Abstract:
A method of acquiring and reconstructing a computed tomography (CT) image is provided. A first scan of the full field of view (FOV) is acquired. A second scan of a smaller target FOV is then acquired by using a collimator to narrow the X-ray beam width. The CT image is iteratively reconstructed by replacing a key-hole region of the full FOV projection data with the target FOV projection data. An exemplary embodiment comprises imaging a heart (target FOV) within a torso (full FOV) over multiple heart beat cycles. A computer readable medium is further provided, including a program configured to reconstruct a CT image using the key-hole method.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 11/731,612 entitled “ITERATIVE RECONSTRUCTION OF TOMOGRAPHIC IMAGE DATA METHOD AND SYSTEM”, filed Mar. 30, 2007, which is herein incorporated by reference. 
    
    
     BACKGROUND 
     The invention relates generally to non-invasive imaging. More particularly, the invention relates to methods and systems for targeted iterative reconstruction for use in non-invasive imaging. 
     In the fields of medical imaging and security screening, non-invasive imaging techniques have gained importance due to benefits that include unobtrusiveness, ease, and speed. In medical and research contexts, these imaging systems are used to image organs or tissues beneath the surface of the skin. A number of non-invasive imaging modalities exist today. A particular modality may be selected based upon the organ or tissue to be imaged, upon the spatial and/or temporal resolution desired, or upon whether structural or functional characteristics are of interest. Certain of these non-invasive imaging modalities collect tomographic data that includes sets of line integrals from multiple directions. Examples of these imaging modalities include X-ray computed tomography (CT) imaging, positron emission tomography (PET) imaging, single photon emission computed tomography (SPECT) imaging, magnetic resonance imaging (MRI) using projection reconstruction, and X-ray tomosynthesis. 
     Certain applications of these imaging modalities require high-resolution images of a targeted field of view (FOV) that is less than the full scan FOV of the imaging system. For example, in cardiac imaging, a high-resolution image of a small sub-region of the patient&#39;s anatomy may be desired. However, in X-ray tomography, reconstruction of the measured projection data may rely on measured projection data from outside the targeted FOV. While reconstruction of this targeted FOV is generally straightforward for analytical reconstruction algorithms (such as filtered back projection), iterative reconstruction techniques typically consider the targeted FOV and the regions of the full scan FOV that surround the targeted FOV. This is because iterative reconstruction techniques attempt to match the estimated projection data (derived from forward projection of an estimated image) to the measured projection data. However, if the estimated projection data do not support the signal from outside the targeted FOV, the estimated projection data cannot correctly match the measured projection data. 
     In general, the signal from outside the targeted FOV should be accounted for in the image reconstruction. If the signal from outside the targeted FOV is not accounted for, the entire signal from outside the targeted FOV may be assigned to the periphery of the targeted FOV or may produce aliasing artifacts inside the targeted FOV. This approach may result in a visible artifact at the periphery of the reconstructed image and quantitatively inaccurate regions throughout the reconstructed image. In other cases, when a targeted FOV less than the scan FOV is requested, the full scan FOV may be reconstructed at high resolution. Subsequently, the image for desired targeted FOV may be extracted from this image for the full scan FOV. This approach, however, reconstructs an image for a full pixel grid (e.g., a full scan) even though only a partial pixel grid for the targeted FOV was requested. As the computational time and image storage requirements grow significantly based on the number of pixels in the reconstruction, this approach may be computationally expensive. 
     BRIEF DESCRIPTION 
     The present technique provides a novel method and system for determining the amount of a substance contained within a region. In accordance with one embodiment of the present technique, a method is provided to acquire a computed tomography image. The method includes acquiring full FOV (background) computed tomography projection data, acquiring target FOV computed tomography projection data, and reconstructing the computed tomography image based on the background a and the target region projection data. 
     In accordance with another embodiment of the present technique, a method is provided to acquire a computed tomography (CT) image. The method includes acquiring a scout image of a patient with a computed tomography scanner, determining a target position in the scout image, determining a target field of view, positioning the target field of view near to an axis of rotation of the CT scanner, performing a full scan, and performing a target scan, wherein the target scan comprises blocking X-ray beams that are not transmitted through the target field of view. 
     In accordance with yet another embodiment of the present technique, a method is provided to reconstruct a CT image. The method includes masking out data in a target (key-hole) region of a full-FOV sinogram that is based on full-FOV projection data, and iteratively reconstructing the CT image based on replacing the masked out keyhole data with target FOV projection data. 
     In accordance with yet another embodiment of the present technique, a computer readable medium is provided, including a program configured to reconstruct a CT image based on full scan data associated with a CT full scan and target scan data associated with a CT target scan. Reconstructing the CT image comprises replacing at least a portion of the full scan data associated with a target field of view with at least a portion of the target scan data associated with the target field of view. 
    
    
     
       DRAWINGS 
       These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
         FIG. 1  is a diagrammatical illustration of an exemplary CT imaging system, in accordance with one aspect of the present technique; 
         FIG. 2  is a diagrammatical illustration of a full FOV scan of a patient torso, in accordance with one aspect of the present technique; 
         FIG. 3  is a diagrammatical illustration of a target FOV scan of a patient heart region, in accordance with one aspect of the present technique; 
         FIG. 4  is a flowchart depicting one technique for image reconstruction, in accordance with one aspect of the present technique; 
         FIG. 5  is a flowchart depicting one technique for determining background projection data for an area outside a targeted FOV, in accordance with one aspect of the present technique; 
         FIG. 6  is a diagrammatical view of measured projection data for a scan FOV, in accordance with one aspect of the present technique; 
         FIG. 7  is a diagrammatical view of a reconstructed image of the measured projection data of  FIG. 6 , in accordance with one aspect of the present technique; 
         FIG. 8  is a diagrammatical view of the reconstructed image of  FIG. 7  with the pixels corresponding to the targeted FOV masked out, in accordance with one aspect of the present technique; 
         FIG. 9  is a diagrammatical view of a forward projection of the reconstructed image of  FIG. 8 , in accordance with one aspect of the present technique; 
         FIG. 10  is a flowchart depicting one technique for utilizing background projection data in an iterative reconstruction algorithm, in accordance with one aspect of the present technique; 
         FIG. 11  is a flowchart depicting a technique for utilizing background projection data in an iterative reconstruction algorithm, in accordance with one aspect of the present technique; 
         FIG. 12  is an expanded diagrammatical view of a targeted FOV, in accordance with one aspect of the present technique; and 
         FIG. 13  is an expanded diagrammatical view of a targeted FOV, in accordance with one aspect of the present technique. 
     
    
    
     DETAILED DESCRIPTION 
     The embodiments discussed below provide a technique for dynamic CT imaging. More specifically, certain embodiments include acquiring at least one full field of view (FOV) (background) image and a plurality of images of a targeted FOV that is contained with the region associated with the background image. To provide for improved processing performance, in certain embodiments, data in the region of the background image that corresponds to the targeted FOV is replaced with the data from each of the images in the targeted FOV. In some embodiments, one or more of the plurality of images including the targeted FOV are reconstructed based on the data in the targeted FOV and the surrounding data of the background image. Accordingly, in certain embodiments, only a single CT scan of a patient is made to acquire the background image and data for processing and additional CT scans are restricted to the targeted FOV, thereby reducing the dose of X-radiation to the patient while accurately acquiring multiple images of the targeted FOV. In some embodiments, such a technique is used for dynamic imaging of the targeted FOV. In some embodiments, the targeted FOV includes a patient&#39;s heart, such that images of the heart may be taken over a period of time (e.g., one or more heartbeats) and reconstructed to provide a four-dimensional image using a first background image for processing image data of the targeted FOV of the heart. Before a detailed discussion of the system and methods are described in accordance with various embodiments of the present technique, it may be beneficial to discuss embodiments of imaging systems that may incorporate the system and methods described herein. 
     Turning now to the figures,  FIG. 1  is a diagram that illustrates an imaging system  10  for acquiring and processing image data. In the illustrated embodiment, system  10  is a CT system designed to acquire X-ray projection data, to reconstruct the projection data into a tomographic image, and to process the image data for display and analysis, in accordance with the present technique. Though the imaging system  10  is discussed in the context of medical imaging, the techniques and configurations discussed herein are applicable in other non-invasive imaging contexts, such as baggage or package screening or industrial nondestructive evaluation of manufactured parts. In the embodiment illustrated in  FIG. 1 , the CT imaging system  10  includes an X-ray source  12 . As discussed in detail herein, the source  12  may include one or more conventional X-ray sources, such as an X-ray tube, or a distributed source configured to emit X-rays from different locations along a surface. For example, the source  12  may include one or more addressable solid-state emitters. Such solid-state emitters may be configured as arrays of field emitters, including one-dimensional arrays, i.e., lines, and two-dimensional arrays. 
     The source  12  may be positioned proximate to a collimator  14 . The collimator  14  may consist of one or more collimating regions, such as lead or tungsten shutters, for each emission point of the source  12 . The collimator  14  typically defines the size and shape of the one or more X-ray beams  16  that pass into a region in which a subject  18 , such as a human patient is positioned. Each X-ray beam  16  may be generally fan-shaped or cone-shaped, depending on the configuration of the detector array and/or the desired method of data acquisition, as discussed below. An attenuated portion  20  of each X-ray beam  16  passes through the subject  18  and impacts a detector array, represented generally at reference numeral  22 . 
     The detector  22  is generally formed by a plurality of detector elements that detect the X-ray beams  16  after they pass through or around the subject  18 . Each detector element produces an electrical signal that represents the intensity of the X-ray beam  16  incident at the position of the detector element when the beam strikes the detector  22 . Alternatively, each element of detector  22  may count incident photons in the X-ray beam  16  and may also determine their energy. Typically, the X-ray beam  16  is generated and the corresponding electrical signals are acquired at a variety of angular positions around the subject of interest so that a plurality of radiographic projection views can be collected. The electrical signals are acquired and processed to reconstruct an image that is indicative of the features within the subject  18 , as discussed in further detail below. 
     A system controller  24  commands operation of the imaging system  10  to execute examination protocols and to process the acquired data. The source  12  is typically controlled by a system controller  24 . Generally, the system controller  24  furnishes power, focal spot location, control signals and so forth, for the CT examination sequences. The detector  22  is coupled to the system controller  24 , which commands acquisition of the signals generated by the detector  22 . The system controller  24  may also execute various signal processing and filtration functions, such as initial adjustment of dynamic ranges, interleaving of digital image data, and so forth. In the present context, system controller  24  may also include signal-processing circuitry and associated memory circuitry. As discussed in greater detail below, the associated memory circuitry may store programs and routines executed by the system controller  24 , configuration parameters, image data, and so forth. In one embodiment, the system controller  24  may be implemented as all or part of a processor-based system such as a general purpose or application-specific computer system. 
     In the illustrated embodiment of  FIG. 1 , the system controller  24  may control the movement of a linear positioning subsystem  28  and a rotational subsystem  26  via a motor controller  32 . In an embodiment where the imaging system  10  includes rotation of the source  12  and/or the detector  22 , the rotational subsystem  26  may rotate the source  12 , the collimator  14 , and/or the detector  22  about the subject  18 . It should be noted that the rotational subsystem  26  might include a gantry (not shown) comprising both stationary components (stator) and rotating components (rotor). The linear positioning subsystem  28  may enable the subject  18 , or more specifically a patient table that supports the subject  18 , to be displaced linearly. Thus, the patient table may be linearly moved within the gantry or within an imaging volume (e.g., the volume located between the source  12  and the detector  22 ) and enable the acquisition of data from particular areas of the subject  18  and, thus the generation of images associated with those particular areas. Additionally, the linear positioning subsystem  28  may displace the one or more components of the collimator  14 , so as to adjust the shape and/or direction of the X-ray beam  16 . In embodiments comprising a stationary source  12  and a stationary detector  22 , the rotational subsystem  26  may be absent. Similarly, in embodiments in which the source  12  and the detector  22  are configured to provide extended or sufficient coverage along the z-axis (i.e., the axis associated with the main length of the subject  18 ) and/or linear motion of the subject is not required, the linear positioning subsystem  28  may be absent. 
     As will be appreciated by those skilled in the art, the source  12  may be controlled by an X-ray controller  30  disposed within the system controller  24 . The X-ray controller  30  may be configured to provide power and timing signals to the source  12 . In addition, in some embodiments the X-ray controller  30  may be configured to selectively activate the source  12  such that tubes or emitters at different locations within the system  10  may be operated in synchrony with one another or independent of one another. 
     Further, the system controller  24  may comprise a data acquisition system  34 . In this exemplary embodiment, the detector  22  is coupled to the system controller  24 , and more particularly to the data acquisition system  34 . The data acquisition system  34  receives data collected by readout electronics of the detector  22 . The data acquisition system  34  typically receives sampled analog signals from the detector  22  and converts the data to digital signals for subsequent processing by a processor-based system, such as a computer  36 . Alternatively, in other embodiments, the detector  22  may convert the sampled analog signals to digital signals prior to transmission to the data acquisition system  34 . 
     In the depicted embodiment, a computer  36  is coupled to the system controller  24 . The data collected by the data acquisition system  34  may be transmitted to the computer  36  for subsequent processing and reconstruction. For example, the data collected from the detector  22  may undergo pre-processing and calibration at the data acquisition system  34  and/or the computer  36  to produce representations of the line integrals of the attenuation coefficients of the subject  18  and the scanned objects. In one embodiment, the computer  36  contains image-processing circuitry  37  for processing and filtering the data collected from the detector  22 . The processed data, commonly called projections, may then be filtered and backprojected by the image processing circuitry  37  to form an image of the subject  18  and/or the scanned area. As will be appreciated by those skilled in the art, the projections may be reconstructed into an image by using other well-known reconstruction algorithms, such as iterative reconstruction. The image processing circuitry  37  may apply geometry-dependent filtering to the processed data to improve image quality and enhance features or certain regions of interest. The identification and/or enhancement of features or regions of interest through such geometry-dependent filtering may be referred to as “computer-aided geometry determination.” Once reconstructed, the image produced by the system  10  of  FIG. 1  may reveal an internal region of interest of the subject  18  which can be used for diagnosis, evaluation, and so forth. 
     The computer  36  may comprise or communicate with a memory  38  that can store data processed by the computer  36 , data to be processed by the computer  36 , or routines to be executed by the computer  36 , such as for processing image data in accordance with the present technique. It should be understood that any type of computer accessible memory device capable of storing the desired amount of data and/or code may be utilized by such an exemplary system  10 . Moreover, the memory  38  may comprise one or more memory devices, such as magnetic or optical devices, of similar or different types, which may be local and/or remote to the system  10 . The memory  38  may store data, processing parameters, and/or computer programs comprising one or more routines for performing the processes described herein. 
     The computer  36  may also be adapted to control features enabled by the system controller  24  (i.e., scanning operations and data acquisition). Furthermore, the computer  36  may be configured to receive commands and scanning parameters from an operator via an operator workstation  40  which may be equipped with a keyboard and/or other input devices. An operator may, thereby, control the system  10  via the operator workstation  40 . Thus, the operator may observe from the computer  36  the reconstructed image and other data relevant to the system  10 , initiate imaging, select and apply image filters, and so forth. Further, the operator may manually identify features and regions of interest from the reconstructed image, or the operator may review features and regions of interest automatically identified and/or enhanced through computer-aided geometry determination or similar techniques, as discussed herein. Alternatively, automated detection algorithms may be applied to aid in identifying and/or manipulating the features or regions of interest. 
     As illustrated, the system  10  may also include a display  42  coupled to the operator workstation  40 . The display  42  may be utilized to observe the reconstructed images, for instance. Additionally, the system  10  may include a printer  44  coupled to the operator workstation  40  and configured to print a copy of the one or more reconstructed images. The display  42  and the printer  44  may also be connected to the computer  36  directly or via the operator workstation  40 . Further, the operator workstation  40  may include or be coupled to a picture archiving and communications system (PACS)  46 . It should be noted that PACS  46  might be coupled to a remote system  48 , radiology department information system (RIS), hospital information system (HIS) or to an internal or external network, so that others at different locations can gain access to the image data. 
     Although only one operator workstation is depicted, one or more operator workstations  40  may be linked in the system  10  for outputting system parameters, requesting examinations, viewing images, and so forth. In general, displays  42 , printers  44 , workstations  40 , and similar devices supplied within the system  10  may be local to the data acquisition components, or may be remote from these components, such as elsewhere within an institution or hospital, or in an entirely different location, linked to the image acquisition system  10  via one or more configurable networks, such as the Internet, virtual private networks, and so forth. 
     Although the previous discussion discloses typical embodiments of the imaging system  10 , it will be appreciated by those skilled in the art, that similar configurations may be employed to acquire CT images. For example, in one embodiment, the imaging system  10  may include a LightSpeed Volume CT (VCT) manufactured by General Electric Company having headquarters in Fairfield, Conn. The VCT is a 64-detector scanner that captures 64 slices of a patient&#39;s anatomy, where each slice is less than 0.36 mm wide, for a total of about 40 mm width of a patients anatomy. Other embodiments may include other number of slices and slice widths. For example, in one embodiment a 16-slice CT scanner including a spiral acquisition path can be used to capture data over a greater axial FOV. 
       FIGS. 2 and 3  illustrate an exemplary medical application of one aspect of the present technique, namely CT cardiac imaging.  FIG. 2  shows the essential CT scan geometry for a full scan FOV of a patient&#39;s torso including the region of the heart. An X-ray source  12  emits a fan beam of X-rays  16  in which the width of the beam is determined by the position and aperture of the collimator  14 . The X-ray fan beam  16  is wider than the patient&#39;s torso  18  for the full scan FOV image acquisition. After passing through the attenuating tissues of the patient  18 , the remaining X-rays  16  impact the detector  22  that measures the X-ray flux or counts X-ray photons.  FIG. 3  is similar to  FIG. 2  except that the collimator  14  has a narrower aperture that restricts the X-ray fan beam width to encompass the targeted FOV  52  (e.g., heart region). The full scan FOV  18  (e.g., torso) is not fully illuminated by the X-ray beam  16 . Those skilled in the art will recognize that the X-ray dose to the patient may be significantly less during a target FOV scan as compared to a full FOV scan. 
     In this exemplary illustration, the patient&#39;s heart (targeted FOV  52 ) is positioned near the axis of rotation of the CT scanner. That is, the distance from the source  12  to the heart  52  is approximately constant while the source rotates rapidly around the patient  18  during the CT scan. Similarly, the detector  22  also remains approximately equidistant from the heart  52  during the CT scanner rotation. Those skilled in the art will recognize that this central position of the targeted FOV simplifies the required motion of the collimator in adjusting between the full FOV scan and the targeted FOV scan. The required width of the fan beam  16  and the patient dose are minimized by positioning the targeted FOV near the CT scanner axis of rotation. 
     As previously mentioned, an image of a local region (e.g., targeted FOV) that is less than a full FOV (e.g., full scan FOV) for the imaging system  10  may be employed in accordance with certain imaging techniques. For example, in cardiac imaging, a high-resolution image of a small sub-region (e.g., a targeted FOV) of a patient&#39;s anatomy, such as the heart, may be desired. Those of ordinary skill in the art will appreciate that image reconstruction for this targeted FOV using iterative reconstruction techniques may be complicated by a variety of factors. For example, data outside of the targeted FOV may or may not enhance the reconstruction of the portion of the image associated with the targeted FOV. One technique for targeted iterative reconstruction involves ignoring the signal from outside the targeted FOV, which may be referred to as “naïve reconstruction.” Such a technique may result in an anomalous image where the entire signal from outside the targeted FOV is assigned to the periphery of the targeted FOV or the signal from outside the targeted FOV may produce aliasing artifacts within the targeted FOV. In other cases, an image of the full scan FOV may be reconstructed at high resolution from which the image for the targeted FOV may be extracted. Such a technique may be referred to as “brute-force reconstruction.” These techniques for targeted iterative reconstruction, however, may inaccurately handle the signal from outside the targeted FOV and/or may handle the signal in a computationally expensive manner. 
     To address these and associated issues with iterative reconstruction of an image of a targeted FOV, one or more embodiments of the present technique provide a method for image reconstruction. Referring now to  FIG. 4 , a flow chart depicting a method  50  associated with aspects of the present technique is presented. In the present technique, a targeted FOV  52  for a tomographic image may be selected, as depicted by block  54  of  FIG. 4 . As those of ordinary skill in the art will appreciate, the targeted FOV  52  of the tomographic image is less than or equal to the full scan FOV of the imaging system  10 . For example, the full scan FOV may include the entire patient, or a significant portion of a patient, such as the patient&#39;s torso, whereas the targeted FOV may include a region that is less than the full scan FOV, such as the region including and surrounding the patient&#39;s heart. In general, the targeted FOV  52  may be selected by any suitable technique, including by a user, automatically, or semi-automatically. Next, background projection data  56  for the area outside the targeted FOV  52  of the tomographic image may be derived, as depicted at block  58 . Derivation of an exemplary background projection data  56  is described in more detail below with respect to  FIG. 5 . In transmission imaging (e.g., X-ray CT), the background projection data  56  generally may represent attenuation of photon flux from the area outside the targeted FOV  52 . Any suitable technique may be used to determine this background projection data  56 . The background projection data  56  may then be used as an additional factor in the reconstruction of the targeted tomographic image  60  of the targeted FOV  52 , as depicted at block  62 . Reconstructing the targeted image  60  using the background projection data  56  may provide reduced artifacts and/or reduced reconstruction time as compared to alternative reconstruction techniques. Exemplary techniques for determining the background projection data  56  and for reconstructing the targeted image  60  will be discussed in the sections that follow. 
     Referring now to  FIG. 5 , a flowchart depicting an exemplary method  70  is presented for deriving the background projection data  58  of  FIG. 4 . Measured projection data  72  for a scan FOV of the tomographic image may be derived, as depicted at block  74 . In general, the measured projection data  72  may contain data indicative of a full scan FOV. Exemplary measured projection data  72  for a scan FOV is represented as a sinogram in  FIG. 6 , where the vertical axis represents projection angle and the horizontal axis represents detector offset. Returning to  FIG. 5 , this measured projection data  72  may be reconstructed, as depicted at block  76 , to obtain a reconstructed image  78  of the scan FOV. An exemplary reconstructed image  78  of the scan FOV is depicted in  FIG. 7 . In the reconstructed image  78  of  FIG. 7 , the subject imaged is represented by elliptical region  80 , and the feature of interest is represented by elliptical region  82 . As those of ordinary skill in the art will appreciate, any suitable reconstruction technique may be utilized to obtain the reconstructed image  78  of the scan FOV, including analytical reconstruction and iterative reconstruction algorithms. For example, full convergence of an iterative reconstruction algorithm may not be necessary for the image reconstruction of the scan FOV because only a reasonable estimate of the background projection data  56  may be needed. In other words, an image generated with less number of iterations through an iterative reconstruction algorithm may be needed to obtain the background projection data  56  than would be required to accurately reconstruct a reliable image of the scan FOV. In another example, the pixel grid utilized for this reconstruction of the scan FOV may have lower resolution than the pixel grid used for reconstruction of the targeted FOV. 
     Once the reconstructed image  78  for the scan FOV has been obtained at block  76 , pixels inside the targeted FOV may be masked out in the reconstructed image  78 , as depicted at block  84 . By way of example, masking out the pixels may include zeroing the pixels in the reconstructed image  78  that are inside the targeted FOV  52 . An exemplary masked image  86  is depicted by  FIG. 8 . In the masked image  86 , the targeted FOV is represented by numeral  52 , and the masked pixels (all pixels inside the targeted FOV  52 ) are represented by number  88 . To obtain the background projection data  56  for the area outside the targeted FOV  52 , the masked image  86  may be forward projected, as depicted at block  90 . As previously mentioned, the background projection data  56  generally may represent the activity outside the targeted FOV  52 . Referring now to  FIG. 9 , exemplary background projection data  56  is depicted in a background sinogram  92 . 
     Referring now to  FIG. 10 , a flow chart depicting a method  100  associated with aspects of the present technique is presented. More specifically  FIG. 10  depicts one embodiment of acquiring and reconstructing a cardiac CT image, in accordance with the present technique. In the present technique, a patient is prepared prior to conducting a CT scan, as depicted at block  102 . Preparing the patient may include, for example, positioning the patient&#39;s arms above the patient&#39;s head to allow for a reduction in X-radiation dose applied to the patient and to reduce bone artifacts in the image since the arms will not be included in the FOV when the CT scan is performed over certain regions of the patient, such as the patient&#39;s torso and heart. A CT scout image  104  is acquired by moving the patient rapidly through the CT system  10  while conducting a scout scan, as depicted at block  106 . In one embodiment, the scout scan may include a rapid scan of all or a majority of the patient to provide data for reconstructing an image to identify locations of certain regions of interest, such as the patient&#39;s heart. 
     Further, the method generally includes determining a target field of view  112 , as depicted at block  114 . For example, a region surrounding the target (e.g., the heart) may be placed around the initial target position  108 . Such a target field of view  112  may be identified from the CT scout image  104 , in one embodiment. For example, where the CT scout image  104  includes the patient&#39;s heart, a target field of view  112  that identifies a sub-region that includes the patient&#39;s heart may be selected. The selected target FOV  112  may be used in positioning and imaging routines discussed below. Further, in certain embodiments, the target FOV  112  may include regions adjacent to the region of interest, such as those regions surrounding the heart that may enhance the reconstruction of the target FOV  112 . For example, the target FOV  112  may include an additional region around the heart to ensure that the entire heart is imaged and/or to aid in reconstructing an image corresponding to the target FOV  112 , as discussed below. Generally, determining the target FOV  112  is automated (e.g., includes image processing to determine the location of the target), however, determination of the target FOV  112  may be subject to operator verification and editing, or may be performed manually. 
     The target FOV  112  is then preferentially moved to a target FOV position  116 , as illustrated at block  118 . For example, in a preferred embodiment the table supporting the patient may be moved to center the target FOV  112  (e.g., the heart region) on the axis of rotation of the CT system  10  (e.g., the axis of rotation of the source  12  and the detector  22 ). With the target FOV  112  positioned relative to the system  10 , a full scan is conducted and full scan projections  122  are acquired, as depicted at block  120 . The full scan generally includes scanning the target FOV  112  along with regions surrounding the target FOV  112 . For example, where the heart is the desired target of the CT image, and defines, at least partially, the target FOV  112 , the full scan may include a scan of the patient&#39;s torso. Accordingly, the full scan projections  122  may include projections of X-rays that have passed through the patient&#39;s torso. During the full scan, the X-ray beams  16  may include a fan angle that engulfs the target FOV  112  and all or at least a significant portion of the patient&#39;s cross-section. 
     Further, a target scan is conducted to acquire target projections  124 , as depicted at block  126 . In one embodiment, the target scan generally includes reducing the fan angle of the X-ray beams  16  such that they pass primarily through the target FOV  112 . In some embodiments, the fan angle may be adjusted such that the X-ray beams  16  pass through the target FOV  112  and regions proximate to the target FOV  112 , but do not pass through the entire region scanned in the full scan discussed previously with regard to block  120 . In one embodiment, a mechanical filter (e.g., the collimator  14 ) is placed between the source  12  and the patient  18  such that the mechanical filter blocks all of the X-ray beams  16 , except for the portion of the X-ray beams  16  that are transmitted through the target FOV  112 , thereby reducing the X-radiation dose to the patient (e.g., reducing radiation to the patient&#39;s torso). In some embodiments, the mechanical filter, or collimator  14 , is formed from tungsten or other suitable X-ray absorbing material. In some embodiments, the mechanical filter may include a dynamic configuration that enables dynamic adjustment to change and/or tune the configuration (e.g., the shape and width) of the X-ray beams  16 . Further, the mechanical filter may be placed close to the X-ray source  12  to facilitate a small size that is still capable of effectively reducing the width and or shape of the X-ray beams  16 . Further, embodiments may include the implementation of a dynamic bowtie filter, a virtual bowtie filter, or the like to configure the X-ray beams  16 . 
     Generally, the CT target scan, as depicted at block  126 , continues for a given period of time. In one embodiment, such as that including a scan of the patient&#39;s heart, the target scan continues for a portion or all of one heart beat. In other embodiments, the target scan continues for more than one heart beat (e.g., for several heart beats). Acquiring the target scan over one or more heat beats may enable reconstruction of images that correspond to the heart (or similar dynamic organ) in various states during its operation. For example, the period of time may enable acquisition of images that are representative of the heart or a similar organ in the contracted (systole) and relaxed (diastole) states, or states in between. 
     The CT target scan projections  124  include the X-ray data acquired during the CT target scan  126 . Accordingly, a target CT image  128  can be generated via reconstruction of the target image, as depicted at  130 . For example, as discussed in greater detail below, certain embodiments of reconstructing the target image, as depicted at block  130 , may include using the full scan to acquire a generally static background sinogram, masking out a region of the background sinogram where the target FOV  112  is located, and iteratively reconstructing the target CT image  128  by extracting data (e.g., keyhole data) of the target scan projection  124  and combining the keyhole data with the unmasked data of the background sinogram of the full scan projections  122 . In other words, data from a single full scan can be combined with each set of data from the target scans to accurately reconstruct each of the target images associated with each of the target scans. Thus, the X-ray dose to the patient can be reduced while still generating accurate target CT images. Only a single full scan  120  is conducted to provide background data corresponding to generally static portions of a patient (e.g., the areas surrounding the heart that do not move significantly during a breath hold). During the target scan  126  the X-ray beam  16  is restricted by the collimator  14  to the region proximate to the target FOV (e.g., the heart region), thereby avoiding additional X-ray dose to the full FOV (e.g., full torso). 
     As previously mentioned in the discussion of  FIG. 4 , the background projection data  56  may be used as an additional factor in the reconstruction of a targeted image  60 . As those of ordinary skill in the art will appreciate, any suitable reconstruction technique may be used to reconstruct the targeted image  60 , including a variety of iterative reconstruction algorithms. One suitable technique includes utilizing the background projection data  56  as an additive correction term for a projection data estimate from the forward projected image estimate. 
     CT imaging depends on the transmission of at least some X-rays through the attenuating object. The standard imaging equation for X-ray CT is given by equation (1) as follows: 
                       y   ^     i     =         b   i     ·     exp   (     -       ∑   j     ⁢         P   ⁢               i   ,   j       ⁢     μ   j           )       +     S   i               (   1   )               
wherein:
     ŷ i  refers to the mean photon flux detected at the detector i in the presence of the object;   b i  refers to the photon flux that would have been detected at the detector i in the absence of the object;   μ j  refers to the linear attenuation coefficient of the object for the pixel j;   S i  refers to the scatter flux detected at the detector i; and   P i,j  refers to the effective intersection length of the line of response (LOR) i with pixel j.   

     Although X-ray imaging is often performed in the presence of an anti-scatter grid, resulting in S i  approximately 0, in the absence of an anti-scatter grid, S i  can be large enough not to be ignored and can be estimated by other algorithmic means. Accordingly, the data can be pre-corrected for scatter or scatter correction can be incorporated into the reconstruction loop. 
     CT reconstruction may be implemented using any of a variety of suitable reconstruction algorithms. As will be appreciated by those of ordinary skill in the art, CT reconstruction may utilize a Maximum Likelihood Transmission (MLTR) algorithm. An exemplary MLTR algorithm for the corrections in the loop technique that does not implement the present technique is given by equation (2): 
                     μ   j     k   ,     m   +   1         =       μ   j     k   ,   m       +         ∑     i   ∈     S   m         ⁢       P     i   ,   j       ·       (         y   ^     i     -     S   i       )         y   ^     i       ·     (         y   ^     i     -     y   i       )             ∑     i   ∈     S   m         ⁢       P     i   ,   j       ·     [       ∑     j   ′       ⁢       P     i   ,   j       ′       ]     ·     (         y   ^     i     -     S   i       )     ·     (     1   -         y   i     ⁢     S   i           y   ^     i   2         )                     (   2   )               
Wherein:
     μ refers to an image estimate, and μ j   k,m  refers to the image estimate for pixel j at the k th  iteration and the m th  subset of LORs;   P i,j  refers to the effective intersection length of the LOR i with pixel j;   S i  refers to the scatter flux detected at the detector i;   ŷ i  refers to the mean photon flux detected at the detector i in the presence of the object;   yi refers to the measured projection data detected by the i th  LOR; and   S m  refers to the m th  subset of LORs.   

     As will be appreciated, for CT reconstruction, the background projection data generally may represent attenuation of photon flux from the area outside the targeted FOV  52 . Once the background projection data  56  is derived, the background projection data  56  may be used as an additional factor in the reconstruction of the targeted FOV (block  60 ,  FIG. 4 ), such as in a reconstruction utilizing the above-listed MLTR algorithm. Accordingly, equation (2) can be modified to implement the reconstruction technique described herein. An exemplary iterative update equation for the corrections in the loop technique utilizing the background projection data in a MLTR algorithm is given by equation (3). It should be appreciated, however, that the present technique is applicable for implementation using any suitable iterative reconstruction update equation. 
                     μ   j     k   ,     m   +   1         =       μ   j     k   ,   m       +         ∑     i   ∈     S   m         ⁢       P     i   ,   j       ·       (         y   ^     i     -     S   i     -     t   i   bkg       )         y   ^     i       ·     (         y   ^     i     -     y   i       )             ∑     i   ∈     S   m         ⁢       P     i   ,   j       ·     [       ∑     j   ′       ⁢     P     i   ,     j   ′           ]     ·     (         y   ^     i     -     S   i     -     t   i   bkg       )     ·     (     1   -         y   i     ⁡     (       S   i     +     t   i   bkg       )           y   ^     i   2         )                     (   3   )               
wherein:
     μ refers to an image estimate, and μ j   k,m  refers to the image estimate for pixel j at the k th  iteration and the m th  subset of LORs;   P i,j  refers to the effective intersection length of the LOR i with pixel j;   S i  refers to the scatter flux detected at the detector i;   t i   bkj  refers to the projection data resulting from attenuation of photon flux from the area outside the targeted FOV (or the background projection data);   ŷ i  refers to the mean photon flux detected at the detector i th  in the presence of the object;   y i  refers to the measured projection data detected by the i th  LOR; and   S m  refers to the m th  subset of LORs.   

     Referring now to  FIG. 11 , a flowchart depicting this corrections-in-the-loop technique is illustrated. In the illustrated embodiment, an image estimate  154  for the targeted FOV may be obtained, as depicted at block  156 . As will be appreciated, the initial image estimate  154  may take any of a number of forms and may include a uniform image or an estimate obtained from a reconstruction technique, such as filtered back projection. This image estimate  154  may then be forward projected, as depicted in block  158 , to the projection plane to obtain a forward projected image estimate  160 . 
     An exemplary technique for determining the background projection data  56  was discussed above with respect to  FIG. 5 . This background projection data  56  may be then added to the forward projected image estimate  160  as an additive corrective term, as depicted at block  182  to obtain a corrected forward projection  184 . As will be appreciated, the forward projected image estimate  160  may also be corrected for photon scatter, dead time, detector efficiency, scanner geometric effects, and so forth. 
     This corrected forward projection  184  then may be compared to the measured target scan projection data  124 , as depicted at block  186 . For example, this comparison may include taking the ratio of the measured projection data  124  and the corrected forward projection  184  to obtain a correction ratio  164 . As depicted at block  166 , the correction ratio  164  may be back projected to obtain correction image data  168 . An updated estimated image  170  may then be obtained by applying the correction image data  168  to the image estimate  154 , as depicted at block  172 . In one embodiment, the corrected image data  168  and the image estimate  154  are multiplied to obtain the updated image estimate  170  for the targeted FOV. As will be appreciated, the updated image estimate  170  becomes the image estimate  154  to be used in the next iteration. As depicted at block  174 , it is determined whether the number of iterations for generating the image for the targeted FOV exceeds a threshold value. If the number of iterations exceeds the threshold value, the updated image estimate  170  is returned, as depicted at block  176 , as the targeted image  60 . Alternatively, rather than using a threshold value, it may be determined whether convergence between the image estimate  154  and the updated image estimate  170  has reached a desired level. Otherwise, the technique of  FIG. 11  starting at block  156  is performed iteratively. 
     As will be appreciated by those of ordinary skill in the art, the embodiment illustrated by  FIG. 11  may be implemented utilizing the Maximum Likelihood Transmission (MLTR) algorithm. While the MLTR algorithm may be employed as discussed above, other embodiments of the present technique may be implemented using any suitable iterative reconstruction update equation. 
     As will be appreciated by those of ordinary skill in the art the exemplary techniques described herein are applicable to both static reconstruction, as well as motion compensated reconstruction, such as motion compensated CT reconstruction. As mentioned above, the background projection data  56  may also be used as an additional factor in a motion compensated reconstruction. In motion compensated reconstruction, the reconstruction is applied to four dimensions wherein the fourth dimension is time gating. By way of example, multiple gates of data are acquired based on time dependent gating, for example, on respiratory gating or cardiac gating. However, while the multiple gates of data are time dependent, the background projection data  56  derived for use in the motion compensated reconstruction need not be time dependent. For example, a low resolution, motion uncompensated image may be used to derive the background projection data  56 . The motion uncompensated image may be reconstructed from a sum of all the projection gates of data or from single breathhold CT full scan projections  122 . From this motion uncompensated image, the background projection data  56  may be derived, for example, by masking out the pixels within the targeted FOV  52  and then forward projecting the masked image  86 , in accordance with the exemplary embodiment of  FIG. 5 . To obtain the background projection data for each of the projection gates, the background projection data  56  may be scaled by the relative acquisition times of each corresponding projection gate. Exemplary embodiments of the present technique may be implemented using any suitable motion compensated reconstruction update equation. 
     As previously discussed, the exemplary techniques of the present technique provide a method for the iterative reconstruction of an image of a targeted FOV that is less than the full scan FOV. As described above, reconstruction of a targeted image (such as targeted image  60 ) in accordance with embodiments of the present technique may provide reduced artifacts as compared to alternative reconstruction techniques. Artifacts, however, may appear in the targeted image  60  due to a variety of factors. 
     In one instance, pixels that straddle the targeted FOV may result in artifacts on the edges of the targeted image  60 . By way of example, these artifacts may occur when the background projection data  56  representing activity outside the targeted FOV is subtracted from the measured projection data  72 , in accordance with aspects of the present technique. As illustrated by  FIG. 12 , pixels  188 ,  190 ,  192  are shown straddling the targeted FOV  194 . The edge of the targeted FOV is represented on  FIG. 12  by numeral  196 . In the illustrated embodiment, the targeted FOV  194  is defined to include all pixels having a center within the targeted FOV  194 . Accordingly, pixels  188  and  190  are shown within the targeted FOV  194 . Pixel  192 , however, does not have a center within the targeted FOV  194  and, thus, is not shown as within the targeted FOV  194 . Because pixel  192  extends into the targeted FOV  194  while not being defined as within the targeted FOV  194 , artifacts may occur in the reconstructed image. For instance, pixel  192  will not be masked out during determination of the background projection data  56 , in accordance with certain aspects of the present technique. 
     To address this issue, the targeted FOV  194  may be expanded so that any pixel extending partially into the targeted FOV  194 , such as pixel  192 , may be considered within the expanded targeted FOV  198 . For example, the targeted FOV  194  may be expanded beyond the targeted FOV that was originally identified. By way of example, the expanded targeted FOV may be defined as the targeted FOV  194  plus a buffer zone  198 . The edge of the buffer zone  198  is represented on  FIG. 12  by numeral  200 . In one embodiment, this buffer zone  198  may expand beyond the original targeted FOV by a distance equal to sqrt(½) of a pixel width. As illustrated on  FIG. 12 , the targeted FOV  194  may be expanded so that pixel  192  that previously straddled the targeted FOV is inside the buffer zone  198 . 
     In another embodiment, the targeted FOV  194  may be expanded so that the entire pixel  92  (and not just the center) is contained within a second buffer zone  202  for the targeted FOV  194 . The second buffer zone  202  may extend beyond the first buffer zone  198  by a distance of sqrt(½) of a pixel width. The edge of the second buffer zone  202  is represented on  FIG. 13  by numeral  202 . Including the entire pixel  192  within the expanded targeted FOV may ensure, for example, that the pixel  192  will be represented in the targeted reconstruction. As will be appreciated, the targeted image may be constructed for this expanded targeted FOV  198 , in accordance with an embodiment of the present technique. The portion of the targeted image that extends beyond the original targeted FOV  194  may be trimmed so that the final reconstructed image is for the targeted FOV  194 . For example, the reconstructed portion of buffer zone  198  and second buffer zone  202  may be trimmed from the targeted image. 
     The targeted FOV may also be expanded for motion compensated reconstruction. As those of ordinary skill in the art will appreciate, projection data from the different gates may be reconstructed independently and subsequently registered and combined. Alternatively, the motion estimates, on a voxel-by-voxel basis, can be incorporated into an iterative reconstruction algorithm that uses all the projection data. In either case, motion estimates are generally made on a voxel-by-voxel basis. However, motion (such as cardiac or respiratory motion) may cause voxels at the edge of the targeted FOV to move in and out of the targeted FOV. Accordingly, the targeted FOV may be expanded to include a buffer zone. The buffer zone may include the range of motion of all voxels inside the targeted FOV. After image reconstruction, this buffer zone may be trimmed from the reconstructed image. 
     In addition, artifacts in the targeted image may also be due to pixel discontinuities in the reconstructed image of the scan FOV that may forward project as streaks through an image of the targeted FOV. To address this issue, projection data filtering may be applied after a forward projection step, such as after block  158  on  FIG. 11 . In one exemplary embodiment, the projection data filtering may be based on the relative size of the scan FOV pixel versus the element width for the projection data. 
     While the present discussion does not reference image shifts and rotations applied during the course of image reconstruction, those of ordinary skill in the art will appreciate that these shifts and rotations may be applied in accordance with aspects of the present technique. For example, because the targeted reconstruction coordinates are defined with respect to the output coordinate system, which may be rotated with respect to, for example, the CT gantry, the measured projection data for the scan FOV  72  may be reconstructed (block  76 ) and forward projected (block  90 ) with the shift and rotation parameters applied. In one embodiment, the shift and rotation parameters may be ignored in the reconstruction of the measured projection data (block  76 ) while the definition of the targeted FOV includes the shift and rotation parameters. In another embodiment, the shift and rotation parameters may be applied to the reconstruction of the measured projection data (block  76 ). In both instances, the forward projection (block  90 ) should match the reconstruction of the measured projection data (block  76 ). 
     As noted above, while specific reference is made in the present discussion to a X-ray CT imaging system  10 , it should be appreciated that the present technique is not intended to be limited to these or to any specific type of imaging system or modality. In general, the present technique may be used for image reconstruction with transmission imaging modalities that use line integral projection tomography reconstruction. Examples include limited angle tomography (X-ray tomosynthesis) and gamma-ray transmission imaging (as used in some PET and SPECT systems for attenuation map imaging). Such imaging modalities are particularly suited for reconstructing images of a targeted FOV that is less than the scan FOV for the imaging system  10 . 
     While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.