System and method for vascular border detection

Abstract

The present invention uses a radio frequency (RF) signal backscattered from vascular tissue to identify a border on a vascular image. Embodiments of the invention operate in accordance with a data gathering device connected to a computing device and a transducer via a catheter. The transducer is used to gather RF data backscattered from vascular tissue. The RF data is provided to the computing device via the data-gathering device. In one embodiment of the present invention, the computing device includes (i) a data storage device for storing tissue types and related parameters and (ii) an application. The application is used to convert the RF data into the frequency domain and to identify associated parameters. The parameters are compared to the parameters stored in the data storage device to identify the corresponding tissue type. This information is used, possibly with other border-related information, to determine a border on a vascular image.

1 . A method of identifying a border on an image of a vascular object, comprising: 
 acquiring RF data backscattered from vascular tissue;    transforming said RF data into the frequency domain;    identifying a plurality of parameters of said transformed RF data;    using said plurality of parameters and previously stored data to identify at least a first and second portion of said transformed RF data that corresponds to a blood and non-blood portion of said vascular tissue, respectively; and    determining said border on said image of said vascular object in accordance with said first and second portions of said transformed RF data.    
   
   
       2 . The method of  claim 1 , wherein said second portion of said vascular tissue corresponds to a plaque portion of said vascular tissue.  
   
   
       3 . The method of  claim 1 , wherein said step of determining said border further comprises determining the luminal border on said image of said vascular object in accordance with said first and second portions of said transformed RF data.  
   
   
       4 . The method of  claim 3 , wherein said steps of (i) using said plurality of parameters and previously stored data to identify a least a first and second portion of said transformed RF data and (ii) determining said border further comprises: 
 using said plurality of parameters and previously stored data to identify at least a third portion of said transformed RF data that corresponds to at least a third tissue type of said vascular tissue, said third tissue type being selected from a list of tissue types consisting of medial tissue and adventitial tissue; and    determining the medial-adventitial border on said image of said vascular object in accordance with said second and third portion of said transformed RF data.    
   
   
       5 . The method of  claim 4 , wherein said third tissue type corresponds to both medial and adventitial tissues.  
   
   
       6 . The method of  claim 1 , wherein said step of determining said border further comprises determining said border on said image of said vascular object in accordance with gradient information, said gradient information being derived from said RF data.  
   
   
       7 . The method of  claim 1 , further comprising the step of using other-border data from at least one other image of said vascular object to approximate said border on said image of said vascular object.  
   
   
       8 . The method of  claim 7 , wherein said step of determining said border further comprises determining said border on said image of said vascular object in accordance with gradient information, said gradient information being derived from said RF data.  
   
   
       9 . The method of  claim 6 , wherein said gradient information further comprises gradient-force data and gradient-border data.  
   
   
       10 . The method of  claim 1 , further comprising the step of filtering said first and second portions of said transformed RF data before they are used to determine said border.  
   
   
       11 . The method of  claim 10 , wherein said step of filtering further comprises filtering said second portion of said transformed RF data to reduce the amount of non-blood particles visible in an image of said second portion of said transformed RF data.  
   
   
       12 . The method of  claim 11 , wherein said step of filtering further comprises filtering said first portion of said transformed RF data to reduce the number of tissue types visible in an image of said first portion of said transformed RF data.  
   
   
       13 . The method of  claim 1 , wherein said step of determining said border further comprises determining said border on said image of said vascular object in accordance with spectral-force data and spectral-border data, said spectral-force data and said spectral-border data being derived from said transformed RF data.  
   
   
       14 . The method of  claim 1 , wherein said step of determining said border further comprises determining said border on said image of said vascular object in accordance with at least one algorithm, said at least one algorithm being selected from a list of algorithms consisting of a continuity algorithm, a curvature algorithm, and a relatedness algorithm.  
   
   
       15 . The method of  claim 1 , wherein said step of transforming said RF data further comprises transforming said RF data into the autoregressive (AR) frequency power spectrum.  
   
   
       16 . The method of  claim 1 , wherein said step of identifying a plurality of parameters further comprises identifying at least one parameter of said transformed RF data, said at least one parameter being selected from a list of parameters consisting of maximum power, minimum power, frequency at maximum power, frequency at minimum power, y intercept, slope, mid-band fit, and integrated backscatter.  
   
   
       17 . The method of  claim 1 , wherein said step of acquiring RF data further comprises acquiring RF data that is both backscattered from said vascular tissue and gated to electrocardiogram (ECG) information.  
   
   
       18 . The method of  claim 1 , wherein said step of identifying a plurality of parameters of said transformed RF data further comprises identifying at least one parameter of said RF data.  
   
   
       19 . The method of  claim 18 , wherein said at least one parameter of said RF data comprises tissue depth.  
   
   
       20 . A method of identifying at least one boundary on a vascular image, comprising: 
 acquiring RF data backscattered from vascular tissue;    transforming said RF data into the frequency domain;    identifying at least one parameter of said transformed RF data;    using said at least one parameter and previously stored data to identify at least one tissue type and transformed RF data corresponding thereto (corresponding RF data);    using said RF data to determine gradient information pertaining to said at least one boundary on said vascular image; and    using at least said gradient information and said corresponding RF data to determine said at least one boundary on said vascular image.    
   
   
       21 . The method of  claim 20 , wherein said step of using at least said gradient information and said corresponding RF data further comprises using at least said gradient information and said transformed RF data to determine said at least one boundary on said vascular image.  
   
   
       22 . The method of  claim 20 , wherein said at least one parameter is selected from a list of parameters consisting of maximum power, minimum power, frequency at maximum power, frequency at minimum power, y intercept, slope, mid-band fit, and integrated backscatter.  
   
   
       23 . The method of  claim 20 , wherein said step of using said gradient information and said corresponding RF data further comprises using other-boundary data to determine said at least one boundary on said vascular image.  
   
   
       24 . The method of  claim 23 , further comprising the step of using said corresponding. RF data to determine spectral-force data and spectral-boundary data, wherein at least said spectral-force data, said spectral-boundary data, and said gradient information are used to determine said at least one boundary on said vascular image.  
   
   
       25 . The method of  claim 23 , further comprising the step of using said gradient information to determine gradient-force data and gradient-border data, wherein at least said gradient-force data, said gradient-boundary data and corresponding RF data are used to determine said at least one boundary on said vascular image.  
   
   
       26 . The method of  claim 24 , further comprising the step of using said gradient information to determine gradient-force data and gradient-border data, wherein at least said gradient-force data, said spectral-force data, said gradient-border data, and said spectral-border data are used to determine said at least one boundary on said vascular image.  
   
   
       27 . The method of  claim 24 , further comprising the step of filtering said corresponding RF data before it is used to determine said spectral-force data and said spectral-boundary data.