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Objectives: To compare the gamma distribution (GD), intravoxel incoherent motion (IVIM), and monoexponential (ME) models in terms of their goodness-of-fit, correlations among the parameters, and the effectiveness in the differential diagnosis of various orofacial lesions. Methods: A total of 85 patients underwent turbo spin-echo diffusion-weighted imaging with six b-values. The goodness-of-fit of three models was assessed using Akaike Information Criterion. We analysed the correlations and compared the effectiveness in the differential diagnosis among the parameters of GD model (κ, shape parameter; θ, scale parameter; fractions of diffusion: ƒ1, cellular component; ƒ2, extracellular diffusion; ƒ3, perfusion component), IVIM model (D, true diffusion coefficient; D*, pseudodiffusion coefficient; f, perfusion fraction), and ME model (apparent diffusion coefficient, ADC). Results: The GD and IVIM models showed a better goodness-of-fit than the ME model (p < 0.05). ƒ1 had strong negative correlations with D and ADC (ρ = –0.901 and –0.937, respectively), while ƒ3 had a moderate positive correlation with f (ρ = 0.661). Malignant entity presented significantly higher ƒ1 and lower D and ADC than benign entity (p < 0.0001). Malignant lymphoma had significantly higher ƒ1 in comparison to squamous cell carcinoma (p = 0.0007) and granulation (p = 0.0075). The trend in ƒ1 was opposite to the trend in D. Malignant lymphoma had significant lower ƒ3 than squamous cell carcinoma (p = 0.005) or granulation (p = 0.0075). Conclusions: The strong correlations were found between the GD- and IVIM-derived parameters. Furthermore, the GD model’s parameters were useful for characterising the pathological structure in orofacial lesions.

This study conducted a receiver operating characteristic (ROC) analysis by applying improved cluster signal-to-noise (CSN) analysis to digital intraoral radiographs and develop an observer-free method of analyzing image quality related to the observer performance in the detection task.Two aluminum step phantoms with a thickness interval of 1.0 mm were used for this study. One phantom had holes of increasing depth (from 0.05 to 0.35 mm) and the other had no holes. Phantom images were obtained under various exposure dose and image capture modes using a dental X-ray unit, a photostimulable phosphor imaging plate, and scanner system. These phantom images were analyzed using the FindFoci plugin in ImageJ software. Subsequently, true positive rates (TPRs) and false positive rates (FPRs) were calculated by analyzing phantom images with and without holes. We constructed ROC curves by plotting the TPRs against the FPRs and calculated the area under the ROC curve (AUC). Using the same phantom images with holes, eight observers assessed the number of detectable holes. Correlations between observer detection performance and AUC values were evaluated.AUC values increased as the exposure dose increased and showed different tendencies depending on the image capture mode. The AUC values showed a high correlation with observer detection performance (r = 0.76).AUC values obtained from CSN analysis reflect image quality and replace the observer detection performance test of image quality.

Objective: To determine whether the γ distribution (GD) model of diffusion MRI is useful in the evaluation of the isocitrate dehydrogenase (IDH) mutation status of glioblastomas. Methods: 12 patients with IDH-mutant glioblastomas and 54 patients with IDH-wildtype glioblastomas were imaged with diffusion-weighted imaging using 13 b-values from 0 to 1000 s/mm2. The shape parameter (κ) and scale parameter (θ) were obtained with the GD model. Fractions of three different areas under the probability density function curve (f1, f2, f3) were defined as follows: f1, diffusion coefficient (D) < 1.0×10−3 mm2/s; f2, D > 1.0×10−3 and −3 mm2/s; f3, D > 3.0 × 10−3 mm2/s. The GD model-derived parameters measured in gadolinium-enhancing lesions were compared between the IDH-mutant and IDH-wildtype groups. Receiver operating curve analyses were performed to assess the parameters' diagnostic performances. Results: The IDH-mutant group’s f1 (0.474 ± 0.143) was significantly larger than the IDH-wildtype group’s (0.347 ± 0.122, p = 0.0024). The IDH-mutant group’s f2 (0.417 ± 0.131) was significantly smaller than the IDH-wildtype group’s (0.504 ± 0.126, p = 0.036). The IDH-mutant group’s f3 (0.109 ± 0.060) was significantly smaller than the IDH-wildtype group’s (0.149 ± 0.063, p = 0.0466). The f1 showed the best diagnostic performance among the GD model-derived parameters with the area under the curve value of 0.753. Conclusion: The GD model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and was useful in the differentiation of these tumors. Advances in knowledge: Diffusion MRI based on the γ distribution model could well describe the pathological features of IDH-mutant and IDH-wildtype glioblastomas, and its use enabled the significant differentiation of these tumors. The γ distribution model may contribute to the non-invasive identification of the IDH mutation status based on histological viewpoint.