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    Can Trace-Weighted Images Be Used to Estimate Diffusional Kurtosis Imaging–Derived Indices of Non-Gaussian Water Diffusion in Head and Neck Cancer? | American Journal of Neuroradiology  
    



















    

































    
  
  




    
    

  

      


    

      

  
  
  

    

  

    

  

                
    

      

  

    

  
      
  
  

    


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LetterLETTER

  
      

  
      
Can Trace-Weighted Images Be Used to Estimate Diffusional Kurtosis Imaging–Derived Indices of Non-Gaussian Water Diffusion in Head and Neck Cancer?
  
    	
M. Giannelli, C. Marzi, M. Mascalchi, S. Diciotti and C. Tessa

  
    	
American Journal of Neuroradiology September 2019,  40 (9) E44-E45; DOI: https://doi.org/10.3174/ajnr.A6167 

  
  
  



  


  
  



			

		

	
	
 	
	  
  

		
		

			

			  

  
      
  
  

    
  


  
  



  
      
  
  

    

  

    

  
      
  
  

    
We read with great interest the appealing article by Tu et al,1 aimed at assessing the role of histogram-based descriptors of the non-Gaussian diffusional kurtosis imaging (DKI) model2 in treatment-response prediction of nasopharyngeal carcinoma. They found that histogram-based analysis of DKI-derived indices of “kurtosis” and “diffusivity” may potentially predict the neoadjuvant chemotherapy and short-term radiation therapy response in locoregionally advanced nasopharyngeal carcinoma, therefore possibly modifying the treatment regimen. Considering that DKI is a quantitative diffusion MR imaging technique, we think it would be of practical interest to discuss in greater detail the approach used to estimate kurtosis and diffusivity in this study and in various DKI applications in general.
Tu et al1 used only 3 orthogonal diffusion-weighting directions as is usual in extracranial DKI studies. While a rigorous application of DKI would require the use of at least 15 diffusion-weighting directions and 2 non-null b-values,2 this simplification (based on the assumption of a relatively low diffusion anisotropy degree for tumor lesions) allows reducing the scan time, which is a pivotal issue in numerous extracranial applications of diffusion MR imaging. In this context, the estimation of kurtosis and diffusivity as the average of directional kurtosis and diffusivity obtained separately along the 3 orthogonal directions (namely K and D) hence represents the best possible approximation3 for rotational invariant mean kurtosis (ie, the average of directional kurtosis along all spatial directions) and mean diffusivity, respectively.2 However, it is likely that Tu et al1 estimated K and D by fitting the DKI model to trace-weighted images (TWIs) (ie, the geometric means of acquired diffusion-weighted images (DWIs), along the 3 orthogonal diffusion-weighting directions), albeit not explicitly reported.
This fact represents an important methodologic issue that should be adequately discussed. Indeed, while the approach of using TWIs has been widely adopted in many extracranial applications of DKI, it is mathematically as well as theoretically incorrect and yields no advantage in terms of acquisition time.3,4 Notably, a simulation study has shown that this approach can potentially affect the estimation of K.3 In particular, even for true K values ∼ 1 (as typically observed in human tissue) and a low diffusion anisotropy degree (<0.2), the percentage error in the K estimation caused by the use of TWIs instead of single DWIs along the 3 orthogonal directions can range up to more than 30%, resulting in greater uncertainty introduced by DWIs noise for typical signal-to-noise ratio values. In addition, a recent in vivo study has demonstrated that the fit of the DKI model to TWIs can introduce bias and error in the estimation of K and D of head and neck cancer, which can be non-negligible for single lesions.4 The median (95% confidence interval) errors in K and D were 5.1% (0.8%, 32.6%) and 1.7% (−2.5%, 5.3%), respectively.
In conclusion, toward an optimization of methods in quantitative DKI of the head and neck and extracranial regions in general, the use of at least 15 diffusion-weighting directions and 2 non-null b-values would be conceptually recommendable.2 Nonetheless, the widespread and growing tendency to obtain K and D by fitting the DKI model to TWIs should possibly be avoided because it can affect the estimation of DKI-derived indices.3,4
REFERENCES
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    1. 

    2. Tu  N, 
    3. Zhong  Y, 
    4. Wang  X, et al
    . Treatment response prediction of nasopharyngeal carcinoma based on histogram analysis of diffusional kurtosis imaging. AJNR Am J Neuroradiol 2019;40:326–33 doi:10.3174/ajnr.A5925 pmid:30630832
    Abstract/FREE Full Text
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    2. Jensen  JH, 
    3. Helpern  JA
    . MRI quantification of non-Gaussian water diffusion by kurtosis analysis. NMR Biomed 2010;23:698–710 doi:10.1002/nbm.1518 pmid:20632416
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    2. Giannelli  M, 
    3. Toschi  N
    . On the use of trace-weighted images in body diffusional kurtosis imaging. Magn Reson Imaging 2016;34:502–07 doi:10.1016/j.mri.2015.12.013 pmid:26706136
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  4. 4.↵
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    2. Marzi  S, 
    3. Minosse  S, 
    4. Vidiri  A, et al
    . Diffusional kurtosis imaging in head and neck cancer: on the use of trace-weighted images to estimate indices of non-Gaussian water diffusion. Med Phys 2018;45:5411–19 doi:10.1002/mp.13238 pmid:30317646
    CrossRefPubMed
  


  
  



  
      
  
  

    
  


  
  



  




  


  
  



  
      
  
  

    
  


  
  



  
      
  
  

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Can Trace-Weighted Images Be Used to Estimate Diffusional Kurtosis Imaging–Derived Indices of Non-Gaussian Water Diffusion in Head and Neck Cancer?
  
    	
M. Giannelli, C. Marzi, M. Mascalchi, S. Diciotti, C. Tessa

  
    	
American Journal of Neuroradiology Sep 2019, 40 (9) E44-E45; DOI: 10.3174/ajnr.A6167 

  
  
  



  


  
  



  
      
  
  

    

  
    
  
  
    
  
  
    
  


  


  
  



  
      
  
  

    
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