3-d ultrasound imaging device and methods

Abstract

The present disclosure includes a method of diagnosing a condition of bodily tissue using a computer, the method comprising comparing, using a computer, a 3D tissue model derived from an ultrasound scan of the bodily tissue with at least one 3D tissue model having common tissue with the bodily tissue, and diagnosing a condition of the bodily tissue responsive to comparing the 3D tissue models.

What is claimed is: 
     
         1 - 20 . (canceled) 
     
     
         21 . A method of diagnosing a condition of bodily tissue using a computer, the method comprising:
 comparing, using a computer, a 3D tissue model derived from an ultrasound scan of the bodily tissue with at least one 3D tissue model having common tissue with the bodily tissue;   diagnosing a condition of the bodily tissue responsive to comparing the 3D tissue models; and   displaying a visual output responsive to diagnosing the condition.   
     
     
         22 . The method of  claim 21 , wherein comparing the 3D tissue models includes comparing the 3D tissue model derived from the ultrasound scan of the bodily tissue with a plurality of 3D tissue models each having common tissue with the bodily tissue. 
     
     
         23 . The method of  claim 21 , wherein:
 the at least one 3D tissue model comprises a plurality of 3D tissue models each having common tissue with the bodily tissue;   the comparing, using the computer, includes using a neural network to compare the 3D tissue model derived from the ultrasound scan of the bodily tissue with the plurality of 3D tissue models each having common tissue with the bodily tissue; and   the diagnosing the condition of the bodily tissue includes outputting a diagnosis by the neural network responsive to the comparing.   
     
     
         24 . The method of  claim 23 , wherein the diagnosing includes an indication that the bodily tissue is at least one of normal, torn, degenerative, and fractured. 
     
     
         25 . The method of  claim 23 , wherein the plurality of 3D tissue models of the neural network include a training data set comprising a plurality of 3D tissue models each having a known diagnosis. 
     
     
         26 . The method of  claim 21 , wherein:
 the 3D tissue model derived from the ultrasound scan of the bodily tissue includes both bone and cartilage; and   the at least one 3D tissue model having common tissue with the bodily tissue includes both bone and cartilage.   
     
     
         27 . The method of  claim 21 , wherein:
 the at least one 3D tissue model having common tissue with the bodily tissue comprises a 3D baseline tissue model;   the comparing includes identifying portions of the 3D tissue model derived from the ultrasound scan of the bodily tissue that exceed a predetermined statistical variance limit with respect to the 3D baseline tissue model.   
     
     
         28 . The method of  claim 21 , wherein:
 the 3D tissue model derived from the ultrasound scan of the bodily tissue includes a fluid collection in cases of internal hemorrhage;   the fluid collection is the result of at least one of blunt trauma and perforating trauma; and,   diagnosing the condition includes identifying whether the fluid collection is the result of at least one of blunt trauma and perforating trauma.   
     
     
         29 . A method of diagnosing, using a computer, a condition of a bodily tissue associated with an internal hemorrhage causing an abnormal fluid collection, the method comprising:
 evaluating, using a computer, a 3D tissue model derived from an ultrasound scan of the bodily tissue associated to identify an abnormal fluid collection; and,   diagnosing a condition of the bodily tissue responsive to evaluating the 3D tissue model including identifying whether the fluid collection is the result of at least one of blunt trauma and perforating trauma.   
     
     
         30 . The method of  claim 29 , wherein evaluating the 3D tissue model includes correlating a location of the fluid collection with at least one of an associated organ or vascular injury. 
     
     
         31 . The method of  claim 29 , wherein the 3D tissue model derived from the ultrasound scan of the bodily tissue visualizes the fluid collection using a volume imaging mode. 
     
     
         32 . A method of diagnosing a condition of bodily tissue using a computer, the method comprising:
 using a neural network to process raw ultrasound data generated during an ultrasound scan of the bodily tissue to at least one of classify a new case concerning the bodily tissue and categorize an injury to the bodily issue; and,   diagnosing a condition of the bodily tissue responsive to at least one of classifying the new case and categorizing the injury.   
     
     
         33 . The method of  claim 32 , wherein classifying the new case includes classifying the new case as at least one of a trauma condition, a soft tissue damage condition, and a bone fracture condition. 
     
     
         34 . The method of  claim 32 , wherein the neural network is trained with a training set of vectors, where each of the vectors include 3D ultrasound data. 
     
     
         35 . The method of  claim 32 , wherein the neural network is trained to differentiate between normal tissue anatomy and abnormal tissue anatomy. 
     
     
         36 . The method of  claim 32 , wherein the neural network is trained with a training set of vectors, where each of the vectors include 3D ultrasound data.