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Purpose: To compare the diagnostic value of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted imaging (DWI) for differentiating benign and malignant hepatic lesions. Methods: This prospective study was approved by our Institutional Review Board and the patients provided written informed consent. Magnetic resonance imaging was acquired for 56 patients with suspected liver disease. This identified 90 focal liver lesions with a maximum diameter >10 mm, of which 47 were benign and 43 were malignant. Using home-built software, two radiologists measured the DWI parameters of hepatic lesions for three models: the apparent diffusion coefficient (ADC) from a mono-exponential model; the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from a bi-exponential model; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) from a stretched exponential model. The parameters were compared between benign and malignant hepatic lesions. Results: ADC, D, D*, f, and DDC values were significantly lower for malignant hepatic lesions than for benign lesions (P < 0.0001–0.03). Although logistic regression analysis demonstrated that DDC was the only statistically significant parameter for differentiating benign and malignant lesions (P = 0.039), however, the areas under the receiver operating characteristic curve for differentiating benign and malignant lesions were comparable between ADC (0.98) and DDC (0.98) values. Conclusion: DDC values obtained from the stretched exponential model could be also used as a quantitative imaging biomarker for differentiating benign and malignant hepatic lesions, however, the diagnostic performance was comparable with ADC values.

To evaluate the use of deep-learning-based image reconstruction (DLIR) algorithms in dynamic contrast-enhanced computed tomography (CT) of the abdomen, and to compare the image quality and lesion conspicuity among the reconstruction strength levels.This prospective study included 59 patients with 373 hepatic lesions who underwent dynamic contrast-enhanced CT of the abdomen. All images were reconstructed using four reconstruction algorithms, including 40% adaptive statistical iterative reconstruction-Veo (ASiR-V) and DLIR at low, medium, and high-strength levels (DLIR-L, DLIR-M, and DLIR-H, respectively). The signal-to-noise ratio (SNR) of the abdominal aorta, portal vein, liver, pancreas, and spleen and the lesion-to-liver contrast-to-noise ratio (CNR) were calculated and compared among the four reconstruction algorithms. The diagnostic acceptability was qualitatively assessed and compared among the four reconstruction algorithms and the conspicuity of hepatic lesions was compared between5 and ≥5 mm lesions.The SNR of each anatomical structure (p0.0001) and CNR (p0.0001) were significantly higher in DLIR-H than the other reconstruction algorithms. Diagnostic acceptability was significantly better in DLIR-M than the other reconstruction algorithms (p0.0001). The conspicuity of hepatic lesions was highest when using 40% ASiR-V and tended to lessen as the reconstruction strength level was getting higher in DLIR, especially in5 mm lesions; however, all hepatic lesions could be detected.DLIR improved the SNR, CNR, and image quality compared with 40% ASiR-V, while making it possible to decrease lesion conspicuity using higher reconstruction strength.

This study aims to compare the diagnostic values of mono-exponential, bi-exponential, and stretched exponential diffusion-weighted imaging (DWI) in differentiating hepatic hemangiomas and liver metastases.This prospective study was approved by our institutional review board, and written informed consent was obtained from all patients. In this study, 244 patients with known or suspected liver disease underwent magnetic resonance imaging. Among them, 37 patients who had focal hepatic lesions with a maximum diameter of ≥10 mm were evaluated. Using home-built software, two radiologists measured the DWI parameters of hepatic lesions for the three models: the apparent diffusion coefficient (ADC) from a mono-exponential model; the true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) from a bi-exponential model; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) from a stretched exponential model. The parameters were compared between hepatic hemangiomas and liver metastases.In total, 64 focal hepatic lesions were evaluated, of which 22 were identified to be hepatic hemangiomas and 42 were liver metastases. ADC, D, f, and DDC values were significantly lower in liver metastases than in hepatic hemangiomas (P 0.0001, 0.0001, 0.015, and 0.0001, respectively); whereas, the α value was significantly higher in liver metastases than in hepatic hemangiomas (P = 0.028). The areas under the ROC curve (AUCs) for differentiating hepatic hemangiomas and liver metastases in ADC, D, D*, f, DDC, and α were 0.940, 0.908, 0.608, 0.686, 0.952, and 0.667, respectively. The AUC values of ADC and DDC were significantly greater than those of D* (P 0.0001), f (P = 0.0001), and α values (P = 0.0001).ADC and DDC values from the mono-exponential and stretched exponential models could be considered as quantitative imaging biomarkers for differentiating hepatic hemangiomas and liver metastases.

To assess the efficacy of diffusion-weighted (DW) MR imaging for differentiating between Warthin’s tumor and oncocytoma of the parotid gland. Forty-five histopathologically confirmed parotid gland tumors (41 Warthin’s tumors and 4 oncocytomas) were examined by MR imaging including DW imaging. 18F-fluorodeoxyglucose PET/CT was also performed in 9 Warthin’s tumors and 3 oncocytomas. We retrospectively reviewed the images and compared the MR signal intensities, apparent diffusion coefficients (ADCs), and maximum standardized uptake values (SUVmax) of the solid components between the two pathologies. The signal intensity ratios (SIRs) on both T2-weighted images (0.92 ± 0.18 vs 0.65 ± 0.13, p

Objective:This study aimed to assess the imaging features and natural course of clavicle fracture following neck dissection (ND).Methods and materials:Eight patients with clavicle fracture following ND were included. Because bilateral clavicle fractures occurred in one patient, a total of nine clavicle fractures following ND were assessed using CT images. Pathological fracture due to bone metastasis or local recurrence was carefully ruled out.Results:The time interval from ND to clavicle fracture ranged from 2 to 8 months (median, 4 months). On CT images, all the nine fractures occurred in the proximal clavicles and displayed increased bone marrow density with extraosseous soft-tissue mass formation. Osteolysis of the clavicle was not observed. Intra articular fractures were observed in three (33%), displaced fractures in two (22%), and free bone fragments in three (33%) cases. Further follow-up using CT showed that six (67%) fractures resulted in pseudoarthrosis. For the remaining three (33%) fractures with bone union during follow-up, the time interval between clavicle fracture and bone union ranged from 4 to 16 months (median, 6 months).Conclusion:Proximal clavicle fractures, which are rarely observed following ND, always display extraosseous soft-tissue mass formation without osteolysis. They frequently result in pseudoarthrosis; however, occasionally, they also result in bone union within 24 weeks.Advances in knowledge:The location of proximal clavicle, the lack of osteolysis, and the presence of free bone fragments may be the key to diagnosis of clavicle fracture following ND.

Purpose To determine the optimal window settings for monochromatic images with various energy levels in single-source dual-energy computed tomography (DECT) of the abdomen. Materials and methods Two hundred consecutive patients underwent contrast-enhanced DECT to screen tumor metastases and/or recurrences after surgery for malignant tumors. Two independent radiologists reviewed eight energy levels (40, 45, 50, 55, 60, 65, 70, and 75 kilo-electron volts [keV]) of the portal venous phase monochromatic images. For each keV image, the radiologists adjusted the window level (WL) and width (WW) using settings of 40 HU of WL and 350 HU of WW on 65 keV images as a reference and recorded these values. After removing the top and bottom 5% of the data, the optimal WL and WW in each energy levels were obtained by rounding the median values. In 7 of 200 patients with a total of 23 liver metastases, the tumor-to-liver contrast-to-noise ratio (CNR) was calculated and compared among the energy levels. Results The optimal WLs and WWs at each energy level were as follows: 40 keV, 140 and 680 HU; 45 keV, 110 and 570 HU; 50 keV, 90 and 490 HU; 55 keV, 70 and 440 HU; 60 keV, 60 and 390 HU; 65 keV, 40 and 350 HU; 70 keV, 30 and 320 HU; and 75 keV, and 20 and 300 HU. The best CNR was obtained using 65 keV images (P Conclusion We clarified the optimal WL and WW available for the preset window settings in abdominal DECT.