It is important to fully automate the evaluation of gadoxetate disodium-enhanced arterial phase images because the efficient quantification of transient severe motion artifacts can be used in a variety of applications. Our study proposes a fully automatic evaluation method of motion artifacts during the arterial phase of gadoxetate disodium-enhanced MR imaging.The proposed method was based on the construction of quality-aware features to represent the motion artifact using MR image statistics and multidirectional filtered coefficients. Using the quality-aware features, the method calculated quantitative quality scores of gadoxetate disodium-enhanced images fully automatically. The performance of our proposed method, as well as two other methods, was acquired by correlating scores against subjective scores from radiologists based on the 5-point scale and binary evaluation. The subjective scores evaluated by two radiologists were severity scores of motion artifacts in the evaluation set on a scale of 1 (no motion artifacts) to 5 (severe motion artifacts).Pearson's linear correlation coefficient (PLCC) and Spearman's rank-ordered correlation coefficient (SROCC) values of our proposed method against the subjective scores were 0.9036 and 0.9057, respectively, whereas the PLCC values of two other methods were 0.6525 and 0.8243, and the SROCC values were 0.6070 and 0.8348. Also, in terms of binary quantification of transient severe respiratory motion, the proposed method achieved 0.9310 sensitivity, 0.9048 specificity, and 0.9200 accuracy, whereas the other two methods achieved 0.7586, 0.8996 sensitivities, 0.8098, 0.8905 specificities, and 0.9200, 0.9048 accuracies CONCLUSIONS: This study demonstrated the high performance of the proposed automatic quantification method in evaluating transient severe motion artifacts in arterial phase images.
The majority of strokes are caused by ischemia and result in brain tissue damage, leading to problems of the central nervous system including hemiparesis, dysfunction of language and consciousness, and dysfunction of perception. The purpose of this study was to investigate the effects of Poly(ADP-ribose) polymerase(PARP) on necrosis in neuronal cells that have undergone needle electrode electrical stimulation(NEES) prior to induction of ischemia. Ischemia was induced in male SD rats(body weight 300g) by occlusion of the common carotid artery for 5 min, after which the blood was reperfused. After induction of brain ischemia, NEES was applied to Zusanli(ST 36), at 12, 24 and 48 hours. Protein expression was investigated using immuno-reactive cells, which react to PARP antibodies in cerebral nerve cells, and Western blotting. The results were as follows: In the cerebral cortex, the number of PARP reactive cells after 24 hours significantly decreased(p
Abstract We aimed to predict molecular subtypes of breast cancer using radiomics signatures extracted from synthetic mammography reconstructed from digital breast tomosynthesis (DBT). A total of 365 patients with invasive breast cancer with three different molecular subtypes (luminal A + B, luminal; HER2-positive, HER2; triple-negative, TN) were assigned to the training set and temporally independent validation cohort. A total of 129 radiomics features were extracted from synthetic mammograms. The radiomics signature was built using the elastic-net approach. Clinical features included patient age, lesion size and image features assessed by radiologists. In the validation cohort, the radiomics signature yielded an AUC of 0.838, 0.556, and 0.645 for the TN, HER2 and luminal subtypes, respectively. In a multivariate analysis, the radiomics signature was the only independent predictor of the molecular subtype. The combination of the radiomics signature and clinical features showed significantly higher AUC values than clinical features only for distinguishing the TN subtype. In conclusion, the radiomics signature showed high performance for distinguishing TN breast cancer. Radiomics signatures may serve as biomarkers for TN breast cancer and may help to determine the direction of treatment for these patients.
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility. We tackle the less-explored yet desired functionality in face de-identification based on the two factors. First, we focus on the challenging issue to obtain a high level of privacy protection in the de-identification task while uncompromising the image quality. Second, we analyze the facial attributes related to identity and non-identity and explore the trade-off between the degree of face de-identification and preservation of the source attributes for enhanced data utility. Based on the analysis, we develop Facial Identity Controllable GAN (FICGAN), an autoencoder-based conditional generative model that learns to disentangle the identity attributes from non-identity attributes on a face image. By applying the manifold k-same algorithm to satisfy k-anonymity for strengthened security, our method achieves enhanced privacy protection in de-identified face images. Numerous experiments demonstrate that our model outperforms others in various scenarios of face de-identification.
Training a convolutional neural network (CNN) to reduce noise in low-dose CT (LDCT) images typically relies on supervised learning, which requires input-target pairs of noisy LDCT and corresponding full-dose CT (FDCT) images. Although previous approaches have shown promising results in LDCT image denoising, it is difficult to acquire clinical datasets of LDCT-FDCT image pairs, which require additional and unnecessary radiation dose delivery to patients. In this paper, we propose a self-supervised learning approach to training a denoising model with LDCT images alone. As a means of self-supervision, the proposed approach utilizes the inter- and intra-slice correlation of LDCT images. In order to learn the intra-slice correlation within an LDCT image, some pixels are intentionally blinded and the deep neural network is trained to recover the blind spots by comparing the model output with the original input itself. In addition, we employ the inter-slice correlation by processing adjacent LDCT images and comparing the denoised image to a thicker and less noisy LDCT slice at the same location. For efficient self-supervised learning, we adopt a twostage training strategy with offline pretraining and online finetuning. The proposed approach is thoroughly evaluated with public and private clinical LDCT datasets. Both image quality measures and clinical assessments indicate that the self-supervised denoising model simultaneously reduces noise level and restores anatomical information in LDCT images from the images alone.
To evaluate the usefulness of the radiomic model in predicting early (≤2 years) and late (>2 years) recurrence after curative resection in cases involving a single hepatocellular carcinoma (HCC) 2-5 cm in diameter using preoperative gadoxetic acid-enhanced magnetic resonance imaging (MRI), in comparison with the clinicopathologic model.This retrospective study included 167 patients with surgically resected and pathologically confirmed single HCC 2-5 cm in diameter (n = 167, training set:validation set = 128:39) who underwent preoperative gadoxetic acid-enhanced MRI between January 2010 and December 2015. A radiomic model, a clinicopathologic model, and a combined clinicopathologic-radiomic (CCR) model were built using a random survival forest to predict disease-free survival (DFS) in the following conditions: early DFS versus late DFS, dynamic phases, and the peritumoral area included in the segmentation.The radiomic model showed a prognostic performance comparable with the clinicopathologic model only with 3-mm peritumoral border extension [c-index difference (radiomic-clinicopathologic), -0.021, P = 0.758]. The CCR model with the 3-mm border extension showed the highest c-index value but no statistically significant improvement over the clinicopathologic model [CCR, 0.716 (0.627-0.799); clinicopathologic model, 0.696 (0.557-0.799)].The prognostic value of the preoperative radiomic model with 3-mm border extension showed comparable performance with that of the postoperative clinicopathologic model for predicting DFS of early recurrence of HCC using gadoxetic acid-enhanced MRI. This suggests the importance of including peritumoral changes in the radiomic analysis of HCC.
The purpose of this study was to evaluate the performance of the deep convolutional neural network (DCNN) in differentiating between tuberculous and pyogenic spondylitis on magnetic resonance (MR) imaging, compared to the performance of three skilled radiologists. This clinical retrospective study used spine MR images of 80 patients with tuberculous spondylitis and 81 patients with pyogenic spondylitis that was bacteriologically and/or histologically confirmed from January 2007 to December 2016. Supervised training and validation of the DCNN classifier was performed with four-fold cross validation on a patient-level independent split. The object detection and classification model was implemented as a DCNN and was designed to calculate the deep-learning scores of individual patients to reach a conclusion. Three musculoskeletal radiologists blindly interpreted the images. The diagnostic performances of the DCNN classifier and of the three radiologists were expressed as receiver operating characteristic (ROC) curves, and the areas under the ROC curves (AUCs) were compared using a bootstrap resampling procedure. When comparing the AUC value of the DCNN classifier (0.802) with the pooled AUC value of the three readers (0.729), there was no significant difference (P = 0.079). In differentiating between tuberculous and pyogenic spondylitis using MR images, the performance of the DCNN classifier was comparable to that of three skilled radiologists.
Abstract Purpose: The purpose of this research was to evaluate the feasibility of reduced-intensity unrelated cord-blood transplantation (RI-UCBT) in adult patients with advanced hematological diseases. Experimental Design: Thirty patients (median age, 58.5 years; range, 20–70 years) with advanced hematological diseases underwent RI-UCBT at Toranomon Hospital between September 2002 and August 2003. Preparative regimen composed of fludarabine 25 mg/m2 on days −7 to −3, melphalan 80 mg/m2 on day −2, and 4 Gy total body irradiation on day −1. Graft-versus-host disease prophylaxis was composed of cyclosporin alone. Results: Twenty-six patients achieved primary neutrophil engraftment after a median of 17.5 days. Median infused total cell dose was 3.1 × 107/kg (range, 2.0–4.3 × 107/kg). Two transplant-related mortalities occurred within 28 days of transplant, and another 2 patients displayed primary graft failure. Cumulative incidence of complete donor chimerism at day 60 was 93%. Grade II-IV acute graft-versus-host disease occurred in 27% of patients, with median onset 36 days. Primary disease recurred in 3 patients, and transplant-related mortality within 100 days was 27%. Estimated 1-year overall survival was 32.7%. Excluding 7 patients with documented infection, 19 patients displayed noninfectious fever before engraftment (median onset, day 9). Manifestations included high-grade fever, eruption, and diarrhea. The symptoms responded well to corticosteroid treatments in 7 of 13 treated patients. Conclusion: This study demonstrated the feasibility of RI-UCBT in adults.
'%3e%3cg id='b'%3e%3cpath id='a' stroke='%23000' stroke-width='2' d='M-6-25H6m-12 3H6m-12 3H6'/%3e%3cuse xlink:href='%23a' y='44'/%3e%3c/g%3e%3cpath stroke='%23fff' d='M0 17v10'/%3e%3ccircle r='12' fill='%23cd2e3a'/%3e%3cpath fill='%230047a0' d='M0-12A6 6 0 0 0 0 0a6 6 0 0 1 0 12 12 12 0 0 1 0-24z'/%3e%3c/g%3e%3cg transform='rotate(-123.69)'%3e%3cuse xlink:href='%23b'/%3e%3cpath stroke='%23fff' d='M0-23.5v3M0 17v3.5m0 3v3'/%3e%3c/g%3e%3c/svg%3e)
