Geometric biopsy plan optimization

Abstract

The present invention is directed to a method for calculating tumor detection probability of a biopsy plan and for generating a 3D biopsy plan that maximizes tumor detection probability. A capsule shaped volume is modeled to represent the volume that a biopsy core may sample. An optimization method is used to generate a 3D biopsy plan that maximizes probability of tumor detection for predefined biopsy core numbers and length. Risk of detecting insignificant tumors, also determined by size, and probability of a false negative result is automatically calculated. The present invention also includes a method to determine number and length of biopsy cores required for individual patients determined by the balance of the insignificant/significant probability of detection, prostate size and shape, based upon the previously explained 3D biopsy plan generation method.

1 . A method of calculating tumor detection probability of a biopsy plan comprising:
 calculating significant and insignificant tumor detection probability;   generating a three-dimensional biopsy plan that increases the probability of the significant and insignificant tumor detection probability;   calculating probability of a false negative detection of tumor using the three-dimensional biopsy plan to create a revised three-dimensional biopsy plan; and   determining a number and length of biopsy cores required to execute the revised three-dimensional biopsy plan.   
     
     
         2 . The method of  claim 1  further comprising implementing the method using a non-transitory computer readable medium. 
     
     
         3 . The method of  claim 1  further comprising calculating tumor detection probability with steps comprising:
 setting a bounding box for a tumor detection area and a voxel size to discretize this volume at a predetermined level of resolution; 
 iterating through all voxels; 
 checking if a voxel center is within the tumor detection area, and if so add it to a set Γ; 
 iterating through all voxels of set Γ; 
 verifying if the voxel center falls within any of the biopsy cores of a set Π; 
 counting the voxel with a center that falls within any of the biopsy cores of set Π as sampled by adding it to a set Ω; and 
 calculating tumor prediction probability as the ratio of the number of elements of the Ω and Γ sets. 
 
     
     
         4 . The method of  claim 1  further comprising calculating tumor detection probability with steps comprising:
 setting a volume of a tumor detection area and a voxel size to discretize the volume of the tumor detection area at a predetermined level of resolution to a set of voxels Γ; 
 defining the tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of the tumor to be detected; 
 iterating through all voxels of F and checking if a voxel center is within the tumor detection area of the biopsy cores of the plan; 
 adding the voxel center to the sampled voxel set Ω; and 
 calculating tumor prediction probability as the ratio of the number of elements of the detected voxel set Ω and tumor search area voxel set Γ. 
 
     
     
         5 . The method of  claim 1  further comprising detecting tumors in the prostate gland. 
     
     
         6 . The method of  claim 1  further comprising detecting tumors in any organ with a boundary that is segmentable as a surface. 
     
     
         7 . The method of  claim 1  further comprising representing the biopsy cores as a capsule with a cylindrical volume having hemispherical end caps. 
     
     
         8 . The method of  claim 1  further comprising setting a tumor detection area. 
     
     
         9 . The method of  claim 1  further comprising generating the three-dimensional biopsy plan for significant tumors for a predefined number of biopsy cores and lengths. 
     
     
         10 . The method of  claim 1  further comprising generating the three-dimensional biopsy plan for insignificant tumors for a predefined number of biopsy cores and lengths. 
     
     
         11 . The method of  claim 1  further comprising defining a tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of a tumor to be detected. 
     
     
         12 . A system for calculating tumor detection probability of a biopsy plan comprising:
 a source of image data capable of reconstructing a target organ in three-dimensions;   a non-transitory computer readable medium programmed for:   calculating significant and insignificant tumor detection probability from the image data;   generating a three-dimensional biopsy plan that increases the probability of the significant and insignificant tumor detection probability;   calculating probability of a false negative detection of tumor using the three-dimensional biopsy plan to create a revised three-dimensional biopsy plan; and   determining a number and length of biopsy cores required to execute the revised three-dimensional biopsy plan.   
     
     
         13 . The system of  claim 12  further comprising a computing device. 
     
     
         14 . The system of  claim 12  further comprising calculating tumor detection probability with steps comprising:
 setting a bounding box for a tumor detection area and a voxel size to discretize this volume at a predetermined level of resolution; 
 iterating through all voxels; 
 checking if a voxel center is within the tumor detection area, and if so add it to a set Γ; 
 iterating through all voxels of set Γ; 
 verifying if the voxel center falls within any of the biopsy cores of a set Π; 
 counting the voxel with a center that falls within any of the biopsy cores of set Π as sampled by adding it to a set Ω; and 
 calculating tumor prediction probability as the ratio of the number of elements of the Ω and Γ sets. 
 
     
     
         15 . The system of  claim 12  further comprising calculating tumor detection probability with steps comprising:
 setting a volume of a tumor detection area and a voxel size to discretize the volume of the tumor detection area at a predetermined level of resolution to a set of voxels Γ; 
 defining the tumor detection area of a biopsy core as a capsule surrounding the biopsy core with a cylindrical volume having hemispherical end caps of the diameter of the tumor to be detected; 
 iterating through all voxels of Γ and checking if a voxel center is within the tumor detection area of the biopsy cores of the plan; 
 adding the voxel center to the sampled voxel set Ω; and 
 calculating tumor prediction probability as the ratio of the number of elements of the detected voxel set Ω and tumor search area voxel set Γ. 
 
     
     
         16 . The system of  claim 12  further comprising detecting tumors in the prostate gland. 
     
     
         17 . The system of  claim 12  further comprising detecting tumors in any organ with a boundary that is segmentable as a surface. 
     
     
         18 . The system of  claim 12  further comprising representing the biopsy cores as a capsule with a cylindrical volume having hemispherical end caps. 
     
     
         19 . The system of  claim 12  further comprising setting a tumor detection area. 
     
     
         20 . The system of  claim 12  further comprising a biopsy device.