Diffusional kurtosis imaging in head and neck cancer: On the use of trace-weighted images to estimate indices of non-Gaussian water diffusion

Simona Marzi, Silvia Minosse, Antonello Vidiri, Francesca Piludu, Marco Giannelli

Research output: Contribution to journalArticle

Abstract

Purpose: While previous studies have demonstrated the feasibility and potential usefulness of quantitative non-Gaussian diffusional kurtosis imaging (DKI) of the brain, more recent research has focused on oncological application of DKI in various body regions such as prostate, breast, and head and neck (HN). Given the need to minimize scan time during most routine magnetic resonance imaging (MRI) acquisitions of body regions, diffusion-weighted imaging (DWI) with only three orthogonal diffusion weighting directions (x, y, z) is usually performed. Moreover, as water diffusion within malignant tumors is generically thought to be almost isotropic, DWI with only three diffusion weighting directions is considered sufficient for oncological application and it represents the de facto standard in body DKI. In this context, since the kurtosis tensor and diffusion tensor cannot be obtained, the averages of the three directional (Kx, Ky, Kz) and (Dx, Dy, Dz) — namely K and D, respectively — represent the best-possible surrogates of directionless DKI-derived indices of kurtosis and diffusivity, respectively. This would require fitting the DKI model to the diffusion-weighted images acquired along each direction (x, y, z) prior to averaging. However, there is a growing tendency to perform only a single fit of the DKI model to the geometric means of the images acquired with diffusion-sensitizing gradient along (x, y, z), referred to as trace-weighted (TW) images. To the best of our knowledge, no in vivo studies have evaluated how TW images affect estimates of DKI-derived indices of K and D. Thus, the aim of this study was to assess the potential bias and error introduced in estimated K and D by fitting the DKI model to the TW images in HN cancer patients. Methods: Eighteen patients with histologically proven malignant tumors of the HN were enrolled in the study. They underwent pretreatment 3 T MRI, including DWI (b-values: 0, 500, 1000, 1500, 2000 s/mm2). Some patients had multiple lesions, and thus a total of 34 lesions were analyzed. DKI-derived indices were estimated, voxel-by-voxel, using single diffusion-weighted images along (x, y, z) as well as TW images. A comparison between the two estimation methods was performed by calculating the percentage error in D (Derr) and K (Kerr). Also, diffusivity anisotropy (Danis) and diffusional kurtosis anisotropy (Kanis) were estimated. Agreements between the two estimation methods were assessed by Bland–Altman plots. The Spearman rank correlation test was used to study the correlations between Kerr/Derr and Danis/Kanis. Results: The median (95% confidence interval) Kerr and Derr were 5.1% (0.8%, 32.6%) and 1.7% (−2.5%, 5.3%), respectively. A significant relationship was observed between Kerr and Danis (correlation coefficient R = 0.694, P < 0.0001), as well as between Kerr and Kanis (R = 0.848, P < 0.0001). Conclusions: In HN cancer, the fit of the DKI model to TW images can introduce bias and error in the estimation of K and D, which may be non-negligible for single lesions, and should hence be adopted with caution.

Original languageEnglish
Pages (from-to)5411-5419
Number of pages9
JournalMedical Physics
Volume45
Issue number12
DOIs
Publication statusPublished - Dec 1 2018

Keywords

  • diffusion-weighted imaging
  • diffusional kurtosis imaging
  • head and neck cancer
  • non-Gaussian water diffusion
  • quantitative imaging

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

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