Optimal b-values for diffusion kurtosis imaging of the liver and pancreas in MR examinations

Pasicz, Katarzyna; Podgórska, Joanna; Jasieniak, Jakub; Fabiszewska, Ewa; Skrzyński, Witold; Anysz-Grodzicka, Agnieszka; Cieszanowski, Andrzej; Kukołowicz, Paweł; Grabska, Iwona

doi:10.1016/j.ejmp.2019.09.238

Cited by 13 publications

(9 citation statements)

References 21 publications

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“…A drawback which hampers its use in routine practice is the long acquisition time due to scan acquisition at multiple b values. There are studies showing application of DKI for the assessment of the pancreas[ 84 , 85 ]. Shi et al [ 83 ] found that the radiomics model of DKI and T2 weighted imaging could improve the diagnostic accuracy for PNENs.…”

Section: Mrimentioning

confidence: 99%

Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms

Ramachandran¹,

Madhusudhan²

2022

WJG

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Gastroenteropancreatic neuroendocrine neoplasms comprise a heterogeneous group of tumors that differ in their pathogenesis, hormonal syndromes produced, biological behavior and consequently, in their requirement for and/or response to specific chemotherapeutic agents and molecular targeted therapies. Various imaging techniques are available for functional and morphological evaluation of these neoplasms and the selection of investigations performed in each patient should be customized to the clinical question. Also, with the increased availability of cross sectional imaging, these neoplasms are increasingly being detected incidentally in routine radiology practice. This article is a review of the various imaging modalities currently used in the evaluation of neuroendocrine neoplasms, along with a discussion of the role of advanced imaging techniques and a glimpse into the newer imaging horizons, mostly in the research stage.

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Section: Mrimentioning

confidence: 99%

Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms

Ramachandran¹,

Madhusudhan²

2022

WJG

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“…Further comparison of IVIM perfusion‐related parameters, derived with optimized b‐values schemes, with pure perfusion sequences such as Dynamic Susceptibility Contrast (DSC) or Dynamic Contrast Enhancement (DCE) MRI, as suggested in Le Bihan 2 and Zampini et al 7 . will be considered, along with additional comparison/integration of the IVIM model with alternative multi‐compartment models, such as Diffusion Kurtosis Imaging (DKI) 34,38,39 …”

Section: Discussionmentioning

confidence: 99%

Optimizing b‐values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model

Paganelli

Zampini²,

Morelli

et al. 2023

J Applied Clin Med Phys

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To define an optimal set of b-values for accurate derivation of diffusion MRI parameters in the brain with segmented Intravoxel Incoherent Motion (IVIM) model. Methods: Simulations of diffusion signals were performed to define an optimal set of b-values targeting different perfusion regimes, by relying on an optimization procedure which minimizes the total relative error on estimated IVIM parameters computed with a segmented fitting procedure. Then, the optimal bvalues set was acquired in vivo on healthy subjects and skull base chordoma patients to compare the optimized protocol with a clinical one. Results: The total relative error on simulations decreased of about 40% when adopting the optimal set of 13 b-values (0 10 20 40 50 60 200 300 400 1200 1300 1400 1500 s/mm 2 ), showing significant differences and increased precision on D and f estimates with respect to simulations with a non-optimized b-values set. Similarly, in vivo acquisitions demonstrated a dependency of IVIM parameters on the b-values array, with differences between the optimal set of b-values and a clinical non-optimized acquisition. IVIM parameters were compatible to literature values, with D (0.679/0.701 [0.022/0.008] ⋅10 −3 mm 2 /s), f (5.49/5.80 [0.70/1.14] %), and D* (8.25/7.67 [0.92/0.83] ⋅10 −3 mm 2 /s) median [interquartile range] estimates for white matter/gray matter in volunteers and D (0.

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“…MD derived by DKI has been shown to have a higher diagnostic performance to assess response to electrochemotherapy than conventional DWI parameters and could be used to identify responders and nonresponders among patients with pancreatic cancer (26). To the best of our knowledge, so far, only a few reports have utilized DKI to differentiate PNETs and SPTs (10,26). Jang et al found that the mean ADC value in SPTs was significantly lower than that in PNETs (6).…”

Section: Discussionmentioning

confidence: 99%

“…Thirdly, in this study, the tumor was contoured slice by slice to obtain the entire neoplastic DKI parameters. However, DKI parameters were obtained from the largest tumor section in some related reports (10,12). We assumed that the DKI quantitative parameters of the entire tumor could provide a more comprehensive tumor characterization.…”

Section: Discussionmentioning

confidence: 99%

“…Diffusion kurtosis imaging (DKI) can provide a more accurate model of diffusion and capture the non-Gaussian diffusion parameters for tissue heterogeneity. DKI has been successfully applied for assessment of pancreas and pancreatic disease (9,10). We speculated that DKI might provide more optimal identification characteristics for differentiation between PNETs and SPTs.…”

Section: Introductionmentioning

confidence: 99%

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Radiomics Analysis Based on Diffusion Kurtosis Imaging and T2 Weighted Imaging for Differentiation of Pancreatic Neuroendocrine Tumors From Solid Pseudopapillary Tumors

et al. 2020

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Objective: To develop and validate a radiomics model of diffusion kurtosis imaging (DKI) and T2 weighted imaging for discriminating pancreatic neuroendocrine tumors (PNETs) from solid pseudopapillary tumors (SPTs). Materials and Methods: Sixty-six patients with histopathological confirmed PNETs (n = 31) and SPTs (n = 35) were enrolled in this study. ROIs of tumors were manually drawn on each slice at T2WI and DWI (b = 1,500 s/mm 2) from 3T MRI. Intraclass correlation coefficients were used to evaluate the interobserver agreement. Mean diffusivity (MD) and mean kurtosis (MK) were derived from DKI. The least absolute shrinkage and selection operator regression were used for feature selection. Results: MD and MK had a moderate diagnostic performance with the area under curve (AUC) of 0.71 and 0.65, respectively. A radiomics model, which incorporated sex and age of patients and radiomics signature of the tumor, showed excellent discrimination performance with AUC of 0.97 and 0.86 in the primary and validation cohort. Moreover, the new model had better diagnostic performance than that of MD (P = 0.023) and MK (P = 0.004), and showed excellent differentiation with a sensitivity of 95.00% and specificity of 91.67% in primary cohort, and the sensitivity of 90.91% and specificity of 81.82% in the validation cohort. The accuracy of radiomics analysis, radiologist 1, and radiologist 2 for diagnosing SPTs and PNETs were 92.42, 77.27, and 78.79%, respectively. The accuracy of radiomics analysis was significantly higher than that of subjective diagnosis (P < 0.05). Conclusions: Radiomics model could improve the diagnostic accuracy of SPTs and PNETs and contribute to determining an appropriate treatment strategy for pancreatic tumors.

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Optimal b-values for diffusion kurtosis imaging of the liver and pancreas in MR examinations

Cited by 13 publications

References 21 publications

Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms

Advances in the imaging of gastroenteropancreatic neuroendocrine neoplasms

Optimizing b‐values schemes for diffusion MRI of the brain with segmented Intravoxel Incoherent Motion (IVIM) model

Radiomics Analysis Based on Diffusion Kurtosis Imaging and T2 Weighted Imaging for Differentiation of Pancreatic Neuroendocrine Tumors From Solid Pseudopapillary Tumors

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