Patent Publication Number: US-10762626-B2

Title: Activity image reconstruction using anatomy data

Description:
FIELD 
     This disclosure is related to medical imaging generally, and more specifically to systems combining functional imaging with anatomical imaging techniques. 
     BACKGROUND 
     Positron emission tomography (PET) allows detection of cancer and heart disease. PET is considered a functional imaging method, because PET images can show the concentration of a radiotracer in different regions of the imaged organ over the course of time. The radiotracer is injected into the patient at a known location (e.g., the aorta). Sensors (e.g., silicon photomultipliers, SiPM) detect annihilation of positron pairs at various locations over time. The annihilation events indicate the blood flow and radiotracer uptake in the tissue of interest. 
     Compared to spatial anatomic images (e.g., computed tomography, CT or magnetic resonance imagery, MRI), PET images have lower spatial resolution, lower signal to noise ratio, and can appear more blurry. Also, PET images are captured over a longer period of time, and may have artifacts due to patient motion. As a result, the boundaries between organs in CT and MR images are sharper than PET images. 
     Many medical imaging systems incorporate spatial information from CT or MR imaging into PET image reconstruction to better define anatomical boundaries and improve image quality. 
     SUMMARY 
     In some embodiments, a method for reconstructing medical images comprises: identifying a plurality of organs in a body of a subject based on an anatomic image; assigning a plurality of voxels in the body to respective ones of the plurality of organs based on the anatomic image; and reconstructing activity images of the body using respectively different processing for the voxels assigned to each respective one of the plurality of organs. 
     In some embodiments, a system for reconstructing medical images comprises a non-transitory, machine-readable storage medium coupled to receive medical image data. The machine-readable storage medium contains instructions. A processor is coupled to the machine-readable storage medium for executing the instructions. The instructions configure the processor for performing a method comprising: identifying a plurality of organs in a body of a subject based on an anatomic image; assigning a plurality of voxels in the body to respective ones of the plurality of organs based on the anatomic image; and reconstructing activity images of the body using respectively different processing for the voxels assigned to each respective one of the plurality of organs. 
     In some embodiments, a non-transitory, machine-readable storage medium contains instructions, such that when a processor executes the instructions, the instructions configure the processor for reconstructing medical images by: identifying a plurality of organs in a body of a subject based on an anatomic image; assigning a plurality of voxels in the body to respective ones of the plurality of organs based on the anatomic image; and reconstructing activity images of the body using respectively different processing for the voxels assigned to each respective one of the plurality of organs. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1A  is a three dimensional (3D) rendering of an anatomy map diagram of a person. 
         FIG. 1B  is a diagram mapping voxels in the segmented anatomy map of  FIG. 1A  to anatomy data collected from a patient by a computed tomography (CT) scanner. 
         FIG. 2  is a flow chart of a method for applying anatomy data in reconstructing activity images using a similarity map. 
         FIGS. 3A-3C  show an example applying MR anatomy prior data in reconstructing a brain image. 
         FIGS. 4A-4C  show an example applying CT anatomy prior data in reconstructing a brain image. 
         FIGS. 5A-5D  show an example adaptively applying CT anatomy prior data in reconstructing a whole body image using a similarity map. 
         FIGS. 6A-6D  show an example applying CT anatomy prior data in reconstructing a whole body image using adaptive regularization. 
         FIG. 7A  is a schematic diagram showing different kinetic models for different organs. 
         FIG. 7B  is a schematic diagram showing a table lookup to determine the applicable kinetic model for a given voxel. 
         FIG. 8  is a flow chart of a method for applying different kinetic models in reconstructing activity images for different organs. 
         FIG. 9  is a flow chart of an embodiment of a method for applying different kinetic models in reconstructing activity images for different organs. 
         FIG. 10  is a flow chart of an embodiment of an example of the method of  FIG. 9 . 
         FIGS. 11A-11E  show application of CT data for parametric images of the aorta. 
         FIGS. 12A-12D  show application of CT data for parametric images of the liver. 
         FIG. 13  is a schematic diagram of an apparatus for PET/CT scanning. 
     
    
    
     DETAILED DESCRIPTION 
     This description of the exemplary embodiments is intended to be read in connection with the accompanying drawings, which are to be considered part of the entire written description. 
     A single static or dynamic positron emission tomography (PET) image reconstruction algorithm can be applied to reconstruct an entire volume (e.g., the patient&#39;s whole torso, or the patient&#39;s torso and head). This may include applying uniform regularization strength throughout the image, and using a uniform Patlak model among all organs in the image. 
     Embodiments described herein apply organ-based regularization or organ-based kinetic models in static/parametric image reconstruction based on an anatomy map. In some embodiments, an anatomy map can be used to adaptively regularize emission image reconstruction. For example, the anatomy map can assign each voxel to a respective organ, and each organ can have a respective regularization strength (e.g., 0%, 100%, or a value between 0% and 100%) for image reconstruction. Alternatively, the anatomy map can assign each voxel to a respective organ, and assign each organ to a respective kinetics model. The PET images can be reconstructed by applying the respective kinetics model corresponding to each voxel, according to the anatomy map. 
     In some embodiments, as described herein, the reconstruction parameters or algorithms can be adapted according to human anatomy. Different organs have different physiology and anatomy structures. In dynamic reconstruction, different organs may follow different kinetics models. For example, in brain imaging, anatomy prior may be different depending on whether computed tomography (CT) information or magnetic resonance (MR) information is used. For example, in point spread function (PSF) reconstruction, different widths of PSF can be applied in the brain area and torso region, respectively. In maximum a posteriori (MAP) image reconstruction, different regularization strengths may be applied to different organs, respectively. 
     Incorporating the anatomy map into activity image reconstruction can provide a more intelligent reconstruction algorithm. For example, reconstruction can apply the anatomy prior, if the organ to be imaged has good correlation with anatomy image or not. In MR/PET, using the T1 image, the anatomy prior can be applied in MR/PET. In PET/CT brain imaging one can turn off anatomy prior. 
       FIG. 13  shows a schematic diagram of a medical imaging system  1 . In some embodiments, the system  1  includes an anatomy image scanner  2   a  and an activity (emission) image scanner  2   b . The anatomy image scanner  2   a  can be a computed tomography (CT) or magnetic resonance (MR) scanner. The activity (emission) image scanner  2   a  can be a positron emission tomography (PET) scanner or a single-photon emission computerized tomography (SPECT) scanner. The system  1  comprises: an examination table  3  for a patient  4  who can be moved on the examination table  3  through an opening  5  of the scanners  2   a ,  2   b , a control device  6 , a processor  7  and a drive unit  8 . The control device  6  activates the scanners  2  and receives (from the scanners  2   a ,  2   b ) signals which are picked up by the scanners  2   a ,  2   b . The scanner  2   a  picks up x-rays (if scanner  2   a  is a CT scanner) or radio waves (if scanner  2   a  is an MR scanner) With the aid of the scanners  2   b  gamma radiation can be collected (if scanner  2   b  is a PET scanner or a SPECT scanner). Also disposed in the scanners  2   a ,  2   b  is a ring of detector blocks  9   a ,  9   b  (collectively referred to as  9 ) for acquiring photons which are created by annihilation of electrons and positrons in the detector blocks  9   a ,  9   b . Although only  2  detector blocks  9   a ,  9   b  are shown in  FIG. 13  for ease of viewing, scanners  2   a ,  2   b  can have many detector blocks  9  arranged in a cylinder around the circumference of the scanners  2   a ,  2   b . The control device  6  is further operable to receive signals from the detector blocks  9   a ,  9   b  and is capable of evaluating these signals for creating PET or SPECT images. The control device  6  further activates the drive unit  8  in order to move the examination table  3  in a direction Z together with the patient  4  through the opening  5  of the scanners  2   a ,  2   b . The control device  6  and the processor  7  can, for example, comprise a computer system with a screen, a keyboard and a non-transitory, machine readable storage medium  12  (hereinafter, “storage medium”) on which electronically-readable control information is stored, which is embodied so that it carries out the method described below when the storage medium  12  is used in conjunction with the processor  7  and the control device  6 . 
     A tool as described in U.S. Patent Application Publication Nos. U.S. 2018/0260951 A1 and US 2018/0260957 A1 by Siemens (both of which are incorporated by reference herein) can be used. The tool is able to accurately segment organs from anatomy images ( FIGS. 1A and 1B ). The tool is based on an automatic algorithm that detects appropriate landmarks and then segment organs from 3D CT/MR volumes using a deep image-to-image network (DI2IN), employing a convolutional encoder-decoder architecture combined with multi-level feature concatenation and deep super-vision. 
     The anatomy map in the 3D rendering of  FIG. 1A  can be overlaid with one or more CT or MR images, as shown in  FIG. 1B , so that each voxel of the PET or single-photon emission computerized tomography (SPECT) images can be reconstructed according to the organ to which that voxel is assigned based on the anatomic image data. 
     After segmentation, each organ may be assigned to an identification table. For example, each organ may be assigned a respective integer (Table 1). The integer numbers corresponding to each organ can be mapped to respective kinetics models, anatomy prior, regularization strength, or the like, or combinations thereof. The anatomy map can comprise a non-transitory, machine-readable storage medium storing a database. In some embodiments, the database can contain a three-dimensional (3D) array, in which each element of the array represents a respective voxel, and contains the identifier (e.g., integer number) representing the organ to which the voxel belongs. 
     
       
         
           
               
               
               
             
               
                   
                 TABLE 1 
               
               
                   
                   
               
               
                   
                 Organ 
                 Integer number 
               
               
                   
                   
               
             
            
               
                   
               
            
           
           
               
               
               
            
               
                   
                 liver 
                 3 
               
               
                   
                 right lung 
                 4 
               
               
                   
                 left lung 
                 5 
               
               
                   
                 left kidney 
                 6 
               
               
                   
                 right kidney 
                 7 
               
               
                   
                 heart 
                 10 
               
               
                   
                 aorta 
                 11 
               
               
                   
                 spleen 
                 13 
               
               
                   
                 brain 
                 16 
               
               
                   
                 bones 
                 19 
               
               
                   
                 remaining organs 
                 0 
               
               
                   
                   
               
            
           
         
       
     
     In some embodiments, each integer number can be used to reference a respective table or table entry defining parameters (e.g., K 1 , V 0 ) to be used for modeling the organ associated with the integer number. In other embodiments, the model parameters can be incorporated into the segmentation table, so that every voxel has a respective entry with an identifier (e.g., integer number) and a set of kinetics model parameters. 
     This anatomy map can guide emission image reconstruction in adaptive regularization ( FIG. 2 ) and/or can use different kinetics models for different organs ( FIGS. 7A-8 ). 
     Referring first to  FIG. 2 , in step  200  the emission data are collected. In some embodiments, a single medical imaging system includes an MR scanner or CT scanner for collecting anatomy images, and a PET scanner or SPECT scanner for collecting emission data representing radiotracer concentration. In some embodiments, anatomy and activity data (e.g., PET and CT data) are both collected while the patient remains on the scanner bed, without leaving the bed in between. 
     At step  202 , the anatomy data (MR or CT data) are segmented into organs. Each voxel is identified with a respective organ. For example, the segmenting may be performed using machine learning, with a neural network trained with a set of organs from previous patients identified by trained inspectors. The neural network is trained to classify each voxel as belonging to a particular organ. In other embodiments, the voxels can be assigned to organs using an image recognition algorithm (e.g., feature extraction) or a clustering algorithm. 
     At step  204 , a respective identifier (e.g., integer number) is assigned to each voxel, corresponding to the organ to which that voxel belongs. 
     At step  206 , the system builds a similarity map (according to equations (1) to (4), based on the anatomy (MR or CT) image for each voxel. In some embodiments, the similarity map excludes voxels assigned to one or more organs to which the anatomy prior is not to be applied. For example, in some embodiments, the system determines whether the functional (PET or SPECT) image values are correlated with the anatomical (MR or CT) image values. 
     The similarity map can be built using a radial Gaussian kernel. The PET image value x at a pixel j,k is given by equation (1):
 
 x=Kα   (1)
 
where the kernel K is defined by equation (2), and α is the coefficient image defined by equation (3).
 
     The kernel function K(f j ,f k ) for each pair of anatomical pixels j and k is defined by equation (2). 
                     K   ⁡     (       f   j     ,     f   k       )       =     exp   (       -              f   j     -     f   k            2         σ   2       )             (   2   )               
where f j  and f k  are anatomical feature vectors for pixels j and k, respectively, K is a kernel function, and the parameter σ controls the edge sensitivity.
 
     For expectation maximization (EM), the coefficient image is defined by equation (3). 
                     α     n   +   1       =         α   n         K   T     ⁢       P   T     ⁡     (     1   AN     )           ⁢     (       K   T     ⁢     P   T     ⁢     y       PK   ⁢           ⁢     α   n       +     A   ⁡     (     NR   +   S     )             )               (   3   )               
where P∈   M     d     ×N     v    is the system matrix with p ij  denoting the probability of detecting an event originating in voxel j in detector pair i, and r is a vector encompassing random and scattered events, and M d  and N v  represent the number of detector bins and voxels, respectively. A is attenuation correction factor, N is normalization factor, and S is simulated scatter sinogram. The similarity matrix (map) K for a given organ is given by equation (4)
 
     
       
         
           
             
               
                 
                   
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     Referring again to  FIG. 2 , at step  208 , the similarity map is applied in iterative image reconstruction, e.g., with the Ordered Subsets Expectation Maximization (OSEM) algorithm in equation (3). 
     At step  210 , the system outputs the reconstructed images. 
     The anatomy map of  FIG. 1B  and Table 1 can be used to specifically identify the organs that have good correlation between activity and the attenuation map in building the similarity matrix according to equation (4). 
     In equation (4), the similarity values with adjacent voxels f k  are calculated for voxels f j  in organs that have good correlation between anatomy and activity, and no similarity values need to be calculated for voxels in organs that do not have good correlation between anatomy and activity. For example, PET and CT data for the brain are known to have poor correlation, so there is no need to calculate similarity values for the voxels in the brain. This adaptive calculation of similarity matrix can be controlled by the factor organ(f j ) in equation (4) since organ(f j ) keeps track of which organ voxel f j  belongs to. 
       FIGS. 3A-3C  show an example where there is good correlation between (functional) PET image reconstruction with no anatomy prior (shown in  FIG. 3A ) and the MR anatomy prior images of the brain ( FIG. 3B ). Both  FIGS. 3A and 3B  show details of the soft tissue of the brain. Consequently, reconstructing the PET image data of  FIG. 3A  using the anatomy prior information from the MR images in  FIG. 3B  provides a smoother image, as shown in  FIG. 3C . Similarly, there is good correlation between the anatomy and emission information for the torso (not shown in  FIGS. 3A-3C ), regardless of whether MR or CT data are used for the torso. 
       FIGS. 4A-4C  show an example where there is poor correlation between (activity) PET image reconstruction of the brain with no anatomy prior (shown in  FIG. 4A ) and the CT anatomy prior images of the brain ( FIG. 4B ).  FIG. 4A  shows details of the soft tissue of the brain, but the CT images in  FIG. 4B  only show the bone. Consequently, reconstructing the PET image data of  FIG. 4A  using the anatomy prior information from the CT images in  FIG. 4B  over-smooths the image, as shown in  FIG. 4C , causing loss of detail while reducing noise. Thus, it can be advantageous to exclude CT anatomy prior data for the skull from a similarity map for the brain in step  206  of  FIG. 2 . 
     In  FIGS. 5A-5D , a similarity matrix was built for all voxels that are located outside of the brain based on the attenuation map. The method distinguishes regions having different attenuation properties, assigning linear attenuation coefficients to them to provide an attenuation map to correct the PET emission data during reconstruction. The attenuation map prior can be successfully applied in the region outside of the brain. The reconstructed brain is not smoothed. 
       FIG. 5A  shows the result of standard uptake value (SUV) reconstruction without using any anatomy prior. The torso portions of the image are noisy. 
       FIG. 5B  shows the PET data from  FIG. 5A , reconstructed using anatomy prior data from a corresponding CT image to the torso and the brain. The torso portion of the image is improved by noise reduction while retaining acceptable detail, but detail is lost in the brain portion of the image, since the brain anatomy (CT) data are not correlated with the brain activity (PET) data. 
       FIG. 5C  shows the image reconstructed using the similarity matrix for all voxels located outside the brain. The torso portion of the image benefits from noise reduction, similar to the torso in  FIG. 5B , but the brain portion retains detail, similar to the brain portion of  FIG. 5A . In this instance, the benefit of retaining detail in the brain in  FIG. 5C  exceeds the cost of foregoing noise reduction in the brain. 
       FIG. 5D  shows the anatomy map overlaid with the CT data. The anatomy map from MR/CT can be used to design more intelligent reconstruction algorithms by knowing to which organ each voxel belongs. 
     In various embodiments, the system can selectively and/or variably apply anatomy prior data for reconstruction of PET or SPECT images, depending on the correlation between anatomy and activity data for each individual organ. The system can apply different regularization or anatomy prior to different organs. 
     In some embodiments, the system can apply different kinetics models to different organs for parametric imaging to increase accuracy and signal to noise ratio. 
       FIGS. 6A-6D  show another example using adaptive regularization strength with quadratic prior. As noted above, it can be advantageous to use MR or CT anatomy prior if the anatomic data and activity data are highly correlated, and it can be advantageous to reconstruct the PET images without using anatomy prior if the anatomic data and activity data have very low correlation. Adaptive regularization strength allows use of reduced regularization strength based on CT anatomy prior for intermediate correlation between anatomic data and activity data. Adaptive regularization can strike a balance between reducing noise and preserving detail. 
       FIGS. 6A and 6B  are coronal and sagittal views of a patient reconstructed with uniform regularization strength: 100% regularization strength for the torso (based on the CT data) and 100% regularization strength for the brain (based on the CT data). The brain portion of the image is over-smoothed.  FIGS. 6C-6D  show another method of reconstructing an image from the same PET data as  FIGS. 6A and 6B  using adaptive regularization strength. In  FIGS. 6C-6D , the regularization strength applied to the brain was one third of that applied to the rest of human body. By using a reduced regularization strength in  FIGS. 6C and 6D , better resolution was preserved in the brain region (compared to  FIGS. 6A and 6B ), while providing an acceptable noise level. This is only one example, and the regularization strength applied to any given organ can be varied to any value between 0% and 100%. 
     Alternatively, the anatomy map can be applied to parametric imaging. For example, Patlak model may be sufficient for tumor (hot spot) imaging. However, the parametric images (Ki and Dv) are noisy compared to SUV images, and the linear Patlak model may be not accurate for parametric imaging for some organs. In some embodiments, the system can apply different kinetics models to different organs. Applying different kinetic models to different organs may increase signal to noise ratio of parametric images. 
     In some embodiments, the anatomy map or segmentation table is used to determine to which organ each voxel is assigned, and each organ is assigned to a respective kinetics model.  FIGS. 7A and 7B  schematically show an indexing method for determining which parameters are included in the kinetics model for a given organ. For example, as shown in  FIG. 7B , if the segmentation table record corresponding to a given voxel contains the integer number  1 , the model parameters for that voxel are identified in the first entry (e.g., row or column) of the model table. In this case, the first row of the model table indicates that a linear model is used, and the parameters K 1  and V 0  will be identified, and parametric K 1  and V 0  images will be reconstructed. Similarly, the remaining entries (rows or columns) of the model table identify the parameters of the models used for other organs. 
     For example, in some implementations of parametric imaging systems, the Patlak model was applied to all of the voxels in the image. A Patlak model is a linear model based on equation (5): 
                       R   ⁡     (   t   )           C   p     ⁡     (   t   )         =       K   ⁢         ∫   0   t     ⁢         C   p     ⁡     (   τ   )       ⁢   d   ⁢           ⁢   τ           C   p     ⁡     (   t   )           +     V   0               (   5   )               
where R is an amount of tracer in a region of interest, C p (t) is the concentration of the tracer in blood, K is the rate of entry into the peripheral (irreversible) compartment, and V 0  is the distribution volume of the tracer in the central (reversible) compartment.
 
     The model of equation (5) assumes that all voxels follow the linear model regardless of which organ the voxels are in. However, many organs exhibit more complex behavior. For example,  FIG. 7A  shows four different schematic models for the aorta, myocardium, brain, and liver, respectively. The aorta is considered as a pass-through, with no tracer uptake, and no change in tracer concentration between the inlet to the aorta and exit from the aorta. The myocardium can be modeled as having one reversible compartment C1, with respective constants K 1  and K 2  defining tracer influx and outflux, respectively. The brain can be modeled as having a reversible compartment C 1  and an irreversible compartment C 2 . The parameter K 3  is added, denoting uptake by the irreversible compartment C 2 . The liver can be modeled as having two reversible compartments C 1  and C 2 . A liver outflux parameter K 4  is added. 
     The anatomy map also allows organ-specific parametric imaging and can increase signal to noise ratio of parametric images. The anatomy map can be derived from high resolution MR or CT images. In static or parametric emission image reconstruction, the correlation information between anatomy (e,g., MR and/or CT) and emission (PET or SPECT) images, allow more accurate kinetics modeling, to de-noise parametric images, and also to provide more desirable correction effects that adapt to clinical needs. 
       FIG. 8  is a flow chart of a method applying different kinetics models to different organs in dynamic imaging. At step  800 , the emission data are collected. In some embodiments, a single medical imaging system includes an MR scanner or CT scanner for collecting anatomy images, and a PET scanner or SPECT scanner for collecting emission data representing radiotracer concentration. 
     At step  802 , the anatomy data (MR or CT data) are segmented into organs. Each voxel is identified with a respective organ. Any segmentation method can be used, such as those discussed above with respect to step  202  of  FIG. 2 . 
     At step  804 , a respective identifier (e.g., integer number) is assigned to each voxel, corresponding to the organ to which that voxel belongs. 
     At step  806 , the system maps each identifier (e.g., integer number) to a respective kinetics model. For example, the mapping can be as shown in  FIG. 7A  or  FIG. 7B . 
     At step  808 , the method loops through each organ to apply the respective kinetics model corresponding to each voxel. 
     At step  810 , the system outputs parametric images for each of the model parameters. 
       FIG. 9  shows an exemplary embodiment for applying different kinetic models to respective organs. 
     At step  900 , the dynamic sinogram data, organ segmentation map, attenuation map, and the blood input function are input to the system. In some embodiments, the sinogram data and organ segmentation map can be captured using a scanner having a PET or SPECT acquisition scanner and a CT or MRI scanner. In other embodiments, the sinogram data and organ segmentation map can be previously stored data accessed from a non-transitory, machine-readable storage medium. 
     At step  902 , each organ is assigned to a respective kinetics model. For simplicity, the remaining steps in  FIG. 10  are based on a linear Patlak model, but in other examples, one or more other models, such as multi-compartment models and/or non-linear models are used. 
     At step  904 , each parametric image is initialized. For example, all voxels for each parametric image can initially be set to a uniform value (e.g., all black, all white, or all gray. 
     Steps  908  to  916  perform the main loop. 
     At step  908 , the system calculates frame emission images using expectation maximization. The frame emission images include a respective SUV image for each time point at which the sinogram data are collected. The first time step  908  is performed, the frame emission images are calculated using the initial parameter values from step  904 . Subsequently, each iteration of step  908  is performed using the parametric images from the previous iteration of the main loop. 
     At step  910 , each frame image (SUV image corresponding to each collection time point during the scan) is updated based on the estimation from the previous time step. For example, the frame image corresponding to the second time point is updated based on the frame image corresponding to the first time point. 
     Steps  912  and  914  form an inner loop to perform kinetic modeling. The inner loop updates the parametric images based on the frame images. 
     Step  912  updates parametric images (e.g., the Ki an Dv images) for an organ based on its respective assigned kinetics model using the frame emission images. For each voxel, a line or curve (depending on the respective model assigned to each organ) is fit to the frame image data for that voxel over all of the time points, and the parameter values (e.g., Ki and Dv) are determined. 
     At step  914 , the updates to the parametric images in step  912  are repeated for each organ. 
     At step  916 , the main loop from step  908  to  916  is repeated until a desired number of iterations have been performed. 
     At step  918 , once the desired number of iterations are performed, the respective parametric images for each organ are output. 
       FIG. 10  shows an exemplary embodiment for applying different kinetic models to respective organs, where the organs include at least the aorta. 
     At step  1000 , the dynamic sinogram data, organ segmentation map, attenuation map, and the blood input function are input to the system. In some embodiments, the scanner is operated in continuous bed motion (CBM) mode. In other embodiments, step-and-scan mode is used. In some embodiments, the sinogram data and organ segmentation map can be captured using a scanner having a PET or SPECT acquisition scanner and a CT or MRI scanner. In other embodiments, the sinogram data and organ segmentation map can be previously stored data accessed from a non-transitory, machine-readable storage medium. 
     At step  1002 , the system calculates the blood tracer concentration C B (t) and the integral of C B (t) for each time point, for each axial slice of the scan. In some embodiments, the method to calculate image slice reference time of different scan passes for parametric PET are based on finely sampled “bed tags”. Bed tags are coordinate pairs accurately encoding position and time information of the bed throughout the scan. In a system scanning in CBM mode, bed tags can be recorded periodically, providing an accurate record of position versus time regardless of bed speed and acceleration. In other embodiments, the system scans in step-and-shoot mode. 
     At step  1004 , each parametric image (e.g., Ki and DV) is initialized. For example, all voxels for each parametric image can initially be set to a uniform value (e.g., all black, all white, or all gray. 
     Steps  1005  to  1016  perform the main loop. 
     Steps  1005  and  1007  constitute a regularization step. At step  1005  the system calculates the mean parameter values for each parameter (e.g., Ki, DV) for the voxels in each organ. In the example of  FIG. 10 , the mean parameter values are assigned for the aorta. Ki and DV are calculated for each voxel using equations (6) and (7), respectively: 
     
       
         
           
             
               
                 
                   
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     if voxel j belongs to aorta in anatomy map, the means are calculated by:
 
 K   ij =mean( K   ij (aorta))
 
 DV   k =mean( DV   j (aorta))
 
     At step  1007 , the mean parameter values computed over all the voxels is assigned to each voxel in each organ. In the example of  FIG. 10 , each voxel is set to the mean parameter values Ki and DV computed in step  1005 . 
     At step  1008 , the system calculates frame emission images using (the Patlak) equation (8).
 
 x   j ( t,K   i     j     ,DV   j )= K   i     j   ∫ o   t   C   p (τ) dτ+DV   j   C   p ( t )  (8)
 
     The frame emission images include a respective SUV image for each time point at which the sinogram data are collected. The first time step  1008  is performed, the frame emission images are calculated using the initial parametric images from step  1004 . Subsequently, each iteration of step  1008  is performed using the parametric images from the previous iteration of the main loop (steps  1005 - 1016 ). 
     At step  1010 , each frame image (SUV images corresponding to each collection time point during the scan) is updated based on the estimation from step  1008 . The updates are performed according to equation (9) 
     
       
         
           
             
               
                 
                   
                     
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     Steps  1012  and  1014  form an inner loop to perform kinetic modeling. 
     Step  1012  updates parametric images (e.g., the Ki an Dv images) for an organ based on its respective assigned kinetics model using the frame emission images. For each voxel, a line or curve is fit to the frame image data for that voxel over all of the time points, and the parameter values (e.g., Ki and Dv) are determined. 
     At step  1014 , the updates to the parametric images in step  1012  are repeated for each organ. 
     At step  1016 , the main loop from step  1008  to  1016  is repeated until a desired number of iterations have been performed. 
     At step  1017 , once the desired number of iterations are performed, the processor outputs Ki and DV parametric images. 
       FIGS. 11A-11E  compare parametric images of the same subject before and after application of the aorta anatomy map. 
       FIG. 11A  shows a sagittal view CT image of the subject. The aorta is labeled and is readily distinguished from surrounding organs. The CT image of  FIG. 11A  has low noise. 
       FIG. 11B  shows the sagittal view Ki parametric image of the same subject, without applying the aorta map information of  FIG. 11A . The aorta is labeled in  FIG. 11B , but the image contains a large amount of noise. 
       FIG. 11C  shows the sagittal view Ki parametric image of the same subject, after applying the aorta map information of  FIG. 11A . The aorta is labeled in  FIG. 11C . Noise is substantially reduced relative to  FIG. 11B , and the image quality of the aorta is improved. 
       FIG. 11D  shows the sagittal view DV parametric image of the same subject, without applying the aorta map information of  FIG. 11A . The aorta is labeled in  FIG. 11D . The image contains a large amount of noise. 
       FIG. 11E  shows the sagittal view DV parametric image of the same subject, after applying the aorta map information of  FIG. 11A . The aorta is labeled in  FIG. 11E . Noise is substantially reduced, and the image quality of the aorta is improved. 
       FIGS. 12A-12D  show a similar improvement in parametric images of the liver obtained by applying the liver map to PET parametric image processing. 
       FIG. 12A  shows the anterior view Ki parametric image of the subject, without applying the liver map. The liver is labeled in  FIG. 12A , but the image contains a large amount of noise. 
       FIG. 12B  shows the sagittal view Ki parametric image of the same subject, after applying the liver map information. The liver is labeled in  FIG. 12B . Noise is substantially reduced relative to  FIG. 12A , and the image quality of the liver is improved. 
       FIG. 12C  shows the anterior view DV parametric image of the subject, without applying the liver map. The liver is labeled in  FIG. 12C , but the image contains a large amount of noise. 
       FIG. 12D  shows the sagittal view DV parametric image of the same subject, after applying the liver map information. The liver is labeled in  FIG. 12D . Noise is substantially reduced relative to  FIG. 12C , and the image quality of the liver is improved. 
     Thus, the signal to noise ratio in the aorta or liver region of parametric images can be improved by including the anatomy information of the aorta or liver (or other organ of interest) in the nested Patlak image reconstruction. The techniques described above can be applied to parametric imaging of other organs. 
     The anatomy map can also be used in other data correction methods, such as motion correction, scatter correction, and point spread function. In various embodiments, the anatomy map can also be used for the following aspects, either alone, or in any combination: 
     (a) Applying respectively different regularization strength over different organs in maximum a posterior (MAP) image reconstruction; 
     (b) Applying data correction algorithms adaptively. For example, we can turn off point spread function (PSF) off in the brain protocol and turn on PSF in the whole body protocol. The system can apply different correction methods (e.g., point spread function, scatter scaling) to different regions of the human body. 
     (c) Applying different PSF width to different parts of the human body. For example, for the brain, the method can use a smaller point spread function, and for the torso region, use a larger width of the point spread function. The radiologist can use a smaller width of point spread function for regions with large amounts of detail (e.g., the brain) to see less blurring in the brain. For the torso, the radiologist can apply a point spread function with a larger width to reduce noise where there is less detail, and reduce noise more. 
     (d) Applying motion correction more intelligently. For motion correction, once the segmentation map is obtained, the system can associate every voxel with the correct organ to which it belongs. The system can apply motion correction for voxels in organs likely to have motion, and omit motion correction for organs which are less likely to have motion. For example, if the patient  4  is stationary on the bed  3 , the brain does not have much motion, but the lung and heart have substantial motion during respiration, so the system can apply motion correction to the voxels assigned to organs in the torso (e.g., lung and heart), but not use motion correction for voxels assigned to the brain. 
     (e) Applying anatomy information regarding lesions. If lesion information is available, the system can include lesion information in the anatomy map, and can reconstruct image region better. For example, the lesion (e.g., malignant tumor) can be treated as a separate organ in the anatomy map, and the system can apply a kinetic model to the lesion different from the kinetic model used for the organ on which the lesion is located. The system can thus obtain more accurate blood activity information with respect to the lesion. 
     The methods and system described herein may be at least partially embodied in the form of computer-implemented processes and apparatus for practicing those processes. The disclosed methods may also be at least partially embodied in the form of tangible, non-transitory machine readable storage media encoded with computer program code. The media may include, for example, RAMs, ROMs, CD-ROMs, DVD-ROMs, BD-ROMs, hard disk drives, flash memories, or any other non-transitory machine-readable storage medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the method. The methods may also be at least partially embodied in the form of a computer into which computer program code is loaded and/or executed, such that, the computer becomes a special purpose computer for practicing the methods. When implemented on a general-purpose processor, the computer program code segments configure the processor to create specific logic circuits. The methods may alternatively be at least partially embodied in a digital signal processor formed of application specific integrated circuits for performing the methods. 
     Although the subject matter has been described in terms of exemplary embodiments, it is not limited thereto. Rather, the appended claims should be construed broadly, to include other variants and embodiments, which may be made by those skilled in the art.