces a phase contrast image of a limited size. Hence, it is desirable to find a way of obtaining a phase contrast image of any desired size without directly using the phase contrast imaging device.

SUMMARY

According to an aspect of the present disclosure, a system is provided. The system may include a storage device storing a set of instructions and at least one processor in communication with the storage device. When the at least one processor executes the set of instructions, the system may be directed to obtain an absorption contrast image of a first object. The system may be further directed to obtain a phase contrast image generation model, wherein the phase contrast image generation model may be associated with at least one sample absorption contrast image and at least one sample phase contrast image of a second object. The system may be further directed to execute the phase contrast image generation model to generate a phase contrast image of the first object based on the absorption contrast image of the first object.

According to another aspect of the present disclosure, a method is provided. The method may be implemented on a computing device having a storage device storing a set of instructions, and at least one processor communicated with the storage device. The method may include obtaining an absorption contrast image of a first object. The method may further include obtaining a phase contrast image generation model, wherein the phase contrast image generation model may be associated with at least one sample absorption contrast image and at least one sample phase contrast image of a second object. The method may further include executing the phase contrast image generation model to generate a phase contrast image of the first object based on the absorption contrast image of the first object.

According to another aspect of the present disclosure, a system is provided. The system may include an acquisition module and a processing module. The acquisition module may be configured to obtain an absorption contrast image of a first object. The acquisition module may be further configured to obtain a phase contrast image generation model, wherein the phase contrast image generation model may be associated with at least one sample absorption contrast image and at least one sample phase contrast image of a second object. The processing module may be configured to execute the phase contrast image generation model to generate a phase contrast image of the first object based on the absorption contrast image of the first object.

According to another aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium may include executable instructions that when executed by at least one processor of an electronic device, directs the at least one processor to perform actions. The actions may include obtaining an absorption contrast image of a first object. The actions may further include obtaining a phase contrast image generation model, wherein the phase contrast image generation model may be associated with at least one sample absorption contrast image and at least one sample phase contrast image of a second object. The actions may further include executing the phase contrast image generation model to generate a phase contrast image of the first object based on the absorption contrast image of the first object.

In some embodiments, the absorption contrast image of the first object and the at least one sample absorption contrast image of the second object may be obtained by scanning with a same scanning parameter and reconstruction with a same reconstruction parameter.

In some embodiments, the phase contrast image generation model may be a neural network model.

In some embodiments, the method may further include obtaining a dark field image generation model, and executing the dark field image generation model based on the absorption contrast image of the first object to generate a dark field image of the first object.

In some embodiments, the dark field image generation model may be trained based on the at least one sample absorption contrast image of the second object and at least one sample dark field image of the second object.

According another aspect of the present disclosure, a system is provided. The system may include a storage device storing a set of instructions and at least one processor in communication with the storage device. When the at least one processor executes the set of instructions, the system may be directed to obtain a preliminary model. The system may be further directed to obtain at least one pair of training images of an object including a sample absorption contrast image and a corresponding sample phase contrast image of the object. The system may be further directed to train the preliminary model based on the at least one pair of training images to generate a phase contrast image generation model.

According to another aspect of the present disclosure, a method is provided. The method may be implemented on a computing device having a storage device storing a set of instructions, and at least one processor communicated with the storage device. The method may include obtaining a preliminary model. The method may further include obtaining at least one pair of training images of an object including a sample absorption contrast image and a corresponding sample phase contrast image of the object. The method may further include training the preliminary model based on the at least one pair of training images to generate a phase contrast image generation model.

According to another aspect of the present disclosure, a system is provided. The system may include an acquisition module and a training module. The acquisition module may be configured to obtain a preliminary model. The acquisition module may be further configured to obtain at least one pair of training images of an object including a sample absorption contrast image and a corresponding sample phase contrast image of the object. The training module may be configured to train the preliminary model based on the at least one pair of training images to generate a phase contrast image generation model.

According to another aspect of the present disclosure, a non-transitory computer readable medium is provided. The non-transitory computer readable medium may include executable instructions that, when executed by at least one processor of an electronic device, directs the at least one processor to perform actions. The actions may include obtaining a preliminary model. The actions may further include obtaining at least one pair of training images of an object including a sample absorption contrast image and a corresponding sample phase contrast image of the object. The actions may further include training the preliminary model based on the at least one pair of training images to generate a phase contrast image generation model.

In some embodiments, the obtaining the pair of training images of the second object may further include obtaining a sample photon signal of the object and separating the sample photon signal into a sample absorption contrast signal and a sample phase contrast signal. The obtaining the pair of training images of the second object may further include generating the sample absorption contrast image based on the sample absorption contrast signal and generating the corresponding sample phase contrast image based on the sample phase contrast signal.

In some embodiments, the obtaining the sample photon signal of the second object may further include scanning the object using a synchrotron light source.

In some embodiments, the object may be a simulated object. The generating the at least one sample photon signal may further include performing at least one numerical simulation on the object to obtain the at least one sample photon signal.

In some embodiments, the phase contrast image generation model may be a neural network model.

In some embodiments, the preliminary model may be trained using a deep learning algorithm.

In some embodiments, the training the preliminary model based on the at least one pair of training images to generate a phase contrast image generation model may further include executing the preliminary model based on the sample absorption contrast image to generate at least one output image. The training the preliminary model based on the at least one pair of training images to generate a phase contrast image generation model may further include training the preliminary model by minimizing the difference between the at least one output image and the corresponding sample phase contrast image to generate the phase contrast image generation model.

DETAILED DESCRIPTION

The present disclosure relates to a method and system for generating a phase contrast image. The method illustrates a way of generating the phase contrast image based on an absorption contrast image and a phase contrast image generation model. The present disclosure also includes a system and method for training a phase contrast image generation model based on a neural network model.

FIG. 1is a schematic diagram illustrating an exemplary imaging system according to some embodiments of the present disclosure. As shown inFIG. 1, the imaging system100may include a scanner110, a network120, one or more terminals130, a processing device140, and a storage150. All the components in the imaging system100may be interconnected via the network120.

The scanner110may scan an object and generate scanned data relating to the object. In some embodiments, the scanner110may be a medical imaging device, for example, an X-ray device, a computed tomography (CT) device, a digital radiography (DR) device, etc. The scanner110may include a gantry111, a detector112, a detecting region113, and a table114. In some embodiments, the scanner110may also include a radioactive scanning source115. The gantry111may support the detector112and the radioactive scanning source115. An object may be placed on the table114for scanning. The radioactive scanning source115may emit radioactive rays to the object. The detector112may detect radiation events (e.g., X-ray) emitted from the detecting region113. In some embodiments, the scanner110may be a CT scanning device and the detector112may include an electric circuit for detecting and receiving X-ray signals.

The network120may include any suitable network that can facilitate exchange of information and/or data for the imaging system100. In some embodiments, one or more components of the imaging system100(e.g., the scanner110, the terminal130, the processing device140, the storage150, etc.) may communicate information and/or data with one or more other components of the imaging system100via the network120. For example, the processing device140may obtain image data from the scanner110via the network120. As another example, the processing device140may obtain user instructions from the terminal130via the network120. The network120may be and/or include a public network (e.g., the Internet), a private network (e.g., a local area network (LAN), a wide area network (WAN)), etc.), a wired network (e.g., an Ethernet network), a wireless network (e.g., an 802.11 network, a Wi-Fi network, etc.), a cellular network (e.g., a Long Term Evolution (LTE) network), a frame relay network, a virtual private network (“VPN”), a satellite network, a telephone network, routers, hubs, witches, server computers, and/or any combination thereof. Merely by way of example, the network120may include a cable network, a wireline network, a fiber-optic network, a telecommunications network, an intranet, a wireless local area network (WLAN), a metropolitan area network (MAN), a public telephone switched network (PSTN), a Bluetooth™ network, a ZigBee™ network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network120may include one or more network access points. For example, the network120may include wired and/or wireless network access points such as base stations and/or internet exchange points through which one or more components of the imaging system100may be connected to the network120to exchange data and/or information.

The terminal(s)130may include a mobile device130-1, a tablet computer130-2, a laptop computer130-3, or the like, or any combination thereof. In some embodiments, the mobile device130-1may include a smart home device, a wearable device, a mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or any combination thereof. In some embodiments, the wearable device may include a bracelet, a footgear, eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the mobile device may include a mobile phone, a personal digital assistance (PDA), a gaming device, a navigation device, a point of sale (POS) device, a laptop, a tablet computer, a desktop, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include a virtual reality helmet, virtual reality glasses, a virtual reality patch, an augmented reality helmet, augmented reality glasses, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a Google Glass™, an Oculus Rift™, a Hololens™, a Gear VR™, etc. In some embodiments, the terminal(s)130may be part of the processing device140.

The processing device140may process data and/or information obtained from the scanner110, the terminal130, and/or the storage150. In some embodiments, the processing device140may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, the processing device140may be local or remote. For example, the processing device140may access information and/or data stored in the scanner110, the terminal130, and/or the storage150via the network120. As another example, the processing device140may be directly connected to the scanner110, the terminal130and/or the storage150to access stored information and/or data. In some embodiments, the processing device140may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the processing device140may be implemented by a computing device200having one or more components as illustrated inFIG. 2.

In some embodiments, the storage150may be connected to the network120to communicate with one or more other components in the imaging system100(e.g., the processing device140, the terminal130, etc.). One or more components in the imaging system100may access the data or instructions stored in the storage150via the network120. In some embodiments, the storage150may be directly connected to or communicate with one or more other components in the imaging system100(e.g., the processing device140, the terminal130, etc.). In some embodiments, the storage150may be part of the processing device140.

FIG. 2is a schematic diagram illustrating an exemplary hardware and software components of a computing device according to some embodiments of the present disclosure.

The computing device200may be a general purpose computer or a special purpose computer, both may be used to implement an imaging system of the present disclosure. In some embodiments, the processing device140may be implemented on the computing device200, via its hardware, software program, firmware, or a combination thereof. For example, the computing device200may obtain a preliminary model. The computing device200may train the preliminary model based on at least one sample absorption contrast image and at least one sample phase contrast image to obtain a trained model. The computing device200may execute a phase contrast image generation model (e.g., the trained model) to generate a phase contrast image based on an absorption contrast image. Although only one such computer is shown, for convenience, the computer functions relating to the CT imaging as described herein may be implemented in a distributed manner on a number of similar platforms, to distribute the processing load.

The computing device200, for example, may include COM ports250connected to and from a network connected thereto to facilitate data communications. The computing device200may also include a central processing unit (CPU)220, in the form of one or more processors, for executing program instructions. The exemplary computer platform may include an internal communication bus210, program storage and data storage of different forms, for example, a disk270, and a read only memory (ROM)230, or a random access memory (RAM)240, for various data files to be processed and/or transmitted by the computer. The exemplary computer platform may also include program instructions stored in the ROM230, RAM240, and/or another type of non-transitory storage medium to be executed by the CPU220. The methods and/or processes of the present disclosure may be implemented as the program instructions. The computing device200also includes an I/O component260, supporting input/output between the computer and other components therein such as user interface elements280. The computing device200may also receive programming and data via network communications.

The computing device200may also include a hard disk controller communicated with a hard disk, a keypad/keyboard controller communicated with a keypad/keyboard, a serial interface controller communicated with a serial peripheral equipment, a parallel interface controller communicated with a parallel peripheral equipment, a display controller communicated with a display, or the like, or any combination thereof.

Merely for illustration, only one CPU and/or processor is described in the computing device200. However, it should be noted that the computing device200in the present disclosure may also include multiple CPUs and/or processors, thus operations and/or method steps that are performed by one CPU and/or processor as described in the present disclosure may also be jointly or separately performed by the multiple CPUs and/or processors. For example, if in the present disclosure the CPU and/or processor of the computing device200executes both operation A and operation B, it should be understood that operation A and operation B may also be performed by two different CPUs and/or processors jointly or separately in the computing device200(e.g., the first processor executes operation A and the second processor executes operation B, or the first and second processors jointly execute operations A and B).

FIG. 3is a schematic diagram illustrating an exemplary hardware and/or software components of an exemplary mobile device that is configured to implement a specific system disclosed in the present disclosure. As illustrated inFIG. 3, the mobile device300may include an antenna310, a display320, a graphic processing unit (GPU)330, a CPU340, an I/O350, a storage360, and a memory390. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in the mobile device300. In some embodiments, a mobile operating system370(e.g., iOS™, Android™, Windows Phone™, etc.) and one or more applications380may be loaded into the memory390from the storage360in order to be executed by the CPU340. The applications380may include a browser or any other suitable mobile apps for receiving and rendering information relating to image processing or other information from the processing device140. User interactions with the information stream may be achieved via the I/O350and provided to the processing device140and/or other components of the imaging system100via the network120. In some embodiments, a user may input parameters to the imaging system100, via the mobile device300, for the imaging system100to execute a phase contrast image generation model. Alternatively or additionally, a user may input parameters to the imaging system100, via the mobile device300, for the imaging system100to train a preliminary model.

In order to implement various modules, units and their functions described above, a computer hardware platform may be used as hardware platforms of one or more elements (e.g., the processing device140and/or other sections of the system100described inFIG. 1). Since these hardware elements, operating systems and program languages are common; it may be assumed that persons skilled in the art may be familiar with these techniques and they may be able to provide information required in the imaging according to the techniques described in the present disclosure. A computer with the user interface may be used as a personal computer (PC), or other types of workstations or terminal devices. After being properly programmed, a computer with the user interface may be used as a server. It may be considered that those skilled in the art may also be familiar with such structures, programs, or general operations of this type of computer device. Thus, extra explanations are not described for the Figures.

FIG. 4is a schematic diagram illustrating an exemplary processing device according to some embodiments of the present disclosure. The processing device140may include an acquisition module410, a training module420, a processing module430, and a storage module440.

The acquisition module410may obtain an image, a model, or other information. In some embodiments, the image may include an absorption contrast image, a phase contrast image, a dark field image, or the like, or any combination thereof. The model may include a phase contrast image generation model, a dark field image generation model, or the like, or any combination thereof.

The training module420may be configured to train a preliminary model and obtain a trained model. The preliminary model and/or the trained model may be a neural network model. The preliminary model may be trained using a deep learning algorithm. The preliminary model and/or the trained model may be executed to generate a phase contrast image based on an absorption contrast image. In some embodiments, the training module420may include an acquisition unit610, a separation unit620, a training unit630, and a processing unit640. More descriptions regarding the training module420may be found elsewhere in the present disclosure. See, e.g.,FIG. 6and the descriptions thereof.

The processing module430may be configured to execute a model and/or generate an image. In some embodiments, the processing module430may execute the phase contrast image generation model to generate a phase contrast image based on an absorption contrast image. When executing the phase contrast image generation model, the processing module430may apply the model to an absorption contrast image. In a case that specialized models corresponding to different organs and tissues of the object are generated, the processing module430may first identify the organs and/or tissues in the absorption contrast image and input the absorption contrast image to the corresponding model. For example, for an absorption contrast image relating to a head of a patient, a phase contrast image generation model for a head may be selected and used by the processing module430.

In some embodiments, the processing module430may execute a dark field image generation model to generate a dark field image based on an absorption contrast image. The dark field image generation model may be trained based on at least one sample absorption contrast image and at least one sample dark field image.

The storage module440may be configured to store the information during the process of generating a phase contrast image. In some embodiments, the storage module410may store an absorption contrast image of a first object, at least one sample absorption contrast image of a second object, at least one sample phase contrast image of the second object, or the like, or any combination thereof. Other modules of the processing device140(e.g., the acquisition module410, the training module420, or the processing module430) may access the information stored in the storage module440.

The modules in the processing device140may be connected to or communicate with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN), a Wide Area Network (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC), or the like, or any combination thereof. Two or more of the modules may be combined into a single module, and any one of the modules may be divided into two or more modules. For example, the acquisition module410and the processing module430may be combined as a single module that performs the corresponding functions. As another example, the training module420and the processing module430may be integrated into a single module that performs the corresponding functions. As a further example, the training module420may be omitted and models may be trained by an external device. Such models may be stored in, e.g., the storage module440, or retrieved from an external device by, e.g., the acquisition module410.

FIG. 5is a flowchart illustrating an exemplary process for determining a phase contrast image according to some embodiments of the present disclosure. The process500may be executed by the processing device140. For example, the process500may be implemented as a set of instructions (e.g., an application) stored in the storage, e.g., ROM230, RAM240, the storage150, the storage390, the storage module440, a storage device external to and accessible by the imaging system100. The CPU220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process500.

In510, the acquisition module410may obtain an absorption contrast image of a first object. In some embodiments, the first object may include a specific portion of a body, a specific organ, or a specific tissue, such as the head, the brain, the neck, the body, a shoulder, an arm, the thorax, the heart, the stomach, a blood vessel, a soft tissue, a knee, a foot, or the like, or any combination thereof. In some embodiments, the object may be a human patient, or a portion thereof. The object may be an animal, a substance, a material, or the like, or any combination thereof. In some embodiments, the absorption contrast image may be obtained via an X-ray imaging device, for example, a digital radiography (DR) device, a computed tomography (CT) device, a C-arm X-ray machine, a four-dimensional CT device, or the like, or any combination thereof. An absorption contrast image may be a shadowgraph. The contrast of grey values of pixels in the absorption contrast image may be generated due to different attenuation coefficient (also referred to as attenuation rate) of various parts in the first object. For example, different parts of an object may attenuate or absorb the X-ray dose differently based on their attenuation coefficient. A detector (e.g., the detector112) may detect the intensities of X-ray that passes through different parts of the object and impinges on the detector112. An absorption contrast image of the object may be reconstructed based on the different intensities of X-ray detected by the detector.

In some embodiments, the absorption contrast image may be of any size. For example, the length (or width) of the absorption contrast image may vary between 3 cm and 100 cm. Merely by way of example, the length (or width) of the absorption contrast image may be 15 cm, 30 cm, 40 cm, 44 cm, 50 cm, 60 cm, etc. In some embodiments, the size of the absorption contrast image of the first object and the size of a sample absorption contrast image (that may be used to train the model applicable to the absorption contrast image) may be different. For example, a sample absorption contrast image may be a small image with a length (or width) varying between 3 cm and 15 cm. The absorption contrast image may be a large image with a length (or width) varying between 5 cm and 100 cm. The absorption contrast image may include an axial image, a coronal image, a sagittal image, or the like, or any combination thereof. The absorption contrast image may be a three-dimensional (3D) image including a stack of two-dimensional (2D) images.

In520, the acquisition module410may obtain a phase contrast image generation model. The phase contrast image generation model may be employed to generate a phase contrast image based on the absorption contrast image. In some embodiments, the phase contrast image generation model may be generated by training based on at least one sample absorption contrast image and at least one sample phase contrast image of a second object that is different from the first object being imaged. The absorption contrast image of the first object and the at least one sample absorption contrast image of the second object may be obtained by scanning with one or more same scanning parameters and reconstruction based on one or more same reconstruction parameters. The scanning parameters may include a tube voltage, a tube current, a tube frequency, a scanning mode, the duration of a scan, or the like, or any combination thereof. The reconstruction parameters may include an image layer thickness in reconstruction, a type of filter, filtering strength, or the like, or any combination thereof. In some embodiments, the absorption contrast image of the first object and the at least one sample absorption contrast image of the second object may be obtained by scanning using a radiation source of a same or similar energy level. For example, both the absorption contrast image of the first object and the at least one sample absorption contrast image of the second object may be obtained by scanning using a radiation source of 120 kV.

In some embodiments, the phase contrast image generation model may be a neural network model. The neural network model may include an artificial neural network (ANN) model, a biological neural network model, a convolutional neural network (CNN), or the like, or any combination thereof. More descriptions regarding the training of the phase contrast image generation may be found elsewhere in the present disclosure. See, e.g.,FIG. 7and the descriptions thereof. More descriptions regarding the structure of a neural network may be found elsewhere in the present disclosure. See, e.g.,FIG. 8and the descriptions thereof.

In530, the processing module430may execute the phase contrast image generation model to generate a phase contrast image of the first object based on the absorption contrast image of the first object. The phase contrast image may correspond to the first object. For example, if the absorption contrast image is an absorption contrast image of a head, the phase contrast image may be a phase contrast image of the head. In some embodiments, the phase contrast image generation model may be a universal model or a specialized model. A universal model may be used to generate a phase contrast image corresponding to one or more of multiple types of absorption contrast images of multiple objects or multiple sections of an object. The universal model may be trained based on a plurality of sample absorption contrast images and a plurality of sample phase contrast images that are associated with one or more of different sections of a type of object (e.g., a human body, a type of animals (e.g., dogs, cats)). In some embodiments, the plurality of sample absorption contrast images and the plurality of sample phase contrast images that are used in training may collectively cover a whole human body. A specialized model may correspond to a specific object or body section. For example, a specialized model of a brain may be used for generating a phase contrast image of a brain based on an absorption contrast image of the brain. If an absorption contrast image of a chest is used with the specialized model of a brain, no phase contrast image may be generated or the generated phase contrast image may be of low quality. As another example, the use of a specialized model for a specific group of objects may be limited to processing images of objects of that group, e.g., for children of a certain age group, the use of the model may be limited to absorption contrast images of children of that age group. A specialized model may be trained based on sample absorption contrast images and sample phase contrast images relating to a particular object, or a particular section of an object, or objects of a certain group, or a particular section of objects of a certain group.

In some embodiments, the acquisition module410may obtain a dark field image generation model. The processing module430may execute the dark field image generation model to generate a dark field image based on the absorption contrast image. In some embodiments, the dark field image generation model may be trained based on the at least one sample absorption contrast image of the second object and at least one sample dark field image of the second object.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations and modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. For example, one or more other optional operations (e.g., an operation of storing intermediate or final results) may be added in the exemplary process500.

FIG. 6is a schematic block diagram illustrating an exemplary training module according to some embodiments of the present disclosure. As shown inFIG. 6, the training module420may include an acquisition unit610, a separation unit620, a training unit630, and a processing unit640.

The acquisition unit610may be configured to obtain a preliminary model. The preliminary model may be a general model with a default inner structure (also referred to as inner parameters). The acquisition unit610may obtain at least one sample photon signal. Alternatively or additionally, the acquisition unit610may obtain at least one sample absorption contrast image and at least one sample phase contrast image. In some embodiments, the acquisition unit610may obtain a half-trained model or a trained model.

The separation unit620may be configured to perform a separation operation. In some embodiments, the separation unit620may separate a photon signal into a sample absorption contrast signal and a sample phase contrast signal. In some embodiments, a photon signal may be separated by an information extraction technique. Exemplary information extraction techniques may include a phase-stepping technique, a reverse-projection technique, a Fourier transform technique, a window-Fourier-transform technique, a conjugate ray pairs algorithm, or the like, or any combination thereof.

The training unit630may be configured to train the preliminary model. The training unit630may train the preliminary model based on at least one sample absorption contrast image and at least one sample phase contrast image. The training unit630may train the preliminary model to generate a phase contrast image generation model. In some embodiment, the preliminary model may be trained using a deep learning algorithm. More descriptions regarding the process for training the preliminary model may be found elsewhere in the present disclosure. See, e.g.,FIG. 7and the descriptions thereof.

The processing unit640may be configured to process information generated or obtained and to generate a processing result accordingly. In some embodiments, the processing unit640may execute the preliminary model based on one of the at least one sample absorption contrast image to generate an output image. The processing unit640may determine a difference between the output image and the sample phase contrast image corresponding to the sample absorption contrast image. The processing unit640may determine whether a preset condition is satisfied.

FIG. 7is a flowchart illustrating an exemplary process for training a model according to some embodiments of the present disclosure. The process700may be executed by the processing device140. For example, the process700may be implemented as a set of instructions (e.g., an application) stored in the storage150, ROM230, RAM240, the storage390, the storage module440, and/or a storage device external to and accessible by the imaging system100. The CPU220may execute the set of instructions, and when executing the instructions, it may be configured to perform the process700. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process700may be accomplished with one or more additional operations not described and/or without one or more of the operations discussed. Additionally, the order in which the operations of the process as illustrated inFIG. 7and described below is not intended to be limiting.

In710, the acquisition unit610may obtain a preliminary model. The preliminary model may be trained to provide a phase contrast image generation model. In some embodiments, the preliminary model may be a neural network model. In some embodiments, the preliminary model may be predefined according to different situations. For example, the inner structure or the parameters of the preliminary model may be predefined according to one or more characteristics (e.g., size, complexity) of a specific object that the preliminary model (and/or a trained model) is associated with. In some embodiments, the preliminary model may be a phase contrast image generation model applicable for multiple objects, and the training process700may retrain the phase contrast image generation model for a desired group of objects or a section thereof.

In720, the acquisition unit610may obtain at least one sample photon signal. A sample photon signal of an object may be processed to provide a sample absorption contrast signal and a sample phase contrast signal of the object. For example, a refractive index may be obtained based on a sample photon signal. The sample absorption contrast signal and the sample phase contrast signal may relate to the real and imaginary part of the refractive index, respectively.

Merely by way of example, the absorption contrast signal μ may be expressed as:

μ=4⁢πλ⁢∫⁢β⁢⁢dz,(1)β=λ4⁢π⁢Σk⁢⁢Nk⁢uk,(2)
where ukdenotes the absorption cross-sectional area, λ denotes the wave length of X-ray, and k denotes the wave number.

The phase absorption contrast signal ϕ may be expressed as:

The refractive index may be expressed as:
n=1−δ+iβ,(5)
where n denotes the refractive index (e.g., the refractive index of the object), δ, the real part of the refractive index, relates to the phase absorption contrast signal ϕ in formula (3), and β, the imaginary part of the refractive index, relates to the absorption contrast signal μ in formula (1).

In some embodiments, a sample photon signal may be obtained by scanning an object using a synchrotron light source. In some embodiments, the object may be a simulated object. At least one simulated scan may be performed on the simulated object to generate the at least one sample photon signal. For example, a numerical simulation (e.g., a Monte Carlo simulation) may be performed on the simulated object to generate the at least one sample photon signal.

In730, the separation unit620may separate each of the at least one sample photon signal into a sample absorption contrast signal and a sample phase contrast signal. In some embodiments, a sample photon signal may be separated by an information extraction technique. Exemplary information extraction techniques may include a phase-stepping technique, a reverse-projection technique, a Fourier transform technique, a window-Fourier-transform technique, a conjugate ray pairs algorithm, or the like, or any combination thereof. The phase stepping algorithm may include a cross phase stepping algorithm, an electromagnetic phase stepping algorithm, etc.

In740, the acquisition unit610may obtain at least one pair of training images including a sample absorption contrast image and a corresponding sample phase contrast image. As used herein, an absorption contrast image may be considered corresponding to a phase contrast image if at least part of the absorption contrast image and the phase contrast image is associated with a same object or a same section of an object. In some embodiments, the sample absorption contrast image and the sample phase contrast signals may be obtained by image reconstruction based on the sample absorption contrast signal and the corresponding sample phase contrast signal, respectively. In some embodiments, the sample absorption contrast signal and the corresponding sample phase contrast signal may be determined by the separation unit620based on a photon signal as described elsewhere. In some embodiments, the sample absorption contrast signal and the corresponding sample phase contrast signal may be acquired directly by scanning an object. In some embodiments, the sample absorption contrast signal and the corresponding sample phase contrast signal (or the corresponding sample absorption contrast image and sample phase contrast image) may be retrieved from an image library. In some embodiments, the sample absorption contrast image may correspond to the sample phase contrast image. For example, the sample absorption contrast image and the sample phase contrast image may correspond to the same object.

In some embodiments, the sample absorption contrast image and the sample phase contrast image of the object may be obtained separately. For example, the sample absorption contrast image of the object may be obtained via an X-ray imaging device, and the sample phase contrast image of the object may be obtained by an X-ray grating phase contrast imaging device. In some embodiments, when the object is a simulated object, the sample absorption contrast image and the sample phase contrast image of the object may be obtained by numerical simulations (e.g., a Monte Carlo simulation).

In some embodiments, the size of the absorption contrast image of an object being imaged and the size of a sample absorption contrast image (used to train the model) used to train the preliminary model may be different. For example, a sample absorption contrast image may be a small image with a length (or width) varying between 3 cm and 15 cm. The absorption contrast image of the object being imaged may be a large image with a length (or width) varying between 5 cm and 100 cm.

In some embodiments, the process700may be an iterative process. For example, the preliminary model may be trained by executing multiple iterations by the training unit630. In each iteration, operations750,760,770, and780may be executed with respect to a pair of sample absorption contrast image and a corresponding sample phase contrast image of a same object, and the preliminary model may be updated accordingly. The iterative process may end when a preset condition is satisfied in780.

In750, the processing unit640may execute the preliminary model or updated preliminary model based on a sample absorption contrast image to generate an output image. The output image may be an output phase contrast image corresponding to the sample absorption contrast image.

In760, the processing unit640may determine a difference between the output image and the sample phase contrast image corresponding to the sample absorption contrast image. In some embodiments, the difference between the output image and the sample phase contrast image may be assessed in terms of a loss function. The loss function may include but not limited to an L1 norm loss function, an L2 norm loss function, a quadratic cost function, a cross-entropy loss function, a log-likelihood cost function, or the like, or any combination thereof.

In770, the processing unit640may update the preliminary model (or further update an updated preliminary model) based on the difference. In some embodiments, the preliminary model may be updated by different strategies. For example, if the difference between the output image and the sample phase contrast image in the present iteration is less than a threshold (e.g., the difference determined in the preceding iteration), part or all parameters of the preliminary model may be updated. If the difference between the output image and the sample phase contrast image in the present iteration is great than the difference in the preceding iteration, the preliminary model is not updated in the current round of iteration.

In780, the processing unit640may determine whether a preset condition is satisfied. If the preset condition is satisfied, the process700may proceed to790; otherwise, the process700may proceed back to750. In some embodiments, the preset condition may include training the preliminary model by all the sample absorption contrast images and the corresponding sample phase contrast images that are available. As another example, the preset condition may include that the difference between the out image and the sample phase contrast image is less than a threshold in one or more consecutive iterations. As a further example, the preset condition may include that the difference between the out image and the sample phase contrast image in the present iteration does not change in a preset number of iterations. As still a further example, the preset condition may be that the trained model converges indicated by, e.g., the parameters of the training model do not change or change within a range over a certain number of iterations.

In790, the processing unit640may provide a trained model. The trained model may be the phase contrast image generation model used to generate a phase contrast image based on the absorption contrast image. More descriptions about using the trained model may be found inFIG. 5and the related descriptions.

FIG. 8is a schematic diagram illustrating the structure of an exemplary neural network model. The neural network model may be used to construct a phase contrast image generation model. In some embodiments, the neural network model may include an artificial neural network (ANN) model, a biological neural network (BNN) model, a convolutional neural network (CNN) model, or the like, or any combination thereof. Taking a CNN model as an example, the CNN model may be executed based on a collection of connected units called artificial neurons (analogous to axons in a biological brain). Each connection (synapse) between the neurons may facilitate a signal transmission from one neuron to another. The receiving (postsynaptic) neuron may process the signal and downstream neurons connected to it. Typically, neurons are organized in layers.

As shown inFIG. 8, the neural network model may include a plurality of layers. Different layers of the plurality of layers may perform different kinds of transformations based on their inputs. For example, a CNN model may include an input layer, a convolutional layer, a pooling layer, a full connected layer, etc. The input of the neural network model810may include at least one sample absorption contrast image. For example, each of x1, x2, . . . xNmay represent one of the at least one sample absorption contrast image. The output of the neural network model820may include at least one sample phase contrast image. For example, each of y1, y2, . . . ymmay represent one of the at least one sample phase contrast image. During a training process, the model may develop its inner structure or parameters of layers based on its input and output.

FIG. 9AandFIG. 9Bare schematic diagrams illustrating an exemplary absorption contrast image and a corresponding phase contrast image, respectively.FIG. 9Ashows an absorption contrast image obtained by scanning an object using an X-ray imaging device.FIG. 9Bshows a phase contrast image generated by a method disclosed in the present disclosure (e.g., by executing the phase contrast image generation model based on the absorption contrast image inFIG. 9A). It can be seen that the phase contrast image shown inFIG. 9Bhas a better quality (e.g., higher signal-to-noise ratio, higher contrast) with respect to soft tissues than the absorption contrast image shown inFIG. 9A.

In some embodiments, the phase contrast image shown inFIG. 9Band the absorption contrast image shown inFIG. 9Amay be used to train the preliminary model. In some embodiments, the processing device140(e.g., the processing module430) may execute a phase contrast image generation model to generate the phase contrast image shown inFIG. 9Bbased on the absorption contrast image shown inFIG. 9A.