DRR generation using a non-linear attenuation model

Abstract

A method and system are presented for generating a DRR of an anatomical region so that the visibility within the DDR of one or more skeletal reference structures is enhanced. 3D scan data, which have been obtained from a 3D scan of the object conducted at a 3D scan energy level, are provided. The 3D scan data are modified to compensated for a difference between the ratio of bone-to-tissue attenuation at the 3D scan energy level, and the ratio of bone-to-tissue attenuation at the intensity of the imaging beam which the DRR emulates. The modified 3D scan data are related to the raw 3D scan data by a non-linear, exponential relationship. A plurality of hypothetical rays are cast through the modified 3D scan data, from the known geometry of the imaging beam. The 3D scan data are integrated along each hypothetical ray, and the integrated values are projected onto an imaging plane.

1 . A method of generating a digitally reconstructed radiograph (DRR) from 3D scan data of an object so as to increase the visibility within the DRR of one or more structures in the object, the DRRs representing a radiographic projection image of the object that would be obtained with a 2D imaging beam of a known intensity, origination position, and angle if the object were positioned as shown in the 3D scan data, the method comprising:
 providing 3D scan data of the object, the 3D scan data resulting from a 3D scan conducted at a 3D scan energy level;   modifying the 3D scan data to compensate for a difference between the attenuation of the one or more structures at the scan energy level and the attenuation of the one or more structures at the known intensity of the 2D imaging beam;   casting a plurality of hypothetical rays through the modified 3D scan data from the known intensity, the known origination position, and angle of the 2D imaging beam; and   integrating the 3D scan data along each hypothetical ray, and projecting the integrated value of the 3D scan data onto an imaging plane.   
   
   
       2 . The method in accordance with  claim 1 , wherein the 3D scan data comprise at least one of CT scan data, MRI scan data, PET scan data, and ultrasound scan data. 
   
   
       3 . The method in accordance with  claim 1 , wherein the 3D scan data comprise a plurality of CT numbers representing the image intensity of corresponding 3D CT voxels, each CT voxel representing a corresponding volume element of the object, each CT number representing the attenuated intensity of an x-ray CT beam that has been generated at a CT scan energy level and that has traversed the corresponding volume element of the object. 
   
   
       4 . The method in accordance with  claim 3 , wherein each CT voxel is disposed within one of a plurality of axial voxel slices, each axial voxel slice representing a corresponding axial slice of the object. 
   
   
       5 . The method in accordance with  claim 4 , wherein the act of integrating the CT numbers along the hypothetical ray comprises:
 performing a bi-linear interpolation for the CT voxels encountered by the ray; and   for each voxel of interest, performing a one-dimensional polynomial interpolation over the voxel of interest and for voxels on each adjacent voxel slice.   
   
   
       6 . The method in accordance with  claim 1 , wherein the object comprises an anatomical region, and the one or more structures comprise at least one reference structure within the anatomical region, and at least one target within the anatomical region. 
   
   
       7 . The method in accordance with  claim 6 , wherein the reference structure comprises a skeletal structure. 
   
   
       8 . The method in accordance with  claim 6 , wherein the anatomical region comprises at least one of: a cervical region of the spine; a thoracic region of the spine; and a lumbar region of the spine. 
   
   
       9 . The method in accordance with  claim 6 , wherein the anatomical region comprises bone and tissue, and wherein the act of modifying the 3D scan data compensates for a difference between the ratio of bone-to-tissue attenuation at the energy level of the 3D scan, and the ratio of bone-to-tissue attenuation at the known intensity of the imaging beam. 
   
   
       10 . A system for generating a digitally reconstructed radiograph (DRR) from 3D scan data of an object to increase the visibility within the DRR of one or more structures in the object, the DRRs representing a radiographic projection image of the object that would be obtained with a 2D imaging beam of a known intensity, origination position, and angle if the object were positioned as shown in the 3D scan data, the system comprising:
 means for providing 3D scan data of the object, wherein the 3D scan data are obtained from a 3D scan conducted at a 3D scan energy level;   a scan data modifier configured to compensate for a difference between the attenuation of the one or more structures at the 3D scan energy level, and the attenuation of the one or more structures at the known intensity of the 2D imaging beam; and   a DRR generator configured to generate at least one DRR of the object from the 3D scan data, the DRR generator comprising:   a ray casting subsystem configured to cast a plurality of hypothetical rays through the modified 3D scan data at the known intensity of the 2D imaging beam and from the known origination position and angle of the 2D imaging beam;   a CT number integrator configured to integrate the 3D scan data along each hypothetical ray; and   a projector configured to project the integrated values of the 3D scan data onto an imaging plane.   
   
   
       11 . The system in accordance with  claim 10 , wherein the 3D scan data comprise at least one of CT scan data, MRI scan data, PET scan data, and ultrasound scan data. 
   
   
       12 . The system in accordance with  claim 10 , wherein the 3D scan data comprise a plurality of CT numbers representing the image intensity of corresponding 3D CT voxels, each CT voxel representing a corresponding volume element of the object, each CT number representing the attenuated intensity of an x-ray CT beam that has been generated at a CT scan energy level and that has traversed the corresponding volume element of the anatomical region. 
   
   
       13 . The system in accordance with  claim 12 , wherein each CT voxel is disposed within one of a plurality of axial voxel slices, each axial voxel slice representing a corresponding axial slice of the object. 
   
   
       14 . The system in accordance with  claim 12 , wherein the CT number integrator comprises:
 a bi-linear interpolator configured to perform bi-linear interpolation on the CT voxels encountered by each ray; and   a polynomial interpolator configured to perform, for each voxel of interest within a voxel slice, a one-dimensional polynomial interpolation over the voxel of interest and for voxels on each adjacent voxel slice.   
   
   
       15 . The system in accordance with  claim 10 , wherein the object comprises an anatomical region, and the one or more structures comprise at least one reference structure within the anatomical region, and at least one target within the anatomical region. 
   
   
       16 . The system in accordance with  claim 15 , wherein the reference structure comprises a skeletal structure. 
   
   
       17 . The system in accordance with  claim 15 , wherein the anatomical region comprises at least one of: a cervical region of the spine; a thoracic region of the spine; and a lumbar region of the spine. 
   
   
       18 . The system in accordance with  claim 15 , wherein the anatomical region comprises at least some bone and at least some tissue, and wherein the scan data modifier is configured to compensate for a difference between the ratio of bone-to-tissue attenuation at the energy level of the 3D scan, and the ratio of bone-to-tissue attenuation at the known intensity of the imaging beam. 
   
   
       19 . The system in accordance with  claim 10 , wherein the means for providing 3D scan data comprises at least one of a CT scanner, an MRI scanner, a PET scanner, and an ultrasound scanner. 
   
   
       20 . An image registration system for registering at least one 2D image of an anatomical region with previously generated 3D scan data of the anatomical region, the anatomical region including at least one treatment target and at least one reference structure, wherein the 2D image is generated in near real time by detecting one or more radiographic imaging beams after the imaging beams have traversed at least a portion of the anatomical region, the imaging beams having known intensities and known positions and angles relative to the anatomical region, the system comprising:
 means for providing the 3D scan data of the anatomical region;   a scan data modifier configured to modify the 3D scan data so as to compensate for a difference between the ratio of bone-to-tissue attenuation at the energy level of the 3D scan, and the ratio of bone-to-tissue attenuation at the energy level of the imaging beam used for the near real-time 2D image;   a digitally reconstructed radiograph (DRR) generator configured to generate at least one DRR of the anatomical region, using the 3D scan data and the known locations, angles, and intensities of the imaging beams;   a motion field generator configured to generate a 3D full motion field within the DRR by estimating a plurality of local motion fields within the DRR; and   a parameter determiner configured to determine from the 3D full motion field a set of non-rigid transformation parameters that represent the difference in the position and orientation of the treatment target as shown in the 2D image, as compared to the position and orientation of the treatment target as shown in the DRR; and   means for adjusting the position and orientation of the treatment target to correct for the difference.   
   
   
       21 . A system in accordance with  claim 20 , wherein the DRR generator comprises:
 a ray casting subsystem configured to cast a plurality of hypothetical rays through the modified 3D scan data at the known intensity and from the known origination position and angle of the imaging beam;   a CT number integrator configured to integrate the 3D scan data along each hypothetical ray; and   a projector configured to project the integrated values of the 3D scan data onto an imaging plane.   
   
   
       22 . The system in accordance with  claim 20 , wherein the motion field generator comprises:
 a mesh grid generator configured to generate in the 2D image a mesh grid having a plurality of mesh nodes;   a nodal motion estimator configured to estimate, for each mesh node in the 2D image, at least one nodal motion vector that describes a matching of the mesh node with a corresponding mesh node in the DRR;   
     and
 a motion field interpolator configured to determine a local motion vector for each of a plurality of points of interest within the 2D image, by interpolating from the nodal motion vectors of the mesh nodes that surround each point of interest. 
 
   
   
       23 . The system in accordance with  claim 20 , wherein the mesh grid generator is configured to repeat, at each of a plurality of mesh resolution levels, the act of generating of the mesh grid;
 wherein nodal motion estimator is configured to repeat, at each of the plurality of mesh resolution levels, the act of estimating the at least one nodal motion vector for each mesh node; and   wherein the interpolated nodal motion vectors comprise nodal motion vectors that have been estimated at a final one of the plurality of mesh resolution levels.   
   
   
       24 . The system in accordance with  claim 20 , further comprising a motion field reconstructor configured to reconstruct the nodal motion vector for a mesh node if any nodal motion vector estimated for any mesh node is unreliable. 
   
   
       25 . A method of registering a near real-time 2D x-ray image of an anatomical region with 3D scan data representative of a preoperative image of the anatomical region, the anatomical region including at least one reference structure and at least one treatment target, the method comprising:
 modifying the 3D scan data, and reconstructing from the modified 3D scan data at least one digitally reconstructed radiograph (DRR);   generating a 3D motion field by estimating one or more 2D local motion fields within the DRR, and constructing a full 3D motion field from the local motion fields; and   determining from the full 3D motion field a set of non-rigid transformation parameters that represent the difference in the position and orientation of the reference structure and the treatment target, as shown in the 2D x-ray image, as compared to the position and orientation of the reference structure and the treatment target, as shown in the DRR; and   adjusting the position and orientation of the treatment target to correct for the difference.   
   
   
       26 . The method in accordance with  claim 27 , wherein the anatomical region comprises at least some bone and at least some tissue, and wherein the 3D scan data are modified to compensate for a difference between the ratio of bone-to-tissue attenuation at the energy level of the 3D scan, and the ratio of bone-to-tissue attenuation at the energy level of the x-ray imaging beam used for the near real-time x-ray image 
   
   
       27 . The method in accordance with  claim 25 , wherein the act of generating the 3D motion field comprises:
 generating in the 2D x-ray image a mesh grid having a plurality of mesh nodes;   for each mesh node in the 2D x-ray image, estimating at least one nodal motion vector that describes a matching of the mesh node with a corresponding mesh node in the DRR; and   determining a local motion vector for each of a plurality of points of interest within the first image, by interpolating from the nodal motion vectors of the mesh nodes that surround the point of interest.   
   
   
       28 . The method in accordance with  claim 27 , further comprising repeating, at each of a plurality of mesh resolution levels, the acts of generating in the 2D x-ray image a mesh grid having a plurality of mesh nodes and estimating at least one nodal motion vector for each mesh node; and
 wherein the nodal motion vectors of the mesh nodes that surround the point of interest comprise nodal motion vectors that have been estimated at a final one of the plurality of mesh resolution levels.   
   
   
       29 . The method in accordance with  claim 28 , further comprising determining whether any nodal motion vector that was estimated for any mesh node is unreliable, and if so, reconstructing the nodal motion vector for the mesh node. 
   
   
       30 . The method in accordance with  claim 28 , further comprising passing, at each mesh resolution level, one or more nodal motion vectors that have been estimated at the current mesh resolution level onto a subsequent mesh resolution level. 
   
   
       31 . The method in accordance with  claim 28 , wherein at each mesh resolution level subsequent to the first mesh resolution level, the act of estimating the at least one nodal motion vector comprises:
 i) using the nodal motion vectors passed on from the previous mesh resolution level as estimates for nodal motion vectors for the first subset of mesh nodes;   ii) interpolating from the estimated nodal motion vectors for the first subset of mesh nodes to estimate the nodal motion vectors for the second subset of mesh nodes; and   iii) for each mesh node in both the first and second subsets in the first image, refining the nodal vector that was estimated for the mesh node.   
   
   
       32 . The method in accordance with  claim 31 , wherein refining the estimated nodal motion vector for a mesh node in the 2D x-ray image comprises:
 defining a block centered around the mesh node in the 2D x-ray image;   searching for a matching mesh node in the DRR that maximizes a similarity measure between the block in the 2D x-ray image and another block centered around the matching mesh node in the DRR; and   revising the local motion vector so that the nodal motion vector describes a mapping of the mesh node in the 2D x-ray image onto the matching mesh node in the DRR.