Systems and methods for improved tractography images

Abstract

The present disclosure discusses systems and methods for identifying biomarkers that can help with the diagnosis, prognosis, and treatment choices of patients with neurodegenerative diseases. Diffusion based magnetic resonance imaging can often fail for patients with a neurodegenerative disease because parameters fractional anisotropy, mean diffusivity, and radial diffusivity are based on simple models that can fail in the presence of neurodegeneration, such as demyelination. The present disclosure discusses systems and methods that enhance dMRI images and enable tractography to be performed on images of a damaged nervous system. The damaged tracks identified by the present system can be used as a biomarker for the assessment of patients. In some implementations, the biomarkers are converted into clinical scales that can be used to compare patients to one another or over time.

What is claimed: 
     
         1 . A method comprising:
 receiving anatomical image data and diffusion-weighted data of a patient's nervous system;   generating a segment from the anatomical image data identifying a first volume of voxels;   generating enhanced diffusion-weighted data in the segment responsive to receiving the diffusion-weighted data;   generating a track comprising a second volume of voxels in the enhanced diffusion-weight diffusion-weighted data;   registering the segment identifying the first volume of voxels with the track in the enhanced diffusion-weight diffusion-weighted data, the segment located in the same space as the track, the segment configured for comparison with the track comprising the second volume of voxels; and   calculatingdetermining a damage score tofor the track based on the first volume of voxels, the second volume of voxels, and the segment registered with the track.   
     
     
         2 . The method of  claim 1 , wherein each of the one or more segments correspondthe segment corresponds to damage identified in the anatomical image data. 
     
     
         3 . The method of  claim 2 , wherein the damage comprises at least one of a brain lesion and a plaque. 
     
     
         4 . The method of  claim 1 , wherein generating the enhanced diffusion-weighted data comprises convolving the diffusion-weighted data with a kernel representing Brownian motion. 
     
     
         5 . The method of  claim 1 , wherein the track is one of a corpus callosum, an optical radiation, and a corticospinal tract. 
     
     
         6 . The method of  claim 1 , further comprising generating the track responsive to a track template. 
     
     
         7 . The method of  claim 1 , further comprising determining a number of voxels of the second volume of voxels contained within the segment. 
     
     
         8 . The method of  claim 7 , wherein calculatingdetermining the damage score is calculated responsive to the number of voxels of the second volume of voxels contained within the segment and the second volume of voxels. 
     
     
         9 . The method of  claim 1 , further comprising calculatingdetermining a clinical score responsive to the determined damage to the track. 
     
     
         10 . The method of  claim 1 , wherein the anatomical image data comprises one or more of T1, T2, HARDI, and MillfMRI data. 
     
     
         11 . A system comprising:
 an assessment engine comprising one or more processors and a processor readable medium storing instructions that when executed by the one or more processors cause the one or more processors to:
 receive anatomical image data and diffusion-weighted data of a patient's nervous system; 
 generate a segment from the anatomical image data identifying a first volume of voxels; 
 generate an enhanced diffusion-weighted data responsive to the diffusion-weighted data; 
 generate a track comprising a second volume of voxels in the enhanced diffusion-weight diffusion-weighted data; 
 register the segment identified as the first volume of voxels with the track in the enhanced diffusion-weight diffusion-weighted data, the segment located in the same space as the track, the segment configured for comparison with the track comprising the second volume of voxels; and 
 calculatedetermine a damage score tofor the track based on the first volume of voxels, the second volume of voxels, and the segment registered with the track. 
   
     
     
         12 . The system of  claim 11 , wherein each of the one or more segments correspondthe segment corresponds to damage identified in the anatomical image data. 
     
     
         13 . The system of  claim 12 , wherein the damage comprises at least one of a brain lesion and a plaque. 
     
     
         14 . The system of  claim 12 , wherein the track is one of a corpus callosum, an optical radiation, and a corticospinal tract. 
     
     
         15 . The system of  claim 11 , wherein execution of the instructions further cause the one or more processors to convolve the diffusion-weighted data with a kernel representing Brownian motion to generate the enhanced diffusion-weighted data. 
     
     
         16 . The system of  claim 11 , wherein execution of the instructions further cause the one or more processors to generate the track responsive to a track template. 
     
     
         17 . The system of  claim 11 , wherein execution of the instructions further cause the one or more processors to determine a number of voxels of the second volume of voxels contained within one of the segment. 
     
     
         18 . The system of claim  11   17 , wherein execution of the instructions further cause the one or more processors to calculatedetermine the damage responsive to the number of voxels of the second volume of voxels contained within one of the segment and the second volume of voxels. 
     
     
         19 . The system of  claim 11 , wherein execution of the instructions further cause the one or more processors to calculatedetermine a clinical score responsive to the determined damage to the track. 
     
     
         20 . The system of  claim 11 , wherein the anatomical image data comprises one or more of T1, T2, HARDI, and fMRI data.