Abnormality finding in projection images

Abstract

The present invention includes a utility for determining the severity of a stenosis in a blood vessel. In one aspect, a method for improving DSA image quality includes: (1) registration of the mask and bolus images prior to subtracting procedure to reduce the artifacts from misalignment; (2) enhancement of the registered DSA image by an anisotropic diffusion technique and a nonlinear normalization technique; and (3) detecting the boundary of blood vessel and quantitatively measuring percentage stenosis, which may be done automatically.

1 . A method for use in the quantitative measurement of a blood vessel blockage, comprising:
 operating an imaging system to obtain a series of mask images and a series of bolus images corresponding to a region of interest;   operating a processor to register the mask images to the bolus images to generate a registered mask image and subtract the registered mask image form the bolus image to produce a motion compensated DSA image;   operating the processor to process the DSA image to enhance contrast in at least the region of interest of the DSA image;   receiving a user input identifying a lumen within the region of interest; and   based on the user input, operating the processor to autonomously identify a boundary of the lumen and calculate a stenosis percentage for the lumen.   
     
     
         2 . The method of  claim 1 , wherein registering the series of mask images and the series of bolus images further comprises averaging said series to produce an average mask image and an average bolus image, wherein the average mask image is registered to the average bolus image. 
     
     
         3 . The method of  claim 1 , wherein registering comprises applying an inverse-consistency constraint to a deformation of the mask frame to the bolus frame. 
     
     
         4 . The method of  claim 3 , wherein the inverse-consistency constraint comprises a B-spline parameterization. 
     
     
         5 . The method of  claim 1 , wherein enhancing the region of inertest comprises:
 operating the processor to diffuse the DSA image to remove background noise from the DSA image.   
     
     
         6 . The method of  claim 5 , further comprising:
 operating the processor to perform a non-linear normalization on the DSA image, wherein a contrast between the lumen and the background of the DSA image is increased.   
     
     
         7 . The method of  claim 1 , wherein receiving the user input identifying the lumen within the region of interest comprises one of:
 receiving at least two user selected points associated with a centerline of said lumen;   receiving a plurality of user selected points associated with edges of the lumen.   
     
     
         8 . The method of  claim 7 , wherein operating the processor to autonomously identify a boundary of the lumen and calculate a stenosis percentage for the lumen further comprises at least one of:
 initial edge detection;   edge refinement; and   lesion measurement.   
     
     
         9 . The method of  claim 8 , wherein initial edge detection based on the at least two points associated with the centerline of the lumen, comprises
 defining an initial centerline in the lumen;   projecting a plurality of perpendicular lines relative to the centerline; and   for each perpendicular line, identifying a boundary point between the lumen and the background based on gradients calculated along said perpendicular line, wherein a plurality of the boundary points define an initial edge of the lumen.   
     
     
         10 . The method of  claim 8 , wherein edge refinement comprises fitting a contour to the initial edge, wherein the contour is smoothed to define a refined edge. 
     
     
         11 . The method of  claim 8 , wherein lesion measurement comprises:
 using the processor to calculate
 (a) minimum and maximum diameters of said lumen; 
 (b) lesion length; 
 (c) a stenosis reference (“normal”) diameter; and 
 (d) percent stenosis. 
   
     
     
         12 . The method of  claim 1 , wherein the steps of operating a processor comprise operating a GPU based processor. 
     
     
         13 . The method of  claim 1 , wherein said series of mask and bolus frames are acquired using at least one of the following imagining modalities:
 X-ray;   CT;   MRI; and   ultrasound.   
     
     
         14 . The method of  claim 1 , wherein operating a processor to register the mask image to the bolus images to generate a registered mask image comprises:
 registering a 3-D mask image to a 3-D bolus image to generate a 3-D registered mask image.   
     
     
         15 . The method of  claim 1 , wherein operating a processor to register the mask image to the bolus images to generate a registered mask image comprises:
 performing an initial registration at a reduced resolution to align global structures; and   performing a secondary registration to align local structures in the region of interest.   
     
     
         16 . A system for use in the quantitative measurement of a blood vessel blockage, comprising:
 an imaging system operative to obtain a series of mask images and a series of bolus images corresponding to a region of interest;   an image processing system operative to:
 register the mask image to the bolus images and generate a registered mask image; 
 subtract the registered mask image form the bolus image to produce a motion compensated DSA image; 
 process the DSA image to enhance contrast in at least the region of interest of the DSA image; 
 receive a user input identifying a lumen within the region of interest; and 
 based on the user input, operating the processor to autonomously identify a boundary of the lumen and calculate a stenosis percentage for the lumen. 
   
     
     
         17 . The system of  claim 16 , wherein the imaging system comprises an X-ray imaging system. 
     
     
         18 . A method for use in the quantitative measurement of a blood vessel blockage, comprising:
 operating an imaging system to obtain a series of mask images and a series of bolus images corresponding to a region of interest;   operating a processor to register the mask images to the bolus images to generate a registered mask image and subtract the registered mask image form the bolus image to produce a motion compensated DSA image;   receiving a user input identifying a lumen within the region of interest; and   based on the user input, operating the processor to autonomously calculate:
 (a) minimum and maximum diameters of said lumen; 
 (b) lesion length; 
 (c) a stenosis reference (“normal”) diameter; and 
 (d) percent stenosis of the lumen. 
   
     
     
         19 . The method of  claim 18 , further comprising identifying edges for the lumen by:
 defining an initial centerline in the lumen;   projecting a plurality of perpendicular lines relative to the centerline; and   for each perpendicular line, identifying a boundary point between the lumen and the background based on gradients along said perpendicular line, wherein a plurality of the boundary points define an edge of the lumen.