Quantitative evaluation of simultaneous spatial and temporal regularization in liver perfusion studies using low-dose dynamic contrast-enhanced CT

The purpose of this study was to quantitatively evaluate the performance of different simultaneous spatial and temporal regularizers in liver perfusion studies using low-dose dynamic contrast-enhanced computed tomography (DCE-CT). A digital liver phantom was used to simulate chronic liver disease (CLD) and hepatocellular carcinoma (HCC) based on clinical data. Low-dose DCE-CT images were reconstructed using regularizers and a primal-dual algorithm. Subsequently, hepatic perfusion parameter (HPP) images were generated using a dual-input single-compartment model and linear least-squares method. In the CLD model, the effect of regularizers on the input functions (IFs) was examined by calculating the areas under the curves (AUCs) of the IFs, and the HPP estimation accuracy was evaluated by calculating the error and coefficient of variation (CV) between the HPP values obtained by the above methods and true values. In the HCC model, the ratios of the mean HPP values inside and outside the tumor were calculated. The AUCs of IFs decreased with increasing regularization parameter (RP) values. Although the AUC of arterial IF did not significantly depend on the regularizers, that of portal IF did. The error and CV were reduced using low-rank and sparse decomposition (LRSD). Total generalized variation (TGV) combined with LRSD (LTGV) was generally superior to the other regularizers in terms of HPP estimation accuracy and range of available RP values in both the CLD and HCC models. However, striped artifacts were more remarkable in the HPP images obtained by the TGV and LTGV than in those obtained by the other regularizers. The results suggest that the LRSD and LTGV are useful for improving the accuracy of HPP estimation using low-dose DCE-CT and for enhancing its practicality. This study will help select a suitable regularizer and/or RP value for low-dose DCE-CT liver perfusion studies.Comment: 32 pages, 1 table, 10 figure

Inversion boundaries developed by etching on{1010} of ZnO crystal

Etch pattern on surfaces of ZnO crystal has been observed by using an E.M. and an S.E.M. Two kinds of boundaries on which stacking orders of Zn and O atomic planes change have been developed on the plane {1010} by using etching technique. Hydrofluoric acid (～46%) was used as etchant. One boundary on which stacking of Zn and O planes changes from one order, -O.Zn-O.Zn-, to the other order, -Zn.O-Zn.O-, is a twin boundary. While, the other boundary on which stacking of Zn and O planes changes from the latter order to the former one is not considered as a twin one. Results of observation suggest to us that the decrease in the growth rate of the crystal needle is due to the generation of twin boundaries in the crystal

Radiological prediction of tumor invasiveness of lung adenocarcinoma on thin-section CT

To evaluate thin-section computed tomography (CT) (TSCT) features that differentiate adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and to determine the size of solid portion on CT that correlates to pathological invasive components. Forty-eight patients were included. Nodules were classified into ground-glass nodule (GGN), part-solid, solid, and heterogeneous. Visual density of GGNs was subjectively evaluated using reference standard images: faint GGN (Ga), −400 HU; and mixed (Ga + Gb, Ga + Gc, and Gb + Gc). The evaluated TSCT findings included margin of nodule, distribution of solid portion, distribution of air bronchiologram, and pleural indentation. The longest diameters of the solid portion and the entire tumor were measured. Invasive diameters were measured in pathological specimens. Twenty-two AISs (16 GGNs [7 Ga, 5 Gb, 2 Gc, 1 Ga + Gc, 1 Gb + Gc], 4 part-solids, and 2 heterogeneous), 6 MIAs (1 GGN [Gb + Gc], 3 part-solids, and 2 solids), and 20 IVAs (1 GGN [Gb], 3 part-solids, and 16 solid) were found. The longest diameter (mean ± standard deviation) of the solid portion and total tumor were 9.7 ± 9.7 and 18.9 ± 5.6 mm, respectively. Significant differences in TSCT findings between AIS and IVA were margin of nodule (Pearson chi-squared test, P = 0.004), distribution of air bronchiologram (P = 0.0148), and pleural indentation (P = 0.0067). A solid portion >5.3 mm on TSCT indicated MIA or IVA, and >7.3 mm indicated IVA (receiver operating characteristic analysis, P 7.3 mm on TSCT indicates IVA

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma

To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-operative chest computed tomography (CT) with 0.625-mm slice thickness were included in this retrospective study. Twenty-four cases of adenocarcinoma in situ (AIS), 20 cases of minimally invasive adenocarcinoma (MIA), and 46 cases of invasive adenocarcinoma (IVA) were pathologically diagnosed. Three radiologists of different levels of experience diagnosed each nodule by using previously documented CT findings to predict pathological invasiveness. DL was structured using a 3-dimensional (3D) convolutional neural network (3D-CNN) constructed with 2 successive pairs of convolution and max-pooling layers, and 2 fully connected layers. The output layer comprises 3 nodes to recognize the 3 conditions of adenocarcinoma (AIS, MIA, and IVA) or 2 nodes for 2 conditions (AIS and MIA/IVA). Results from DL and the 3 radiologists were statistically compared.No significant differences in pathological diagnostic accuracy rates were seen between DL and the 3 radiologists (P>. 11). Receiver operating characteristic analysis demonstrated that area under the curve for DL (0.712) was almost the same as that for the radiologist with extensive experience (0.714; P=. 98). Compared with the consensus results from radiologists, DL offered significantly inferior sensitivity (P=. 0005), but significantly superior specificity (P=. 02).Despite the small training data set, diagnostic performance of DL was almost the same as the radiologist with extensive experience. In particular, DL provided higher specificity than radiologists

Ultra-High-Resolution Computed Tomography of the Lung: Image Quality of a Prototype Scanner

Purpose: The image noise and image quality of a prototype ultra-high-resolution computed tomography (U-HRCT) scanner was evaluated and compared with those of conventional high-resolution CT (C-HRCT) scanners. Materials and Methods: This study was approved by the institutional review board. A U-HRCT scanner prototype with 0.25 mm × 4 rows and operating at 120 mAs was used. The C-HRCT images were obtained using a 0.5 mm × 16 or 0.5 mm × 64 detector-row CT scanner operating at 150 mAs. Images from both scanners were reconstructed at 0.1-mm intervals; the slice thickness was 0.25 mm for the U-HRCT scanner and 0.5 mm for the C-HRCT scanners. For both scanners, the display field of view was 80 mm. The image noise of each scanner was evaluated using a phantom. U-HRCT and C-HRCT images of 53 images selected from 37 lung nodules were then observed and graded using a 5-point score by 10 board-certified thoracic radiologists. The images were presented to the observers randomly and in a blinded manner. Results: The image noise for U-HRCT (100.87 ± 0.51 Hounsfield units [HU]) was greater than that for C-HRCT (40.41 ± 0.52 HU; P <.0001). The image quality of U-HRCT was graded as superior to that of C-HRCT (P <.0001) for all of the following parameters that were examined: margins of subsolid and solid nodules, edges of solid components and pulmonary ves sels in subsolid nodules, air bronchograms, pleural indentations, margins of pulmonary vessels, edges of bronchi, and interlobar fissures. Conclusion: Despite a larger image noise, the prototype U-HRCT scanner had a significantly better image quality than the C-HRCT scanners

Role of Dlg5/lp-dlg, a Membrane-Associated Guanylate Kinase Family Protein, in Epithelial-Mesenchymal Transition in LLc-PK1 Renal Epithelial Cells

Discs large homolog 5 (Dlg5) is a member of the membrane-associated guanylate kinase adaptor family of proteins, some of which are involved in the regulation of epithelial-to-mesenchymal transition (EMT). Dlg5 has been described as a susceptibility gene for Crohn's disease; however, the physiological function of Dlg5 is unknown. We show here that transforming growth factor-β (TGF-β)-induced EMT suppresses Dlg5 expression in LLc-PK1 cells. Depletion of Dlg5 expression by knockdown promoted the expression of the mesenchymal marker proteins, fibronectin and α-smooth muscle actin, and suppressed the expression of E-cadherin. In addition, activation of JNK and p38, which are stimulated by TGF-β, was enhanced by Dlg5 depletion. Furthermore, inhibition of the TGF-β receptor suppressed the effects of Dlg5 depletion. These observations suggest that Dlg5 is involved in the regulation of TGF-βreceptor-dependent signals and EMT