Importance of Fractional b Value for Calculating Apparent Diffusion Coefficient in DWI

Ogura, Akio; Hatano, Isamu; Osakabe, Kohki; Yamaguchi, N; Koyama, Daisuke; Watanabe, H.

doi:10.2214/ajr.15.15945

Cited by 15 publications

(7 citation statements)

References 47 publications

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“…It was also observed that sampling low b-values increased parameter error as it pushed the slope to be artificially steeper due to perfusion effects. It was previously suggested that all non-zero b-values be greater than the fractional b-value (17). Sampling more high b-values appeared to slightly increase TPEsIVIM in contrast with a previous study (28).…”

Section: Associations With Clinicopathologic Factorscontrasting

confidence: 77%

“…Portions of the cohort crucial parameter affecting ADC calculation (6,(13)(14)(15)(16). Furthermore, finding the b-value at which the contribution of perfusion-effects to the diffusional signal is negligible (the so-called fractional b-value) is critical in obtaining a perfusion-insensitive ADC (17,18). Due to the multistep estimation process of estimating IVIM parameters, the choice of b-value threshold in the initial estimation of the pure diffusion coefficient (D) and perfusion fraction (f) affects the final fitting (19,20).…”

Section: In Vivomentioning

confidence: 99%

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B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer

et al. 2020

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Section: Associations With Clinicopathologic Factorscontrasting

confidence: 77%

Section: In Vivomentioning

confidence: 99%

B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer

et al. 2020

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“…This was possible because they both relate to cell density. However, mean ADC was slightly lower than mean D app , probably because mean D app was calculated according to multiple b values (30). MEM is a Gaussian diffusion model, but DKI is a non-Gaussian diffusion model.…”

Section: Discussionmentioning

confidence: 99%

Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

Jiang

Liu

et al. 2020

Acta Radiol

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Background There is still little research about histogram analysis of diffusion kurtosis imaging (DKI) using in prostate cancer at present. Purpose To verify the utility of histogram analysis of DKI model in detection and assessment of aggressiveness of prostate cancer, compared with monoexponential model (MEM). Material and Methods Twenty-three patients were enrolled in this study. For DKI model and MEM, the Dapp, Kapp, and apparent diffusion coefficient (ADC) were obtained by using single-shot echo-planar imaging sequence. The pathologies were confirmed by in-bore magnetic resonance (MR)-guided biopsy. Regions of interest (ROI) were drawn manually in the position where biopsy needle was put. The mean values and histogram parameters in cancer and noncancerous foci were compared using independent-samples T test. Receiver operating characteristic curves were used to investigate the diagnostic efficiency. Spearman’s test was used to evaluate the correlation of parameters and Gleason scores. Results The mean, 10th, 25th, 50th, 75th, and 90th percentiles of ADC and Dapp were significantly lower in prostate cancer than non-cancerous foci ( P < 0.001). The mean, 50th, 75th, and 90th percentiles of Kapp were significantly higher in prostate cancer ( P < 0.05). There was no significant difference between the AUCs of two models (0.971 vs. 0.963, P > 0.05). With the increasing Gleason scores, the 10th ADC decreased ( ρ = −0.583, P = 0.018), but the 90th Kapp increased ( ρ = 0.642, P = 0.007). Conclusion Histogram analysis of DKI model is feasible for diagnosing and grading prostate cancer, but it has no significant advantage over MEM.

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“…The b-value corresponds to diffusion effects. The ADC is calculated on the basis of the difference in the signal intensity on DW images obtained at two different b values, corresponding to an exponential decrease in signal intensities [ 27 , 28 ].…”

Section: Resultsmentioning

confidence: 99%

Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review

et al. 2022

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Background: Glioblastoma is the most frequent malignant primitive brain tumor in adults. The treatment includes surgery, radiotherapy, and chemotherapy. During follow-up, combined chemoradiotherapy can induce treatment-related changes mimicking tumor progression on medical imaging, such as pseudoprogression (PsP). Differentiating PsP from true progression (TP) remains a challenge for radiologists and oncologists, who need to promptly start a second-line treatment in the case of TP. Advanced magnetic resonance imaging (MRI) techniques such as diffusion-weighted imaging, perfusion MRI, and proton magnetic resonance spectroscopic imaging are more efficient than conventional MRI in differentiating PsP from TP. None of these techniques are fully effective, but current advances in computer science and the advent of artificial intelligence are opening up new possibilities in the imaging field with radiomics (i.e., extraction of a large number of quantitative MRI features describing tumor density, texture, and geometry). These features are used to build predictive models for diagnosis, prognosis, and therapeutic response. Method: Out of 7350 records for MR spectroscopy, GBM, glioma, recurrence, diffusion, perfusion, pseudoprogression, radiomics, and advanced imaging, we screened 574 papers. A total of 228 were eligible, and we analyzed 72 of them, in order to establish the role of each imaging modality and the usefulness and limitations of radiomics analysis.

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Importance of Fractional b Value for Calculating Apparent Diffusion Coefficient in DWI

Abstract: The ADC value fluctuated when the b values used for calculation were higher than the fractional b value. Therefore, it is important to determine the fractional b value of the target tissue.

Cited by 15 publications

References 47 publications

B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer

B-Value Optimization in the Estimation of Intravoxel Incoherent Motion Parameters in Patients with Cervical Cancer

Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model

Pseudoprogression in Glioblastoma: Role of Metabolic and Functional MRI-Systematic Review

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