Abstract:
A method, system, and software are provided for joint reconstruction of three-dimensional images using multiple imaging modalities. In an exemplary embodiment, the present approach includes providing a first dataset acquired via a first imaging technique or a first image generated from the first dataset, providing a second dataset acquired via a second imaging technique or a second image generated from the second dataset, and generating a volumetric dataset by extracting information from the first and second datasets or images. The first imaging technique may have better resolution than the second imaging technique in a first direction, and the second imaging technique may have better resolution than the first imaging technique in a second direction. There is provided a system and one or more tangible, machine readable media for performing the act of generating the volumetric dataset by extracting information from the first and second datasets or images.

Description:
BACKGROUND 
       [0001]    The present approach relates generally to the field of medical imaging, and more specifically to the fields of tomosynthesis and ultrasound imaging. In particular, the present approach relates to the combination of data acquired during tomosynthesis and ultrasound. 
         [0002]    In modern healthcare facilities, medical diagnostic and imaging systems are used for identifying, diagnosing, and treating diseases. Diagnostic imaging refers to any visual display of structural or functional patterns of organs or tissues for a diagnostic evaluation. Currently, a number of modalities exist for medical diagnostic and imaging systems. These include, for example, ultrasound systems, X-ray imaging systems (including tomosynthesis systems), molecular imaging systems, computed tomography (CT) systems, positron emission tomography (PET) systems and magnetic resonance imaging (MRI) systems. 
         [0003]    One such imaging technique is tomosynthesis, in which X-ray attenuation data is obtained for a region of interest over a limited angular range and used to construct volumetric or generally three-dimensional images. For example, tomosynthesis may be employed to acquire mammography information whereby a breast of a patient may be non-invasively examined or screened to visualize and detect abnormalities, such as lumps, fibroids, lesions, calcifications, and so forth. Such X-ray imaging and tomosynthesis systems are generally effective for detailed characterization of benign and cancerous structures such as calcifications and masses embedded in the breast tissue. 
         [0004]    Another known imaging technique is ultrasound. An ultrasound imaging system uses an ultrasound probe for transmitting ultrasound signals into an object, such as the breast of the patient being imaged, and for receiving reflected ultrasound signals there from. The reflected ultrasound signals received by the ultrasound probe are processed to reconstruct an image of the object. Ultrasound imaging is useful as an alternate tool for diagnosis, such as for differentiating benign cysts and masses. 
         [0005]    Generally, when such tomosynthesis and ultrasound data are collected for a given volume, the resulting images are collected and analyzed independently. At best, the images are compared side-by-side to determine if any abnormalities seen in images produced using one modality are also present in images produced using the other modality. However, there is complementary information in the tomosynthesis and ultrasound datasets, not only concerning different tissue characteristics that are made visible though the use of these different modalities, but also in terms of the inherent resolution exhibited by these imaging systems. In particular, tomosynthesis imaging exhibits a poor depth resolution in combination with a very good in-plane resolution, while ultrasound imaging exhibits a good depth-resolution combined with a somewhat reduced in-plane resolution. 
       BRIEF DESCRIPTION 
       [0006]    There is provided a method for generating an imaging dataset including providing a first dataset acquired via a first imaging technique or a first image generated from the first dataset, providing a second dataset acquired via a second imaging technique or a second image generated from the second dataset, and generating a volumetric dataset by extracting information from the first and second datasets or images. The first imaging technique may have better resolution than the second imaging technique in a first direction and the second imaging technique may have better resolution than the first imaging technique in a second direction. 
         [0007]    There is further provided tangible, machine readable media, with code executable to perform the act of generating a volumetric dataset by extracting information from a first dataset acquired using a first imaging technique or a first image generated from the first dataset and a second dataset acquired using a second imaging technique or a second image generated from the second dataset. The first imaging technique may have better resolution than the second imaging technique in a first direction and the second imaging technique may have better resolution than the first imaging technique in a second direction. 
         [0008]    In addition, there is provided a system including a computer configured to generate a volumetric dataset by extracting information from a first dataset acquired using a first imaging technique or a first image generated from the first dataset and a second dataset acquired using a second imaging technique or a second image generated from the second dataset. The first imaging technique may have better resolution than the second imaging technique in a first direction and the second imaging technique may have better resolution than the first imaging technique in a second direction. 
     
    
     
       DRAWINGS 
         [0009]    These and other features, aspects, and advantages of the present approach 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: 
           [0010]      FIG. 1  is a diagrammatic representation of one embodiment of a mammography imaging system in accordance with aspects of the present approach; 
           [0011]      FIG. 2  is a diagrammatic representation of one embodiment of an ultrasound imaging system in accordance with aspects of the present approach; and 
           [0012]      FIGS. 3-7  are flow charts illustrating exemplary embodiments or aspects of the present approach. 
       
    
    
     DETAILED DESCRIPTION 
       [0013]    The present approach is directed towards joint reconstruction of images with better resolutions in different directions. For example, tomosynthesis and ultrasound images may advantageously be combined in a joint reconstruction to leverage the better in-plane resolution in tomosynthesis and the better resolution in the direction of wave propagation in ultrasound. In the simplest embodiment, images acquired with different techniques or modalities may have different resolution characteristics in different orthogonal directions, such as the X, Y, and Z planes, however it should be understood that the present approach is not limited to these cases. In other examples, a cranio-caudal (CC) tomosynthesis image may be combined with a medio-lateral oblique (MLO) tomosynthesis image in an improved joint reconstruction according to the present approach. Likewise, one or more conventional mammography images or single X-ray projection images may be used as one of the modalities according to the present approach. In addition, the present approach need not be limited to joint reconstruction of images acquired using two techniques but may be applied to images acquired using more than two techniques. For example, a MLO tomosynthesis image, a CC tomosynthesis image, and an ultrasound image may be combined in a three-way joint reconstruction. This approach may be applied to the field of mammography, where improved imaging is needed to provide improved sensitivity and specificity through early detection of malignant growths and to improve the correct classification of imaged structures by reducing the rate of incorrect classifications of benign cysts and masses. However, as will be appreciated by those of ordinary skill in the art, the present approach may also be applied in other medical and non-medical contexts. 
         [0014]    The present specification describes the use of tomosynthesis and ultrasound as exemplary imaging modalities. However, it should be appreciated that the present approach may employ other imaging modalities or the same type of imaging modality operated using different scan parameters, protocols, trajectories, or orientations which result in the acquisition of image data that has different resolution characteristics in different directions. For convenience, the term imaging technique will be used herein to describe the acquisition of images using a given modality and/or a given configuration, such as a given orientation, that results in image data being acquired with resolution characteristics that are better in one direction relative to another direction. For example, acquisition of breast images using a tomosynthesis system and an ultrasound system from the same orientation constitute two distinct imaging techniques due to the distinctly separate imaging modalities and due to the different resolution characteristics of these modalities. For instance, image data acquired at a given orientation by an ultrasound system may have superior resolution in the wave-propagation direction relative to images acquired by a tomosynthesis system with the breast at the same orientation. Conversely, images acquired by the tomosynthesis system may have superior in-plane resolution (i.e., parallel to a detector) than images acquired by the ultrasound system with the breast at the same orientation. Further, a single imaging modality employed at different orientations or using different scan parameters or configurations may be considered as constituting two distinct imaging techniques, as used herein. For example, using a tomosynthesis system to acquire breast images in a CC orientation and in a MLO orientation constitute separate imaging techniques due to the different resolution characteristics in the acquired image data, i.e., the “in-plane” image data for each of these techniques is essentially orthogonal. With this clarification that an imaging technique, as used herein, encompasses both images acquired using different modalities (at the same or different orientations) or the same modality but at different orientations or using different scan parameters or configurations, the following discussion is provided. 
         [0015]    Turning now to the drawings, and referring first to  FIG. 1 , an exemplary tomosynthesis imaging system  10  for use in accordance with the present approach is illustrated diagrammatically. As depicted, the tomosynthesis imaging system  10  includes an image data acquisition system  12 . The image data acquisition system  12  includes an X-ray source  14 , an X-ray detector  16  and a compression assembly  18 . The tomosynthesis imaging system  10  further includes a system controller  22 , a motor controller  24 , data acquisition and image-processing module  26 , an operator interface  28  and a display module  30 . 
         [0016]    The X-ray source  14  further includes an X-ray tube and a collimator configured to generate a beam of X-rays when activated. The X-ray tube is one example of the X-ray source  14 . Other types of the X-ray sources  14  may include solid state X-ray sources having one or more emitters. The X-ray source  14  may be movable in one, two or three dimensions, either by manual or by automated means. The image data acquisition system  12  may move the X-ray source  14  via tracks, ball-screws, gears, belts, and so forth. For example, the X-ray source  14  may be located at an end of a mechanical support, such as a rotating arm or otherwise adjustable support, which may be moved by the image data acquisition system  12  or by an operator. Instead of, or in combination with, a mechanical displacement of the X-ray source  14 , different view angles may be achieved through individually addressable source points. 
         [0017]    The X-ray detector  16  may be stationary, or may be configured to move either independently or in synchrony with the X-ray source  14 . In a present embodiment, the X-ray detector  16  is a digital flat panel detector. The image data acquisition system  12  may move the X-ray detector  16 , if mobile, via tracks, ball-screws, gears, belts, and so forth. In one embodiment, the X-ray detector  16  also provides support for an object, such as a breast  17  of a patient to be imaged, thereby forming one part of the compression assembly  18 . In other embodiments, the X-ray detector may be disposed immediately or proximately beneath a bottom plate of compression assembly  18 , i.e., in such an embodiment, the breast  17  does not rest directly on the detector  16  but on a plate or other compressing support above the detector  16 . 
         [0018]    The compression assembly  18 , whether including two compression plates or a compression plate and the detector  16 , is configured to compress the patient breast  17  for performing tomosynthesis imaging and to stabilize the breast  17  during the imaging process to minimize patient motion while data is acquired. In one embodiment, the breast is compressed to near uniform thickness. In the depicted embodiment, the compression assembly  18  includes at least one mammography compression plate  20 , which may be a flat, inflexible plate, deformable sheet, or alternative compression device. In one embodiment, the mammography compression plate  20  is configured to be radiolucent to transmit X-rays and is further configured to be sonolucent to transmit ultrasound signals. The compression assembly  18  may be used to stabilize the imaged breast  17  during acquisition of both the tomosynthesis and the ultrasound datasets, thereby enabling the acquisition of co-registered tomosynthesis X-ray images, ultrasound images, and Doppler images. 
         [0019]    The system controller  22  controls operation of the image data acquisition system  12  and provides for any physical motion of the X-ray source  14  and/or the X-ray detector  16 . In the depicted embodiment, movement is, in turn, controlled through the motor controller  24  in accordance with an imaging trajectory for use in tomosynthesis. Therefore, by means of the image data acquisition system  12 , the system controller  22  may facilitate acquisition of radiographic projections at various angles relative to a patient. The system controller  22  further controls an activation and operation of other components of the system, including collimation of the X-ray source  14 . Moreover, the system controller  22  may be configured to provide power and timing signals to the X-ray source  14 . The system controller  22  may also execute various signal processing and filtration functions. In general, the system controller  22  commands operation of the tomosynthesis imaging system  10  to execute examination protocols and to acquire resulting data. 
         [0020]    For example, in the depicted embodiment, the system controller  22  controls a tomosynthesis data acquisition and image-processing module  26 . The tomosynthesis data acquisition and image-processing module  26  communicates with the X-ray detector  16  and typically receives data from the X-ray detector  16 , such as a plurality of sampled analog signals or digitized signals resulting from exposure of the X-ray detector to X-rays. The tomosynthesis data acquisition and image-processing module  26  may convert the data to digital signals suitable for processing and/or may process sampled digital and/or analog signals to generate volumetric images of the breast  17 . 
         [0021]    The operator interface  28  may include a keyboard, a mouse, and other user interaction devices. The operator interface  28  can be used to customize settings for the tomosynthesis imaging and for effecting system level configuration changes as well as for allowing operator activation and operation of the tomosynthesis imaging system  10 . In the depicted embodiment, the operator interface  28  is connected to the tomosynthesis data acquisition and image-processing module  26 , the system controller  22  and the display module  30 . The display module  30  presents a reconstructed image of an object, or of a region of interest within the object, based on data from the data acquisition and image-processing module  26 . As will be appreciated by those skilled in the art, digitized data representative of individual picture elements or pixels is processed by the tomosynthesis data acquisition and image-processing module  26  to reconstruct the desired image. The image data, in either raw or processed forms, may be stored in the system or remotely for later reference and image reconstruction. 
         [0022]      FIG. 2  illustrates an exemplary ultrasound imaging system  32  for use in conjunction with the present approach. As depicted, the ultrasound imaging system  32  includes an ultrasound probe  34 , an ultrasound data acquisition and image-processing module  36 , which includes beam-formers and image reconstruction and processing circuitry, an operator interface  38 , a display module  40  and a printer module  42 . In a hybrid imaging system based upon both X-ray and ultrasound techniques, certain of these components or modules may be partially or fully integrated to perform image acquisition and processing for both systems. 
         [0023]    The ultrasound imaging system  32  uses the ultrasound probe  34  for transmitting a plurality of ultrasound signals into an object, such as the breast  17  of a patient being imaged, and for receiving a plurality of reflected ultrasound signals therefrom. The ultrasound probe  34 , according to aspects of the present approach, includes at least one transducer for generating ultrasound waves or energy from mechanical or electromechanical impulses and vice versa. As will be appreciated by those of ordinary skill in the art, the plurality of reflected ultrasound signals from the object carry information about thickness, size, and location of various tissues, organs, tumors, and anatomical structures in relation to transmitted ultrasound signals. The plurality of reflected ultrasound signals received by the ultrasound probe  34  are processed for constructing an image of the object. In certain embodiments, the ultrasound probe  34  can be hand-held or mechanically positioned using a robotic assembly. The ultrasound imaging system  32  may also incorporate beam steering technology to reach all areas of the imaged breast. In addition, according to an embodiment of the present approach, the ultrasound imaging system  32  may use compounding, that is, a suitable combination of signals from the same area of the breast  17  that leads to improved ultrasound image quality. 
         [0024]    The ultrasound data acquisition and image-processing module  36  sends signals to and receives information from the ultrasound probe  34 . Thus, the ultrasound data acquisition and image-processing module  36  controls strength, beam focus or forming, duration, phase, and frequency of the plurality of ultrasound signals transmitted by the ultrasound probe  34 , and decodes the information contained in the plurality of reflected ultrasound signals from the object to a plurality of discernable electrical and electronic signals. Once the information is obtained, an ultrasound image of the object located within a region of interest is reconstructed in accordance with generally known reconstruction techniques. 
         [0025]    The operator interface  38  may include a keyboard, a mouse, and other user interaction devices. The operator interface  38  can be used to customize a plurality of settings for an ultrasound examination, to effect system level configuration changes, and to allow operator activation and operation of the ultrasound imaging system  32 . The operator interface  38  is connected to the ultrasound data acquisition and image-processing module  36 , the display module  40  and to the printer module  42 . The display module  40  receives image information from the ultrasound data acquisition and image-processing module  36  and presents the image of the object within the region of interest of the ultrasound probe  34 . The printer module  42  is used to produce a hard copy of the ultrasound image in either gray-scale or color. As noted above, some or all of these system components may be integrated with those of the tomosynthesis X-ray system described above. 
         [0026]    Turning now to  FIG. 3 , an exemplary embodiment of the present approach is illustrated in a flow chart. At least one tomosynthesis dataset  46  may be acquired via the system described in reference to  FIG. 1  or via an alternate tomosynthesis imaging system. Likewise, at least one ultrasound dataset  48  may be acquired via the system described in reference to  FIG. 2  or via an alternate ultrasound imaging system. Alternatively, the present approach may be applied to previously-acquired tomosynthesis and/or ultrasound data. Raw data from the tomosynthesis and ultrasound imaging systems may have been processed to produce volumetric datasets  46  and  48 . For example, the tomosynthesis dataset  46  may have been suitably reconstructed from a set of individual projection images that were gain-corrected, log-corrected, or corrected for some geometrical effects, such as path length between source and each pixel, effective pixel area, or path length through tissue. In addition, the tomosynthesis projection data may have been scatter corrected or may be virtually scatter-free, such as in slot-scanning systems. 
         [0027]    In an exemplary process  44 , at least one tomosynthesis dataset  46  and at least one ultrasound dataset  48  may be registered in a step  50 . In this step  50 , the datasets  46  and  48  may be aligned such that their respective coordinate systems correspond. The registration may be rigid or non-rigid, with varying degrees of flexibility. Depending on the resolution of the datasets  46  and  48 , the registration may also include an interpolation step, such as, for example, tri-linear interpolation or nearest neighbor interpolation, to map both datasets to the same voxel grid. In cases where the datasets are acquired together they may be intrinsically registered to one another, in which case the registration step  50  may be omitted or only an interpolation may be performed. In illustrations of further embodiments of the present approach this registration step is omitted, however it should be understood that registration may be required if the tomosynthesis and ultrasound datasets are not intrinsically registered. This may be especially important in situations where the imaged breast is not in the same position while the two datasets are acquired. 
         [0028]    Registered datasets  52  may be compared to one another to derive a suitable color or gray-scale mapping in a step  54 . This derivation may employ a method, such as mutual information, wherein some similarity criterion between the datasets is minimized. The mapping function may be one-to-one, where any gray value in the ultrasound dataset corresponds to a single associated attenuation value in the tomosynthesis dataset and vice versa, many-to-one, where more than one gray value in one dataset may be assigned to a single gray value in the other dataset, one-to-many, or many-to-many. A mapping algorithm  56  may be derived such that each color or gray-scale value represented in the ultrasound dataset can be assigned a corresponding X-ray attenuation value, where the assigned attenuation value is derived from the mapping between the tomosynthesis and the ultrasound dataset. Once the mapping algorithm  56  is derived, it may be applied to the ultrasound dataset  48  in a step  58 . 
         [0029]    The resulting jointly reconstructed dataset  60  may go through a post-processing step  62 . This step  62  may include, for example, coloring (i.e., assigning gray-scale or color values to voxels) the jointly reconstructed dataset  60  such that both ultrasound and X-ray characteristics of the imaged anatomy are properly represented. For example, if two regions “look” different in the ultrasound dataset  48  but are mapped to the same X-ray attenuation value, such as when the ultrasound to X-ray gray-scale mapping is many-to-one, then the regions may be represented by different colors in post-processing step  62 . In one embodiment of the present approach, the jointly reconstructed dataset  60  may be represented in gray-scale values while complementary information from the ultrasound dataset may be overlaid in colors. In another embodiment of the present approach, post-processing step  62  may include reconstructing fine X-ray detail by using, for example, sparseness of data and non-linear techniques such as order statistics-based reconstruction (OSBR). In OSBR, the image data from the projection images is backprojected, then combined. Unlike in simple backprojection, where the backprojected values at each voxel are combined using an averaging operator, the backprojected values in OSBR are combined, for example, by using a voting scheme. That is, if more than half of the backprojected values indicate that the gray level in a position should be higher, then it is increased correspondingly. In another example, the reconstructed voxel value is generated as the average of all backprojected values with the exception of some of the largest and smallest values. Other order-statistics based operators may be used as well, such as median and mode. Other suitable techniques to combine the backprojected data may also be used. The sparseness of the residual projection data after re-coloring the ultrasound dataset can then be used to effectively “place” voxels of certain types of tissue at the correct locations in the dataset  60 , thereby improving the resolution within the reconstructed dataset  60 . In addition, the post-processing step  62  may include preparing the jointly reconstructed dataset  60  for display and displaying the jointly reconstructed three-dimensional image  66 . 
         [0030]    In another exemplary embodiment of the present approach, illustrated in  FIG. 4 , at least one tomosynthesis projection dataset  70  and at least one ultrasound dataset  72  are used as input in a process  68 . Ultrasound dataset  72  may be processed in a step  74  such that subsets  76  of the ultrasound dataset are specified. This processing may be a quantization, in which the registered ultrasound dataset  72  is divided into discrete ranges of color or gray-scale values. For example, one range may include gray-scale values from 0.5 to 0.6. In this example, all voxels of the registered ultrasound dataset  72  which have a gray-scale value from 0.5 to 0.6 would be grouped into a single subset  76 . This quantization may cover the entire range of gray-scale values present in the registered ultrasound dataset  72  such that every voxel is placed into a subset  76 , or the quantization may apply only to gray-scale values which are present in medically relevant sections of the registered ultrasound dataset  72 . The gray-scale levels that separate the different ranges of values, as well as the number of different ranges of values, may be adaptively chosen (e.g., by using suitable clustering techniques). They may also be chosen based on prior knowledge of the imaging physics. They may also be chosen manually, or in a semi-automatic fashion. The same technique may be applied to colored ultrasound data. Alternatively, the processing step  74  may include segmenting the registered ultrasound dataset  72  into homogeneous regions based on texture or visible edges according to techniques known in the art and assigning a different label to each segment. The term “homogeneous” may refer to image gray-scale or color values, as well as tissue-type characteristics (which may be reflected, e.g., in homogeneous properties of the image texture). Each homogeneous region may then be a subset  76  of the ultrasound dataset. In one embodiment of the present approach, processing step  74  may include over-segmentation such that there is a high confidence that data within each region is homogenous. 
         [0031]    In a step  78 , all locations or voxels within a subset  76  of the ultrasound dataset are assigned a value of one while locations or voxels within all other subsets  76  are assigned a value of zero, and the corresponding volume is then projected according to the tomosynthesis acquisition geometry in order to form a basis image  80 . Each basis image  80  may be a family of images, including one image for each projection angle in the tomosynthesis mode. Alternatively, the basis image  80  may be a subset of all of the images or a single image. The registered tomosynthesis projection dataset  70  is then approximated by linear combination or weighted sum of the basis images  80  in a step  82 . That is, each basis image  80  is assigned a weight such that the weighted sum of all the basis images  80  is approximately equal to the tomosynthesis projection dataset  70 . These weights may then represent the X-ray attenuation values  84  most representative of each basis image  80 . The derived X-ray attenuation values  84  may then be applied to the ultrasound subsets  76  in order to form a linear combination of the subsets  76  in a step  86 . The dataset created in this linear combination step  86  represents a jointly reconstructed dataset  88  in which each quantized or segmented subset  76  of the ultrasound dataset has been assigned an X-ray attenuation value corresponding to the registered tomosynthesis dataset  70 . The resulting jointly reconstructed dataset  88  may be post-processed in a step  90  using techniques similar to that of post-processing step  62 . Finally, a jointly reconstructed three-dimensional image  94  may be generated or displayed. 
         [0032]    Turning now to  FIG. 5 , an exemplary embodiment of the present approach designated as process  96  is illustrated in a flow chart. At least one ultrasound dataset  100  may be analyzed to detect horizontal edges in a step  102 . In this technique, “horizontal” means in a direction generally orthogonal to the direction of wave propagation as described above in reference to  FIG. 2 . The horizontal edges correspond to discontinuities in depth relative to the ultrasound probe. The orientation of the horizontal edges does not need to be strictly horizontal, but could include any orientation that may be roughly aligned with this orientation. In its most general embodiment, any edge orientation may be used. In practice, the “horizontal” plane will often be parallel to the compression plates  20  as described above in reference to  FIG. 2 . In an embodiment of the present approach, a level of confidence in the accuracy of the horizontal edge information  104  may be determined based on the resolution of the ultrasound dataset and other factors. This horizontal edge information  104  may then be combined with at least one tomosynthesis dataset  98  to reconstruct a dataset with improved horizontal edge information in a step  106 . In one embodiment, the confidence level of the horizontal edge information  104  may contribute to how much weight the information  104  is given in the reconstruction step  106 . A jointly reconstructed dataset  108  may then be post-processed in a step  110 . A jointly reconstructed three-dimensional image  114  may then be produced. 
         [0033]    In accordance with another embodiment of the present approach, a process  116  is illustrated in  FIG. 6 . As in the embodiment described in reference to  FIG. 5 , at least one ultrasound dataset  120  may be analyzed to detect horizontal edges in a step  122 . Confidence levels for the detected horizontal edges may also be determined. Horizontal edge information  124  with higher confidence levels may be given more weight and edges with lower confidence levels may be given less weight or disregarded in reconstruction step  126 . A jointly reconstructed tomosynthesis dataset  128  may be reconstructed from at least one tomosynthesis dataset  118  using, for example, Markov random fields (MRF) or similar techniques in a step  126 , where the horizontal edge information  124  may be injected as a local smoothness constraint or lack thereof. This constraint may also reflect the confidence associated with the different edge locations. The algorithm for the reconstruction step  126  may encourage “smooth” behavior, except in locations where the dataset  118  or the horizontal edge information  124  does not support this assumption. 
         [0034]    In a parallel track of process  116 , the tomosynthesis dataset  118  may be analyzed to detect vertical edges in a step  130 . In this embodiment, “vertical” is in a direction generally along the X-ray beam, as described in reference to  FIG. 1 . In practice, the “vertical” plane will generally be perpendicular to the X-ray detector  16  and compression plates  20  as described above in reference to  FIG. 1 . In addition, confidence levels for the detected vertical edges may be determined. The derived vertical edge information  132  and the associated confidence levels may then be combined with the registered ultrasound dataset  120  to produce a jointly reconstructed ultrasound dataset  136  in a reconstruction step  134 . Steps  122  through  134  may then be repeated until further iterations fail to yield substantial improvements in the jointly reconstructed datasets  128  and  136 . Finally, the jointly reconstructed datasets  128  and  136  may undergo post-processing in a step  140 . A jointly reconstructed three-dimensional image  142  may then be displayed. The datasets  128  and  136  may be a single combined multi-parameter (or multi-modality) dataset, which reflects both tomosynthesis and ultrasound characteristics. Reconstruction steps  126  and  134  may also be a combined step that utilizes information from the tomosynthesis dataset  118  and ultrasound dataset  120 , as well as the previously-estimated multi-parameter datasets  128  and/or  136 , in an iterative process. 
         [0035]    In another embodiment, edge information may be extracted jointly from both datasets  118  and  120 , where the confidence levels for one edge orientation are higher in one modality and the confidence levels for another edge orientation are higher in another modality. For example, objective function based approaches may be used, where the objective function reflects the different confidence levels. The objective function may be minimized or maximized, depending on the formulation. An exemplary objective function approach may use active contours, or snakes, as known in the literature. This approach may also incorporate prior information about the imaged anatomy, such as from an atlas. For example, the atlas may be registered to the imaged anatomy, and the initial estimate of the location of edges may be derived from the atlas. 
         [0036]    Turning now to  FIG. 7 , in an exemplary embodiment of the present approach, at least one tomosynthesis dataset  148  and at least one ultrasound dataset  150  may be combined with prior information  152  in a step  154  to produce registered datasets  156 . In this context, prior information  152  may include an anatomical atlas, such as, for example, geometrical shape models, models of tissue distribution, or models of tissue composition within certain regions of the anatomy or of the image. Alternatively, the prior information  152  may include other images of the anatomy, such as, for example, a CT scan, a MR scan, or previous tomosynthesis or ultrasound scans. According to an embodiment of the present approach, the prior information  152  may include only a subset of a structures or descriptive information. For example, prior information  152  may include a constraint that the X-ray attenuation values only correspond to two values, those of fatty tissue and fibroglandular tissue. In one embodiment of the present approach, the tomosynthesis and ultrasound datasets  148  and  150  may be intrinsically registered and registration step  154  may be merely used to align the datasets with the prior information  152 . 
         [0037]    In a step  158 , information from the datasets  156  registered to the prior information  152  may be used to classify each voxel in the imaged volume, creating a classified dataset  160 . That is, each voxel may be assigned a value or label based on information obtained from two or more of the tomosynthesis imaging, the ultrasound imaging, and the prior knowledge gained from the anatomical atlas. For example, based on models of tissue distribution, the subcutaneous fat layer may be easily identifiable in the ultrasound dataset, and this information may flow directly into the joint reconstruction. Alternatively, techniques from multi-sensor fusion may be applied to classify the volume in step  158  in accordance with an embodiment of the present approach. That is, each voxel in the registered datasets  156  may be placed into a class, such as, for example, fat, fibroglandular tissue, or calcifications. The classes could also contain anatomical information, such as subcutaneous fat layer, duct, Cooper&#39;s ligaments, etc. The classification may, for example, be based on two or more of the ultrasound dataset, the raw ultrasound data, the X-ray projections, the tomosynthesis reconstruction, the prior information, and a first stage classification which may have acted on a reduced set of data and which may have an associated confidence level. Once the combined datasets have been classified, color or gray-scale values may be assigned to each class in a step  162 . The jointly reconstructed dataset  164  may then be post-processed in a step  166  to produce a jointly reconstructed three-dimensional image  168 . 
         [0038]    Prior information  152  may also be used in other embodiments of the present technique, such as, for example, reconstruction steps  126  and  134  of process  116 , illustrated in  FIG. 6 . In process  116 , prior information  152  may be utilized to assign class information to voxels in the reconstructed datasets  128  and  136 . 
         [0039]    In addition, the objective function based approach described in relation to  FIG. 6  may be applied to the process  146  of  FIG. 7 . For example, the objective function may contain penalty terms for class membership, smoothness, length of edges between regions, or other classification information. While  FIG. 6  refers to an embodiment employing primarily edge-based segmentation and reconstruction,  FIG. 7  refers to an embodiment employing primarily region-based segmentation and reconstruction. Combined, hybrid approaches may also be used. Furthermore, the registration step may also be performed in conjunction with the multi-modality reconstruction, in an integrated processing step. 
         [0040]    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.