Diffusion-weighted imaging of uterine adenomyosis: Correlation with clinical backgrounds and comparison with malignant uterine tumors.

AIM: To elucidate correlation between signal intensity on diffusion-weighted images (SI-DWI) and clinical backgrounds for uterine adenomyosis and to compare SI-DWI of adenomyosis and malignant uterine tumors. METHODS: This study examined 46 adenomyosis patients diagnosed using magnetic resonance imaging and 25 patients with surgically confirmed malignant uterine myometrial tumor. First, adenomyosis cases were classified visually into high-intensity and low-intensity groups based on the SI-DWI compared with that of normal uterine myometrium. Secondly, correlation was assessed between SI-DWI of adenomyosis and patient clinical background information such as age, menopausal status, menstrual cycle and dysmenorrhea severity. Third, quantitative comparison was made of low-intensity adenomyosis (LIA), high-intensity adenomyosis (HIA) and malignant tumor groups for the signal intensity ratio (SIR) on DWI and the apparent diffusion coefficient (ADC). Their diagnostic performance was evaluated using logistic regression analysis and receiver operating characteristic (ROC) analysis. RESULTS: The 46 adenomyosis cases were classified as 26 low-intensity and 20 high-intensity cases. Significant correlation was found only for menstrual cycle phases. HIA had significantly lower SIR and higher ADC than malignant tumor. The ADC of HIA was significantly higher than that of LIA. The combination of SIR and ADC showed excellent diagnostic performance (area under ROC curve, 0.99). CONCLUSION: There is a variation in signal intensity on DWI of uterine adenomyosis and it is associated with menstrual cycle phase. Adenomyosis with high signal intensity on DWI can be differentiated from malignant lesions by its lower signal intensity on DWI and higher ADC than that found for malignant uterine tumors, however overlaps exist.

Quantitative and Qualitative Evaluation of Convolutional Neural Networks with a Deeper U-Net for Sparse-View Computed Tomography Reconstruction.

RATIONALE AND OBJECTIVES: To evaluate the utility of a convolutional neural network (CNN) with an increased number of contracting and expanding paths of U-net for sparse-view CT reconstruction. MATERIALS AND METHODS: This study used 60 anonymized chest CT cases from a public database called "The Cancer Imaging Archive". Eight thousand images from 40 cases were used for training. Eight hundred and 80 images from another 20 cases were used for quantitative and qualitative evaluation, respectively. Sparse-view CT images subsampled by a factor of 20 were simulated, and two CNNs were trained to create denoised images from the sparse-view CT. A CNN based on U-net with residual learning with four contracting and expanding paths (the preceding CNN) was compared with another CNN with eight contracting and expanding paths (the proposed CNN) both quantitatively (peak signal to noise ratio, structural similarity index), and qualitatively (the scores given by two radiologists for anatomical visibility, artifact and noise, and overall image quality) using the Wilcoxon signed-rank test. Nodule and emphysema appearance were also evaluated qualitatively. RESULTS: The proposed CNN was significantly better than the preceding CNN both quantitatively and qualitatively (overall image quality interquartile range, 3.0-3.5 versus 1.0-1.0 reported from the preceding CNN; p < 0.001). However, only 2 of 22 cases used for emphysematous evaluation (2 CNNs for every 11 cases with emphysema) had an average score of ≥ 2 (on a 3 point scale). CONCLUSION: Increasing contracting and expanding paths may be useful for sparse-view CT reconstruction with CNN. However, poor reproducibility of emphysema appearance should also be noted.

MR findings of uterine PEComa in patients with tuberous sclerosis: report of two cases.

Tuberous sclerosis complex (TSC), a rare autosomal dominant neurocutaneous disorder, is characterized by the presence of benign congenital tumors in multiple organs. Neoplasms with perivascular epithelioid cell differentiation (PEComas), including angiomyolipoma (AML) and lymphangioleiomyomatosis (LAM), can occur in association with TSC. This report describes two cases of uterine PEComas presenting characteristic MR imaging features reflecting pathological findings. From MR images, both cases showed single or multiple large, irregularly shaped or lobulated hemorrhagic lesions within the myometrium. They differed from typical adenomyotic cysts in their large size and irregular margins. Histopathologic analysis revealed that the hemorrhage was caused by adenomyosis and tumor cells that proliferated in surrounding stroma of the hemorrhagic lesions, compatible with PEComas. Microscopic observation revealed an infiltrative growth pattern of PEComas, with small nodules formed. The tumor lesions, however, were difficult to detect on MR images. The myometrium showed normal appearance on both T1-weighted and T2-weighted images in both cases. We speculate that PEComas may infiltrate extensively into the myometrium even when the myometrium shows almost normal radiologic appearance.