Method and system for parametric noise modulation in X-ray imaging

Abstract

The present invention is entitled "Methods and Systems for Parametric Noise Modulation in X-ray Imaging". The present invention provides various methods and systems for x-ray imaging. In one embodiment, a method includes: acquiring an image of a subject; generating a noise modulation map including an estimate of the noise in each pixel of the image based on the image and a plurality of parameters; selectively reducing the noise in the image based on the noise modulation map to generate a final image; and displaying the final image. In this way, the radiation dose during imaging can be reduced while maintaining or even improving the image quality.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to U.S. Provisional Patent Application No. 62 / 941,696, filed on November 27, 2019, which is incorporated herein by reference in its entirety. Background Art

[0003] Embodiments of the subject matter disclosed herein relate to x-ray imaging.

[0004] Imaging techniques such as x-ray imaging allow for the non-invasive acquisition of images of internal structures or features of a subject, such as a patient. Digital x-ray imaging systems produce digital data that can be reconstructed into radiographic images. In a digital x-ray imaging system, radiation from a source is directed toward the subject. A portion of the radiation passes through the subject and strikes a detector. The detector includes an array of discrete picture elements or detector pixels, and generates an output signal based on the amount or intensity of radiation striking each pixel area. The output signal is then processed to generate an image that can be displayed for viewing. These images are used to identify and / or examine internal structures and organs within the patient's body. Summary of the invention

[0005] In one embodiment, a method includes: acquiring an image of a subject; generating a noise modulation map including an estimate of noise in each pixel of the image based on the image and a plurality of parameters; selectively reducing noise in the image based on the noise modulation map to generate a final image; and displaying the final image. In this way, radiation dose during imaging can be reduced while maintaining or even improving image quality.

[0006] It should be understood that the above brief description is provided to introduce in a simplified form selected concepts that are further described in the detailed description. It is not meant to identify key or essential features of the claimed subject matter, the scope of which is solely defined by the claims that follow the detailed description. Furthermore, the claimed subject matter is not limited to implementations that solve any disadvantages noted above or in any part of this disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The present invention will be better understood by reading the following description of non-limiting embodiments with reference to the accompanying drawings, in which:

[0008] Figure 1 shows a pictorial view of an imaging system according to one embodiment;

[0009] Figure 2 shows a block schematic diagram of an exemplary imaging system according to one embodiment;

[0010] Figure 3shows a block diagram illustrating an exemplary noise modulation pattern generator according to one embodiment;

[0011] Figure 4 shows a high-level flow chart illustrating an exemplary method of parametric noise modulation for image noise reduction according to one embodiment;

[0012] Figure 5 shows an exemplary original image without noise reduction applied according to one embodiment;

[0013] Figure 6 According to one embodiment, Figure 5 An exemplary noise modulation diagram generated from an original image of

[0014] Figure 7 According to one embodiment, Figure 5 An exemplary processed image generated from an original image;

[0015] Figure 8 According to one embodiment, Figure 7 An exemplary denoised image generated by processing the image;

[0016] Fig. 9 According to one embodiment, Figure 7 The processed images and Figure 8 An exemplary noise image generated by the denoised image; and

[0017] Fig.10 According to one embodiment, a Figure 6 The noise modulation diagram of Fig. 9 The noisy image and Figure 8 An exemplary noise modulated image generated from a denoised image. DETAILED DESCRIPTION

[0018] The following description relates to various embodiments of x-ray imaging. In particular, systems and methods for parameter noise reduction for x-ray imaging are provided. Figure 1 and Figure 2The imaging system depicted may use an x-ray source and an x-ray detector to acquire radiographic images of a subject, such as a patient. Such radiographic images typically exhibit noise generated by quantum and electron sources, which results in random variations in image brightness, giving the image a mottled, grainy, textured, or snowy appearance. Although noise imparts a generally undesirable appearance to the image, the most significant effect is that the noise may obscure or reduce the visibility of certain features within the image. In addition to increasing the dose to the patient to improve image quality, noise may also be reduced via image processing techniques. The resulting perceived noise is not equal across the entire x-ray image. For example, in a typical x-ray chest examination, areas of low photon counts (such as the abdomen) may exhibit more noise than lower attenuation areas (such as the lungs) where higher photon counts have been registered in the acquired raw image. In addition, the size of the pixels displayed on the viewing screen also affects the appearance of noise. Systems for selectively modulating noise suppression such as Figure 3 The depicted system includes a noise modulation map generator that models a noise metric on a pixel-by-pixel basis in an acquired image based on a plurality of parameters. Figure 4 As shown in the depicted method, the noise modulation map thus acquired can be used to selectively reduce the noise of the entire image based on the estimated noise metric. The method provided herein for selectively modulating noise based on various parameters is achieved by Figures 5 to 10 The exemplary images shown illustrate.

[0019] Although the systems and methods provided herein are described with reference to x-ray imaging technology, it should be understood that the technology provided herein can also be applied to various imaging modes, including various x-ray imaging modes (e.g., single energy, dual energy, X-ray tomography, image pasting, fluoroscopy, mammography, etc.), computed tomography (CT), and positron emission tomography.

[0020] Figure 1 An exemplary CT system 100 configured for CT imaging is shown. Specifically, the CT system 100 is configured to image a subject 112 (such as a patient, an inanimate object, one or more manufactured parts) and / or a foreign object (such as a dental implant, a stent, and / or a contrast agent present within the body). In one embodiment, the CT system 100 includes a gantry 102, which in turn may also include at least one x-ray source 104, which is configured to project an x-ray radiation beam 106 for imaging the subject 112. Specifically, the x-ray source 104 is configured to project the x-rays 106 toward a detector array 108 positioned on an opposite side of the gantry 102. Although Figure 1Only a single x-ray source 104 is shown, but in some embodiments, multiple x-ray radiation sources and detectors may be employed to project multiple x-rays 106 so as to acquire projection data at different energy levels corresponding to the patient. In some embodiments, the x-ray source 104 may implement dual energy gem spectrum imaging (GSI) by fast peak kilovolt voltage (kVp) switching. In some embodiments, the x-ray detector employed is a photon counting detector capable of distinguishing x-ray photons of different energies. In other embodiments, two sets of x-ray tube detectors are used to generate dual energy projections, where one set of x-ray tubes is set to low kVp and the other set to high kVp. It should therefore be understood that the methods described herein may be implemented with single energy acquisition techniques as well as dual energy acquisition techniques.

[0021] In certain embodiments, the CT system 100 further includes an image processor unit 110 configured to reconstruct an image of a target volume of the subject 112 using an iterative or analytical image reconstruction method. For example, the image processor unit 110 may reconstruct an image of the target volume of the patient using an analytical image reconstruction method such as filtered back projection (FBP). As another example, the image processor unit 110 may reconstruct an image of the target volume of the subject 112 using an iterative image reconstruction method such as advanced statistical iterative reconstruction (ASIR), conjugate gradient (CG), maximum likelihood expectation maximization (MLEM), model-based iterative reconstruction (MBIR), etc. As further described herein, in some examples, in addition to iterative image reconstruction methods, the image processor unit 110 may also use analytical image reconstruction methods such as FBP.

[0022] In some CT imaging system configurations, a radiation source projects a cone beam that is collimated to lie in the XYZ plane of a Cartesian coordinate system and is often referred to as the "imaging plane". The radiation beam passes through an object being imaged, such as a patient or subject 112. The beam, after being attenuated by the object, impinges upon an array of radiation detectors. The intensity of the attenuated radiation beam received at the detector array depends on the attenuation of the radiation beam by the object. Each detector element of the array produces a separate electrical signal that is a measure of the attenuation of the beam at the detector location. Attenuation measurements from all detector elements are acquired individually to produce a transmission profile.

[0023] In some CT systems, a gantry is used to rotate the radiation source and detector array around the object to be imaged in the imaging plane so that the angle at which the radiation beam intersects the object is constantly changing. A set of radiation attenuation measurements (e.g., projection data) from the detector array at one gantry angle is called a "view". A "scan" of an object includes a set of views made at different gantry angles or viewing angles during one rotation of the radiation source and detector. It is conceivable that the benefits of the methods described herein derive from medical imaging modalities other than CT, so as used herein, the term "view" is not limited to the use described above with respect to projection data from one gantry angle. The term "view" is used to mean a data acquisition whenever there are multiple data acquisitions from different angles (whether from CT, positron emission tomography (PET) or single photon emission CT (SPECT) acquisitions), and / or any other modality (including modalities yet to be developed) and their combination in fusion embodiments.

[0024] The projection data is processed to reconstruct an image corresponding to a two-dimensional slice acquired through the object, or in some examples where the projection data includes multiple views or scans, an image corresponding to a three-dimensional rendering of the object. A method for reconstructing an image from a set of projection data is known in the art as a filtered back projection technique. Transmission and emission tomography reconstruction techniques also include statistical iterative methods such as maximum likelihood expectation maximization (MLEM) and ordered subset expectation reconstruction techniques and iterative reconstruction techniques. The method converts the attenuation measurements from the scan into an integer called a "CT number" or "Hou's unit", which is used to control the brightness of the corresponding pixel on the display device.

[0025] To reduce the total scan time, a "helical" scan can be performed. To perform a "helical" scan, the patient is moved while data for a specified number of slices is acquired. Such systems produce a single spiral from a cone beam helical scan. The spiral mapped out by the cone beam produces projection data from which an image in each specified slice can be reconstructed.

[0026] As used herein, the phrase "reconstructing an image" is not intended to exclude embodiments of the invention in which data representing an image is generated rather than a visible image. Thus, as used herein, the term "image" broadly refers to both a visible image and data representing a visible image. However, many embodiments generate (or are configured to generate) at least one visible image.

[0027] Figure 2 Shows something like Figure 1 1. An exemplary imaging system 200 of the CT system 100 of FIG. According to aspects of the present disclosure, the imaging system 200 is configured to image a subject 204 (eg, Figure 1In one embodiment, the imaging system 200 includes a detector array 108 (see Figure 1 ). The detector array 108 also includes a plurality of detector elements 202 that together sense the x-ray beam 106 that passes through a subject 204 (such as a patient) (see Figure 1 ) to collect corresponding projection data. Therefore, in one embodiment, the detector array 108 is manufactured in a multi-slice configuration including multiple rows of cells or detector elements 202. In such a configuration, one or more additional rows of detector elements 202 are arranged in a parallel configuration for collecting projection data.

[0028] In certain embodiments, the imaging system 200 is configured to traverse different angular positions around the subject 204 to collect the desired projection data. Thus, the gantry 102 and components mounted thereon can be configured to rotate about the rotation center 206 to collect, for example, projection data at different energy levels. Alternatively, in embodiments where the projection angle relative to the subject 204 changes over time, the mounted components can be configured to move along a generally curved line rather than along a section of a circle.

[0029] Thus, as the x-ray source 104 and the detector array 108 rotate, the detector array 108 collects data of the attenuated x-ray beam. The data collected by the detector array 108 then undergoes pre-processing and calibration to condition the data to represent the line integral of the attenuation coefficient of the scanned subject 204. The processed data is often referred to as a projection.

[0030] In some examples, individual detectors or detector elements 202 in detector array 108 may include photon counting detectors that register interactions of individual photons into one or more energy bins. It should be appreciated that the methods described herein may also be implemented using energy integrating detectors.

[0031] The acquired projection data sets may be used for base material decomposition (BMD). During BMD, the measured projections are converted into a set of material density projections. The material density projections may be reconstructed to form a pair or set of material density maps or images for each corresponding base material (such as bone, soft tissue, and / or contrast agent maps). The density maps or images may then be correlated to form a volume rendering of the base material (e.g., bone, soft tissue, and / or contrast agent) in the imaging volume.

[0032] Once reconstructed, the base material image produced by the imaging system 200 shows the internal features of the subject 204 represented by the density of the two base materials. The density image can be displayed to show these features. In traditional methods of diagnosing medical conditions (such as disease states), and more generally diagnosing medical events, a radiologist or physician will consider a hard copy or display of a density image to discern characteristic features of interest. Such features can include lesions, size and shape of specific anatomical structures or organs, as well as other features that should be discernible in the image based on the skill and knowledge of the individual practitioner.

[0033] In one embodiment, the imaging system 200 includes a control mechanism 208 to control the movement of components, such as the rotation of the gantry 102 and the operation of the x-ray source 104. In certain embodiments, the control mechanism 208 also includes an x-ray controller 210 configured to provide power and timing signals to the x-ray source 104. Additionally, the control mechanism 208 includes a gantry motor controller 212 configured to control the rotational speed and / or position of the gantry 102 based on imaging requirements.

[0034] In certain embodiments, the control mechanism 208 also includes a data acquisition system (DAS) 214 that is configured to sample analog data received from the detector elements 202 and convert the analog data into digital signals for subsequent processing. The DAS 214 can also be configured to selectively aggregate analog data from a subset of the detector elements 202 into a so-called macro detector, as further described herein. The data sampled and digitized by the DAS 214 is transmitted to a computer or computing device 216. In one example, the computing device 216 stores the data in a storage device or mass storage device 218. For example, the storage device 218 can include a hard drive, a floppy disk drive, a compact disk-read / write (CD-R / W) drive, a digital versatile disk (DVD) drive, a flash drive, and / or a solid-state storage drive.

[0035] In addition, the computing device 216 provides commands and parameters to one or more of the DAS 214, the x-ray controller 210, and the gantry motor controller 212 to control system operations, such as data acquisition and / or processing. In certain embodiments, the computing device 216 controls system operations based on operator input. The computing device 216 receives operator input, which includes, for example, commands and / or scan parameters, via an operator console 220 operatively coupled to the computing device 216. The operator console 220 may include a keyboard (not shown) or a touch screen to allow an operator to specify commands and / or scan parameters.

[0036] Although Figure 2Only one operator console 220 is shown, but more than one operator console may be coupled to the imaging system 200, e.g., for inputting or outputting system parameters, requesting exams, plotting data, and / or viewing images. In addition, in certain embodiments, the imaging system 200 may be coupled to multiple displays, printers, workstations, and / or the like located locally or remotely, e.g., within an institution or hospital or at an entirely different location, via one or more configurable wired and / or wireless networks (such as the Internet and / or a virtual private network, a wireless telephone network, a wireless local area network, a wired local area network, a wireless wide area network, a wired wide area network, etc.).

[0037] In one embodiment, for example, the imaging system 200 includes or is coupled to a picture archiving and communication system (PACS) 224. In an exemplary embodiment, the PACS 224 is further coupled to a remote system (such as a radiology department information system, a hospital information system) and / or to an internal or external network (not shown) to allow operators at different locations to supply commands and parameters and / or gain access to image data.

[0038] The computing device 216 uses operator-supplied and / or system-defined commands and parameters to operate a table motor controller 226, which in turn can control a table 228, which can be a motorized table. Specifically, the table motor controller 226 can move the table 228 to properly position the subject 204 in the gantry 102 to acquire projection data corresponding to a target volume of the subject 204.

[0039] As previously described, the DAS 214 samples and digitizes the projection data acquired by the detector elements 202. Subsequently, the image reconstructor 230 uses the sampled and digitized x-ray data to perform high-speed reconstruction. Figure 2 The image reconstructor 230 is shown as a separate entity, but in certain embodiments, the image reconstructor 230 may form part of the computing device 216. Alternatively, the image reconstructor 230 may not be present in the imaging system 200, and instead the computing device 216 may perform one or more functions of the image reconstructor 230. Furthermore, the image reconstructor 230 may be located locally or remotely, and may be operably connected to the imaging system 200 using a wired or wireless network. In particular, one exemplary embodiment may use computing resources in a "cloud" network cluster for the image reconstructor 230.

[0040] In one embodiment, the image reconstructor 230 stores the reconstructed image in the storage device 218. Alternatively, the image reconstructor 230 may transmit the reconstructed image to the computing device 216 to generate usable patient information for diagnosis and evaluation. In certain embodiments, the computing device 216 may transmit the reconstructed image and / or the patient information to a display or display device 232 that is communicatively coupled to the computing device 216 and / or the image reconstructor 230. In some embodiments, the reconstructed image may be transmitted from the computing device 216 or the image reconstructor 230 to the storage device 218 for short-term or long-term storage.

[0041] Various methods and processes further described herein (such as those described below in reference Figure 4 The methods described herein may be stored as executable instructions in a non-transitory memory on a computing device (or controller) in the imaging system 200. In one embodiment, the image reconstructor 230 may include such executable instructions in the non-transitory memory and may apply the methods described herein to reconstruct an image from the scan data. In another embodiment, the computing device 216 may include instructions in the non-transitory memory and may apply the methods described herein, at least in part, to the reconstructed image after receiving the reconstructed image from the image reconstructor 230. In another embodiment, the methods and processes described herein may be distributed on the image reconstructor 230 and the computing device 216.

[0042] In one embodiment, the display 232 allows the operator to assess the imaged anatomy. The display 232 may also allow the operator to select a volume of interest (VOI) and / or request patient information, such as via a graphical user interface (GUI), for subsequent scanning or processing.

[0043] Figure 3A block diagram illustrating an exemplary noise modulation map generator 305 according to one embodiment is shown. The noise modulation map generator 305 generates a noise modulation map 307 based on the original image 302. Specifically, the noise modulation map generator 305 estimates the noise modulation map 307, which includes a pixel-by-pixel noise metric for the original image 302, based on one or more acquisition parameters 310, display parameters 330, and subject parameters 340. The noise modulation map generator 305 can be implemented, for example, in the computing device 216 of the imaging system 200 or in the image processor unit 110 of the system 100. In some examples, the noise modulation map generator 305 can include a trained deep learning model, such as a deep neural network, or another type of deep learning model configured to accept the original image 302 and a plurality of parameters as input and output the noise modulation map 307. In other examples, the noise modulation map generator 305 can include a linear or nonlinear regression model configured to accept the original image 302 and a plurality of parameters as input and output the noise modulation map 307.

[0044] As an illustrative and non-limiting example, the noise metric may include an estimate of the signal-to-noise ratio (SNR) at each pixel location in the original image 302. As another example, the noise metric may include an estimate of the contrast-to-noise ratio (CNR) at each pixel location in the original image 302, for example.

[0045] The noise modulation map 307 thus includes an estimate of how much noise is present in each pixel of the original image 302. As further described herein, the noise modulation map 307 is used to determine how much noise to remove from the image. For example, when the noise metric includes SNR, the noise metric ranges from zero to one, where a value of zero indicates that the pixel is entirely noise and contains no x-ray signal, and where a value of one indicates that the pixel is entirely x-ray signal and contains no noise. By using the noise modulation map 307 to modulate the noise reduction distribution in the image, more noise is removed from pixels with low SNR than from pixels with high SNR. In this way, noise suppression is not equal or global across the entire image, but depends on the signal level of the original image 302.

[0046] While a noise metric such as an estimate of the SNR may be determined solely from the raw image 302 , the noise modulation map generator 305 models the noise metric based on additional parameters such as acquisition parameters 310 , display parameters 330 , and subject parameters 340 that may further affect the noise.

[0047] Acquisition parameters 310 include parameters related to the acquisition of raw image 302 that may affect image noise. For example, acquisition parameters 310 may include, but are not limited to, one or more of the following: tube peak kilovolt voltage (kVp) 312, tube current (mA) 314, focal spot size 316, source-to-image distance (SID) 318, spectral filtering 320, grid size 322, and detector pixels 324. kVp 312, for example, has a strong impact on noise level because x-rays generated by a higher kVp 312 will have higher energy than x-rays generated by a lower kVp 312, and therefore will have a correspondingly higher penetration power, thereby improving the SNR of the overall image. Similarly, mA 314 may have a strong impact because the amount of x-rays generated by a higher mA 314 is greater than the amount of x-rays generated by a lower mA 314. Focal spot size 316 affects noise level because focal spot size 316 determines the imaging area on the detector. The SID 318 affects the noise level because the SID 318 determines the distance that the x-rays travel from the x-ray source to the x-ray detector, so for a larger SID 318, more x-rays may be scattered, so that the SNR may be reduced relative to a smaller SID 318. The spectral filtering 320 affects the noise level because the type of spectral filtering 320 (e.g., copper filtering, aluminum copper filtering, etc.) affects the spectrum of the x-rays, thereby also changing the response of the detector. Enhanced spectral filtering 320 results in a reduction in x-ray counts, so the SNR may be reduced for enhanced spectral filtering 320. The grid size 322 includes the size of the anti-scatter grid positioned in front of the detection surface of the x-ray detector, so the grid size 322 of the anti-scatter grid affects the SNR by affecting the amount of scattered x-rays detected by the x-ray detector. Detector pixel 324 affects the noise level because the noise characteristics of a large detector pixel are different from the noise characteristics of a small detector pixel. For the same dose, a large detector pixel receives more x-ray counts due to its increased area compared to a small detector pixel, so the SNR can increase or decrease depending on the size of the detector pixel 324.

[0048] Display parameters 330 include parameters related to the display of an image for viewing by a user that may affect image noise. For example, display parameters 330 may include, but are not limited to, one or more of a display device 332, image processing 334, and a field of view (FOV) 336. The type of display device 332 affects image noise because different screen technologies, frame rates, etc. associated with the display device 332 affect how a user may perceive an image. Similarly, image processor 334 affects image noise because different types of processing may increase or decrease apparent image noise. FOV 336 affects image noise because for a smaller FOV, pixels in the smaller FOV are magnified or scaled to the full window screen size, so the pixel size displayed is affected by FOV 336. For example, for a smaller FOV 336, noise may be substantially magnified. By considering how an image is displayed to a user based on display parameters 330, noise modulation map generator 305 may more effectively model applicable noise modulation methods.

[0049] The subject parameters 340 include parameters related to the subject imaged in the raw image 302 that may affect the image noise. For example, the subject parameters 340 may include, but are not limited to, patient thickness 342. The patient thickness 342 affects the noise level because a patient with a greater patient thickness 342 will strongly attenuate x-rays, thereby reducing the SNR.

[0050] In some examples, the noise modulation map generator 305 may generate the noise modulation map 307 based on all of the acquisition parameters 310, the display parameters 330, the subject parameters 340, and the original image 302. In other examples, the noise modulation map generator 305 may model the noise based on a weighted combination of the acquisition parameters 310, the display parameters 330, the subject parameters 340, and the original image 302, depending on the relative impact of each parameter on the noise level. In still other examples, the noise modulation map generator 305 may generate the noise modulation map 307 based on the original image 302 and the display parameters 330, the subject parameters 340, and the original image 302. Figure 3 304. The noise modulation map 307 may be generated by the noise modulation map generator 305 based on the original image 302, the kVp 312, and the detector pixels 324.

[0051] Therefore, noise modulation is a model derived from the quantum statistics of the acquired x-ray image and various parameters, such as kVp, SID, spectral filtering, patient thickness, grid size, focal spot size, detector quantum efficiency, detector pixel size, display device and FOV. The noise modulation model attempts to estimate the SNR at each pixel position in the image. The model is obtained by linear or nonlinear regression of a large number of x-ray acquisitions using a series of acquisition parameters. The SNR model can also be obtained by training a deep learning model with the acquired data set to estimate the local SNR value. The noise modulation map thus obtained by the model map measures how much noise is suppressed at each position of the x-ray image pixel by pixel. As further described herein, for chest examinations, abdominal areas that exhibit lower SNR will remove a larger portion of the noise, while lung areas that exhibit higher SNR will retain a larger portion of their noise in the final image. Although SNR is discussed as a noise metric option for determining how much noise to suppress or retain in the final image, it should be understood that other noise metrics such as CNR can be used without departing from the scope of the present disclosure.

[0052] Figure 4 A high-level flow chart illustrating an exemplary method 400 of parametric noise modulation for image noise reduction according to one embodiment is shown. Specifically, the method 400 involves modeling the noise distribution throughout the image based on various parameters, and modulating the amount of noise reduction throughout the image. Figures 1 to 3 The method 400 is described with reference to the systems and components of the present disclosure, but it should be understood that the method 400 can be implemented with other systems and components without departing from the scope of the present disclosure. As an illustrative example, the method 400 can be implemented as instructions in a non-transitory memory of the computing device 216, which can be executed by the computing device 216 to perform the operations described herein.

[0053] The method 400 begins at 405. At 405, the method 400 determines an acquisition protocol and a display protocol. The acquisition protocol includes a set of acquisition parameters related to image acquisition, such as acquisition parameters 310. The display protocol includes a set of display parameters related to the manner in which the image is displayed, such as display parameters 330. In some examples, via the operator console 220, a user of the imaging system 200 may select an acquisition protocol and a display protocol from a plurality of acquisition protocols and display protocols. Alternatively, the method 400 may automatically determine the acquisition protocol and / or the display protocol based on a clinical task for imaging a subject or based on default settings of the acquisition protocol and / or the display protocol. Furthermore, in some examples, the method 400 may additionally determine one or more subject parameters related to the subject to be imaged, such as subject parameters 340.

[0054] Continuing at 410, the method 400 controls the imaging system to acquire a raw image according to the acquisition protocol. For example, the method 400 may control the x-ray source 104 and the detector array 108 of the imaging system 200 according to the acquisition parameters of the acquisition protocol to acquire a raw image of the subject 204. As an illustrative and non-limiting example, Figure 5 An exemplary raw image 500 is shown without any processing applied. Since the raw image 500 depicts the photon counts for each pixel, the legend 505 indicates that higher photon counts are depicted in white and lower photon counts are depicted in black, indicating areas where lower or higher x-ray attenuation occurs, respectively.

[0055] After acquiring the raw image, the method 400 continues to 415. At 415, the method 400 generates a noise modulation map based on the raw image and a plurality of parameters. For example, the method 400 inputs the raw image acquired at 410 together with one or more acquisition parameters 310 determined by the acquisition protocol, display parameters 330 determined by the display protocol, and one or more subject parameters 340 into the noise modulation map generator 305 to generate the noise modulation map 307. As an illustrative and non-limiting example, Figure 6 An exemplary noise modulation map 600 generated from the original image 500 is shown. The noise modulation map 600 includes an estimate of a noise metric, such as SNR, for each pixel of the original image 500, which ranges from zero to one, as shown in the legend 605. Thus, pixels with a value of one correspond to regions where the x-ray signal substantially exceeds the noise, while pixels with a value of zero correspond to regions where the noise substantially exceeds the x-ray signal.

[0056] Continuing at 420, method 400 processes the raw image acquired at 410 to generate a processed image. For example, method 400 may process the raw image to normalize the photon counts, thereby changing the range of pixel intensity values ​​and / or mapping the photon counts to pixel values. As an illustrative and non-limiting example, Figure 7 An exemplary processed image 700 generated from the original image 500 is shown, where the legend 705 indicates that the pixel values ​​range from zero to 0.4. By normalizing the photon counts and remapping them to the pixel values ​​depicted in the processed image 700, the internal structures are more easily visible compared to the original image 500.

[0057] After preprocessing the original image to generate the processed image, method 400 continues to 425. At 425, method 400 applies noise reduction to the processed image to generate a smoothed image. For example, noise reduction may include a noise filter applied to the processed image, or another type of noise reduction technique that smoothes the processed image. Applying noise reduction to the processed image globally reduces noise throughout the processed image. As an illustrative and non-limiting example, Figure 8An exemplary denoised or smoothed image 800 generated from processed image 700 is shown, where legend 805 indicates that the pixel values ​​are relatively consistent with the pixel values ​​of processed image 700 .

[0058] Rather than simply outputting the smoothed image obtained at 425, method 400 uses the noise modulation map obtained at 415 to modulate the noise distribution in the image. To this end, at 430, method 400 subtracts the smoothed image from the processed image to generate the noise image. Thus, the noise image includes the noise removed from the processed image in the smoothed image. As an illustrative and non-limiting example, Fig. 9 An exemplary noisy image 900 generated from the processed image 700 and the denoised image 800 is shown. Legend 905 depicts the extent of noise removed from the processed image 700 during the global noise filtering at 425 .

[0059] Continuing at 435, method 400 blends the noisy image with the smoothed image based on the noise modulation map to generate a final image. For example, method 400 may multiply the noisy image with the noise modulation map to modulate the noise. Method 400 may then add the modulated noise to the smoothed image to selectively reintroduce at least some of the noise removed from the processed image into the smoothed image. As an illustrative and non-limiting example, Fig.10 An exemplary noise modulation image 1000 generated from the noise image 900 and the smoothed image 800 based on the noise modulation map 600 is shown. The legend 1005 indicates that the range of pixel values ​​is consistent with the smoothed image 800. As depicted by the noise modulation map 600, the SNR in the lungs is significantly higher than the SNR in the abdomen of the imaged subject. Therefore, the noise filtered out from the lung region may actually include valid signals rather than noise, and therefore when the noise image 900 is multiplied by the noise modulation map 600 and the modulated noise is re-added to the smoothed image 800, the signal is also re-added to the lung region. In contrast, the noise filtered out from the abdomen region may correspond to actual noise rather than x-ray signals, and therefore the lower SNR value of the noise modulation map 600 in the abdomen region causes less noise in the noise image 900 to be re-added to the smoothed image 800.

[0060] At 440, method 400 displays the final image according to the display protocol, such as noise modulated image 1000. In the example where the noise modulation map is generated based on the display protocol, the noise of the final image is improved. Method 400 then returns.

[0061] Thus, a method for x-ray imaging includes adaptively reducing noise based on a noise metric such as SNR, such that low SNR regions are subjected to higher noise suppression than high SNR regions. For example, using a noise modulation map as described herein allows a strong filter kernel to be applied to an image during denoising, while the modulation of the noise achieves effective denoising in low SNR regions and limits the effect of strong smoothing operations in high SNR regions of the image.

[0062] A technical effect of the present disclosure includes noise reduction modulation performed on the entire image based on a noise metric. Another technical effect of the present disclosure includes selective modulation of noise in an image based on acquisition parameters. Another technical effect of the present disclosure includes an improved estimate of the signal-to-noise ratio in an x-ray image. Yet another technical effect of the present disclosure includes improved image quality. Another technical effect of the present disclosure includes reducing radiation dose while maintaining image quality.

[0063] In one embodiment, a method includes: acquiring an image of a subject; generating a noise modulation map including an estimate of noise in each pixel of the image based on the image and multiple parameters; selectively reducing noise in the image based on the noise modulation map to generate a final image; and displaying the final image.

[0064] In a first example of the method, generating a noise modulation map includes inputting an image and a plurality of parameters into a noise modulation model that outputs a noise modulation map. In a second example of the method that optionally includes the first example, the noise modulation model includes a trained deep learning model. In a third example of the method that optionally includes one or more of the first and second examples, the noise modulation model includes a linear or nonlinear regression model. In a fourth example of the method that optionally includes one or more of the first to third examples, the plurality of parameters include at least two parameters selected from the following: acquisition parameters associated with image acquisition, display parameters associated with final image display, and subject parameters associated with the imaged subject. In a fourth example of the method that optionally includes one or more of the first to third examples, the acquisition parameters include one or more of the following: a tube voltage applied to an x-ray source during image acquisition, a tube current applied to an x-ray source during image acquisition, a focal spot size, a distance between an x-ray source and an x-ray detector, an amount of spectral filtering, a size of an anti-scatter grid disposed in front of the x-ray detector, and a detector pixel of the x-ray detector. In a fifth example of the method that optionally includes one or more of the first to fourth examples, the display parameters include one or more of the following: a display device type, an image processing technique, and a field of view used to display the final image. In a sixth example of the method that optionally includes one or more of the first to fifth examples, the subject parameters include subject thickness. In a seventh example of the method that optionally includes one or more of the first to sixth examples, the method also includes applying noise reduction to the image to generate a smoothed image, and subtracting the smoothed image from the image to generate a noisy image, wherein selectively reducing noise in the image based on the noise modulation map to generate the final image includes mixing the noisy image with the smoothed image according to the noise modulation map.

[0065] In another embodiment, a method includes: controlling an x-ray source and an x-ray detector to acquire an image of a subject; measuring an estimate of noise in each pixel of the image based on the image and multiple parameters; applying noise reduction to the entire image to generate a smoothed image; subtracting the smoothed image from the image to generate a noisy image; multiplying a noise modulation map with the noise image to generate a modulated noise image; adding the modulated noise image to the smoothed image to generate a final image; and displaying the final image.

[0066] In a first example of the method, measuring an estimate of noise in each pixel of an image includes inputting the image and a plurality of parameters into a noise modulation model, which outputs an estimate of noise in each pixel of the image. In a second example of the method that optionally includes the first example, the estimate of noise in each pixel of the image includes an estimate of a signal-to-noise ratio determined for each pixel based on the image and the plurality of parameters. In a third example of the method that optionally includes one or more of the first and second examples, the noise modulation model includes one of a trained deep learning model, a linear regression model, or a nonlinear regression model. In a fourth example of the method that optionally includes one or more of the first to third examples, the plurality of parameters include a peak tube kilovolt voltage applied to an x-ray source during acquisition of an image, and a size of a detector pixel of an x-ray detector. In a fifth example of the method that optionally includes one or more of the first to fourth examples, the multiple parameters also include one or more of the following: a tube current applied to the x-ray source during image acquisition, a focal spot size, a distance between the x-ray source and the x-ray detector, an amount of spectral filtering, a size of an anti-scatter grid disposed in front of the x-ray detector, a type of display device used to display the final image, an image processing technique, a field of view used to display the final image, and a thickness of the subject.

[0067] In another embodiment, a system includes: an x-ray source for generating x-rays; an x-ray detector configured to detect x-rays; a display device; and a processor configured with instructions in a non-volatile memory that, when executed, cause the processor to: acquire an image of a subject; generate a noise modulation map based on the image and multiple parameters, the noise modulation map including an estimate of the noise in each pixel of the image; selectively reduce noise in the image based on the noise modulation map to generate a final image; and display the final image via the display device.

[0068] In a first example of the system, in order to generate a noise modulation map, the processor is configured with instructions in a non-volatile memory, which, when executed, causes the processor to input an image and multiple parameters into a noise modulation model that outputs a noise modulation map. In a second example of the system that optionally includes the first example, the noise modulation model includes one or more of a trained deep learning model, a linear regression model, and a nonlinear regression model. In a third example of the system that optionally includes one or more of the first and second examples, the multiple parameters include two or more of the following: a peak tube kilovolt voltage applied to the x-ray source during image acquisition, a tube current applied to the x-ray source during image acquisition, a focal spot size, a distance between the x-ray source and the x-ray detector, an amount of spectral filtering, a size of an anti-scatter grid disposed in front of the x-ray detector, a size of a detector pixel in the x-ray detector, a type of display device used to display the final image, an image processing technique used to process the image, a field of view used to display the final image, and a thickness of the subject. In a fourth example of the system that optionally includes one or more of the first to third examples, the processor is further configured with instructions in the non-volatile memory that, when executed, cause the processor to: preprocess the image to generate a processed image, the preprocessing including at least normalization of the photon counts; apply noise reduction to the entire processed image to generate a smoothed image; subtract the smoothed image from the processed image to generate a noise image; multiply the noise modulation map with the noise image to generate a modulated noise image; and add the modulated noise image to the smoothed image to generate a final image.

[0069] As used herein, the elements or steps listed in the singular and beginning with the word "one" or "a kind of" should be understood as not excluding a plurality of the elements or steps, unless such exclusion is explicitly stated. In addition, the reference to "an embodiment" of the present invention is not intended to be interpreted as excluding the existence of additional embodiments that also include the cited features. In addition, unless explicitly stated to the contrary, the embodiment of "including", "comprising" or "having" an element or multiple elements with a specific characteristic may include additional such elements without the characteristic. The terms "including" and "in..." are used as the concise language equivalents of the corresponding terms "including" and "wherein". In addition, the terms "first", "second" and "third" etc. are only used as marks, and are not intended to impose numerical requirements or specific positional order on their objects.

[0070] This written description uses examples to disclose the invention, including the best mode, and also to enable a person of ordinary skill in the relevant art to practice the invention, including making and using any devices or systems and performing any included methods. The patentable scope of the invention is defined by the claims, and may include other examples that occur to a person of ordinary skill in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insignificant differences from the literal language of the claims.

Claims

1. A method comprising: acquiring images of the subject; generating a noise modulation map based on the image and a plurality of parameters, the noise modulation map comprising an estimate of noise in each pixel of the image; applying noise reduction to the image to generate a smooth image; subtracting the smoothed image from the image to generate a noisy image; selectively reducing noise in the image based on the noise modulation map to generate a final image, including blending the noisy image with the smoothed image according to the noise modulation map; as well as The final image is displayed. 2 . The method of claim 1 , wherein generating the noise modulation map comprises inputting the image and the plurality of parameters into a noise modulation model that outputs the noise modulation map.

3. The method of claim 2, wherein the noise modulation model comprises a trained deep learning model. The method of claim 2 , wherein the noise modulation model comprises a linear or nonlinear regression model.

5. The method of claim 2, wherein the plurality of parameters comprises at least two parameters selected from the group consisting of: acquisition parameters associated with the acquisition of the image, display parameters associated with the display of the final image, and subject parameters associated with the imaged subject.

6. The method of claim 5, wherein the acquisition parameters include one or more of: a tube voltage applied to an x-ray source during image acquisition, a tube current applied to the x-ray source during image acquisition, a focal spot size, a distance between the x-ray source and an x-ray detector, an amount of spectral filtering, a size of an anti-scatter grid disposed in front of the x-ray detector, and detector pixels of the x-ray detector.

7. The method of claim 5, wherein the display parameters include one or more of: a type of display device used to display the final image, an image processing technique, and a field of view. The method of claim 5 , wherein the subject parameter comprises a thickness of the subject.

9. The method of claim 1 , wherein selectively reducing noise in the image based on the noise modulation map to generate the final image comprises: multiplying the noise estimate in each pixel of the image by the noise image to generate a modulated noise image; as well as The modulated noise image is added to the smoothed image to generate the final image.

10. A method comprising: controlling an x-ray source and an x-ray detector to acquire images of a subject; measuring an estimate of noise in each pixel of the image based on the image and a plurality of parameters; applying noise reduction to the entire image to generate a smooth image; subtracting the smoothed image from the image to generate a noisy image; multiplying the noise estimate in each pixel of the image by the noise image to generate a modulated noise image; adding the modulated noise image to the smoothed image to generate a final image; as well as The final image is displayed.

11. The method of claim 10, wherein measuring the noise estimate in each pixel of the image comprises inputting the image and the plurality of parameters into a noise modulation model, the noise modulation model outputting the noise estimate in each pixel of the image.

12. The method of claim 11, wherein the estimate of noise in each pixel of the image comprises an estimate of a signal-to-noise ratio determined for each pixel based on the image and the plurality of parameters.

13. The method of claim 11, wherein the noise modulation model comprises one of a trained deep learning model, a linear regression model, or a nonlinear regression model.

14. The method of claim 10, wherein the plurality of parameters include a peak tube kilovolt voltage applied to the x-ray source during acquisition of the image, and a size of a detector pixel of the x-ray detector.

15. The method of claim 14, wherein the plurality of parameters further comprises one or more of: a tube current applied to the x-ray source during image acquisition, a focal spot size, a distance between the x-ray source and the x-ray detector, an amount of spectral filtering, a size of an anti-scatter grid disposed in front of the x-ray detector, a type of display device used to display the final image, an image processing technique, a field of view used to display the final image, and a thickness of the subject.

16. A system comprising: An x-ray source, the x-ray source is used to generate x-rays; an x-ray detector configured to detect the x-rays; Display devices; as well as A processor configured with instructions in a non-transitory memory, the instructions, when executed, causing the processor to: acquiring images of the subject; generating a noise modulation map based on the image and a plurality of parameters, the noise modulation map comprising an estimate of noise in each pixel of the image; applying noise reduction to the image to generate a smooth image; subtracting the smoothed image from the image to generate a noisy image; selectively reducing noise in the image based on the noise modulation map to generate a final image, including blending the noisy image with the smoothed image according to the noise modulation map; as well as The final image is displayed via the display device.

17. A system according to claim 16, wherein, in order to generate the noise modulation map, the processor is configured with instructions in the non-volatile memory, and the instructions, when executed, cause the processor to input the image and the multiple parameters into a noise modulation model that outputs the noise modulation map.

18. The system of claim 17, wherein the noise modulation model comprises one or more of a trained deep learning model, a linear regression model, and a nonlinear regression model.

19. The system of claim 16, wherein the plurality of parameters comprises two or more of: a peak tube kilovolt voltage applied to the x-ray source during image acquisition, a tube current applied to the x-ray source during image acquisition, a focal spot size, a distance between the x-ray source and the x-ray detector, an amount of spectral filtering, a size of an anti-scatter grid disposed in front of the x-ray detector, a size of detector pixels in the x-ray detector, a type of the display device used to display the final image, an image processing technique used to process the image, a field of view used to display the final image, and a thickness of the subject.

20. The system of claim 16, wherein the processor is further configured with instructions in the non-transitory memory that, when executed, cause the processor to: preprocessing the image to generate a processed image, the preprocessing comprising at least normalization of photon counts; applying noise reduction to the entire processed image to generate a smooth image; subtracting the smoothed image from the processed image to generate a noisy image; multiplying the noise modulation map and the noise image to generate a modulated noise image; as well as The modulated noise image is added to the smoothed image to generate a final image.

Images