Abstract:
A system and method (i.e, utility) are disclosed for positioning a needle in three-dimensions based on patient related statistics for extracting tissue during biopsy procedures. Aspects of the utility can be applied independently or serve as an aid to the urologist when regions of interest are hard to discern in an ultrasound image. Regions of interest that correspond to high cancer risk regions (e.g., statistically) are automatically superimposed on an ultrasound image of a patient in real time. Additionally a statistical map based on one or more demographic parameters of a patient and containing cancer probability locations are also automatically mapped on the ultrasound image in real time displaying potential cancer locations. Aspects of the system are also capable of displaying optimal needle placement positions based on statistical priors and will be able to accurately navigate the needle to that position for biopsy extraction and/or treatment.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims priority under 35 U.S.C. §119 to U.S. Provisional Application No. 60/747,565 entitled “Prostate Target Identification System” having a filing date of May 18, 2006 and U.S. Provisional Application No. 60/913,178 entitled “An Improved Method for 3-D Biopsy” having a filing date of Apr. 20, 2007; the entire contents of both of these applications are incorporated by reference herein. 
    
    
     FIELD 
     The present invention relates to medical imaging. One aspect is directed to image guided surgery using 3D patient related statistics. One application is in aiding a urologist during prostate biopsy to find potential cancerous tissue sites for extraction often in the absence of any significant features or distinguishing characteristics of cancers in 3D ultrasound images. 
     BACKGROUND 
     The Center for Prostate Disease Research (CPDR) has projected that there will be over 200,000 new cancer cases and 27,000 deaths from prostate cancer in the year 2007. Prostate cancer alone accounts for roughly 29% of cancer incidences in men. According to the National Cancer Institute (NCI), a man&#39;s chance of developing prostate cancer increases drastically from 1 in 10,000 before age 39 to 1 in 45 between 40-59 and 1 in 7 after age 60. The overall probability of developing prostate cancer from birth to death being close to 1 in 6. 
     Traditionally either elevated Prostate Specific Antigen (PSA) level or Digital Rectal Examination (DRE) has been widely used as a standard for prostate cancer detection. For a physician to diagnose prostate cancer, a biopsy of the prostate must be performed. This is done on patients that have either abnormal PSA levels or an irregular digital rectal exam (DRE), or on patients that have had previous negative biopsies but continue to have elevated PSA. Biopsy of the prostate requires that a number of tissue samples (i.e, cores) be obtained from various regions of the prostate. For instance, the prostate may be divided into six regions (i.e., sextant biopsy), apex, mid and base bilaterally, and one representative sample is randomly obtained from each sextant. Such random sampling continues to be the most commonly practiced method although it has received criticism in recent years on its inability to sample regions where there might be significant volumes of malignant tissues resulting in high false negative detection rates. Further using such random sampling it is estimated that the false negative rate is about 30% on the first biopsy. That is, 30% of the men had cancer, but the biopsy procedure missed finding it. Thus, many men will require a second and sometimes a third prostate biopsy, at the discretion of their physician. This can result in increased patient anxiety, health care costs, and/or delayed cancer diagnosis. 
     Accordingly, to improve the detection of cancer during biopsy, researchers have discussed different sampling schemes as well as using more cores or sampling different regions for improving detection rates. In the latter regard, it has been proposed to obtain samples from additional regions (e.g., 10 core biopsy) not sampled by standard sextant biopsy. Others have noted the difference in cancer likelihood in the different zones of the prostate (e.g. inhomogeneous distribution) and proposed more complete sampling of regions that have a higher likelihood of being cancerous. In addition to studies verifying inhomogeneous spatial distribution of cancers there is also the possibility of cancers occurring in specific regions based on age, PSA level and ethnicity. 
     SUMMARY OF THE INVENTION 
     To perform a biopsy of a prostate, an image (e.g., 3-D ultrasound image) may be acquired and utilized to guide a biopsy needle to locations on or within the prostate. The present inventors have recognized that the ability to combine statistical data (e.g., cancer data by prostate region) with the image may allow medical personnel to obtain biopsy cores from (or perform procedures on) regions of the prostate having a greater probability of containing cancerous cells if cancer is indeed present. More specifically, it has been determined that the occurrence and location of a number of prostate cancers may be based on one or more demographic characteristics (e.g., age, ethnicity, etc.) and that by utilizing such information, the effectiveness of a biopsy procedure may be improved. 
     That is, the systems and method (i.e, utilities) discussed herein use previously gathered statistical information regarding various zones within the prostate where cancer resides and a probability map of cancer locations from expert (histologist) based ground truth selection. There are several utilities that may work together to arrive at a 3D target site for biopsy sampling. Initially, a prostate is identified within an ultrasound volume. The identified prostate is mapped, in real time, to a shape model whose contents include statistical information and/or zone related information that is previously determined and stored. Accordingly, one utility involves the training of a prostate shape model and the corresponding association of statistical information with the shape model and another utility involves fitting the shape model to fit patient image/data and the transfer of statistical information from the shape model to the patient image. Such a shape model may be a 3D model such that it can be fit to a 3D ultrasound image. Accordingly, such statistical data may be transferred to locations within the 3D ultrasound image as well as onto the surface of the image. 
     The statistical information transferred to the patient image/data may contain information regarding the various zones of the prostate and also cancer probability maps specific to patient related data (age, PSA level and ethnicity). Such data (e.g., cancer probability maps) may allow targeting or treating areas/regions to specific to each patient while still focusing on zones where cancers are most prevalent. For instance, such statistical data may be overlaid onto the patient image to allow guiding a biopsy device to a region that is statistically at risk for cancer based on one or more patient specific parameters including, without limitation, demographic parameters (age, ethnicity, etc.), PSA levels etc. As utilized herein, overlaid includes the incorporation of statistical data onto and/or into a 3D patient image as well as onto 2D patient images. 
     Statistics are generated from a large database of ground truth images. The procedure begins with the collection of data from histology specimens that are outlined and labeled. These labels correspond to whether cancer is present or not at a 3-D location. Several such samples are collected are used to compile statistics on the presence of cancer locations. The database of such images whose cancer characteristics are known is referred to as ground truth data. These ground truth images are all fitted to a common anatomical frame that contains labels that mark landmark locations of the prostate, whether cancer is present or not. Cancer probability maps are then computed from this data and a cancer probability map/atlas or more generally look-up-table (i.e., LUT) is created. This LUT can be used for biopsy guidance. 
     When a new patient comes in for biopsy, the acquired 3-D ultrasound image is fit to the LUT (which could be an image in which the LUT resides) or vice versa. For instance, the image including the LUT may be a shape model that is fit to an acquired ultrasound image. Once the patient image is fit to this model, 3-D statistical data associated with the LUT, including statistical locations of interest, is available (e.g., displayed on and/or within) with the acquired 3-D ultrasound image and can be used to perform biopsy procedures. 
     A shape model may be generated from a database of ultrasound volumes. Such ultrasound volumes may be compiled and segmented either manually or using a segmentation program to obtain several prostate ultrasound surfaces. These surfaces can be used to train a shape model. A shape model may include a mean shape and one or more vectors (e.g., Eigen vectors) that correspond to the principal modes of variation. The projections on these vectors can then be used to describe any shape resembling the training data accurately. The advantage of using shape models is that these projections may represent the direction of largest variance of the data. For instance, 10-15 such projections may adequately represent a large range of shapes accounting for more than 95% of the variance in the data. The projections can be either directly optimized to maximize the similarity between the given shape and the model or the model can be allowed to warp freely and can then be constrained by the requirements of the model that prevent the model from fitting (e.g., warping) into shapes that do not resemble a prostate. 
     Accordingly, one aspect includes obtaining an ultrasound image of a prostate of a patient and fitting a predetermined prostate shape model to that image. Statistical data is then transferred from the prostate shape model to the ultrasound image such that one or more procedures may be performed on the prostate based on the statistical data. For instance, such a procedure may include obtaining at least one biopsy sample from a location of interest within the prostate and/or placing objects within the prostate. 
     Transferring data may include any method of overlaying statistical data onto the ultrasound image of the prostate. When three-dimensional shape models and prostate images are utilized, such overlaying of statistical data may include orienting regions and/or markers associated with statistical data within the three-dimensional ultrasound image. Likewise, information may be overlaid onto the surface of the three-dimensional image. It will be further recognized that such three-dimensional images may be sliced to provide two-dimensional images on which statistical information is present. 
     In one arrangement, performing the procedure includes selecting one or more potentially cancerous regions for biopsy and obtaining a biopsy sample from the selected regions. In conjunction with such performance, the method may include establishing one or more biomarkers on the prostate. Such biomarkers may represent biopsy locations statistically associated with cancer. For instance, the statistical data may include one or more regions that are associated with cancer. A centroid of such regions may be associated with an optimal target location (e.g., biomarker) for obtaining a biopsy sample Accordingly, information from the ultrasound image (e.g., biomarker) may be provided to a guidance instrument for use in guiding a biopsy needle to a location on and/or within the prostate. 
     In a further arrangement, transferring statistical data includes transferring prostate zone information to the ultrasound image. In this regard, the prostate may include various different zones, and statistical size averages associated with such zones may be overlaid onto an ultrasound image. Further statistical histological data associated with each zone may be provided. Accordingly, procedures, such as biopsy, may be performed zone by zone, for example, sequentially. 
     In a further arrangement, statistical data may be associated with specific patient data. In this regard, statistical data based on one or more demographic factors and/or PSA levels may be utilized to select statistical data that is more relevant to a particular patient. In this regard, it is noted that various forms of cancers originate in different locations based on ethnicity and/or other factors. In this regard, by selecting more relevant statistical data and/or associating that data with the shape model, or, providing multiple shape models with different statistical data, improved biopsy may be provided. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates processes for generating a shape model, incorporating statistical information into the shape model and applying the shape model to a prostate image. 
         FIG. 2  illustrates obtaining a prostate image. 
         FIGS. 3A and 3B  illustrate 2D and 3D prostate images. 
         FIG. 4  illustrates a process for labeling ultrasound volumes. 
         FIG. 5  illustrates a process for generating a shape model. 
         FIGS. 6A-6E  illustrate application of a shape model and statistical data to an ultrasound image. 
         FIG. 7  illustrates various zones of prostate. 
         FIGS. 8A-C  illustrate application of zone information to a prostate image. 
         FIG. 9  illustrates a zonal biopsy procedure. 
     
    
    
     DETAILED DESCRIPTION 
     Reference will now be made to the accompanying drawings, which assist in illustrating the various pertinent features of the present disclosure. Although the present disclosure is described primarily in conjunction with transrectal ultrasound imaging for prostate imaging, it should be expressly understood that aspects of the present invention may be applicable to other medical imaging applications. In this regard, the following description is presented for purposes of illustration and description. 
     Presented herein are systems and processes (utilities) to aid urologists (or other medical personnel) in finding optimal target sites for biopsy. Generally, the utilities use statistical information regarding various zones within a prostate where the cancer resides and/or probability maps of cancer locations obtained from an expert (histologist) based ground truth selection. There are several procedures, each of which may include separately novel features, within the utilities that may work together to arrive at the identification of statistically important 3-D target sites. The utilities begin with identifying the prostate first within an ultrasound volume. The identified prostate image (e.g., segmented prostate) is mapped to a previously generated model that includes statistical information in the form of ground truth locations and/or zone related information. The mapping/fitting of the prostate image to the model is achieved in real time and statistical information may be applied to the prostate image such that the statistical information may be utilized for performing one or more procedures (e.g., biopsy, brachytherapy, etc.). 
     As illustrated in  FIG. 1 , there are three main processes in implementing the utility for identifying biopsy locations: a) generation and training of a shape model; b) statistical information collection and combination with the shape model and c) fitting the shape model to patient data and transferring statistical information. However, it will be appreciated that implementing the system may include various sub-processes. For purposes of the discussion herein, the term ‘offline’ is meant to refer to procedures performed prior to a patient visit and the term ‘online’ is meant to refer to procedures performed during a patient visit. Each of these processes and/or sub-processes contains one or more novel aspects alone as well as in combination with the other processes/sub-processes. Accordingly, each process is discussed herein. 
     Shape Model 
     Initially, 3-D ultrasound images of multiple prostates are acquired  102  using, for example a TransRectal UltraSound (TRUS) system. The acquired images may then be converted to 3-D orthogonal voxel data (e.g., ultrasound volumes) having equal resolution in all three dimensions. The images may be acquired in an appropriate manner.  FIG. 2  illustrates a transrectal ultrasound probe being utilized to obtain a plurality of two-dimensional ultrasound images of a prostate  12 . As shown, the probe  10  may be operative to automatically scan an area of interest. In such an arrangement, a user may rotate the acquisition end  14  of the ultrasound probe  10  over an area of interest. Accordingly, the probe  10  may acquire plurality of individual images while being rotated over the area of interest. See  FIGS. 3A-B . Each of these individual images may be represented as a two-dimensional image. See  FIG. 3A . Initially, such images may be in a polar coordinate system. In such an instance, it may be beneficial for processing to translate these images into a rectangular coordinate system. In any case, the two-dimensional images may be combined to generate a 3-D image. See  FIG. 3B . As will be appreciated, such a procedure may be performed on a plurality of patients to obtain a database of ultrasound volumes, which may be utilized to generate a shape model. 
     Referring again to  FIG. 1 , once the ultrasound volumes are obtained  102 , compiled and segmented, either manually or using a segmentation program, the ultrasound volumes may be utilized to train a shape model. However, prior to training the shape model, the segmented surfaces must be labeled. That is, corresponding structures within each segmented surface must be labeled to a common reference frame. This is referred to as a registration process  104 . See  FIG. 1 . In this regard, each of the ultrasound volumes are marked by an expert (e.g., histologist) in feature rich regions or regions that distinguish the shape of the surface. The marked points are called landmarks, and they are correspondingly marked in all of the images of the training data/ultrasound volume database. 
       FIG. 4  illustrates a process utilized in the current embodiment where a non-linear registration method is utilized to align all the images to a common reference. In this regard, the ultrasound volume database is provided  402 . A reference image is selected  404  from the ultrasound database  402 . This image may be randomly picked from the ultrasound volume database  402 . The remaining volumes (n−1) are all aligned  408  with the reference volume  406  associated with the selected image. This alignment is non-linear and may result in a point wise correspondence from one surface to the other, resulting in labeled data  410 . That is, all of the common landmarks of the ultrasound volumes are aligned with a chosen reference surface from the database. The resulting aligned images or ‘labeled correspondences’  106  (See  FIG. 1 ) may then be utilized to train the shape model  108 . 
     A process for training the shape model is provided in  FIG. 5 . As will be appreciated, the training images (i.e., labeled data  502 ) reflect a variety of different geometrical prostate shapes. These different shapes must be taken into account in training the system. To this end, an average shape is created from the training images in the form of a mean shape vector. Generally, creating the average prostate shape involves labeling a set of feature points corresponding to prostate features/landmarks depicted in each training image in the training set of ultrasound volumes. The locations of the labeled feature points from a training images are used to form vector shapes. The average of all the vectors is then computed to produce a mean vector shape that represents the average prostate shape. 
     The ultrasound volumes associated with the labeled data  502  are then Procrustes aligned so as to remove variations in translation, rotation and scaling across the dataset in order to move them into a common frame of reference. Such alignment  504  results in rigidly aligned training volumes  506 . Once the volumes are aligned, a mean shape may be computed  507  to generate a mean shape  508 . In the present arrangement, a principle component analysis (PCA) is performed  510  to identify Eigen values and Eigen vectors  512  that account for the variance in the set of images. A top percentage of the Eigen Vectors are selected  514  that account for more than 95% variance of the entire set of images. Accordingly, the projections on the selected Eigen Vectors  516  can then be utilized to align the shape model (i.e., mean shape) to any other shape. 
     That is, a mean shape and its principal mode of variation are defined  110  (See  FIG. 1 ). These modes of variation can be utilized to fit the mean prostate shape to a prostate image acquired from a patient. Registration of the model to any shape resembling the training shape now becomes a straightforward mathematical process. The projection can be either directly optimized to maximize the similarity between the given shape and the model or the model can be allowed to “warp” freely and may be constrained by requirements of the model that would prevent the model from warping into shapes that do not resemble a prostate. This can in fact be construed as a segmentation problem where a model can be used to warp so as to minimize energy based on the constraints of the shape model or as a registration problem, which is the case where the model is warped so as to best match a segmented prostate surface. 
     Statistical Information Collection. 
     Statistical information collection entails the collection of histology data  120 , which are outlined and labeled  122 . See  FIG. 1 . In this regard, prostate cancer locations are identified and mapped for a large group of patient data. These samples are collected and used to compile statistics on the presence of cancer locations. Reference to the database of images whose cancer characteristics are fully known is referred to as ground truth data. This ground truth data may be utilized to generate a look-up-table or LUT/map/atlas that indicates the probability of various regions of developing cancer. These ground truth images may be generated from histological data including histological slices from actual prostates and/or histological data identified from individual (e.g., 2-D) prostate images. The ground truth images are all mapped to a common anatomical frame and contain labels that mark every location of the prostate, i.e. whether cancer is present or not. Such labels may be selected by a histologist  124 . Cancer probability maps/atlases are then computed from this data. These maps/atlases can be used for biopsy guidance. 
     The specificity of the map/atlas may be further improved by normalizing subgroups of the data separately based on age, ethnicity, PSA levels and/or other demographic factors. In this regard, statistical information may be based on one or more demographic parameters. In any case, cancer probability maps/atlases are computed from histological data which may include actual prostates that have been removed from cancer patients as well as from images of cancerous prostates (e.g., samples). The cancer in the samples may be mapped by a histologist who reviews the sample identifies the location of cancer therein. Accordingly, a database may be generated from a plurality of such prostates to identify which regions of the prostates are likely to identify which regions of the prostates are likely to have cancer (e.g., based on one or more demographics), as well as to identify the exact location of such cancer. 
     Data from separate prostates is labeled to a common reference frame such that the data may be incorporated into a map/atlas that may be utilized to identify areas within a prostate for a given patient. Such labeling may include selecting a volume as a common volume of reference for a set of image volumes. Each of the remaining volumes may be registered to the chosen common volume of reference so as to create an atlas. Then, special coordinates of cancer in each of the remaining image volumes are mapped onto the atlas coordinates in the atlas by transformation that registers the corresponding image volume to the atlas. 
     In this regard, prostate regions that contain cancer may be identified. For instance, if a plurality of the histological samples of different prostates include cancer in a common region, a centroid of that region may be identified. The centroid may be a common point or biomarker of all the map/atlas coordinates and may represent an optimal target position for biopsy to identify cancer within that region of the prostate. That is, the centroid/biomarker may identify an optimal position for sampling for a patient having demographic information and/or PSA levels that match those of a given map/atlas. 
     In any case, once the histological data is labeled into a common 3D reference frame  126 , a map/atlas may be aligned  128  with the mean shape of the shape model discussed above. That is, statistical information of the map/atlas (e.g., regions of increased probability of cancer) may be incorporated into the shape model. This shape model and corresponding statistical information  130  may then be fit to an image of a prostate of a patient in an online procedure. Accordingly, statistical information associated with the regions having a high incidence of cancer may be overlaid onto the surface of the image of the prostate of the patient. Accordingly, these regions may be targeted for biopsy. 
     Fitting the Shape Model to Patient Image 
     As illustrated in  FIG. 1 , the online portion of the utility involves acquiring an image  140  for a new patient. This may be performed as illustrated in  FIG. 2  where a side fire or end fire TRUS probe  10  is utilized to acquire a patient prostate image. However, it will be appreciated that other probes may be may be utilized as well. The probe  10  may also includes a biopsy gun (not shown) that may be attached to the probe. Such a biopsy gun may include a spring driven needle that is operative to obtain a core from desired area within the prostate. In this regard, it may be desirable to generate an image of the prostate  12  while the probe  10  remains positioned relative to the prostate as well as identify target areas for sampling. Further, it may be desirable to register the location of the probe and or needle to the ultrasound images, such that the images may be utilized to direct the needle to an area or/or within the prostate for obtaining a core sample. 
     In any case, once the ultrasound image is acquired it may be segmented  142  to identify the surface of the 3-D volume/capsule  144  and/or the boundaries of individual  2 -D images. Such segmentation may be performed in any known manner. One such segmentation method is provided in co-pending U.S. patent application Ser. No. 11/615,596, entitled “Object Recognition System for Medical Imaging” filed on Dec. 22, 2006, the contents of which are incorporated by reference herein. The segmented image is then provided for combination with the shape model  146  in order to align the map/atlas information with the acquired image. Biopsy locations may then be identified  148 . 
       FIGS. 6A-6E  graphically illustrate the overall process. Though illustrated as 2D figures, it will be appreciated that the shape model, prostate image, statistical regions and biomarkers discussed herein may be three dimensional. Accordingly, the statistical information and biomarkers may be displayed on and/or within the prostate image. Initially, the shape model  202  is provided  6 A. Statistical information  200  (e.g., ground truth data) corresponding to a current patient (e.g., based on demographics, PSA etc) is aligned with the shape model  202  so that a completely defined geometrical deformation shape model  204  including statistical information is provided.  FIG. 6B . The deformation shape model  204  may be based on a set of Eigen vectors that allow the model  204  to only be fitted in ways allowed by predetermined limitations of the model. The model may then be applied (e.g., fit) to an acquired ultrasound prostate image  206 .  FIG. 6C . The result of this fitting procedure is also the transfer of statistical information to the prostate image  206  of the patient. That is, the statistical information may be applied to the prostate image  206  of the patient to provide a combined image with statistical data  208 .  FIG. 6D . A urologist may utilize the combined image  208  to identify regions on the prostate of the current patient that have, for example, higher likelihood of cancer. Accordingly, the urologist may target such regions for biopsy. 
     The identification of target locations (e.g., biomarkers) may allow for use of a positioning system to obtain biopsies from those locations. In this regard, a urologist may use 3-D cancer distribution and/or biomarkers for needle positioning during biopsy. That is, the statistical information applied to the prostate may be reduced into a biomarker framework to generate the cancer biopsy spots as surrogate biomarkers for biopsy. See  FIG. 6E . The concept comes from knowledge of the centroid/central core of the cancer for a 3D cancer region. Since the 3D model will have different 3D regions, one can reduce the 3D regions into 3D centralized spots as biomarkers for biopsy. The reduction of the 3D cancer regions into biomarkers is illustrated in  FIG. 6E . As shown, the biomarkers  210  (e.g., a centroid of potentially cancerous region) are displayed on the on the combined image  208  that provide a target location for biopsy. Further, it will be appreciated that the location of these biomarkers as applied to the prostate image may be output from, for example the imaging device to a positioning device. The positioning device may then guide a biopsy device (e.g., needle) to the biomarker. Such a biopsy device may be incorporated onto, for example, a TRUS ultrasound. 
     The provision of a system that allows for combining statistical information with an image of a patient&#39;s prostate may allow for additional enhanced procedures. For instance, the prostate is formed of three zones including a peripheral zone, a central zone and a transition zone. See  FIG. 7 . Since cancer is most often found in the peripheral zone of the prostate, followed by transition and central zones, biopsy can be performed starting in order of the likelihood of cancer. That is, zonal targeting of biopsy sites can be used to improve the specificity of diagnosis and/or treatment. 
     In order to allow targeting individual zones within a patient&#39;s prostate, the shape model discussed above may also include zonal information. In this regard, during the generation and training of the shape model, data associated with the transitional zone, central zone and/or peripheral zones of multiple prostates may be incorporated into the shape model such that such information may be applied to the prostate image. For instance, as shown in  FIGS. 8A-8C , the transition zone  302 , central zone  304  and peripheral zone  306  may be incorporated into a shape model  300 . The shape model may also include statistical information as discussed above. Again, an ultrasound image of a prostate  206  of a patient may be acquired. See  FIG. 8B . In this arrangement, in addition to applying statistical information regarding cancer locations onto the prostate  206 , the shape model  300 , including the zone information, may be overlaid onto the prostate  206 . In this regard the resulting three-dimensional image may be segmented in order to generate views in which the different zones within the prostate are visible. See  FIG. 8C . 
     The combined view  308  may then be utilized to identify areas within specific zones for biopsy purposes. In one arrangement, the use of such zones may allow for sequential identification of target locations. In this regard, the zones may be identified sequentially within the patient&#39;s prostate. Further, these zones may be selected in the order of importance. In any case, three-dimensional locations within a zone may be ascertained through use of an atlas/map containing statistical information regarding that zonal area. Accordingly, regions of interest within the zone and/or biomarkers may be generated for the zone and may identify one or more points of maximum likelihood for cancer based on the map/atlas. Accordingly, a biopsy of this location may be performed.  FIG. 9  illustrates an overall process for performing a zonal biopsy. The method starts with the obtaining  600  an ultrasound image of a prostate of a patient. In addition, the patient&#39;s information is entered (PSA, age, ethnicity and/or others) and an atlas or look-up table specific to these statistics is retrieved  602  from a database. In any case, the patient&#39;s ultrasound image is acquired, interpolated and reconstructed into 3D. The reconstructed image may then be segmented by any of several segmentation techniques to get a surface outline of the ultrasound prostate. The patient&#39;s surface is then aligned with the zones one at a time. In this regard, zones for sampling are selected  604 . For each zone selected  606 , statistical information from the map/atlas are utilized to identify  608  optimal location for biopsy based on the retrieved statistical data for that zone. A biopsy needle may be guided to the location to obtain a biopsy sample  610 . If another zone is selected  612 , a 3D atlas for that zone may be obtained  614  and utilized to identify  616  one or more target locations for the current zone. This may be repeated until all selected zones in the prostate are sampled. 
     In addition to the above noted functions, the disclosed processes, alone or in combination, also provide one or more of the following advantages. As statistical properties of cancerous regions in a prostate are derived in the 3-D regions, the maps/atlases include all information necessary to guide a biopsy planning process. Further, as the maps/atlases are prepared offline prior to patient visits, this allows the statistical data of the maps/atlases to be quickly selected (e.g., based on demographics, etc.) and applied to an acquired image. Further, as a result of matching the map/atlas to a patient based on patient specific information, the probability of identifying cancerous cells in improved. Further, the utility may allow for the comparison of images a prostate of a patient where the images are acquired at separate times. That is, the utility may allow for the registration of temporally distinct images together. This may allow, for example, comparison of the overall size of the prostate to identify changes. Further, this may allow for identifying previous biopsy locations, obtaining biopsies form previous locations and/or utilizing old biopsy locations to permit sampling of previously un-sampled regions. 
     In a system that uses biomarkers as location identifiers, cancerous regions derived from the histology data may be reduced to 3-D target locations by computing the center of the originating cancers. These biomarkers may accurately represent changes during which a cancer has evolved or spread over a 3-D region. Further, the computation of biomarkers is an offline process and it does not affect the workflow of urologists for biopsy. Another advantage of having the biomarker strategy is that it avoids the occlusion of the prostate image during biopsy. 
     As noted above, sextant biopsy can miss 30% of cancers and other biopsy methods have randomly obtained biopsy samples from all zones of the prostate. Since a majority of cancers are found in the peripheral zone of the prostate, following a zonal concept of biopsy sampling can be very efficient. That is, zones having higher likelihood of cancer may provide a majority or all biopsy samples. Further, combining zonal biopsy with biomarkers provides the added advantage of finding target locations accurately and also improves the efficiency of a biopsy process. That is, the areas (zones) targeted for biopsy sampling may be reduced based on patient specific information and locations within the zones may be limited to those identified as having high probability of cancer. The combined effect of biomarker identifying target locations based on statistical data and obtaining biopsies in a zonal fashion can make the overall biopsy process very efficient while allowing for improved cancer detection. 
     The foregoing description of the present invention has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit the invention to the form disclosed herein. Consequently, variations and modifications commensurate with the above teachings, and skill and knowledge of the relevant art, are within the scope of the present invention. The embodiments described hereinabove are further intended to explain best modes known of practicing the invention and to enable others skilled in the art to utilize the invention in such, or other embodiments and with various modifications required by the particular application(s) or use(s) of the present invention. It is intended that the appended claims be construed to include alternative embodiments to the extent permitted by the prior art.