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Evaluating High Spatial Resolution Diffusion Kurtosis Imaging at 3T: Reproducibility and Quality of Fit.
Kasa, Loxlan W; Haast, Roy A M; Kuehn, Tristan K; Mushtaha, Farah N; Baron, Corey A; Peters, Terry; Khan, Ali R.
Afiliação
  • Kasa LW; Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.
  • Haast RAM; School of Biomedical Engineering, Western University, London, Ontario, Canada.
  • Kuehn TK; Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.
  • Mushtaha FN; School of Biomedical Engineering, Western University, London, Ontario, Canada.
  • Baron CA; Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.
  • Peters T; Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada.
  • Khan AR; Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada.
J Magn Reson Imaging ; 53(4): 1175-1187, 2021 04.
Article em En | MEDLINE | ID: mdl-33098227
ABSTRACT

BACKGROUND:

Diffusion kurtosis imaging (DKI) quantifies the non-Gaussian diffusion of water within tissue microstructure. However, it has increased fitting parameters and requires higher b-values. Evaluation of DKI reproducibility is important for clinical purposes.

PURPOSE:

To assess the reproducibility in whole-brain high-resolution DKI at varying b-values. STUDY TYPE Retrospective. SUBJECTS AND PHANTOMS In all, 44 individuals from the test-retest Human Connectome Project (HCP) database and 12 3D-printed phantoms. FIELD STRENGTH/SEQUENCE Diffusion-weighted multiband echo-planar imaging sequence at 3T and 9.4T. magnetization-prepared rapid acquisition gradient echo at 3T for in vivo structural data only. ASSESSMENT From HCP data with b-values = 1000, 2000, 3000 s/mm2 (dataset A), two additional datasets with b-values = 1000, 3000 s/mm2 (dataset B) and b-values = 1000, 2000 s/mm2 (dataset C) were extracted. Estimated DKI metrics from each dataset were used for evaluating reproducibility and fitting quality in white matter (WM) and gray matter (GM) based on whole-brain and regions of interest (ROIs). STATISTICAL TESTS DKI reproducibility was assessed using the within-subject coefficient of variation (CoV), fitting residuals to evaluate DKI fitting accuracy and Pearson's correlation to investigate the presence of systematic biases. Repeated measures analysis of variance was used for statistical comparison.

RESULTS:

Datasets A and B exhibited lower DKI CoVs (<20%) compared to C (<50%) in both WM and GM ROIs (all P < 0.05). This effect varies between DKI and DTI parameters (P < 0.005). Whole-brain fitting residuals were consistent across datasets (P > 0.05), but lower residuals in dataset B were detected for the WM ROIs (P < 0.001). A similar trend was observed for the phantom data CoVs (<7.5%) at varying fiber orientations for datasets A and B. Finally, dataset C was characterized by higher residuals across the different fiber crossings (P < 0.05). DATA

CONCLUSION:

The study demonstrates that high reproducibility can still be achieved within a reasonable scan time, specifically dataset B, supporting the potential of DKI for aiding clinical tools in detecting microstructural changes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Encéfalo / Imagem de Tensor de Difusão Tipo de estudo: Observational_studies Limite: Adult / Female / Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Encéfalo / Imagem de Tensor de Difusão Tipo de estudo: Observational_studies Limite: Adult / Female / Humans / Male Idioma: En Ano de publicação: 2021 Tipo de documento: Article