Noise and artifact reduction for image scatter correction

Abstract

Imaging devices and associated methods are provided for receiving projection data measured in a primary region and correcting for scatter by processing the imaging data as two separate components: unscatter-corrected data and scatter-only data. Separate image processing (e.g., reconstruction) allows for the use of individualized data processing, including filters, tailored to the source data, to focus on specific aspects of the source data, including, for example, noise and artifact reduction, resolution, edge preservation, etc. Combining the separately processed data improves image quality and optimizes the balance of these aspects.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. patent application Ser. No. 16 / 823,560, filed March 19, 2020, entitled "NOISE AND ARTIFACT REDUCTION FOR IMAGE SCATTER CORRECTION." This application is also related to U.S. patent application Ser. No. 16 / 694,145, filed November 25, 2019, entitled "MULTIMODAL RADIATION APPARATUS AND METHODS," and U.S. patent application Ser. No. 16 / 694,148, filed November 25, 2019, entitled "APPARATUS AND METHODS FOR SCALABLE FIELD OF VIEW IMAGING USING A MULTI-SOURCE SYSTEM," both of which are incorporated herein by reference in their entireties.

[0002] Aspects of the disclosed techniques relate to improving quality during radiological image processing, including, for example, reducing noise and artifacts associated with scatter and scatter correction, and more particularly to processing scatter-corrected images as non-scatter-corrected and scatter-only components. [Background technology]

[0003] Tomography is a noninvasive radiological imaging technique used to generate cross-sectional images of three-dimensional (3D) objects without overlying tissues. Tomography can be classified into transmission tomography, e.g., computed tomography (CT), and emission tomography, such as single photon emission computed tomography (SPECT) and positron emission tomography (PET). CT is a technique based on the transmission of X-rays through a patient to generate images of a portion of the body. Photon emission computed tomography and positron emission tomography provide 3D image information about radionuclides injected into a patient, indicating metabolic and physiological activity within organs.

[0004] In a tomographic scan, one or more rotating detectors (coupled with the rotating radiation source in CT) acquire projections from many different angles around the body. These data are then reconstructed to form a 3D image of the body. For example, reconstructions of tomographic images can be obtained by filtered backprojection and iterative methods.

[0005] The final image quality is limited by several factors, including gamma-ray photon attenuation and scattering, detection efficiency, and spatial resolution of the collimator-detector system. These factors can result in poor spatial resolution, low contrast, and / or high levels of noise. Image data processing (e.g., filtering) techniques can be used to improve image quality.

[0006] In CT, including cone-beam CT, the primary signal detected by a detector element represents the x-rays that leave the tube, penetrate the patient's body, and reach or are detected by the detector. The primary signal x-rays travel along an x-ray path connecting the tube focal point to the detector element for detection. The scatter signal detected by the same element also represents the x-rays scattered within the element. The primary signal allows for the reconstruction of the CT image. However, the scatter signal can degrade the CT image both quantitatively and qualitatively.

[0007] Scatter from various radiation imaging modalities, including CT and cone-beam CT, can account for a significant portion of detected photons. Scatter can negatively impact image quality, including contrast and quantitative accuracy. Consequently, scatter measurement, scatter assessment, and scatter correction can be applied to data processing and image reconstruction, including in cases such as image-guided radiation treatment (IGRT). IGRT utilizes medical imaging modalities, such as CT, to collect images of patients before, during, and / or after treatment.

[0008] Software-based scatter correction in various radiological imaging modalities can significantly increase noise compared to no scatter correction. For example, in low-count scans (low dose and / or large patients), significant scatter noise associated with scatter correction at certain angles can be amplified and appear as noticeable streak artifacts in reconstructed images. Therefore, noise reduction for scatter correction is a challenging task for image generation. Summary of the Invention [Means for solving the problem]

[0009] In one embodiment, a method for generating a radiation image includes receiving radiation data from a radiation imaging device, the radiation data including a primary component and a scatter component; generating a data set without scatter correction based on the radiation data using a first data processing technique; estimating the scatter component of the radiation data; generating a scatter-only data set based on the scatter estimation using a second data processing technique different from the first data processing technique; and generating an image based on the unscatter-corrected data set and the scatter-only data set.

[0010] Features described and / or illustrated with respect to one embodiment may be used in one or more other embodiments in the same or similar manner and / or in combination with or instead of features of the other embodiments.

[0011] The description of the invention does not limit in any way the claims or the terms used in the claims or the scope of the invention. Terms used in the claims have all of their fullest general meanings.

[0012] Embodiments of the present specification are illustrated in the accompanying drawings, which are incorporated into and constitute a part of this specification. These, together with the general description of the invention provided above and the detailed description provided below, serve to illustrate embodiments of the present invention. It will be understood that the boundaries of elements illustrated in the figures (e.g., boxes, groups of boxes, or other shapes) represent one embodiment of the boundaries. In some embodiments, one element may be designed as multiple elements. Or, multiple elements may be designed as one element. In some embodiments, an element shown as an internal component of another component may be implemented as an external component, or vice versa. Additionally, elements may not be drawn to scale. [Brief explanation of the drawings]

[0013] [Figure 1]1 is a flowchart illustrating an exemplary method for generating a radiological image by processing imaging data into a non-scatter corrected component and a scatter-only component.

[0014] [Figure 2] 10 is a flowchart illustrating another exemplary method for generating a radiological image by processing imaging data into a non-scatter corrected component and a scatter-only component.

[0015] [Figure 3] 10 is a flowchart illustrating another exemplary method for generating a radiological image by processing imaging data into a non-scatter corrected component and a scatter-only component.

[0016] [Figure 4] 10 is a flowchart illustrating another exemplary method for generating a radiological image by processing imaging data into a non-scatter corrected component and a scatter-only component.

[0017] [Figure 5] 10 is a flowchart illustrating another exemplary method for generating a radiological image by processing imaging data into a non-scatter corrected component and a scatter-only component.

[0018] [Figure 6] 10 is a flowchart illustrating another exemplary method for generating a radiological image by processing imaging data into a non-scatter corrected component and a scatter-only component.

[0019] [Figure 7A] 1 is an exemplary image produced without scatter correction.

[0020] [Figure 7B] 1 is an exemplary image produced with scatter correction.

[0021] [Figure 7C] 1 is an exemplary image produced with scatter correction followed by a low-pass Gaussian filter.

[0022] [Figure 8A] FIG. 7D is a magnified view of the exemplary image of FIG. 7C.

[0023] [Figure 8B] 10A-10C are exemplary images produced by processing imaging data into a non-scatter corrected component and a scatter-only component.

[0024] [Figure 9] 7A-7C and 8B show a comparison of exemplary noise measurements associated with the exemplary images of FIG.

[0025] [Figure 10] FIG. 9 shows a comparison of exemplary line integral values ​​associated with the exemplary images of FIGS. 7A-7C and 8B along the line profile shown.

[0026] [Figure 11] 11 shows an exploded view of a portion of the line integral values ​​shown in FIG. 10.

[0027] [Figure 12] A comparison of exemplary images with CT numbers associated with conventional image processing, as well as exemplary embodiments for processing imaging data as unscatter-corrected and scatter-only components, are shown.

[0028] [Figure 13] FIG. 1 is a perspective view of an exemplary imaging device in accordance with one aspect of the disclosed technique.

[0029] [Figure 14] FIG. 1 is a schematic diagram of imaging equipment incorporated into an exemplary radiation therapy device, in accordance with one aspect of the disclosed technique.

[0030] [Figure 15]FIG. 2 is a schematic diagram of an exemplary collimated projection onto a detector.

[0031] [Figure 16] 1 is a flowchart illustrating an exemplary method for scatter correction.

[0032] [Figure 17] 1 is a flowchart illustrating an exemplary method of IGRT using an imaging device within a radiation therapy machine. DETAILED DESCRIPTION OF THE INVENTION

[0033] The following include definitions of exemplary terms that may be used throughout this disclosure: Both the singular and plural forms of all words are within their respective meanings.

[0034] As used herein, a "component" may be described as a portion of hardware, a portion of software, or a combination thereof. A portion of hardware includes at least a processor and a portion of memory, the memory including instructions for execution. A component may be associated with an apparatus.

[0035] As used herein, "logic," which is synonymous with "circuitry," includes, but is not limited to, hardware, firmware, software, and / or combinations of each for performing one or more functions or operations. For example, based on desired application or need, logic may include discrete logic such as a software-controlled microprocessor, an application-specific integrated circuit (ASIC), or other programmed logic device and / or controller. Logic may also be embodied entirely as software.

[0036] As used herein, "processor" includes one or more of, but is not limited to, a microprocessor, a microcontroller, a central processing unit (CPU), and virtually any number of processor systems or stand-alone processors, including any combination of a digital signal processor (DSP), a field-programmable gate array (FPGA), a graphics processing unit (GPU), etc. A processor may be associated with various other circuits that support the operation of the processor, such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), clocks, decoders, a memory controller, or an interrupt controller. These support circuits may be internal to or within the processor or a package of electronic components associated with the processor. The support circuits are capable of communicating with the processor. The support circuits are not necessarily shown separately from the processor in block diagrams or other illustrations.

[0037] As used herein, a "signal" includes, but is not limited to, an analog or digital signal, one or more electrical signals containing one or more computer instructions, a bit or bitstream, or the like.

[0038] As used herein, "software" includes, but is not limited to, one or more computer-readable and / or executable instructions that cause a computer, processor, logic, and / or other electronic device to function, perform actions, and / or operate in a desired manner. Instructions may be embodied in various forms, such as routines, algorithms, modules, or programs with specific applications. They may also be coded from dynamically linked sources or libraries.

[0039] While exemplary definitions are provided above, it is Applicant's intention that these and other terms be given their broadest reasonable interpretation consistent with this specification.

[0040] In CT scans, the X-ray reference data (I0) is the signal when the patient (and examination table) is not present. The raw or patient data (I d ), the flux-to-signal ratio for each detector element is calculated. The logarithm of this ratio, if the patient data only has a primary signal (Pr), is the line integral of the patient's linear attenuation along the corresponding x-ray path. CT images can then be reconstructed from the line integrals measured at all detector elements at many angles around the patient.

[0041] The detected signal includes both the primary signal (Pr) and the scattered signal (Sc), in this case I d = Pr + Sc, so the ratio of reference to detector signal I / I d The direct calculation of is not the integral of the patient's line attenuation along the x-ray path (I) due to the presence of the scatter component (Sc) in the signal. Obviously, the correct line integral must be I = log(I0 / Pr). However, since this includes scatter, Equation 1: Id=log(I0 / I d ) (1) The calculated ratio is shown below. In this case, I d =Pr+Sc.

[0042] When scatter (Sc) is positive and there is no scatter correction, the calculated line integral is smaller than the true line integral (without (Sc)). Reconstruction using the contaminated line integral Id introduces quantitative bias into the image and qualitatively reduces contrast and introduces artifacts into the image.

[0043] To address the above scatter issues, medical CT systems can use hardware approaches to minimize scatter beforehand and during data acquisition, and apply software approaches to correct for residual scatter in the measured data, the latter also being referred to as scatter correction.

[0044] The principle of scatter correction is to estimate the scatter (Sc_est) and remove or subtract the estimated scatter from the patient data and calculate the correction line integral according to Equation 2:

number

[0045] If this approach is accurate enough so that the scatter estimate (Sc_est) is identical to the scatter component in the measured data, then Equation 2 will yield accurate line integrals, allowing for accurate CT image reconstruction.

[0046] However, from an image noise perspective, scatter correction increases the noise in the calculated line integrals, which in turn increases the noise in the reconstructed image. The variance (noise) of the line integrals without scatter correction is given by Equation 3:

number

[0047] The variance of the line integral after scatter correction is given by Equation 4:

number

[0048] A comparison of the noise in the scatter-corrected line integral in Equation 4 with the noise in the line integral without scatter correction in Equation 3 shows that even when the scatter estimate (Sc_est) is noise-free, the noise in the calculated line integral is amplified by the factor shown in Equation 5.

number

[0049] The noise amplification in Equation 5 increases as the percentage of scatter in the measurement data increases. For example, if 50% of the measurement data is scatter, the noise is amplified by a factor of four. In a cone-beam CT system using an anti-scatter grid, residual scatter can be 30% of the data. Compared to no scatter correction, Equation 5 predicts approximately a two-fold noise amplification with scatter correction. In a cone-beam CT system without an anti-scatter grid and equipped with a flat-panel detector, scatter can exceed 50% of the total measurement data, with larger patients experiencing more scatter.

[0050] Conventional noise reduction approaches for scatter correction can be used to (a) reduce noise in estimated scatter (corresponding to reducing the noise in the second term on the right-hand side of Equation 4), (b) reduce noise in the scatter-corrected raw data or line integrals, (c) model scatter during iterative reconstruction as an additive term to the estimated primary and compare the sum of the estimated primary and scatter to the measured data, (d) adjust for noise during reconstruction, or (e) filter / denoise the scatter-corrected images.

[0051] While all of these approaches may have benefits in certain situations and to some extent, they also have many drawbacks. For approach (a), even if a completely noise-free scatter image is created, noise is still amplified by the factor shown in Equation 5. Furthermore, noise can be particularly pronounced in CT scans with a large amount of scatter (especially cone-beam CT with a large imaging field of view and no anti-scatter grid). Approach (b) not only has the drawbacks of approach (a), but can also result in loss of signal (resolution and contrast) because the raw data is filtered for noise reduction. Approach (c) requires more sophisticated reconstruction algorithms and longer reconstruction times, and also has limited noise reduction. Approach (d) is challenging due to how the calibration is designed. These challenges are easily understood when considering a scatter-corrected image as a combination of the unscatter-corrected and scatter-corrected components. The unscatter-corrected component has much less noise than the scatter-corrected component. Therefore, applying adjustments to the entire image to optimize noise reduction for the high-noise scatter component tends to over-adjust the non-scatter-corrected component of the image, which is low noise. (e) Post-reconstruction image processing (filtering) / denoising shares the same challenge as (d) in that it cannot optimize two components with significantly different noise levels.

[0052] In embodiments disclosed herein, the scatter-corrected image can be processed as a combination of two components: one without scatter correction and one with scatter only. The line integral in Equation 2 is rewritten as the sum of the two components shown in Equation 6.

number

[0053] The first term on the right hand side of Equation 6 is the line integral without scatter correction. The term in square brackets is the scatter-corrected component of the line integral. For analytical reconstruction, reconstructing the corrected line integral is equivalent to reconstructing the two terms separately to produce two images, and then summing the two images to obtain the final scatter-corrected image. The image reconstructed from the first term is equivalent to a conventional no-scatter image (noSC image). The image reconstructed from the second term can be called a scatter-only image. Notably, Equation 7: CT image = noSC image + scatter only image (7) is shown in.

[0054] In this manner, the scatter estimate (Sc_est) can be removed from the patient data. From the above analysis, it is clear that the scatter-only image contains noise and artifacts associated with scatter correction. The noSC image has even lower noise than the scatter-only image, and the entire CT image is a combination of these two images.

[0055] Traditional noise reduction associated with scatter correction, either in the raw data or reconstructed images, or during the reconstruction process, primarily involves combining the noSC and scatter-only components of the data / image, even though these components have significantly different noise levels. Approaches that optimally suppress noise in the scatter-only component may result in over-smoothing (and therefore resolution degradation) of the noSC component, and approaches that minimize resolution degradation may not be effective in suppressing noise associated with scatter correction.

[0056] The embodiments described herein improve image quality after scatter correction, including, for example, sufficient noise reduction and minimal resolution degradation. In these embodiments, the scatter-only image can be processed separately and differently from the noSC image to reduce noise and artifacts associated with scatter and scatter correction. Due to the higher noise in the scatter-only image, more noise-suppressing data processing techniques (e.g., filters) can be applied to the scatter-only image to optimize noise reduction. However, less noise-suppressing data processing techniques (e.g., filters) can be applied to the noSC image to minimize resolution loss. Thus, the two image components of Equation 7 can be optimized independently, and the combined final image (e.g., a CT image) can be an optimal compromise between noise reduction and resolution preservation.

[0057] In some embodiments, using a noSC image to guide noise reduction of a noisy scatter-only image can have additional benefits. Because the noSC image has a much lower noise level than the scatter-only image, it can be used to guide noise reduction of the scatter-only image. For example, "guiding" can include determining a filter kernel and any associated parameters. This guided noise reduction of the scatter-only image can result in a scatter-only image that is similar to or significantly lower than the noSC image. The edges of the noSC image can provide reliable edge-preserving guidance for data processing (e.g., filtering) of the scatter-only image. Thus, the combined final image (e.g., a CT image) can have a noise level similar to that in the noSC image, while at the same time optimizing edge preservation.

[0058] In various embodiments, the two components on the right hand side of Equation 6 are reconstructed separately, for example, using a higher resolution filter (kernel) to reconstruct the noSC component and a higher smoothing filter (lower resolution kernel) to reconstruct the scatter-only component.

[0059] The following flowcharts and block diagrams illustrate exemplary configurations and methodologies related to scatter correction and / or image generation. The exemplary methodologies may be implemented in logic, software, hardware, or a combination thereof. Additionally, while the procedures and methods are presented sequentially, the blocks may be executed in different orders, including serial and / or parallel. Thus, the following steps, including imaging, image-based pre-exposure steps, and exposure, although shown sequentially, may be executed simultaneously (including in real time). Furthermore, additional or fewer steps may be used.

[0060] FIG. 1 is a flowchart illustrating an exemplary method 100 for generating a radiological image by processing (e.g., by data processing techniques, algorithms, filters, etc.) imaging data into unscatter-corrected and scatter-only components. In this manner, the data processing techniques applied to the unscatter-corrected and scatter-only components are separate and distinct, as opposed to the data processing techniques applied to the combined data. In this embodiment, a patient scan is performed in step 110, and the radiological / patient data (I d ) 115. A scatter estimate (Sc_est) 117 is also generated. The scatter estimate 117 may be generated in any suitable manner based on the patient data 115, including a scatter-only measurement, and / or information related to the scanning equipment, including, for example, the scan model, parameters, settings, etc. In various embodiments, the scatter-only measurement may be from an active portion of the radiation detector that is shielded from direct radiation (primary data), for example, by a beamformer or collimator.

[0061] In this manner, the method continues to process the imaging data as a combination of two components: 1) a component without scatter correction and 2) a component with scatter only, as described in detail above (shown in Equation 7).

[0062] In step 120, the method generates at least one unscatter-corrected data set 145 (e.g., line integrals, images, and / or other data) based on patient data 115 using data processing techniques 122, which may include, for example, a filter. In step 130, the method generates at least one scatter-only data set 155 (e.g., line integrals, images, and / or other data) based on scatter estimates 117 using data processing techniques 132, which may include, for example, another filter. In various embodiments, and as described in other embodiments below, one or more different types of data processing techniques may be used during steps 120, 130, including during different steps or sub-steps of image data processing.

[0063] Data processing techniques or steps, as described herein, include software-based mathematical manipulation of the imaging data (e.g., operations applied to data relating to pixels / voxels of the image data). Primary objectives for applying data processing techniques to the imaging data can include noise suppression, preserving spatial resolution and contrast, smoothing, reducing artifacts, and edge enhancement.

[0064] For example, in various embodiments, a data processing technique or step may include applying one or more filters to the data. In image processing, these filters may include, for example, kernels, convolution matrices, masks, etc. These filters may be used for blurring, sharpening, embossing, edge detection, etc. For example, in some embodiments, this is accomplished by performing a convolution between a kernel and the image. Data processing, including by filters, may be applied to imaging data before, during, and / or after reconstruction. For example, in one embodiment, radiology / patient data (I d) 115 contains the raw x-ray data, which are the values ​​of all measured detector signals during a CT scan. After calibration, for example, the attenuation characteristics of each x-ray signal are accounted for by tube power variations and beam hardening, which correlate with ray position. CT images are reconstructed from these data, which involves the use of mathematical methods such as convolution filtering and backprojection. A convolution filter is a mathematical filter function (kernel) applied during image reconstruction of CT imaging data. Reconstruction filters can include sinc filters (e.g., windowing (e.g., Lanczos, Kaiser), splines, Gaussians, B-splines (e.g., box filters, tent filters), etc. In addition to reconstruction, other filters such as resampling, interpolation, and anti-aliasing can be used.

[0065] Various types of data processing techniques, including those using filters, can be used, for example, to smooth or enhance edges, and can be selected according to the type of source data (e.g., primary data, primary and scatter data, scatter-only data, etc.), application (CT, CBCT, PET, SPECT, etc.), desired computation speed, tissue characteristics, etc. Other types of data processing techniques can include noise reduction, such as by wavelet transform, singular value decomposition, etc. For example, for singular value decomposition, different eigenvalues ​​can be used for the scatter-only and non-scatter components. References to filters in the following embodiments are exemplary, and other types of data processing techniques can be used instead of or in addition to filters.

[0066] In various embodiments, the data processing technique 132 is different from the data processing technique 122. In this case, the data processing techniques (e.g., filters) 122, 132 and their associated parameters are specifically targeted to the associated data 115, 117. In this manner, the data processing techniques 122, 132 can be optimized separately for the source data 115, 117. For example, the use of different data processing techniques 122, 132 during processing of each of the separate data 115, 117 can provide both sufficient noise reduction and minimal resolution degradation. In these embodiments, processing the scatter estimate 117 (scatter-only) separately and in a different manner from the patient data 115 (noSC) can improve quality (e.g., reduce noise and artifacts associated with scatter and scatter correction), as discussed above. Notably, due to, for example, higher noise in the scatter estimate 117, a data processing technique (e.g., filter) 132 with stronger noise suppression (e.g., smoothing kernel) can be applied to the scatter-only image data to optimize noise reduction. In contrast, a data processing technique (e.g., a filter) 122 with lighter noise suppression (e.g., a high-resolution kernel) can be applied to the unscatter-corrected image data to minimize resolution loss. In this approach, the two imaging data components (unscatter-corrected and scatter-only components, shown in Equation 7) are processed (optimized) independently.

[0067] In some embodiments, the unscatter corrected image / data, before or after processing by data processing technique (e.g., filter) 122, can be used to guide (e.g., to determine a filter kernel for noise reduction) the processing of the relatively noisy scatter-only image / data by data processing technique (e.g., filter) 132. As noted above, the unscatter corrected image / data has a much lower noise level than the scatter-only image / data and can therefore be used to guide the scatter-only image / data noise reduction in step 130.

[0068] In step 160, the method generates a patient image 165 based on the unscatter-corrected data set 145 and the scatter-only data set 155 (e.g., by removing scatter from the patient data). For example, in embodiments where the data sets are line integral components, the scatter-only data set 155 can be added to the unscatter-corrected data set 145 to obtain the patient image 165. In an exemplary embodiment, based on the independent processing in steps 120, 130, which includes each of the above data processing techniques (e.g., filters) 122, 132, the final combined image 165 (e.g., a CT image) can provide an optimal compromise between noise reduction and resolution preservation.

[0069] This method 100 can be applied to embodiments that process imaging data (i.e., in the data or volume / image domain) before or after reconstruction, including those detailed in the following embodiments. References to any filters in the following embodiments are used as exemplary data processing. Other types of data processing techniques can be used without, instead of, or in addition to the exemplary filters mentioned.

[0070] 2 is a flow chart illustrating another exemplary method 200 for generating a radiological image by processing imaging data into unscatter-corrected and scatter-only components. In this embodiment, the radiological / patient data (I d ) 115 and scatter estimate (Sc_est) 117 may be generated as described in method 100 , including by patient scan 110 .

[0071] In step 221, the method generates at least one unscatter-corrected line integral based on the patient data 115. The unscatter-corrected line integral is then reconstructed in step 222 and processed in step 225 to generate an unscatter-corrected image 245. These steps 222, 225 may be performed simultaneously or in any order. For example, in this embodiment and other embodiments mentioned below, reconstruction and data processing may be combined (e.g., where reconstruction of data may include data processing), may include one or more of each step, and / or may include one or more data processing techniques. In one embodiment, when steps 222, 225 are performed separately (as shown in FIG. 2), each step may use an associated filter 223, 226. In another embodiment, when steps 222, 225 are performed simultaneously, only one type of filter 223 may be used. As described above, processing may be performed before, during, and / or after reconstruction in various embodiments. In one embodiment, steps 221, 222, and 225 may be associated with an exemplary implementation of step 120.

[0072] In step 231, the method generates at least one scatter-only line integral based on the scatter estimate 117. The scatter-only line integral is then reconstructed in step 232 and processed in step 235 to generate a scatter-only image 255. These steps 232, 235 may be performed simultaneously or in any order. In one embodiment, if steps 232, 235 are performed separately (as shown in FIG. 2), each step may use an associated filter 233, 236. In another embodiment, if steps 232, 235 are performed simultaneously, only one type of filter 233 may be used. As described above, processing may be performed before, during, and / or after reconstruction in various embodiments. In one embodiment, steps 231, 232, and 235 may be associated with an exemplary implementation of step 130.

[0073] As noted above, in various embodiments, one or more of the filters 233, 236 are different from one or more of the filters 223, 226. In this case, the filters and their associated parameters may be specifically targeted to the associated data 115, 117. As detailed above, the filters may be optimized separately for the source data 115, 117, independently optimizing the processing (filtering) of the two imaging data components (unscattered and scatter only, as shown in Equation 7).

[0074] In some embodiments, the unprocessed or processed unscatter-corrected image 245 can be used to guide processing (i.e., to determine filter kernels for noise reduction), for example, with filters 233 and / or 236 for the relatively noisy scatter-only image / data. For example, in one embodiment, data processing for the scatter-only data applies a Gaussian filter. This Gaussian filter uses voxel differences in the unscatter-corrected image 245 to determine kernel weights for the Gaussian filter. When designing a filter for the scatter-only data, the unscatter-corrected image 245 (which has lower noise) can be used to determine (guide) the kernel of the filter (e.g., filter 233 or filter 236) used to filter the scatter-only data to generate the scatter-only image 255. In one embodiment, the scatter-only image 255 can be obtained by decreasing the kernel weights of pixels on edges (in the unscatter-corrected image) to increase the weights or increasing the kernel size of areas without edges (in the unscatter-corrected image), followed by using the kernels to filter the scatter-only data. As mentioned above, the non-scatter corrected images / data have much lower noise levels than the scatter-only images / data, and this can be used to guide noise reduction in the scatter-only images / data.

[0075] In step 260, the method generates a patient image based on the un-scatter corrected image 245 and the scatter-only image 255. For example, the scatter-only image 255 can be added to the un-scatter corrected image 245 to obtain the patient image according to, for example, Equation 7.

[0076] 3 is a flow chart illustrating another exemplary method 300 for generating a radiological image by processing imaging data into unscatter-corrected and scatter-only components. In this embodiment, the radiological / patient data (I d ) 115 and scatter estimate (Sc_est) 117 may be generated as described in method 100 , including by patient scan 110 .

[0077] In step 321, the method generates at least one unscatter-corrected line integral based on the patient data 115. The unscatter-corrected line integral is then reconstructed in step 322 and processed in step 325 to generate an unscatter-corrected image 345. These steps 322, 325 may be performed simultaneously or in any order. In one embodiment, if steps 322, 325 are performed separately (as shown in FIG. 3), each step may use an associated filter 323, 326. In another embodiment, if steps 322, 325 are performed simultaneously, only one type of filter 323 may be used. As described above, processing may be performed before, during, and / or after reconstruction in various embodiments. In one embodiment, steps 321, 322, 325 may be associated with an exemplary implementation of step 120.

[0078] In step 331, the method generates at least one scatter-corrected line integral based on the patient data 115 and the scatter estimate 117. The scatter-corrected line integral is then reconstructed in step 332. A filter 333 may be used before, during, or after the reconstruction. Then, in step 334, the method determines the difference between the reconstructed unscatter-corrected line integral from step 322 and the reconstructed scatter-corrected line integral from step 332. For example, in one embodiment, the unscatter-corrected image is subtracted from the scatter-corrected image to obtain scatter-only image data. Then, in step 335, the difference can be processed using filter 336 to generate scatter-only image 355. In one embodiment, steps 331, 332, 334, and 335 may be associated with an exemplary implementation of step 130.

[0079] As noted above, in various embodiments, one or more of the filters 333, 336 are different from one or more of the filters 323, 326. In this case, the filters and their associated parameters may be specifically targeted to the associated data 115, 117. As detailed above, the filters may be optimized separately for the source data 115, 117, independently optimizing the processing (filtering) of the two imaging data components (unscattered and scatter only, as shown in Equation 7).

[0080] In some embodiments, the unprocessed or processed unscatter corrected image 345 can be used, for example, to guide the processing of the relatively noisy scatter-only image / data with filter 336 (i.e., to determine a filter kernel for noise reduction, for example). As noted above, the unscatter corrected image has a much lower noise level than the scatter-only image / data and can therefore be used to guide the noise reduction of the scatter-only image in step 335.

[0081] In step 360, the method generates a patient image based on the unscatter corrected image 345 and the scatter-only image 355. For example, the scatter-only image 355 can be added to the unscatter corrected image 345 to obtain the patient image.

[0082] 4 is a flow chart illustrating another exemplary method 400 for generating a radiological image by processing imaging data into unscatter-corrected and scatter-only components. In this embodiment, the radiological / patient data (I d ) 115 and scatter estimate (Sc_est) 117 may be generated as described in method 100 , including by patient scan 110 .

[0083] In step 421, the method generates at least one unscatter-corrected line integral based on the patient data 115. The unscatter-corrected line integral is then processed in step 422 and reconstructed in step 425 to generate an unscatter-corrected image 445. These steps 422, 425 may be performed simultaneously or in any order. In one embodiment, when steps 422, 425 are performed separately (as shown in FIG. 4), each step may use an associated filter 423, 426. In another embodiment, when steps 422, 425 are performed simultaneously, only one type of filter 426 may be used. As described above, processing may be performed before, during, and / or after reconstruction in various embodiments. In one embodiment, steps 421, 422, 425 may be associated with an exemplary implementation of step 120.

[0084] In step 431, the method generates at least one scatter-only line integral based on the scatter estimate 117. The scatter-only line integral is then processed in step 432 and reconstructed in step 435 to generate a scatter-only image 455. These steps 432, 435 may be performed simultaneously or in any order. In one embodiment, when steps 432, 435 are performed separately (as shown in FIG. 4), each step may use an associated filter 433, 436. In another embodiment, when steps 432, 435 are performed simultaneously, only one type of filter 436 may be used. As described above, processing may be performed before, during, and / or after reconstruction in various embodiments. In one embodiment, steps 431, 432, 435 may be associated with an exemplary implementation of step 130.

[0085] As noted above, in various embodiments, one or more of the filters 433, 436 are different from one or more of the filters 423, 426. In this case, the filters and their associated parameters may be specifically targeted to the associated data 115, 117. As detailed above, the filters may be optimized separately for the source data 115, 117, independently optimizing the processing (filtering) of the two imaging data components (unscattered and scatter only, as shown in Equation 7).

[0086] In some embodiments, the unprocessed or unprocessed unscatter corrected line integrals 421 from step 421 can be used, for example, to guide processing of the relatively noisy scatter-only data with filters 433 and / or 436 (e.g., to determine filter kernels for noise reduction). As noted above, unscatter corrected data has a much lower noise level than scatter-only data and can therefore be used to guide noise reduction of the scatter-only data.

[0087] In step 460, the method generates a patient image based on the unscatter corrected image 445 and the scatter-only image 455. For example, the scatter-only image 455 can be added to the unscatter corrected image 445 to obtain a patient image.

[0088] 5 is a flow chart illustrating another exemplary method 500 for generating a radiological image by processing imaging data into unscatter-corrected and scatter-only components. In this embodiment, the radiological / patient data (I d ) 115 and scatter estimate (Sc_est) 117 may be generated as described in method 100 , including by patient scan 110 .

[0089] In step 521, the method generates at least one unscatter-corrected line integral based on the patient data 115. The unscatter-corrected line integral is then processed in step 522 and reconstructed in step 525 to generate an unscatter-corrected image 545. These steps 522, 525 may be performed simultaneously or in any order. In one embodiment, when steps 522, 525 are performed separately (as shown in FIG. 5), each step may use an associated filter 523, 526. In another embodiment, when steps 522, 525 are performed simultaneously, only one type of filter 526 may be used. As described above, processing may be performed before, during, and / or after reconstruction in various embodiments. In one embodiment, steps 521, 522, 525 may be associated with an exemplary implementation of step 120.

[0090] In step 531, the method generates at least one scatter-corrected line integral based on the patient data 115 and the scatter estimate 117. The scatter-corrected line integral is then reconstructed in step 532. A filter 533 may be used before, during, or after the reconstruction. Then, in step 534, the method determines the difference between the reconstructed unscatter-corrected line integral from step 525 and the reconstructed scatter-corrected line integral from step 532. For example, in one embodiment, the unscatter-corrected image is subtracted from the scatter-corrected image to obtain scatter-only image data. Then, in step 535, the difference can be processed using filter 536 to generate scatter-only image 555. In one embodiment, steps 531, 532, 534, and 535 may be associated with an exemplary implementation of step 130.

[0091] As noted above, in various embodiments, one or more of the filters 533, 536 are different from one or more of the filters 523, 526. In this case, the filters and their associated parameters may be specifically targeted to the associated data 115, 117. As detailed above, the filters may be optimized separately for the source data 115, 117, independently optimizing the processing (filtering) of the two imaging data components (unscattered and scatter only, as shown in Equation 7).

[0092] In some embodiments, the unprocessed or processed unscatter corrected image 545 can be used, for example, to guide the processing of the relatively noisy scatter-only image / data with filter 536 (i.e., to determine, for example, a filter kernel for noise reduction). As noted above, the unscatter corrected image has a much lower noise level than the scatter-only image / data and can therefore be used to guide the noise reduction of the scatter-only image in step 535.

[0093] In step 560, the method generates a patient image based on the un-scatter corrected image 545 and the scatter-only image 555. For example, the scatter-only image 555 can be added to the un-scatter corrected image 545 to obtain the patient image.

[0094] 6 is a flow chart illustrating another exemplary method 600 for generating a radiological image by processing imaging data into unscatter-corrected and scatter-only components. In this embodiment, the radiological / patient data (I d ) 115 and scatter estimate (Sc_est) 117 may be generated as described in method 100 , including by patient scan 110 .

[0095] In step 621, the method generates at least one unscatter-corrected line integral based on the patient data 115. The unscatter-corrected line integral is then processed in step 622 using a filter 623. In step 631, the method generates at least one scatter-only line integral based on the scatter estimate 117. The scatter-only line integral is then processed in step 632 using a filter 633.

[0096] As noted above, in various embodiments, one or more filters 633 are different from one or more filters 623. In this case, the filters and their associated parameters may be specifically targeted to the associated data 115, 117. As detailed above, the filters may be optimized separately for the source data 115, 117, independently optimizing the processing (filtering) of the two imaging data components (unscattered and scatter only, as shown in Equation 7).

[0097] In some embodiments, the pre-processing or post-processing unscatter-corrected line integrals from step 621 can be used, for example, to guide processing of the relatively noisy scatter-only data with filter 633 (i.e., to determine a filter kernel for noise reduction, for example.) As noted above, the unscatter-corrected data has a much lower noise level than the scatter-only data and can therefore be used to guide noise reduction of the scatter-only data.

[0098] Next, in step 640, the method separates the primary data, for example, by determining the difference between the unscatter-corrected line integrals from steps 621 and 622 and the scatter-only line integrals from steps 631 and 632. For example, in one embodiment, the scatter-only line integral data is added to the unscatter-corrected line integral data to obtain primary-only line integral data. Next, in step 650, the primary line integral data can be reconstructed, which includes the use of filter 655. In step 660, the method generates patient image 665.

[0099] Figures 7-11 illustrate the performance of an exemplary embodiment on a cone-beam CT scan of a pelvis phantom. Compared to a traditional approach using a post-reconstruction low-pass filter, the results in this example demonstrate much better preservation of bone boundaries (image edges) with comparable noise reduction. Additionally, the noise pattern is much more natural than in the low-pass filtered image.

[0100] In particular, Figures 7A-7C are cone beam CT images 700 of a pelvic phantom acquired at 125 kVp, 2.5 mAs, 360 fields per rotation, and 24 fields per second with a collimator aperture of approximately 10 cm at the isocenter. Figures 7A-7C illustrate the image quality obtained with conventional image processing without the two-component image processing approach described above (i.e., processing the unscattered and scatter-only components separately using separate filters as described in the method above).

[0101] Figure 7A is an image 710 without scatter correction. Figure 7B is an image 720 with scatter correction. Figure 7C is an image 730 with scatter correction followed by a low-pass Gaussian filter. As shown in these images, the scatter correction used on image 720 minimized the cupping artifact 712 (i.e., reduced intensity in the center of the phantom compared to the periphery of the phantom in image 710), but image noise was amplified and streak artifacts were introduced throughout image 720. Post-reconstruction processing using a low-pass filter on image 730 reduced the noise and amplitude of the streak artifacts, but at the expense of reduced image resolution (e.g., visually blurred bone boundaries).

[0102] 8A-8B show a comparison 800 of image quality obtained with conventional image processing versus an exemplary embodiment implementing the two-component image processing approach described above (i.e., using separate filters to process the no-scatter and scatter-only components separately). In particular, FIG. 8A is a close-up of image 730 of FIG. 7C, which was obtained after conventional scatter correction and using a low-pass Gaussian filter. FIG. 8B is an image 810 of the same pelvis phantom using method 200, which shows reduced noise and artifacts and improved edge preservation compared to the Gaussian-filtered image 730.

[0103] Specifically, the scatter-only image was processed (filtered) based on the noSC image (e.g., 245 in FIG. 2 ) to generate image 810. In this case, the noSC image was used as a guide image for determining or calculating a filtering kernel. As in method 200, the scatter-only image was processed (filtered) using a filter different from the filter used for the noSC image. In this embodiment, the filtering kernel is a local Gaussian kernel of size 7×7×7, and the weight of each voxel is calculated using the voxel's HU difference relative to the central voxel in the noSC image. Edge information from the noSC image is naturally incorporated into the filter (kernel) calculation for the scatter-only image to preserve corresponding edges in the scatter-only image. The resulting image 810 demonstrates effective edge preservation and noise and artifact reduction.

[0104] FIG. 9 shows a comparison 900 of noise measurements associated with images 710, 720, 730, and 810. Among other things, table 910 includes mean levels and noise measurements from different regions-of-interest (ROIs) of images 710, 720, 730, and 810 processed in different ways. Note that the mean values ​​in table 910 are CT numbers +1000. No-SC noise data, SC noise data, and SC+Gau noise data are associated with each of the three images 710, 720, and 730 shown in FIGS. 7A-7C. "Example" noise data is associated with image 810 shown in FIG. 8B. Regions of interest include ROI_center 912, ROI_periphery 914, and line profile 916 (detailed in FIG. 10). The noise level of image 810 is similar to noSC image 710 in ROI_periphery 914 and lower than noSC image 710 in ROI_center 912. SC+Gau image 730 has similar noise to image 810 in ROI_center 912, but lower noise in ROI_periphery 914. However, as shown in FIG. 8, SC+Gau image 730 has very strong residual streak artifacts, which are minimal in image 810. Line profile 916 is used for the line profile comparison shown in FIG. 10.

[0105] FIG. 10 shows a comparison 1000 of line integral values ​​associated with images 710, 720, 730, and 810 along a line profile 916. Note that the line integral value is CT number + 1000. The "NoSc," "Sc," and "Sc_GaussianPF" lines are associated with the line profile 916 through the three images 710, 720, and 730 shown in FIGS. 7A-7C, respectively. The "Example" line is associated with the line profile 916 through image 810 shown in FIG. 8B. FIG. 11 shows an exploded view 1100 of a section 1010 of data. The line profiles further illustrate the effectiveness of the exemplary embodiment in both suppressing noise and streak artifacts and preserving edges. For example, the Sc line has exaggerated peaks and valleys compared to the noSc line. Additionally, the Sc_GaussianPF line is significantly smoother in these regions. In contrast, the example ray removes the scatter component, but without the associated noise, artifacts, loss of resolution, etc.

[0106] FIG. 12 shows a comparison 1200 of images and CT numbers associated with conventional image processing and an exemplary embodiment of the two-component image processing approach for a Catphan scan with annular scanning. The comparison 1200 illustrates significant noise reduction while maintaining the boundaries of small objects (visual assessment). Image 1210 (Non-SC) is not scatter corrected. Image 1220 (SC) is scatter corrected. Image 1230 (Example) is scatter corrected according to the two-component method. To generate the images, a Catphan+ annular scan was performed with an aperture of 4.5 cm at the isocenter. Images 1210, 1220, and 1230 are displayed using an HU window of [-400, 200]. Scan parameters were 125 kV, 2.5 mAs, 480 fields of view / rotation, and 24 frames per second. Table 1250 shows the mean CT number + / - standard deviation for ROI 1212 (center—shown as a circle), ROI 1214 (center—shown as a dashed ellipse), and ROI 1216 (periphery—shown as a solid ellipse). In this case, the mean value is CT number +1000. Image 1230 (Example) used in the exemplary embodiment has much greater noise suppression when compared to scatter-corrected image 1220 (Sc). Small contrast boundaries and contrast are visually identical in the two images 1220, 1230.

[0107] As detailed above, embodiments of the disclosed techniques utilize patient data from imaging scans (I dThe present invention relates to scatter correction of imaging data, including the use of Sc_est and scatter estimation (Sc_est). The imaging scans may be performed with any radiological imaging equipment, including X-ray, CT, CBCT, SPECT, PET, MR, etc. These methods can be used for scatter correction in imaging data from these imaging scans, e.g., with respect to noise and artifact reduction. While some exemplary embodiments emphasize CT scanners and cone-beam CT scanners, the techniques can also be applied to image reconstruction / data processing based on removal of unwanted counts / signals derived from the original counts to generate corrected images, e.g., scatter correction for SPECT, PET, MR, SPECT / CT, PET / CT, PET / MR, etc.

[0108] In various embodiments, imaging scans may be performed using dedicated imaging equipment or imaging equipment integrated into radiation therapy equipment. For example, a radiation therapy device may use an integrated low-energy radiation source for CT for use in conjunction with, or as part of, IGRT. Notably, for example, radiation therapy devices and related methods may combine both a collimated low-energy radiation source for imaging in a gantry using rotational (e.g., helical or step-and-shoot) image acquisition and a high-energy radiation source for therapeutic treatment. This is related to U.S. Patent Application No. 16 / 694,145, filed November 25, 2019, entitled "MULTIMODAL RADIATION APPARATUS AND METHODS," and U.S. Patent Application No. 16 / 694,148, filed November 25, 2019, entitled "APPARATUS AND METHODS FOR SCALABLE FIELD OF VIEW IMAGING USING A MULTI-SOURCE SYSTEM," both of which are incorporated herein by reference in their entireties. In these embodiments, a low energy radiation source (e.g., kilovoltage (kV)) can produce higher quality images than can be produced by using a high energy radiation source (e.g., megavoltage (MV)) for imaging.

[0109] The imaging data acquisition methodology can include, but is not limited to, multiple rotational scans, which can be, for example, continuous (e.g., having a helical source trajectory about a central axis with longitudinal movement of the patient support through the gantry bore), non-continuous stop-and-reverse circumferential scans with incremental longitudinal movement of the patient support, step-and-shoot circumferential scans, etc.

[0110] According to various embodiments, the imaging device collimates the radiation source, including, for example, into a cone beam or fan beam using, for example, a beamformer. In one embodiment, the collimated beam is combined with a gantry that rotates continuously while the patient is moved, thereby enabling helical images to be acquired.

[0111] Detectors (with various row / slice sizes, configurations, dynamic ranges, etc.), scan pitch, and / or dynamic collimation are additional features in various embodiments, including selective exposure of portions of the detector and selective definition of the effective readout area, as described in more detail below.

[0112] Imaging devices and methods can provide selective and variable collimation of a radiation beam emitted by a radiation source. This involves adjusting the shape of the radiation beam to expose less than the entire reactive area of ​​an associated radiation detector (e.g., a radiation detector positioned to receive radiation from the radiation source). By exposing only the primary region of the detector to direct radiation, only scattered radiation in the shaded region of the detector can be received. Scatter measurements in the shaded region of the detector (and in some embodiments, measurements in the peripheral region) can be used to estimate scattered radiation in the primary region of the detector receiving the projection data.

[0113] 13 and 14 , an exemplary imaging device 10 (which may include, for example, an X-ray imaging device) is shown. It will be understood that the imaging device 10 may be associated with and / or incorporated into a radiation therapy device (shown in FIG. 14 ) that can be used for a variety of applications, including, but not limited to, IGRT. The imaging device 10 includes a rotatable gantry system, referred to as a gantry 12, supported by or otherwise housed in a support unit or housing 14. Gantry herein refers to a gantry system comprising one or more gantries (e.g., rings or C-arms) capable of supporting one or more radiation sources and / or associated detectors as they rotate around a target. The rotatable gantry 12 defines a gantry bore 16 through which a patient may be moved and positioned for imaging and / or treatment. According to one embodiment, the rotatable gantry 12 is configured as a slip-ring gantry, providing continuous rotation of the imaging radiation source (X-rays) and associated radiation detectors while providing sufficient bandwidth for high-quality imaging data received at the detectors.

[0114] A patient support 18 is positioned adjacent to the rotatable gantry 12 and is configured to support a patient, typically in a horizontal position, for longitudinal movement in and within the rotatable gantry 12. The patient support 18 is capable of moving the patient, for example, in a direction perpendicular to the plane of rotation of the gantry 12 (along or parallel to the axis of rotation of the gantry 12). The patient support 18 can be operatively coupled to a patient support controller for controlling movement of the patient and the patient support 18. The apparatus 10 is capable of acquiring volumetric and planar-based imaging. For example, in various embodiments, the apparatus 10 may be used to acquire volumetric and / or planar images and perform the associated processing methods described above.

[0115] As shown in FIG. 14, imaging apparatus 10 includes an imaging radiation source 30 coupled to or otherwise supported by a rotatable gantry 12. Imaging radiation source 30 emits a radiation beam (generally shown as 32) for producing high-quality images. In this embodiment, the imaging radiation source is an X-ray source 30, which is configured as a kilovoltage (kV) source (e.g., a medical X-ray source having an energy level ranging from about 20 kV to about 150 kV). The imaging radiation source can be any type of transmission source suitable for imaging. Other imaging transmission sources can be interchangeably used in various other embodiments.

[0116] The imaging apparatus 10 may also include another radiation source 20 coupled to or otherwise supported by the rotatable gantry 12. According to one embodiment, the radiation source 20 is configured as a therapeutic radiation source, such as a high-energy radiation source used to treat tumors within a patient within a region of interest. It will be understood that the therapeutic radiation source may be a high-energy X-ray beam (e.g., a megavoltage (MV) X-ray beam). Generally, the radiation source 20 has a higher energy level (e.g., peak and / or average) than the imaging radiation source 30. Although FIGS. 13 and 14 show the X-ray imaging apparatus 10 with the radiation source 30 mounted on the ring gantry 12, other embodiments may include other types of rotatable imaging apparatus, including, for example, C-arm gantries and robotic arm-based systems.

[0117] A detector 34 (e.g., a two-dimensional flat detector or a curved detector) may be coupled to or otherwise supported by the rotatable gantry 12. The detector 34 (e.g., an X-ray detector) is positioned to receive radiation from the imaging radiation source 30 and may rotate relative to the source 30. The detector 34 may detect or measure the unattenuated radiation dose. Thus, it is possible to infer the actual attenuation in the patient or related patient ROI (compared to that initially generated). The detector 34 may detect or otherwise collect attenuation data from different angles as the radiation source 30 rotates around and emits radiation toward the patient.

[0118] A collimator or beamformer assembly (generally shown as 36) is positioned relative to the imaging source 30 to selectively control and adjust the shape of the radiation beam 32 emitted by the source 30 to selectively expose portions or areas of the reactive area of ​​the detector 34. The beamformer can also control how the radiation beam 32 is positioned on the detector 34. For example, in one embodiment, each readout may capture 3-4 centimeters of projection image data with approximately 1-2 centimeters of unexposed detector area on one or both sides. This may be used to capture scatter data.

[0119] A detector 24 is coupled to or otherwise supported by the rotatable gantry 12 and can be positioned to receive radiation 22 from the therapeutic radiation source 20. The detector 24 can detect or measure the unattenuated radiation dose, thus allowing an inference to be made of the actual attenuation in the patient or associated patient ROI (compared to that initially generated). The detector 24 can detect or otherwise collect attenuation data from different angles as the therapeutic radiation source 20 rotates around and emits radiation toward the patient.

[0120] The therapeutic radiation source 20 may be mounted, configured, and / or moved in the same plane as the imaging source 30 or in a different plane (offset) therefrom. In some embodiments, scattered radiation caused by simultaneous operation of the radiation sources 20, 30 may be reduced by offsetting the emission planes.

[0121] When incorporated into a radiation therapy machine, the imaging device 10 can provide images used to set up (e.g., coordinate and / or register), design, and / or guide a radiation delivery method (treatment). Setup is typically accomplished by comparing current (in-treatment) images with pre-treatment image information. Pre-treatment image information may include, for example, X-ray, CT, CBCT, MR, PET, SPECT, and / or 3D rotational angiography (3DRA) data, and / or any information obtained from these devices or other imaging modalities. In some embodiments, the imaging device 10 can track the movement of the patient, target, or ROI during treatment.

[0122] The reconstruction processor 40 can be operatively coupled to the detectors 24, 34. In one embodiment, the reconstruction processor 40 is configured to generate patient images based on radiation from the radiation sources 20, 30 received by the detectors 24, 34 as described above. It will be understood that the reconstruction processor 40 can be configured to perform the methods described herein. The apparatus 10 can also include a memory 44 suitable for storing information including, but not limited to, software including data processing and reconstruction algorithms, filters and data processing / filter parameters, imaging parameters, prior or otherwise pre-acquired images (e.g., planning images), and image data from a treatment plan.

[0123] The imaging device 10 may include an operator / user interface 48, in which case an operator of the imaging device 10 may interact with or otherwise control the imaging device 10, provide input related to scan or imaging parameters, etc. The operator interface 48 may include any suitable input device, such as a keyboard, mouse, voice-activated controller, etc. The imaging device 10 may also include a display 52 or other human-readable element to provide output to the operator of the imaging device 10. For example, the display 52 may allow the operator to observe reconstructed patient images and other information related to the operation of the imaging device 10, such as imaging or scan parameters.

[0124] 14 , imaging apparatus 10 includes a controller (generally designated as 60) operatively coupled to one or more components of apparatus 10. Controller 60 controls the overall operational capabilities and operation of apparatus 10, including providing power and timing signals to imaging source 30 and / or therapeutic radiation source 20 and a gantry motor controller that controls the rotational speed and position of rotatable gantry 12. It will be understood that controller 60 can include one or more of a patient support controller, a gantry controller, a controller coupled to therapeutic radiation source 20 and / or imaging source 30, a controller of beamformer 36, a controller coupled to detector 24 and / or detector 34, etc. In one embodiment, controller 60 is a system controller capable of controlling other components, devices, and / or controllers.

[0125] In various embodiments, the reconstruction processor 40, the operator interface 48, the display 52, the controller 60 and / or other components may be combined into one or more components or devices.

[0126] The device 10 may include various components, logic, and software. In one embodiment, the controller 60 includes a processor, memory, and software. By way of example and not limitation, the imaging device and / or radiation therapy system may include various other devices and components (e.g., a gantry, radiation source, collimator, detector, controller, power supply, patient support, among others) that can perform one or more routines or steps related to imaging and / or IGRT for a particular application. Routines may include imaging, image-based pre-irradiation steps, and / or irradiation, including the settings, configurations, and / or positions (e.g., path / trajectory) of individual devices, which may be stored in memory. Furthermore, one or more controllers may directly or indirectly control one or more devices and / or components according to one or more routines or processes stored in memory. An example of direct control is setting various radiation source or collimator parameters (e.g., power, speed, position, timing, adjustment, etc.) related to imaging or treatment. An example of indirect control is communicating position, path, speed, etc. to a patient support controller or other peripheral device. The hierarchy of the various controllers that may be associated with the imaging equipment may be arranged in any suitable manner to convey appropriate commands and / or information to the desired devices and components.

[0127] Additionally, those skilled in the art will appreciate that the system and method may be implemented in other computer system configurations. The illustrated aspects of the present invention may be implemented in a distributed computing environment, where certain tasks are performed by local or remote processing devices that are linked through a communications network. For example, in one embodiment, the reconstruction processor 40 may be associated with different systems. In a distributed computing environment, program modules may be located in both local and remote memory storage devices. For example, a remote database, a local database, a cloud computing platform, a cloud database, or a combination thereof may be utilized by the imaging device 10.

[0128] The imaging device 10 can utilize an exemplary environment for implementing various aspects of the present invention, including a computer. The computer includes a controller 60 (e.g., including a processor and memory, which may be memory 44) and a system bus. The system bus can couple system components, including but not limited to the memory for the processor, and can communicate with other systems, controllers, components, devices, and processors. The memory can include read-only memory (ROM), random access memory (RAM), hard drives, flash drives, and any other form of computer-readable medium. The memory can store various software and data, including routines and parameters, which may include, for example, treatment plans.

[0129] Image quality is determined by many factors (e.g., the focal spot size of the imaging source, the dynamic range of the detector, etc.). Scatter is a limiting factor in many imaging techniques and image quality. Various approaches can be used to reduce scatter. One approach is to use an anti-scatter grid (which collimates the scattered rays). However, implementing a scatter grid in a kV imaging system, including those for motion tracking and correction purposes, can be problematic. As noted above, accurate estimation of scatter in the projection data is essential to improve the quality of the image data. In various embodiments, scatter in the projection data acquired in the primary region of the detector 34 can be estimated based on data measured in the shaded region (and peripheral region) of the detector 34.

[0130] FIG. 15 is a schematic diagram of an exemplary projection 1500 collimated onto a detector 1502. Rotation of a radiation source 1506 (e.g., X-ray) is shown emitting a radiation beam 1508 that exposes a primary or central (C) region 1510 of the detector 1502 to direct radiation from the source 1506 (e.g., passing through a target) as the source 1506 rotates about the y-axis. Axial (longitudinal) movement of a patient support (not shown) is along the y-axis and is included as part of the scan. The detector 1502 also has a posterior (B) shaded region 1512 and a front (F) shaded region 1514 that are blocked from direct exposure to the radiation beam 1508 by a beamformer / collimator 1520. The beamformer / collimator 1520 is configured to adjust the shape and / or position of the radiation beam 1508 emitted by the source 1506 onto the detector 1502. The shaded areas 1512, 1514 receive only scattered radiation.

[0131] The collimator 1520 aperture is configured so that the rear (B) end 1512 and front (F) end 1514 of the detector 1502 in the axial or longitudinal direction (toward the examination couch or along the y-axis) are not illuminated by direct radiation 1508. These rear (B) shaded region 1512 (negative longitudinal direction along the rotational y-axis) and front (F) 1514 (positive longitudinal direction along the rotational y-axis) shaded regions can be used for scatter measurements because they do not receive direct radiation. For example, the detector 1502 readout range can be configured to read all or part of the data in one or more shaded regions 1512, 1514 and use the data in the primary region 1510 for scatter estimation. The primary region 1510, or central (C) region, receives both direct illumination and scatter.

[0132] In various embodiments, a data processing system (e.g., including processor 40) can be configured to receive measured projection data in the primary region 1510 and measured scatter data in at least one shaded region 1512, 1514. This then determines estimated scatter in the primary region 1510 based on the measured scatter data in the at least one shaded region 1512, 1514. In some embodiments, the determination of estimated scatter in the primary region 1510 during a current rotation can be based on measured scatter data in at least one shaded region 1512, 1514 during a most recent (prior and / or subsequent) rotation. In other embodiments, measurement data from one or more peripheral regions (adjacent to the primary region and the shaded region) may also be used in the scatter estimation.

[0133] Various techniques and methods can use different scan geometries, detector positioning, and / or beamformer window shapes. In some embodiments, the detectors may also be offset laterally.

[0134] FIG. 16 is a flowchart illustrating an exemplary method 1600 for scatter estimation and correction, such as those described above. Inputs can include any prior data and / or scan design. In this embodiment, step 1610 includes data acquisition. For example, while a radiation source is rotating, projecting a collimated radiation beam toward a target and a radiation detector, the method measures projection data (primary + scatter) in a central (primary) region of the radiation detector and measures scatter using front and / or back oblique peripheral regions of the detector. Data acquisition in step 1610 can also include adjusting the shape / position of the radiation beam using a beamformer before and / or during the scan and / or adjusting the readout region (including determining the reactive region).

[0135] Next, step 1620 includes scatter estimation. For example, this method uses scatter measurements of one or more shaded regions to estimate scatter in the projection data of the central (primary) region. Subsequently, step 1630 includes scatter correction, which may include either of the above two component approaches. The output includes scatter-corrected projection data suitable for imaging. Various embodiments may use different scan geometries, detector positioning / reactive regions, beamformer positioning / window shapes, etc.

[0136] FIG. 17 is a flowchart illustrating an exemplary method 1700 of IGRT using a radiation therapy device (e.g., including imaging device 10). Patient prior image data 1705, which may include prior CT images and may be previously acquired planning images, may be utilized. The prior data 1705 may also include treatment plans, phantom information, models, a priori information, etc. In some embodiments, the prior image data 1705 is generated by the same radiation therapy device, but at an earlier time. In step 1710, patient imaging is performed using a low-energy radiation source (e.g., kV radiation from x-ray source 30). In one embodiment, imaging includes a helical scan with a fan or cone beam geometry. Step 1710 may use the scatter estimation and scatter correction techniques described above to generate one or more high-quality (HQ) images or imaging data 1715. In some embodiments, image quality may be adjusted to optimize a balance between image quality / resolution and dose. In other words, not all images need to be of the highest quality. Alternatively, image quality may be adjusted to optimize or trade off image quality / resolution and image acquisition time. The imaging step 1710 may also include image / data processing to generate a patient image based on the imaging data (e.g., according to the methods described above). An image processing step 1720 is shown as part of the imaging step 1710. In some embodiments, the image processing step 1720 is a different step, including where the image processing is performed on a different device.

[0137] Next, in step 1730, one or more image-based pre-irradiation steps, described below, are performed based at least in part on the imaging data 1715 of step 1710. As described in further detail below, step 1730 can include determining various parameters related to the treatment procedure and (subsequent) imaging planning. In some embodiments, the image-based pre-irradiation step (1730) can require further imaging (1710) before irradiation (1740). Step 1730 can include adapting the treatment plan based on the imaging data 1715 as part of an adaptive radiation treatment routine. In some embodiments, the image-based pre-irradiation step 1730 can include real-time treatment planning. Embodiments can also include simultaneous, overlapping, and / or alternating operation of the imaging and therapeutic radiation sources. Real-time treatment planning can include any or all of these types of imaging and therapeutic radiation actuation techniques (simultaneous, overlapping, and / or alternating).

[0138] Next, in step 1740, delivery of the therapeutic treatment is performed using a source of high-energy radiation (e.g., MV radiation from therapeutic radiation source 20). Step 1740 delivers a therapeutic dose 1745 to the patient according to the treatment plan. In some embodiments, IGRT method 1700 may return to step 1710 for additional imaging at various intervals, followed by image-based pre-irradiation steps (1730) and / or irradiation (1740) as needed. In this manner, high-quality imaging data 1715 may be generated and utilized during IGRT using device 10 capable of delivering adaptive therapy. As described above, steps 1710, 1720, 1730, and / or 1740 may be performed simultaneously, overlapping, and / or alternating.

[0139] In various embodiments, the imaging data is generated using dedicated imaging equipment or imaging equipment integrated into radiation therapy equipment, and the various methods described above can be used for scatter correction.

[0140] In one embodiment, the CT device receives raw data (e.g., I d The system includes a rotating X-ray source and X-ray detector for acquiring a set of X-rays (e.g., Sc_est) and hardware and / or software for measuring and / or generating a set of scatter data (e.g., Sc_est) to cancel / correct for scatter contamination in the raw data. An unscatter-corrected image is reconstructed from the raw data, and a scatter-only image is reconstructed from the scatter data. In this embodiment, the raw data can be used to calculate unscatter-corrected line integrals to reconstruct an unscatter-corrected CT image. The scatter data can be used to calculate scatter-only line integrals according to Equation 6 to reconstruct a scatter-only image. The unscatter-corrected and scatter-only images are processed independently, with the latter being more heavily filtered due to higher noise. The processed unscatter-corrected and processed scatter-only images can be combined to create a final scatter-corrected CT image.

[0141] In another embodiment, volumetric image subtraction may be used to generate a scatter-only image. Here, the scatter data is used with the raw data to generate scatter-corrected line integrals to reconstruct a scatter-corrected image. The raw data can be used to calculate unscatter-corrected line integrals to reconstruct an unscatter-corrected image. The unscatter-corrected image can be subtracted from the scatter-corrected image to obtain a scatter-only image. The unscatter-corrected image and the scatter-only image are processed independently, with the latter being more heavily filtered due to higher noise. The processed unscatter-corrected image and the processed scatter-only image can be combined together to create a final scatter-corrected CT image.

[0142] In various embodiments, the unscatter-corrected image can be used to guide the processing of the scatter-only image while preserving edges in the image, resulting in effective noise and artifact reduction of the scatter-only image. For example, the filter can be a Gaussian filter that uses voxel differences in the unscatter-corrected image to determine kernel weights for the scatter-only image filter. In this manner, edge information in the unscatter-corrected image is used to preserve corresponding edges in the scatter-only image. The unscatter-corrected image can also be used in more advanced edge-preserving processing schemes to improve the processing of the scatter-only image. For example, the processing of the scatter-only image can be based on anisotropic derivative filter parameters obtained with the unscatter-corrected image.

[0143] In another embodiment, different reconstruction schemes can be used to reconstruct the uncorrected and scatter-only images. For example, the uncorrected image can be reconstructed using a higher-resolution kernel than the scatter-only image, and the scatter-only image can be reconstructed using a customized streak artifact reduction algorithm. The scatter-only image can be reconstructed using a different grid to speed up reconstruction time. For example, if the uncorrected image reconstruction uses a 512 x 512 matrix, the scatter-only image reconstruction can use a 256 x 256 matrix for reconstruction to speed up reconstruction time. The reconstructed scatter-only image can be resampled to the same grid as the uncorrected image. The uncorrected image can then be used to guide the processing of the scatter-only image. The resulting scatter-only image can be combined with the uncorrected image to create a final scatter-corrected image.

[0144] In addition to the CT environment highlighted in some of the exemplary embodiments, various other embodiments may utilize raw data (e.g., I dVarious imaging instruments that acquire or generate scatter data (e.g., Sc_est) can use the scatter data to correct raw data from SPECT, PET, etc. If the line integral can be decomposed into a linear combination of uncorrected and scatter-corrected components, similar to Equation 6, the scatter data can be used to correct / correct the line integral. Uncorrected images have lower noise than scatter-only images. The two images can be reconstructed differently to optimize the quality of both, and then combined after reconstruction to obtain the final image. The reconstructed uncorrected and scatter-only images can be processed independently to optimize the quality of both, and then combined to obtain the final image. The uncorrected image can also be used as a guide image to determine the weights of the filtering kernel when processing the scatter-only image.

[0145] In addition to embodiments that use the unscatter-corrected image to guide the processing of the scatter-only image (i.e., operate in the image domain), other embodiments can operate in the data domain. In these embodiments, processing of the generated scatter-only component line integrals can be based on the unscatter-corrected component line integral data as guidance data to preserve edges in the scatter-only component. The resulting scatter-only component line integrals can be reconstructed separately from or together with the unscatter-corrected component line integrals.

[0146] In various embodiments, raw data (e.g., I d) and the measured scatter data (e.g., Sc_est) are used together to reconstruct a scatter-corrected image, and the raw data is used to reconstruct an unscatter-corrected image using various reconstruction algorithms to obtain the image. In some embodiments, the reconstruction can be analytical reconstruction. In some embodiments, the reconstruction can be iterative reconstruction. In various embodiments, the scatter-only image is processed (including filtering, artifact reduction, etc.) separately from the unscatter-corrected image and then combined with the unscatter-corrected image to obtain a final image. In some embodiments, the scatter-only image is generated by subtracting the unscatter-corrected image from the scatter-corrected image. Furthermore, the unscatter-corrected image can be used to guide the processing of the scatter-only image for optimal noise and artifact reduction and edge preservation.

[0147] In general, in various embodiments, the above techniques can be applied to any imaging device. Additionally, any correction approach that modifies the line integral for image reconstruction generates a correction term that modifies the line integral for image reconstruction (e.g., leading to increased image noise and artifacts). For example, the correction term can be a time lag correction term in cone-beam CT using a flat-panel detector. While multiple correction terms, such as a time lag correction term and scatter correction in cone-beam CT, jointly modify the line integral for reconstruction, the line integral can be decomposed into two components, with or without correction, as in Equation 6. Using the above methods, final images with improved quality and performance can be obtained.

[0148] While the disclosed methodology has been shown and described with respect to certain specific aspects, one or more embodiments, it will be apparent that similar changes and modifications will occur to those skilled in the art upon reading and understanding this specification and the accompanying drawings. In particular, with respect to the various functions performed by the above elements (components, assemblies, devices, members, compositions, etc.), the terms used to describe such elements (including references to "means") are intended, unless otherwise indicated, to correspond to any element among the described elements that performs the particular function, even if it is not structurally equivalent to the disclosed structure that performs the function of the exemplary aspects shown herein, one or more embodiments of the disclosed methodology. In addition, while particular features of the disclosed methodology may be described above with respect to only one or more of some illustrated aspects or embodiments, such features can be combined with one or more other features of other embodiments, as may be desirable or advantageous for any given or particular application.

[0149] While the embodiments described herein relate to the systems and methods described above, these embodiments are intended to be exemplary and are not intended to limit the applicability of these embodiments solely to the descriptions set forth herein. While the invention has been illustrated by the description of its embodiments, and the embodiments have been described in some detail, it is not the intention of the applicant to restrict or in any way limit the scope of the appended claims to such details. Further advantages and modifications will be readily apparent to those skilled in the art. Therefore, the invention in its broader aspects is not limited to the specific details, representative apparatus and methods, and illustrative examples shown and described. Accordingly, departures may be made from such details without departing from the spirit or scope of the applicant's general inventive concept.

[0150] Embodiment The following is a non-exhaustive list of exemplary embodiments according to aspects of the present disclosure. 1. A radiological imaging device comprising: a radiation source for emitting radiation; a radiation detector positioned to receive radiation from the radiation source and generate radiation data, the radiation data including a primary component and a scattered radiation component; 1. A data processing system comprising: receiving radiation data, generating an unscatter corrected image based on the radiation data and using a first data processing technique; Estimation of the scatter component of radiation data, generating a scatter-only image based on the scatter estimation and using a second data processing technique different from the first data processing technique; and Generation of images based on unscatter-corrected and scatter-only images; and a data processing system configured to perform the steps of: 2. The radiation source comprises a rotating X-ray source that emits a radiation beam; the radiation detector comprises an X-ray detector positioned to receive radiation from the X-ray source; The equipment, further comprising a beamformer configured to adjust the shape of the radiation beam emitted by the x-ray source, wherein a primary region of the x-ray detector is directly exposed to the radiation beam and at least one shaded region of the x-ray detector is shielded from direct exposure to the radiation beam by the beamformer; 2. An imaging device according to embodiment 1. 3. An imaging device as described in any one of embodiments 1 and 2, wherein the estimation of the scatter component of the radiation data is based on measured scatter data of at least one shaded region. 4. The generation of the unscatter-corrected image includes reconstruction of the radiation data; Generate an image of only scattered radiation Reconstructing a scatter-corrected image based on the radiation data and scatter estimation; and including subtraction of the non-scatter corrected image from the scatter corrected image; An imaging device according to any one of the first to third embodiments. 5. Producing the unscatter-corrected image includes reconstruction of radiation data, and the first data processing technique includes a higher resolution kernel than the second data processing technique; An imaging device according to any one of embodiments 1 to 3, wherein generating a scatter-only image includes reconstructing a scatter estimate using a streak artifact reduction algorithm. 6. Producing the unscatter-corrected image includes reconstructing the radiation data using a first grid; Generate an image of only scattered radiation Reconstructing a scatter-only image using a second grid associated with a faster reconstruction time than the first grid; resampling the reconstructed scatter-only image using a first grid; and processing the scatter-only image based on a third data processing technique determined based on the unscatter-corrected image. An imaging device according to any one of the first to third embodiments. 7. An imaging apparatus according to any one of embodiments 1 to 6, wherein the first data processing technique comprises a high-resolution kernel and the second data processing technique comprises a smoothing kernel. 8. An imaging device according to any one of embodiments 1 to 6, wherein the second data processing technique is determined based on an image without scatter correction. 9. An imaging device according to any one of embodiments 1 to 8, further comprising processing of the scattered radiation only image based on a third data processing technique determined based on the image without scatter correction. 10. An imaging device as described in embodiment 9, wherein the third data processing technique includes a Gaussian filter that uses voxel differences of the image without scatter correction to determine kernel weights of the third data processing technique. 11. An imaging device as described in any one of embodiments 9 and 10, wherein processing the scatter-only image includes using anisotropic derivative filter parameters obtained from the non-scatter-corrected image to process the scatter-only image. 12. An imaging device described in any one of embodiments 1 to 11, wherein a first data processing technique is applied to radiation data before reconstruction of an image without scatter correction, and a second data processing technique is applied to scatter estimation before reconstruction of a scatter-only image. 13. A method for producing a radiological image, comprising: receiving radiation data from a radiation imaging device, the radiation data including a primary component and a scatter component; generating an unscatter-corrected data set based on the radiation data using a first data processing technique; estimating a scatter component of the radiation data; generating a scatter-only data set using a second data processing technique different from the first data processing technique based on the scatter estimation; generating an image based on the unscatter corrected data set and the scatter only data set. 14. Generating an unscatter-corrected data set based on the radiation data using a first data processing technique; generating unscatter corrected line integrals; reconstructing and processing the unscatter corrected line integrals using a first data processing technique to generate an unscatter corrected image; generating a scatter-only data set using a second data processing technique based on the scatter estimation; generating a line integral of only the scattered radiation; reconstructing and processing the reconstructed scatter-only line integrals using a second data processing technique to generate a scatter-only image; and generating an image based on the unscatter-corrected data set and the scatter-only data set includes adding the scatter-only image to the unscatter-corrected image; 14. The method of embodiment 13. 15. The method of any one of embodiments 13 and 14, wherein the second data processing technique is based on images without scatter correction. 16. Generating an unscatter-corrected data set based on the radiation data using a first data processing technique; generating unscatter corrected line integrals; reconstructing and processing the reconstructed un-scatter corrected line integrals using a first data processing technique to generate an un-scatter corrected image; generating a scatter-only data set using a second data processing technique based on the scatter estimation; generating scatter-corrected line integrals; reconstructing scatter-corrected line integrals; determining the difference between the reconstructed unscatter-corrected line integral and the reconstructed scatter-corrected line integral; processing the difference using a second data processing technique to generate a scatter-only image; and generating an image based on the unscatter-corrected data set and the scatter-only data set includes adding the scatter-only image to the unscatter-corrected image; 14. The method of embodiment 13. 17. Generating an unscatter-corrected data set based on the radiation data using a first data processing technique; generating unscatter corrected line integrals; reconstructing and processing the unscatter corrected line integrals using a first data processing technique to generate an unscatter corrected image; generating a scatter-only data set using a second data processing technique based on the scatter estimation; generating a line integral of only the scattered radiation; reconstructing and processing the scatter-only line integrals using a second data processing technique to generate a scatter-only image; and generating an image based on the unscatter-corrected data set and the scatter-only data set includes adding the scatter-only image to the unscatter-corrected image; 14. The method of embodiment 13. 18. Generating an unscatter-corrected data set based on the radiation data using a first data processing technique, generating unscatter corrected line integrals; reconstructing and processing the unscatter corrected line integrals using a first data processing technique to generate an unscatter corrected image; generating a scatter-only data set using a second data processing technique based on the scatter estimation; generating scatter-corrected line integrals; reconstructing the scatter-corrected line integrals to generate a scatter-corrected image; determining the difference between the reconstructed unscatter-corrected line integral and the reconstructed scatter-corrected line integral; processing the difference using a second data processing technique to generate a scatter-only image; and generating an image based on the unscatter-corrected data set and the scatter-only data set includes adding the scatter-only image to the unscatter-corrected image; 14. The method of embodiment 13. 19. Generating an unscatter-corrected data set based on the radiation data using a first data processing technique; generating unscatter corrected line integrals; processing the unscatter corrected line integrals using a first data processing technique; generating a scatter-only data set using a second data processing technique based on the scatter estimation; generating a line integral of only the scattered radiation; processing the scattered-only line integrals using a second data processing technique; and generating images based on the unscatter-corrected data set and the scatter-only data set; separating the line integrals of the primary data based on the difference between the processed unscatter-corrected line integral and the processed scatter-only line integral; and reconstructing line integrals of the primary data to generate an image. 14. The method of embodiment 13. 20. A radiation therapy device comprising: a rotatable gantry system positioned at least partially about the patient support; a first radiation source coupled to the rotatable gantry system and configured as a therapeutic radiation source; a second radiation source coupled to the rotatable gantry system and configured as an imaging radiation source having a lower energy level than the therapeutic radiation source; a radiation detector coupled to the rotatable gantry system and positioned to receive radiation from the second radiation source; 1. A data processing system comprising: receiving radiation data including primary and scatter components; generating an unscatter-corrected data set based on the radiation data using a first data processing technique; Estimation of the scatter component of radiation data, generating a scatter-only data set using a second data processing technique different from the first data processing technique based on the scatter estimation; generating images based on the unscatter-corrected data set and the scatter-only data set; and delivering a dose of therapeutic radiation to the patient via a first radiation source based on the image during the compatible IGRT; and a data processing system configured to perform the steps of:

Claims

1. 1. A radiation imaging device comprising: a radiation source for emitting radiation; a radiation detector positioned to receive radiation from the radiation source and generate radiation data, the radiation data including a primary component and a scattered radiation component; and 1. A data processing system comprising: receiving said radiation data; generating an unscatter corrected image based on the radiation data and using a first data processing technique; estimating the scatter component of the radiation data; generating a scatter-only image based on the estimated scatter component and using a second data processing technique; and generating a final scatter-corrected radiographic image based on the non-scatter-corrected image and the scatter-only image; a data processing system configured to perform generating the unscatter-corrected image includes reconstructing the unscatter-corrected image based on the radiation data; generating the scattered radiation only image, reconstructing a scatter-corrected image based on the radiation data and the estimated scatter component; and a subtraction of the unscatter-corrected image from the scatter-corrected image.

2. the radiation source comprises a rotating X-ray source emitting a radiation beam; the radiation detector comprises an X-ray detector positioned to receive the radiation from the rotating X-ray source; The radiation imaging device comprises: and a beamformer configured to adjust the shape of the radiation beam emitted by the rotating X-ray source, wherein a primary region of the X-ray detector is directly exposed to the radiation beam and a shaded region of the X-ray detector is shielded from direct exposure to the radiation beam by the beamformer.

10. The imaging device of claim 1.

3. The imaging apparatus of claim 2 , wherein the estimation of the scatter component of the radiation data is based on scatter data measured in at least one of the shaded regions.

4. generating the unscatter-corrected image includes reconstructing the unscatter-corrected image based on the radiation data, and the first data processing technique includes a higher resolution kernel than the second data processing technique; generating the scatter-only image includes reconstructing the scatter-only image based on the estimated scatter component using a streak artifact reduction algorithm. An imaging device according to any one of claims 1 to 3.

5. generating the unscatter-corrected image includes reconstructing the unscatter-corrected image based on the radiation data using a first grid; generating the scattered radiation only image, reconstructing the scatter-only image based on the estimated scatter component using a second grid associated with a faster reconstruction time than the first grid; and resampling the reconstructed scatter-only image using the first grid; and processing the scatter-only image based on a third data processing technique determined based on the unscatter-corrected image; the third data processing technique is a Gaussian filter that uses voxel differences in the unscatter corrected image to determine kernel weights for a scatter-only image filter. An imaging device according to any one of claims 1 to 3.

6. 6. The imaging apparatus of claim 1, wherein the first data processing technique comprises a high-resolution kernel and the second data processing technique comprises a smoothing kernel.

7. 7. The imaging apparatus of claim 1, wherein the second data processing technique is determined based on the unscattered image.

8. 6. The imaging instrument of claim 5, wherein processing the scatter-only image includes using anisotropic derivative filter parameters obtained from the non-scatter corrected image to process the scatter-only image.

9. 9. The imaging device of claim 1, wherein the first data processing technique is applied to the radiation data before reconstruction of the non-scatter-corrected image, and the second data processing technique is applied to the estimated scatter component before reconstruction of the scatter-only image based on the estimated scatter component.

10. 1. A method for producing a radiological image, comprising: receiving radiation data from a radiation imaging device, the radiation data including a primary component and a scatter component; generating an unscatter corrected data set based on the radiation data using a first data processing technique; estimating the scatter component of the radiation data; generating a scatter-only data set using a second data processing technique based on the estimated scatter component; generating a final scatter-corrected radiographic image based on the non-scatter-corrected data set and the scatter-only data set; generating the unscatter corrected data set based on the radiation data using the first data processing technique; generating an integral value of the line integral without scatter correction; generating an unscatter-corrected image based on the unscatter-corrected line integrals using the first data processing technique; generating the unscatter-corrected image includes reconstructing the unscatter-corrected image based on integral values ​​of the unscatter-corrected line integrals; generating the scatter-only data set using the second data processing technique based on the estimated scatter component; generating a line integral of only the scattered radiation; generating a scatter-only image based on the integral of the scatter-only line integral using the second data processing technique; generating the scatter-only image includes reconstructing the scatter-only image based on an integral of the scatter-only line integral; and generating the final scatter-corrected radiological image based on the non-scatter-corrected data set and the scatter-only data set comprises adding the scatter-only image to the non-scatter-corrected image; the unscatter-corrected data set comprises unscatter-corrected line integrals and / or images; A method wherein the scatter-only data set includes scatter-only line integrals and / or images.

11. The method of claim 10 , wherein the second data processing technique is based on the unscatter corrected image.

12. generating the unscatter corrected data set based on the radiation data using the first data processing technique; generating an integral value of the line integral without scatter correction; generating an unscatter-corrected image based on the unscatter-corrected line integrals using the first data processing technique; generating the unscatter-corrected image includes reconstructing the unscatter-corrected image based on integral values ​​of the unscatter-corrected line integrals; generating the scatter-only data set using the second data processing technique based on the estimated scatter component; generating a line integral value of the radiation data from which the estimated scattered radiation component has been removed or subtracted; reconstructing a scatter-corrected image based on the integral value of the line integral of the radiation data from which the estimated scatter component has been removed or subtracted; determining a difference between the unscatter-corrected image and the scatter-corrected image; processing the difference using the second data processing technique to generate a scatter-only image; and 11. The method of claim 10, wherein generating the final scatter-corrected radiographic image based on the non-scatter-corrected data set and the scatter-only data set comprises adding the scatter-only image to the non-scatter-corrected image.

13. generating the unscatter corrected data set based on the radiation data using the first data processing technique; generating an integral value of the line integral without scatter correction; processing the unscatter corrected line integrals using the first data processing technique; generating the scatter-only data set using the second data processing technique based on the estimated scatter component; generating a line integral of only the scattered radiation; and processing the integral of the scattered-only line integral using the second data processing technique; and generating the scatter-corrected final radiation based on the non-scatter-corrected data set and the scatter-only data set; Separating the integral value of the line integral of the first component based on the difference between the integral value of the processed line integral without scatter correction and the integral value of the processed line integral with only scatter; generating the final scatter-corrected radiographic image based on the integral value of the line integral of the primary component; The method of claim 10.

14. 1. A radiation therapy device comprising: a rotatable gantry system positioned at least partially about the patient support; a first radiation source coupled to the rotatable gantry system and configured as a therapeutic radiation source; a second radiation source coupled to the rotatable gantry system and configured as an imaging radiation source having a lower energy level than the therapeutic radiation source; a radiation detector coupled to the rotatable gantry system and positioned to receive radiation from the second radiation source; 1. A data processing system comprising: receiving radiation data including primary and scatter components; generating an unscatter corrected data set based on the radiation data using a first data processing technique; estimating the scatter component of the radiation data; generating a scatter-only data set using a second data processing technique based on the estimated scatter component; generating a final scatter-corrected radiographic image based on the non-scatter-corrected data set and the scatter-only data set; and delivering a dose of therapeutic radiation to the patient via the first radiation source based on the scatter-corrected final radiation image during a compatible IGRT; a data processing system configured to perform generating the unscatter corrected data set based on the radiation data using the first data processing technique; generating an integral value of the line integral without scatter correction; generating an unscatter-corrected image based on the unscatter-corrected line integrals using the first data processing technique; generating the unscatter-corrected image includes reconstructing the unscatter-corrected image based on integral values ​​of the unscatter-corrected line integrals; generating the scatter-only data set using the second data processing technique based on the estimated scatter component; generating a line integral of only the scattered radiation; generating a scatter-only image based on the integral of the scatter-only line integral using the second data processing technique; generating the scatter-only image includes reconstructing the scatter-only image based on an integral of the scatter-only line integral; and generating the final scatter-corrected radiological image based on the non-scatter-corrected data set and the scatter-only data set comprises adding the scatter-only image to the non-scatter-corrected image; the unscatter-corrected data set comprises unscatter-corrected line integrals and / or images; A radiation therapy device, wherein the scattered radiation-only data set includes scattered radiation-only line integrals and / or images.

Images