Systems and methods for performing truncation artifact correction

A method for performing truncation artifact correction includes acquiring a projection dataset of a patient, the projection dataset including measured data and truncated data, generating an initial estimate of a boundary between the measured data and the truncated data, using the measured data to revise the initial estimate of the boundary, estimating the truncated data using the revised estimate of the boundary, and using the measured data and the estimated truncated data to generate an image of the patient.

BACKGROUND OF THE INVENTION

The subject matter disclosed herein relates generally to imaging systems, and more particularly, to systems and methods for performing truncation artifact correction.

Computed Tomography (CT) imaging systems typically include an x-ray source and a detector. In operation, the x-rays are transmitted from the x-ray source, through a patient, and impinge upon the detector. The information from the detector, also referred to herein as the measured data, is then utilized to reconstruct a diagnostic image of the patient. However, under some scanning conditions, portions of the patient may extend beyond a region measured by the detector, e.g. when the patient is larger than the scan field of view (SFOV) of the detector. The SFOV is defined as the region for which the patient will be fully measured by the detector in every view. Additionally, the patient may not be properly aligned with the detector. Imaging patients that are larger than SFOV and/or patients that are improperly aligned with the detector may result in image artifacts.

More specifically, the CT imaging system is utilized to reconstruct cross-sectional images of the patient using a plurality of line integrals of the linear attenuation coefficients, e.g. the measured data. However, when the patient extends beyond the SFOV of the detector or the patient is improperly aligned with the detector, the line integrals outside the SFOV, also referred to herein as truncated data, are not known. Typically, the truncated data is therefore set to zero. Image reconstruction is then performed using the measured data and the truncated data. However, the truncated data may result in image artifacts, also referred to herein as truncation artifacts, in the reconstructed images. The truncation artifacts are typically visualized on the reconstructed images as a bright ring at the edge of the detector SFOV.

One known method of reducing truncation artifacts is to set the truncated data to a value other than zero in a technique known as padding. However, while padding may reduce the brightness of the ring at the edge of the detector SFOV, padding still does not provide a very accurate representation of the truncated data outside the detector SFOV.

BRIEF DESCRIPTION OF THE INVENTION

In one embodiment, a method is provided for performing truncation artifact correction. The method includes acquiring a projection dataset of a patient, the projection dataset including measured data and truncated data, generating an initial estimate of a boundary using the measured data and the truncated data, using the measured data to revise the initial estimate of the boundary, estimating the truncated data using the revised estimate of the boundary, and using the measured data and the estimated truncated data to generate an image of the patient.

In another embodiment, a non-transitory computer readable medium is provided. The non-transitory computer readable medium being programmed to instruct a computer to acquire a projection dataset of a patient, the projection dataset including measured data and truncated data, generate an initial estimate of a boundary using the measured data and the truncated data, use the measured data to revise the initial estimate of the boundary, estimate the truncated data using the revised estimate of the boundary, and use the measured data and the estimated truncated data to generate an image of the patient.

In a further embodiment, an imaging system is provided. The imaging system includes a detector and a computer coupled to the detector. The computer is configured to acquire a projection dataset of a patient, the projection dataset including measured data and truncated data, generate an initial estimate of a boundary using the measured data and the truncated data, use the measured data to revise the initial estimate of the boundary, estimate the truncated data using the revised estimate of the boundary, and use the measured data and the estimated truncated data to generate an image of the patient.

DETAILED DESCRIPTION OF THE INVENTION

Described herein are various embodiments for performing truncation artifact correction using an iterative method or algorithm. In various embodiments, the method includes generating an initial estimate of a boundary that is between the measured data and the truncated data, e.g. a line between data that lies within a scan field of view (SFOV) and data that lies outside the SFOV. Thus, when the patient image is segmented, the area within the boundary and representing the patient is initially classified as water and the area outside the boundary is initially classified as air. Thus, the boundary defines where the patient goes from water to air. The iterative method then refines the reconstructed image by improving the estimate of the boundary. The mass outside the SFOV is first assumed to be water. At each iterative step in the method, the reconstruction outside the detector SFOV is thresholded into water and air. The measured x-ray beams are compared to the x-ray beams acquired during a forward projection of the image which has been thresholded outside the SFOV, and the changes are used to dilate or erode the estimate of the patient boundary. More specifically, after each thresholding iteration, the method performs a forward projection and subtracts the data with the measured data. A difference sinogram is then zeroed where no information is measured because of truncation artifacts. An unfiltered backprojection is then applied. Optionally, a filtered backprojection may also be utilized. A resulting difference image along the boundary may be thresholded. More specifically, where the boundary is above the threshold, the boundary is dilated. Where the boundary is below the threshold, the boundary is dilated. In various embodiments, a full difference sinogram may not be estimated. Instead, at points along the object boundary, the difference between the measured rays and the forward projected rays may be used to determine whether the boundary at the point should be dilated, eroded, or kept constant.

FIGS. 1A and 1Bis a flowchart of an exemplary method100for performing truncation artifact correction. Although the method100is described in a medical setting using a Computed Tomography (CT) imaging system, it is contemplated that the benefits of the various embodiments described herein accrue to all CT imaging systems including industrial CT imaging systems such as, for example, a baggage scanning CT system typically used in a transportation center such as, for example, but not limited to, an airport or a rail station.

At102, the method includes acquiring a set of projection data. In various embodiments, the projection data may be acquired using an exemplary imaging system, such as a CT imaging system150shown inFIG. 2. In various embodiments, the CT imaging system150includes an x-ray source152that projects a fan-shaped beam154which is collimated to lie within an X-Y plane of a Cartesian coordinate system and generally referred to as an “imaging plane”. The fan-shaped beam154includes a plurality of x-ray beams156that define the SFOV158of a detector160.

In operation, the x-ray beams156passes through an object being imaged, such as a patient162. The x-ray beams156, after being attenuated by the patient162, impinge upon the detector160. In various embodiments, the detector160includes a plurality of detector elements164. The intensity of the attenuated x-ray beams156received at each of the detector elements164is dependent upon the attenuation of the x-ray beams156by the patient162. More specifically, each detector element164produces an electrical signal that represents the intensity of an impinging x-ray beam156and hence allows estimation of the attenuation of the x-ray beam156as the x-ray beam156passes through the patient162. In various embodiments, the detector160is a multislice detector160that includes a plurality of parallel detector rows (not shown) of detector elements164such that projection data corresponding to a plurality of slices may be acquired simultaneously during a scan.

A group of x-ray attenuation measurements, i.e., projection data180, from the detector160at one gantry angle is referred to as a “view”. A “scan” of the patient162may include a set of views made at different gantry angles, or view angles, during one revolution of the x-ray source152and the detector160. The projection data180is then processed to generate an image that corresponds to a two dimensional slice taken through the patient162.

In various embodiments, a portion164the patient162may extend beyond the SFOV158measured by detector160as shown inFIG. 2. More specifically, for the CT imaging system150to reconstruct images of the patient162, the patient162should be located within the SFOV158of the detector160. Traditional reconstruction methods require all nonzero line measurements to be known for accurate reconstruction to be possible. As a result, the failure to collect attenuation information concerning portions of the patient162extending beyond the detector SFOV158results in truncated views which result in truncation artifacts.

For example,FIG. 3is an exemplary image200that is generated in accordance with various embodiments. As shown inFIG. 3, the image200is generated using non-truncated views202or images that are generated using the measured data, or non-truncated data. Measured data as used herein is data acquired by the detector160and that lies within the detector SFOV158. Additionally, the image200includes truncated views204that cause truncation artifacts206which in the illustrated embodiment, appear as a bright white line near a left side of the image200. Truncated views, or truncated data, as used herein refers to data that lie outside the detector SFOV158, or views which contain some truncated data. Truncated views or data may also refer to data that is within the SFOV158but proximate to an edge of the detector160, for example data acquired by the reference channels170and/or172. Accordingly, and referring again toFIGS. 1A and 1B, at102a projection dataset that includes measured, or non-truncated data, and non-measured, or truncated data is acquired of the patient162.

At104, an initial estimate of the truncated data within the projection dataset180is estimated to identify the truncated data. In various embodiments, the truncated data may be estimated, or extrapolated, using for example, a padding method. In operation, the padding method identifies the last value measured in a specific detector channel and then assigns the last value to the data to the truncated data outside the same detector channel. For example, and referring again toFIG. 2, assume that the last value acquired from a detector element182is one. Accordingly, the value one is assigned to all truncated data that lies in the same detector row, so that the value in missing data is the same value as that of detector element182. It should be realized that the detector160may include a plurality of detector rows. Accordingly, the padding value assigned to each truncated data point is based on the specific detector row of the detector160. Accordingly, truncated data along different detector rows may be assigned the same value, or a different value.

In another embodiment, the truncated data may be modeled or estimated, on a view-by view basis, using a method referred to herein as water cylinder extrapolation. In operation, projections from neighboring channels are utilized to perform the water cylinder extrapolation. More specifically, because the human anatomy typically does not change quickly over a small distance, e.g. a few millimeters, the measurements along a boundary184(shown inFIG. 2) also typically do not vary significantly. Based on the boundary and the slope of the projection measurements obtained at the edge of the detector, a location and a size of a cylindrical water object that can be best fitted to the truncated projection is generated. In operation, a size and location of the water cylinder is therefore estimated based on the weighted average described above. The water cylinder information may then be utilized as an estimate for the truncated data. More specifically, the truncated data is modeled on a view-by-view basis as a cylinder made of water by calculating the slope and offset of the measured data at the boundary182of the truncation in order to uniquely determine the size and location of the imaginary water cylinder. The projections through the water cylinder may then be utilized to estimate the truncated data. In various embodiments, the water cylinder may be resealed or stretched to ensure that the total mass of the water cylinder is consistent throughout. In various other embodiments, a symmetric mirroring method or a polynomial extrapolation method may be utilized to estimate the truncated data. One method for using the water cylinder extrapolation technique is described in U.S. Pat. No. 6,856,666.

Referring again toFIGS. 1A and 1B, at106the measured data and the estimated truncated data are combined to generate a revised dataset.

At108, the revised dataset is reconstructed to generate at least one image of the patient162. For example,FIG. 4is an exemplary image220that may be reconstructed after implementing the water cylinder extrapolation technique described above. In various embodiments, the revised dataset may be reconstructed using a filtered backprojection technique. In operation, the filtered backprojection technique converts the attenuation measurements from the scan information into reconstructions of the object, typically in units called “CT numbers” or “Hounsfield units”, which are used to control the brightness of a corresponding pixel on a display. Optionally, the revised dataset may be reconstructed using any known reconstruction method.

At110, and in various embodiments, the reconstructed image generated at108is segmented as either water or air. More specifically, the Hounsfield units derived at108are utilized to classify or segment the truncated data as either water or air to provide an initial estimate of a location of the boundary184. For example, assume that a single pixel representing a single truncated data point was previously estimated to have a Hounsfield unit value of X. Accordingly, in various embodiments, if X is greater than a predetermined value, the single truncated data point is classified as water. Moreover, if X is less than the predetermined value, the single truncated data point is classified as air. Accordingly, each of the truncated data points is classified as either water or air based on the Hounsfield value assigned at108. In operation, classifying the truncated data as either water or air facilitates reducing errors that may result in shading artifacts, and may occur as a result of implementing, for example, the water cylinder extrapolation technique described above. In various other embodiments, the truncated data may be classified into more than two groups. For example, the truncated data may be classified as water, air, bone, metal, or iodine. In order to improve the quality of the boundary, postprocessing steps may be used after the initial segmentation. For example, on the segmented image, binary image closing or opening may be used to produce a cleaner boundary between air and water.

Referring again toFIGS. 1A and 1B, at112the measured data and the segmented truncated data, i.e the reconstruction, generated at110are forward projected to estimate the x-ray beams that were measured, e.g. the non-truncated data. In operation, forward projecting the projection data provides an estimate of what the measurements should be if the segmentation into water and air were correct. Because the segmentation outside the SFOV158is in general not correct, error exists between the forward projected measurements and the originally measured data. Within the SFOV158, the water cylinder extrapolated result is generally good. However, as the data further outside the SFOV is extrapolated, the reconstructed image may exhibit increased quantities of artifacts. Moreover, the water cylinder extrapolated image is consistent with the original measured data. Accordingly, when the reconstructed image is forward projected, a resulting sinogram, such as the sinogram240shown inFIG. 5, matches the measured data within the SFOV158, but it is not consistent with our prior knowledge of what images should look like. For example, the reconstructed sinogram240may include negative values, streaks, and/or artifacts241.

Accordingly, and referring again toFIGS. 1A and 1B, at114the initial boundary estimate is modified, as described in more detail below, and an error sinogram244is generated as shown inFIG. 7. In general, the sinogram240as described above, may be further refined or improved by iteratively thresholding or segmenting the truncated data into water and air as described above at110. More specifically, when for example, the water cylinder extrapolation technique is utilized, the resultant reconstructed image or sinogram240accurately represents the information acquired during the scan within the SFOV158. Accordingly, while various techniques, such as the water cylinder extrapolation technique, facilitate providing an improved image, further improvement may be desired to more accurately represent the truncated data.

In various embodiments, the initial boundary estimate may be modified or revised on a per data point basis, e.g. modifying each truncated data point, which is a measurement of a line integral. For example, as described above, the truncated data was initially estimated using the water cylinder extrapolation. A resultant image was then reconstructed as shown inFIG. 4and segmented into water and air. Accordingly, at114, the forward projected ray is compared to the measured ray. In one embodiment, if the forward projected ray is larger than the measured ray, there is too much mass along that ray. Accordingly, the boundary184is made smaller, or shrunk (eroded). If the forward projected ray is smaller than the measured ray, there is too little mass along that ray, the boundary184is expanded or dilated. Optionally, the boundary184is reduced or contracted and some of the water is reclassified as air. Repeating step112over all the rays yields an image that is more consistent with the measured data.

In various embodiments, step114may be implemented in sinogram space. For example,FIG. 6is an error sinogram242generated using the difference of the measured data and the forward projected data.FIG. 5, the label240identifies the unfiltered backprojection of the error sinogram242. Accordingly, in various embodiments, the initial boundary estimate may be generated using the error or difference sinogram242, which is segmented to show only the boundary as shown inFIG. 7. This image244may then be thresholded and used to guide the reclassification of boundary pixels from water into air or from air into water.

Referring again toFIGS. 1A and 1B, at116an unfiltered backprojection is performed using the error sinogram244to dilate or erode the initial boundary estimate. In operation, the unfiltered backprojection enables the method to identify a pixel in the image and determine the effects of the reconstruction using the measured rays compared to the forward projected rays. To reduce the computation time of the unfiltered backprojection, a subset of the data may be backprojected (for example, every other view, or every other ray within a view). A filter could be applied if desired.

At118, steps110-116are iteratively repeated until changes or movement of the boundary184is less than a predetermined threshold. For example, if a final iteration does not appreciably expand or contract the boundary184, the iterative process may be completed and the final boundary set at the location determined by the last location identified at step116.

At120, a forward projection is performed to generate the missing or truncated data. More specifically, as described above, a forward projection was utilized for the measured data. At120, data acquired at118is forward projected to generate the missing or truncated data. In the exemplary embodiment, the truncated data is forward projected in the image domain to provide an estimate for the measurements that are outside the SFOV158.

At122, the measured data and the estimated data from the final forward projection at120are combined to generate a final or complete projection dataset. In various embodiments, a blending or smoothing operation may be performed on the final sinogram to facilitate reducing and/or eliminating discontinuities between the measured rays and the forward projected rays. At124, the final projection dataset is utilized to reconstruct a final image of the patient162using any suitable method. In various embodiments, the method100may also include segmenting the image in an area outside the SFOV158to bone or iodine or iodine and water. For each bone, or iodine or metal location, the method may include estimating an expected point spread function at each location and then deconvolving the image by the point spread function to reduce artifacts caused by the bone or metal. This deconvolution could be regularized for stability, e.g. with a Wiener deconvolution.

The methods and algorithms described herein are used to perform truncation artifact correction. The methods and algorithms may be embodied as a set of instructions that are stored on a computer and implemented using, for example, a module330, shown inFIG. 8, software, hardware, a combination thereof, and/or a tangible non-transitory computer readable medium. In one embodiment, a tangible non-transitory computer readable medium excludes signals.

FIG. 8is a pictorial view of an exemplary imaging system300that is formed in accordance with various embodiments.FIG. 9is a block schematic diagram of a portion of the multi-modality imaging system300shown inFIG. 8. The imaging system may be embodied as a computed tomography (CT) imaging system, a positron emission tomography (PET) imaging system, a magnetic resonance imaging (MRI) system, an ultrasound imaging system, an x-ray imaging system, a single photon emission computed tomography (SPECT) imaging system, an interventional C-Arm tomography imaging system, a CT system for a dedicated purpose such as extremity or breast scanning, and combinations thereof, among others. In the exemplary embodiment, the method100is described with respect to a CT imaging system.

Although various embodiments are described in the context of an exemplary dual modality imaging system that includes a computed tomography (CT) imaging system and a positron emission tomography (PET) imaging system, it should be understood that other imaging systems capable of performing the functions described herein are contemplated as being used. Moreover, the various methods described herein may be implemented with a stand-alone CT imaging system.

The multi-modality imaging system300is illustrated, and includes a CT imaging system302and a PET imaging system304. The imaging system300allows for multiple scans in different modalities to facilitate an increased diagnostic capability over single modality systems. In one embodiment, the exemplary multi-modality imaging system300is a CT/PET imaging system300. Optionally, modalities other than CT and PET are employed with the imaging system300. For example, the imaging system300may be a standalone CT imaging system, a standalone PET imaging system, a magnetic resonance imaging (MRI) system, an ultrasound imaging system, an x-ray imaging system, and/or a single photon emission computed tomography (SPECT) imaging system, interventional C-Arm tomography, CT systems for a dedicated purpose such as extremity or breast scanning, and combinations thereof, among others.

The CT imaging system302includes a gantry310that has an x-ray source312that projects a beam of x-rays toward a detector array314on the opposite side of the gantry310. The detector array314includes a plurality of detector elements316that are arranged in rows and channels that together sense the projected x-rays that pass through an object, such as the subject306. The imaging system300also includes a computer320that receives the projection data from the detector array314and processes the projection data to reconstruct an image of the subject306. In operation, operator supplied commands and parameters are used by the computer320to provide control signals and information to reposition a motorized table322. More specifically, the motorized table322is utilized to move the subject306into and out of the gantry310. Particularly, the table322moves at least a portion of the subject306through a gantry opening324that extends through the gantry310.

The imaging system300also includes a module330that is configured to implement various methods and algorithms described herein. The module330may be implemented as a piece of hardware that is installed in the computer320. Optionally, the module330may be implemented as a set of instructions that are installed on the computer320. The set of instructions may be stand alone programs, may be incorporated as subroutines in an operating system installed on the computer320, may be functions in an installed software package on the computer320, and the like. It should be understood that the various embodiments are not limited to the arrangements and instrumentality shown in the drawings.

As discussed above, the detector314includes a plurality of detector elements316. Each detector element316produces an electrical signal, or output, that represents the intensity of an impinging x-ray beam and hence allows estimation of the attenuation of the beam as it passes through the subject306. During a scan to acquire the x-ray projection data, the gantry310and the components mounted thereon rotate about a center of rotation340.FIG. 9shows only a single row of detector elements316(i.e., a detector row). However, the multislice detector array314includes a plurality of parallel detector rows of detector elements316such that projection data corresponding to a plurality of slices can be acquired simultaneously during a scan.

Rotation of the gantry310and the operation of the x-ray source312are governed by a control mechanism342. The control mechanism342includes an x-ray controller344that provides power and timing signals to the x-ray source312and a gantry motor controller346that controls the rotational speed and position of the gantry310. A data acquisition system (DAS)348in the control mechanism342samples analog data from detector elements316and converts the data to digital signals for subsequent processing. For example, the subsequent processing may include utilizing the module330to implement the various methods described herein. An image reconstructor350receives the sampled and digitized x-ray data from the DAS348and performs high-speed image reconstruction. The reconstructed images are input to the computer320that stores the image in a storage device352. Optionally, the computer320may receive the sampled and digitized x-ray data from the DAS348and perform various methods described herein using the module330. The computer320also receives commands and scanning parameters from an operator via a console360that has a keyboard. An associated visual display unit362allows the operator to observe the reconstructed image and other data from computer.

The operator supplied commands and parameters are used by the computer320to provide control signals and information to the DAS348, the x-ray controller344and the gantry motor controller346. In addition, the computer320operates a table motor controller364that controls the motorized table322to position the subject306in the gantry310. Particularly, the table322moves at least a portion of the subject306through the gantry opening324as shown inFIG. 8.

Referring again toFIG. 9, in one embodiment, the computer320includes a device370, for example, a floppy disk drive, CD-ROM drive, DVD drive, magnetic optical disk (MOD) device, or any other digital device including a network connecting device such as an Ethernet device for reading instructions and/or data from a computer-readable medium372, such as a CD-ROM, a DVD or an other digital source such as a network or the Internet, as well as yet to be developed digital means. In another embodiment, the computer320executes instructions stored in firmware (not shown). The computer320is programmed to perform functions described herein, and as used herein, the term computer is not limited to just those integrated circuits referred to in the art as computers, but broadly refers to computers, processors, microcontrollers, microcomputers, programmable logic controllers, application specific integrated circuits, and other programmable circuits, and these terms are used interchangeably herein.

In the exemplary embodiment, the x-ray source312and the detector array314are rotated with the gantry310within the imaging plane and around the subject306to be imaged such that the angle at which an x-ray beam374intersects the subject306constantly changes. A group of x-ray attenuation measurements, i.e., projection data, from the detector array314at one gantry angle is referred to as a “view”. A “scan” of the subject306comprises a set of views made at different gantry angles, or view angles, during one revolution of the x-ray source312and the detector314. In a CT scan, the projection data is processed to reconstruct an image that corresponds to a two dimensional slice taken through the subject306.

Exemplary embodiments of a multi-modality imaging system are described above in detail. The multi-modality imaging system components illustrated are not limited to the specific embodiments described herein, but rather, components of each multi-modality imaging system may be utilized independently and separately from other components described herein. For example, the multi-modality imaging system components described above may also be used in combination with other imaging systems.

As used herein, the term “computer” may include any processor-based or microprocessor-based system including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), logic circuits, GPUs, FPGAs, and any other circuit or processor capable of executing the functions described herein. The above examples are exemplary only, and are thus not intended to limit in any way the definition and/or meaning of the term “computer”. The computer or processor executes a set of instructions that are stored in one or more storage elements, in order to process input data. The storage elements may also store data or other information as desired or needed. The storage element may be in the form of an information source or a physical memory element within a processing machine.