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ObjectivesDeep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).MethodsOur retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared.ResultsThe image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality.ConclusionsDLR improved the quality of abdominal U-HRCT images.Key Points• The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen.• Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.

Objectives We evaluated lower dose (LD) hepatic dynamic ultra-high-resolution computed tomography (U-HRCT) images reconstructed with deep learning reconstruction (DLR), hybrid iterative reconstruction (hybrid-IR), or model-based IR (MBIR) in comparison with standard-dose (SD) U-HRCT images reconstructed with hybrid-IR as the reference standard to identify the method that allowed for the greatest radiation dose reduction while preserving the diagnostic value. Methods Evaluated were 72 patients who had undergone hepatic dynamic U-HRCT; 36 were scanned with the standard radiation dose (SD group) and 36 with 70% of the SD (lower dose [LD] group). Hepatic arterial and equilibrium phase (HAP, EP) images were reconstructed with hybrid-IR in the SD group, and with hybrid-IR, MBIR, and DLR in the LD group. One radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise. The overall image quality was assessed by 3 other radiologists; they used a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). Superiority and equivalence with prespecified margins were assessed. Results With respect to the image noise, in the HAP and EP, LD DLR and LD MBIR images were superior to SD hybrid-IR images; LD hybrid-IR images were neither superior nor equivalent to SD hybrid-IR images. With respect to the quality scores, only LD DLR images were superior to SD hybrid-IR images. Conclusions DLR preserved the quality of abdominal U-HRCT images even when scanned with a reduced radiation dose. Key Points • Lower dose DLR images were superior to the standard-dose hybrid-IR images quantitatively and qualitatively at abdominal U-HRCT. • Neither hybrid-IR nor MBIR may allow for a radiation dose reduction at abdominal U-HRCT without compromising the image quality. • Because DLR allows for a reduction in the radiation dose and maintains the image quality even at the thinnest slice section, DLR should be applied to abdominal U-HRCT scans.

PurposeDeep learning reconstruction (DLR) introduces deep convolutional neural networks into the reconstruction flow. We examined the clinical applicability of drip-infusion cholangiography (DIC) acquired on an ultra-high-resolution CT (U-HRCT) scanner reconstructed with DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).MethodsThis retrospective, single-institution study included 30 patients seen between January 2018 and November 2019. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) in the common bile duct. The overall visual image quality of the bile duct on thick-slab maximum intensity projections was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (not delineated) to 5 (clearly delineated). The difference among hybrid-IR, MBIR, and DLR images was compared.ResultsThe image noise was significantly lower on DLR than hybrid-IR and MBIR images and the CNR and the overall visual image quality of the bile duct were significantly higher on DLR than on hybrid-IR and MBIR images (all: p < 0.001).ConclusionDLR resulted in significant quantitative and qualitative improvement of DIC acquired with U-HRCT.

Deep learning-based spectral CT imaging (DL-SCTI) is a novel type of fast kilovolt-switching dual-energy CT equipped with a cascaded deep-learning reconstruction which completes the views missing in the sinogram space and improves the image quality in the image space because it uses deep convolutional neural networks trained on fully sampled dual-energy data acquired via dual kV rotations. We investigated the clinical utility of iodine maps generated from DL-SCTI scans for assessing hepatocellular carcinoma (HCC). In the clinical study, dynamic DL-SCTI scans (tube voltage 135 and 80 kV) were acquired in 52 patients with hypervascular HCCs whose vascularity was confirmed by CT during hepatic arteriography. Virtual monochromatic 70 keV images served as the reference images. Iodine maps were reconstructed using three-material decomposition (fat, healthy liver tissue, iodine). A radiologist calculated the contrast-to-noise ratio (CNR) during the hepatic arterial phase (CNR a ) and the equilibrium phase (CNR e ). In the phantom study, DL-SCTI scans (tube voltage 135 and 80 kV) were acquired to assess the accuracy of iodine maps; the iodine concentration was known. The CNR a was significantly higher on the iodine maps than on 70 keV images ( p  < 0.01). The CNR e was significantly higher on 70 keV images than on iodine maps ( p  < 0.01). The estimated iodine concentration derived from DL-SCTI scans in the phantom study was highly correlated with the known iodine concentration. It was underestimated in small-diameter modules and in large-diameter modules with an iodine concentration of less than 2.0 mgI/ml. Iodine maps generated from DL-SCTI scans can improve the CNR for HCCs during hepatic arterial phase but not during equilibrium phase in comparison with virtual monochromatic 70 keV images. Also, when the lesion is small or the iodine concentration is low, iodine quantification may result in underestimation.

Because acute small bowel ischemia has a high mortality rate, it requires rapid intervention to avoid unfavorable outcomes. Computed tomography (CT) examination is important for the diagnosis of bowel ischemia. Acute small bowel ischemia can be the result of small bowel obstruction or mesenteric ischemia, including mesenteric arterial occlusion, mesenteric venous thrombosis, and non-occlusive mesenteric ischemia. The clinical significance of each CT finding is unique and depends on the underlying pathophysiology. This review describes the definition and mechanism(s) of bowel ischemia, reviews CT findings suggesting bowel ischemia, details factors involved in the development of small bowel ischemia, and presents CT findings with respect to the different factors based on the underlying pathophysiology. Such knowledge is needed for accurate treatment decisions.

The original version of this article, published on 11 April 2019, unfortunately, contained a mistake. The following correction has therefore been made in the original: The image in Fig. 3c was wrong. The corrected figure is given below. The original article has been corrected.

According to the ADD model [1], deviation from Newton's inverse square law is expected at below sub-millimeter scale. Present study is an experimental investigation of the Newton's gravitational law at a short range scale. We have developed an experimental setup using torsion balance bar, and succeeded to confirm the inverse square law at a centimeter scale. In addition, composition dependence of gravitational constant G is also tested at the centimeter scale, motivated to test the weak equivalence principle.

A new experimental technique for short range gravity measurements using pico-precision displacement sensor using digital image analysis is presented. We have developed a new experimental setup using torsion balance pendulum, aiming to test the Newton's inverse square law at below millimeter scale. Weak equivalence principle can also be tested using the same experimental setup. Detector techniques and the detail of the experimental setup are described.

Allergic asthma is one of most famous allergic diseases, which develops lung and airway inflammation. Recent studies have revealed the relationship between the pathology of allergic asthma and the increase of host-derived DNA in inflamed lung, but the role of the DNA-recognizing innate immune receptor for the inflammation is unknown well. Here we investigated the role of Toll-Like Receptor 9 in the pathogenesis of allergic asthma without synthesized CpG-ODNs. To examine that, we analyzed the pathology and immunology of house-dust-mite (HDM)-induced allergic asthma in Tlr9 –/– mice and TLR9-inhibitory-antibody-treated mice. In Tlr9 –/– mice, airway hyperresponsiveness (AHR) and the number of eosinophils decreased, and production of the Th2 cytokines IL-13, IL-5, and IL-4 was suppressed, compared with in wild-type mice. Interestingly, unlike Th2 cytokine production, IL-17A production was increased in Tlr9 –/– mice. Furthermore, production of IL-2, which decreases IL-17A production, was reduced in Tlr9 –/– mice. Blockade of TLR9 by treatment with TLR9-inhibitory-antibody, NaR9, effectively suppressed the development of allergic asthma pathology. IL-17A production in NaR9-treated mice was enhanced, which is comparable to Tlr9 -/- mice. These results suggest that the TLR9–IL-2 axis plays an important role in Th2 inflammation by modulating IL-17A production in HDM-induced allergic asthma and that targeting of TLR9 might be a novel therapeutic method for allergic asthma.