Comprehensive elucidation of resting-state functional connectivity in anorexia nervosa by a multicenter cross-sectional study.

BACKGROUND: Previous research on the changes in resting-state functional connectivity (rsFC) in anorexia nervosa (AN) has been limited by an insufficient sample size, which reduced the reliability of the results and made it difficult to set the whole brain as regions of interest (ROIs). METHODS: We analyzed functional magnetic resonance imaging data from 114 female AN patients and 135 healthy controls (HC) and obtained self-reported psychological scales, including eating disorder examination questionnaire 6.0. One hundred sixty-four cortical, subcortical, cerebellar, and network parcellation regions were considered as ROIs. We calculated the ROI-to-ROI rsFCs and performed group comparisons. RESULTS: Compared to HC, AN patients showed 12 stronger rsFCs mainly in regions containing dorsolateral prefrontal cortex (DLPFC), and 33 weaker rsFCs primarily in regions containing cerebellum, within temporal lobe, between posterior fusiform cortex and lateral part of visual network, and between anterior cingulate cortex (ACC) and thalamus (p < 0.01, false discovery rate [FDR] correction). Comparisons between AN subtypes showed that there were stronger rsFCs between right lingual gyrus and right supracalcarine cortex and between left temporal occipital fusiform cortex and medial part of visual network in the restricting type compared to the binge/purging type (p < 0.01, FDR correction). CONCLUSION: Stronger rsFCs in regions containing mainly DLPFC, and weaker rsFCs in regions containing primarily cerebellum, within temporal lobe, between posterior fusiform cortex and lateral part of visual network, and between ACC and thalamus, may represent categorical diagnostic markers discriminating AN patients from HC.

Anxiety and its risk factors among non-Japanese residents living in Japan undergoing COVID-19 situation: A cross-sectional survey.

INTRODUCTION: In the context of collective efforts taken in Japan to control the spread of COVID-19, the state of emergency and social distancing have caused a negative impact on the mental health of all residents, including foreign communities in Japan. This study aimed to evaluate the level of anxiety and its associated factors among non-Japanese residents residing in Japan during the COVID-19 pandemic. METHODS: A web-based survey in 13 languages was conducted among non-Japanese residents living in Japan during the COVID-19 situation. The State-Trait Anxiety Inventory assessed the level of anxiety-State (STAI-S) scores prorated from its six-item version. The multivariable logistic regression using the Akaike Information Criterion (AIC) method was performed to identify the associated factors of anxiety among participants. RESULTS: From January to March 2021, we collected 392 responses. A total of 357 valid responses were analyzed. 54.6% of participants suffered from clinically significant anxiety (CSA). In multivariable logistic model analysis, the CSA status or the high level of anxiety was associated with three factors, including having troubles/difficulties in learning or working, decreased sleep duration, and decreased overall physical health (p<0.05). CONCLUSION: Our study suggests several possible risk factors of anxiety among non-Japanese residents living in Japan undergoing the COVID-19 pandemic, including the troubles or difficulties in learning or working, the decrease in sleep duration, and the decrease in overall physical health.

A JROD survey: nationwide overview of radiotherapy data from 2015 to 2021.

The purpose of this survey was to examine the status of radiotherapy in Japan based on the cases registered in the Japanese Radiation Oncology Database (JROD), from 2015 to 2021, and to provide basic data to help improve the usefulness of the JROD in the future. The study population consisted of patients who underwent radiotherapy between 2014 and 2020 and did not opt out of the study. The survey item data analyzed in this study were entered into the database at each radiotherapy institution by referring to medical records from the preceding year. Our results show that the number of registered radiotherapy institutions and cases increased by ~50% in 2019 compared to those in 2015 (to 113 institutions and 60 575 cases, respectively). Among the survey item categories, the registration rate was lowest for prognostic information (13.9% on average over the 7-year period). In terms of the Japanese Society for Radiation Oncology disease site, the breast; lung, trachea and mediastinum and urogenital sites accounted for >50% of the total cases. The average survival and mortality rates over the 7-year study period were 67.4 and 17.4%, respectively. The X-ray radiotherapy completion rate exceeded 90% for all years and across all disease categories. 192Ir-based brachytherapy and 223Ra-based radionuclide therapy accounted for an average of 61.9 and 44.6%, respectively, of all corresponding cases over the 7-year period. In conclusion, this survey enables us to infer the actual status of radiotherapy in Japan based on the analysis of relevant nationwide data.

Deep Learning-based Post Hoc CT Denoising for the Coronary Perivascular Fat Attenuation Index.

RATIONALE AND OBJECTIVES: Coronary inflammation related to high-risk hemorrhagic plaques can be captured by the perivascular fat attenuation index (FAI) using coronary computed tomography angiography (CCTA). Since the FAI is susceptible to image noise, we believe deep learning (DL)-based post hoc noise reduction can improve diagnostic capability. We aimed to assess the diagnostic performance of the FAI in DL-based denoised high-fidelity CCTA images compared with coronary plaque magnetic resonance imaging (MRI) delivered high-intensity hemorrhagic plaques (HIPs). MATERIALS AND METHODS: We retrospectively reviewed 43 patients who underwent CCTA and coronary plaque MRI. We generated high-fidelity CCTA images by denoising the standard CCTA images using a residual dense network that supervised the denoising task by averaging three cardiac phases with nonrigid registration. We measured the FAIs as the mean CT value of all voxels (range of -190 to -30 HU) located within a radial distance from the outer proximal right coronary artery wall. The diagnostic reference standard was defined as HIPs (high-risk hemorrhagic plaques) using MRI. The diagnostic performance of the FAI in the original and denoised images was assessed using receiver operating characteristic curves. RESULTS: Of 43 patients, 13 had HIPs. The denoised CCTA improved the area under the curve (0.89 [95% confidence interval (CI) 0.78-0.99]) of the FAI compared with that in the original image (0.77 [95% CI, 0.62-0.91], p = 0.008). The optimal cutoff value for predicting HIPs in denoised CCTA was -69 HU with 0.85 (11/13) sensitivity, 0.79 (25/30) specificity, and 0.80 (36/43) accuracy. CONCLUSION: DL-based denoised high-fidelity CCTA improved the AUC and specificity of the FAI for predicting HIPs.

Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth.

BACKGROUND: To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses. PURPOSE: To develop a deep learning-based noise-reduction method for CCTA using four-dimensional noise reduction (4DNR) as the ground truth for supervised learning. MATERIAL AND METHODS: \We retrospectively collected 100 retrospective ECG-gated CCTAs. We created 4DNR images using non-rigid registration and weighted averaging three timeline CCTA volumetric data with intervals of 50 ms in the mid-diastolic phase. Our method set the original reconstructed image as the input and the 4DNR as the target image and obtained the noise-reduced image via residual learning. We evaluated the objective image quality of the original and deep learning-based noise-reduction (DLNR) images based on the image noise of the aorta and the contrast-to-noise ratio (CNR) of the coronary arteries. Further, a board-certified radiologist evaluated the blurring of several heart structures using a 5-point Likert scale subjectively and assigned a coronary artery disease reporting and data system (CAD-RADS) category independently. RESULTS: DLNR CCTAs showed 64.5% lower image noise (P < 0.001) and achieved a 2.9 times higher CNR of coronary arteries than that in original images, without significant blurring in subjective comparison (P > 0.1). The intra-observer agreement of CAD-RADS in the DLNR image was excellent (0.87, 95% confidence interval = 0.77-0.99) with original CCTAs. CONCLUSION: Our DLNR method supervised by 4DNR significantly reduced the image noise of CCTAs without affecting the assessment of coronary stenosis.

Generative adversarial network-based post-processed image super-resolution technology for accelerating brain MRI: comparison with compressed sensing.

BACKGROUND: It is unclear whether deep-learning-based super-resolution technology (SR) or compressed sensing technology (CS) can accelerate magnetic resonance imaging (MRI) . PURPOSE: To compare SR accelerated images with CS images regarding the image similarity to reference 2D- and 3D gradient-echo sequence (GRE) brain MRI. MATERIAL AND METHODS: We prospectively acquired 1.3× and 2.0× faster 2D and 3D GRE images of 20 volunteers from the reference time by reducing the matrix size or increasing the CS factor. For SR, we trained the generative adversarial network (GAN), upscaling the low-resolution images to the reference images with twofold cross-validation. We compared the structural similarity (SSIM) index of accelerated images to the reference image. The rate of incorrect answers of a radiologist discriminating faster and reference image was used as a subjective image similarity (ISM) index. RESULTS: The SR demonstrated significantly higher SSIM than the CS (SSIM=0.9993-0.999 vs. 0.9947-0.9986; P < 0.001). In 2D GRE, it was challenging to discriminate the SR image from the reference image, compared to the CS (ISM index 40% vs. 17.5% in 1.3×; P = 0.039 and 17.5% vs. 2.5% in 2.0×; P = 0.034). In 3D GRE, the CS revealed a significantly higher ISM index than the SR (22.5% vs. 2.5%; P = 0.011) in 2.0 × faster images. However, the ISM index was identical for the 2.0× CS and 1.3× SR (22.5% vs. 27.5%; P = 0.62) with comparable time costs. CONCLUSION: The GAN-based SR outperformed CS in image similarity with 2D GRE for MRI acceleration. In addition, CS was more advantageous in 3D GRE than SR.

Individual cognitive therapy reduces frontal-thalamic resting-state functional connectivity in social anxiety disorder.

Introduction: Previous neuroimaging studies in social anxiety disorders (SAD) have reported potential neural predictors of cognitive behavioral therapy (CBT)-related brain changes. However, several meta-analyses have demonstrated that cognitive therapy (CT) was superior to traditional exposure-based CBT for SAD. Objective: To explore resting-state functional connectivity (rsFC) to evaluate the response to individual CT for SAD patients. Methods: Twenty SAD patients who attended 16-week individual CT were scanned pre- and post-therapy along with twenty healthy controls (HCs). The severity of social anxiety was assessed with the Liebowitz Social Anxiety Scale (LSAS). Multi-voxel pattern analysis (MVPA) was performed on the pre-CT data to extract regions associated with a change in LSAS (∆LSAS). Group comparisons of the seed-based rsFC analysis were performed between the HCs and pre-CT patients and between the pre-and post-CT patients. Results: MVPA-based regression analysis revealed that rsFC between the left thalamus and the frontal pole/inferior frontal gyrus was significantly correlated with ∆LSAS (adjusted R2 = 0.65; p = 0.00002). Compared with HCs, the pre-CT patients had higher rsFCs between the thalamus and temporal pole and between the thalamus and superior/middle temporal gyrus/planum temporale (p < 0.05). The rsFC between the thalamus and the frontal pole decreased post-CT (p < 0.05). Conclusion: SAD patients had significant rsFC between the thalamus and temporal pole, superior/middle temporal gyrus, and planum temporale, which may be indicators of extreme anxiety in social situations. In addition, rsFC between the thalamus and the frontal pole may be a neuromarker for the effectiveness of individual CT.

Super-resolution generative adversarial networks with static T2*WI-based subject-specific learning to improve spatial difference sensitivity in fMRI activation.

The spatial resolution of fMRI is relatively poor and improvements are needed to indicate more specific locations for functional activities. Here, we propose a novel scheme, called Static T2*WI-based Subject-Specific Super Resolution fMRI (STSS-SRfMRI), to enhance the functional resolution, or ability to discriminate spatially adjacent but functionally different responses, of fMRI. The scheme is based on super-resolution generative adversarial networks (SRGAN) that utilize a T2*-weighted image (T2*WI) dataset as a training reference. The efficacy of the scheme was evaluated through comparison with the activation maps obtained from the raw unpreprocessed functional data (raw fMRI). MRI images were acquired from 30 healthy volunteers using a 3 Tesla scanner. The modified SRGAN reconstructs a high-resolution image series from the original low-resolution fMRI data. For quantitative comparison, several metrics were calculated for both the STSS-SRfMRI and the raw fMRI activation maps. The ability to distinguish between two different finger-tapping tasks was significantly higher [p = 0.00466] for the reconstructed STSS-SRfMRI images than for the raw fMRI images. The results indicate that the functional resolution of the STSS-SRfMRI scheme is superior, which suggests that the scheme is a potential solution to realizing higher functional resolution in fMRI images obtained using 3T MRI.

Deep Learning-based Post Hoc CT Denoising for Myocardial Delayed Enhancement.

Background To improve myocardial delayed enhancement (MDE) CT, a deep learning (DL)-based post hoc denoising method supervised with averaged MDE CT data was developed. Purpose To assess the image quality of denoised MDE CT images and evaluate their diagnostic performance by using late gadolinium enhancement (LGE) MRI as a reference. Materials and methods MDE CT data obtained by averaging three acquisitions with a single breath hold 5 minutes after the contrast material injection in patients from July 2020 to October 2021 were retrospectively reviewed. Preaveraged images obtained in 100 patients as inputs and averaged images as ground truths were used to supervise a residual dense network (RDN). The original single-shot image, standard averaged image, RDN-denoised original (DLoriginal) image, and RDN-denoised averaged (DLave) image of holdout cases were compared. In 40 patients, the CT value and image noise in the left ventricular cavity and myocardium were assessed. The segmental presence of MDE in the remaining 40 patients who underwent reference LGE MRI was evaluated. The sensitivity, specificity, and accuracy of each type of CT image and the improvement in accuracy achieved with the RDN were assessed using odds ratios (ORs) estimated with the generalized estimation equation. Results Overall, 180 patients (median age, 66 years [IQR, 53-74 years]; 107 men) were included. The RDN reduced image noise to 28% of the original level while maintaining equivalence in the CT values (P < .001 for all). The sensitivity, specificity, and accuracy of the original images were 77.9%, 84.4%, and 82.3%, of the averaged images were 89.7%, 87.9%, and 88.5%, of the DLoriginal images were 93.1%, 87.5%, and 89.3%, and of the DLave images were 95.1%, 93.1%, and 93.8%, respectively. DLoriginal images showed improved accuracy compared with the original images (OR, 1.8 [95% CI: 1.2, 2.9]; P = .011) and DLave images showed improved accuracy compared with the averaged images (OR, 2.0 [95% CI: 1.2, 3.5]; P = .009). Conclusion The proposed denoising network supervised with averaged CT images reduced image noise and improved the diagnostic performance for myocardial delayed enhancement CT. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Vannier and Wang in this issue.

Effect of the COVID-19 pandemic on heatstroke-related ambulance dispatch in the 47 prefectures of Japan.

In 2020, Coronavirus disease 2019 (COVID-19) pandemic has brought a huge impact in daily life and has prompted people to take preventive measures. In the summertime, however, the Japanese government has cautioned that some COVID-19 pandemic conditions may affect the risk to heatstroke. This study investigated how the COVID-19 pandemic setting affected heatstroke-related ambulance dispatches (HSAD). Daily HSAD data and relevant weather parameters from June to September from 2016 to 2020 of 47 prefectures in Japan were obtained from the Fire and Disaster Management Agency (FDMA) database. A binary variable representing COVID-19 impact was created, whereby years 2016 to 2019 were coded as 0, while 2020 as 1. We employed a two-stage analysis in elucidating the impact of COVID-19 pandemic on HSAD. Firstly, we regressed HSAD with the COVID-19 binary variable after adjusting for relevant covariates to obtain prefecture-specific effect estimates. Prefecture-specific estimates were subsequently pooled via random effects meta-analysis in generating the pooled estimate. Pooled Relative Risk (RR) of HSAD during the COVID-19 pandemic was 0.78 (95% Confidential Interval [CI], 0.75-0.82). We found an overall statistically significant decrease in HSAD risk during the COVID-19 pandemic in Japan. Specifically, the decrease in the risk of HSAD may be linked to the COVID-19 precautionary measures such as stay-home request and availability of alternative consultation services, which may have decreased the direct exposure of the population to extreme heat.

Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network.

We have developed a deep learning-based approach to improve image quality of single-shot turbo spin-echo (SSTSE) images of female pelvis. We aimed to compare the deep learning-based single-shot turbo spin-echo (DL-SSTSE) images of female pelvis with turbo spin-echo (TSE) and conventional SSTSE images in terms of image quality.One hundred five and 21 subjects were used as training and test sets, respectively. We performed 6-fold cross validation. In the training process, low-quality images were generated from TSE images as input. TSE images were used as ground truth images. In the test process, the trained convolutional neural network was applied to SSTSE images. The output images were denoted as DL-SSTSE images. Apart from DL-SSTSE images, classical filtering methods were adopted to SSTSE images. Generated images were denoted as F-SSTSE images. Contrast ratio (CR) of gluteal fat and myometrium and signal-to-noise ratio (SNR) of gluteal fat were measured for all images. Two radiologists graded these images using a 5-point scale and evaluated the image quality with regard to overall image quality, contrast, noise, motion artifact, boundary sharpness of layers in the uterus, and the conspicuity of the ovaries. CRs, SNRs, and image quality scores were compared using the Steel-Dwass multiple comparison tests.CRs and SNRs were significantly higher in DL-SSTSE, F-SSTSE, and TSE images than in SSTSE images. Scores with regard to overall image quality, contrast, noise, and boundary sharpness of layers in the uterus were significantly higher on DL-SSTSE and TSE images than on SSTSE images. There were no significant differences in the CRs, SNRs, and respective scores between DL-SSTSE and TSE images. The score with regard to motion artifacts was significantly higher on DL-SSTSE, F-SSTSE, and SSTSE images than on TSE images. The score with regard to the conspicuity of ovaries was significantly higher on DL-SSTSE images than on F-SSTSE, SSTSE, and TSE images (P < .001).DL-SSTSE images showed higher image quality as compared with SSTSE images. In comparison with conventional TSE images, DL-SSTSE images had acceptable image quality while keeping the advantage of the motion artifact-robustness and acquisition time efficiency in SSTSE imaging.

Deep Learning Electronic Cleansing for Single- and Dual-Energy CT Colonography.

Electronic cleansing (EC) is used for computational removal of residual feces and fluid tagged with an orally administered contrast agent on CT colonographic images to improve the visibility of polyps during virtual endoscopic "fly-through" reading. A recent trend in CT colonography is to perform a low-dose CT scanning protocol with the patient having undergone reduced- or noncathartic bowel preparation. Although several EC schemes exist, they have been developed for use with cathartic bowel preparation and high-radiation-dose CT, and thus, at a low dose with noncathartic bowel preparation, they tend to generate cleansing artifacts that distract and mislead readers. Deep learning can be used for improvement of the image quality with EC at CT colonography. Deep learning EC can produce substantially fewer cleansing artifacts at dual-energy than at single-energy CT colonography, because the dual-energy information can be used to identify relevant material in the colon more precisely than is possible with the single x-ray attenuation value. Because the number of annotated training images is limited at CT colonography, transfer learning can be used for appropriate training of deep learning algorithms. The purposes of this article are to review the causes of cleansing artifacts that distract and mislead readers in conventional EC schemes, to describe the applications of deep learning and dual-energy CT colonography to EC of the colon, and to demonstrate the improvements in image quality with EC and deep learning at single-energy and dual-energy CT colonography with noncathartic bowel preparation. ©RSNA, 2018.

Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT.

In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.

Clinical impact of pre-transplant gut microbial diversity on outcomes of allogeneic hematopoietic stem cell transplantation.

Post-transplant microbial diversity in the gastrointestinal tract is closely associated with clinical outcomes following allogeneic hematopoietic stem cell transplantation (allo-HSCT). However, little is known about the impact of the fecal microbiota before allo-HSCT. We analyzed fecal samples approximately 2 weeks before conditioning among 107 allo-HSCT recipients between 2013 and 2015. Microbial analysis was performed using 16S rRNA gene sequencing. Operational taxonomic unit-based microbial diversity was estimated by calculating the Shannon index. Patients were classified into three groups based on the diversity index: low (<2), intermediate (2, 3), and high (>3) diversity (18 (16.8%), 48 (44.9%), and 41 (38.3%) patients, respectively). There were no significant differences in the 20-month overall survival, cumulative incidence of relapse, and non-relapse mortality among three groups. The cumulative incidence of grade II to IV acute graft-versus-host disease (aGVHD) was similar among the three groups (low 55.6%; intermediate 35.4%; high 48.8%, p = 0.339, at day 100). Furthermore, we found no differences in the cumulative incidence of grade II to IV acute gastrointestinal GVHD among the three groups (low 38.9%; intermediate 21.3%; high 24.4%, p = 0.778, at day 100). Regarding the composition of microbiota before allo-HSCT, aGVHD patients showed a significantly higher abundance of phylum Firmicutes (p < 0.01) and a lower tendency for Bacteroidetes (p = 0.106) than non-aGVHD patients. Maintenance of Bacteroidetes throughout allo-HSCT may be a strategy to prevent aGVHD.

Dental health behavior of parents of children using non-fluoride toothpaste: a cross-sectional study.

BACKGROUND: One of the dental health goals of Health Japan 21, in which the Japanese government clarified its health policy, was to ensure the use of fluoride toothpaste in 90% or more of schoolchildren. This goal was not achieved. The aim of this cross-sectional questionnaire study was to evaluate the characteristics of parents whose children use non-fluoride toothpaste. METHODS: In December 2010, questionnaire forms were sent to 18 elementary schools or school dentists. Students (6-12 years old) were asked to take the forms home for their parents to fill in, and to bring the completed questionnaire to school. The collected questionnaires were mailed from schools to the author's institution by the end of March 2011. The relationship between fluoride in toothpaste and reasons for choice of toothpaste, the child's toothbrushing habits, and attitude toward child caries prevention was examined in the 6,069 respondents who answered all the questions for the analyses and indicated that their children use toothpaste. RESULTS: Non-fluoride toothpaste users accounted for 5.1% of all toothpaste users. Among the children using non-fluoride toothpaste, significantly greater numbers gave 'anti-gingivitis', 'halitosis prevention' or 'tartar control' as reasons for choice of toothpaste; did not give 'has fluoride', 'is cheaper' or 'tastes good' as reasons for choice of toothpaste; or used toothpaste sometimes, or were in 4th - 6th grades. There was no significant relationship between use of non-fluoride toothpaste and measures taken for caries prevention in children. Multilevel (first level: individual, second level: school) logistic regression analysis indicated that use of non-fluoride toothpaste was significantly related to: giving 'anti-gingivitis' (odds ratio: 1.44) as a reason for choice of toothpaste; not giving 'has fluoride' (0.40), 'tastes good' (0.49) or 'is cheaper' (0.50) as the reason for choice of toothpaste; to toothbrushing less often (twice a day: 1.34, once a day or less: 1.46) and to using toothpaste less often (sometimes: 1.39). CONCLUSIONS: It is necessary to teach parents that dental caries is the dental health issue with the highest priority for children, and therefore fluoride toothpaste should be used.