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Purpose: To evaluate and compare the image quality and diagnostic accuracy of Artificial Intelligence-assisted Compressed Sensing (ACS) sequences for lumbar disease, as an acceleration method for MRI combining parallel imaging, half-Fourier, compressed sensing and neural network and routine 2D sequences for lumbar spine. Methods: We collected data from 82 healthy subjects and 213 patients who used 2D ACS accelerated sequences to examine the lumbar spine while 95 healthy subjects and 234 patients used routine 2D sequences. Acquisitions included axial T2WI, sagittal T2WI, T1WI, and T2-fs sequences. All obtained images of these subjects were analyzed in the light of calculating image quality factors such as signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) for selected regions of interest. The lumbar image quality, artifacts and visibility of lesion structure were assessed by two radiologists independently. Differences between the evaluation values above were tested for statistical significance by the Wilcoxon signed-ranks test. Inter-observer agreements of image quality between two radiologists were measured using Cohen's kappa correlation coefficient. Results: The ACS accelerated sequences not only reduced the scanning time by 18.9%, but also retained basically the same image quality as the routine 2D sequences in both healthy subjects and patients. Artifacts are less produced on ACS accelerated sequences compared with routine 2D sequences (p < 0.05). Apart from this, there were no significant differences in quantitative SNR, CNR measurements and qualitative scores within reviewing radiologists for each group (p > 0.05). Moreover, inter-observer agreement between two radiologists in scoring image quality was substantial consistently for ACS accelerated sequences and routine sequences (kappa = 0.622– 0.986). Conclusion: Compared with routine 2D sequences, ACS accelerated sequences allow for faster lumbar spine imaging with similar imaging quality and present reliable diagnostic accuracy, which can potentially improve workflow and patient comfort in musculoskeletal examinations. [ABSTRACT FROM AUTHOR]

Purpose: The three-dimensional (3D) sequence of magnetic resonance imaging (MRI) plays a critical role in the imaging of musculoskeletal joints; however, its long acquisition time limits its clinical application. In such conditions, compressed sensing (CS) is introduced to accelerate MRI in clinical practice. We aimed to investigate the feasibility of an isotropic 3D variable-flip-angle fast spin echo (FSE) sequence with CS technique (CS-MATRIX) compared to conventional 2D sequences in knee imaging. Methods: Images from different sequences of both the accelerated CS-MATRIX and the corresponding conventional acquisitions were prospectively analyzed and compared. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the structures within the knees were measured for quantitative analysis. The subjective image quality and diagnostic agreement were compared between CS-MATRIX and conventional 2D sequences. Quantitative and subjective image quality scores were statistically analyzed with the paired t-test and Wilcoxon signed-rank test, respectively. Diagnostic agreements of knee substructure were assessed using Cohen's weighted kappa statistic. Results: For quantitative analysis, images from the CS-MATRIX sequence showed a significantly higher SNR than T2-fs 2D sequences for visualizing cartilage, menisci, and ligaments, as well as a higher SNR than proton density (pd) 2D sequences for visualizing menisci and ligaments. There was no significant difference between CS-MATRIX and 2D T2-fs sequences in subjective image quality assessment. The diagnostic agreement was rated as moderate to very good between CS-MATRIX and 2D sequences. Conclusion: This study demonstrates the feasibility and clinical potential of the CS-MATRIX sequence technique for detecting knee lesions The CS-MATRIX sequence allows for faster knee imaging than conventional 2D sequences, yielding similar image quality to 2D sequences. [ABSTRACT FROM AUTHOR]

Non‑small cell lung cancer (NSCLC) is one of leading causes of cancer‑associated mortality, with a high number of cases caused by metastasis. The early diagnosis of cancer contributes to the successful treatment of patients with lung cancer. The aim of the present study was to analyze the efficacy of marker gene detection and computed tomography (CT) in diagnosing human lung cancer. Lung cancer marker genes, including carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), tissue polypeptide antigen (TPA), pro‑gastrin‑releasing peptide (ProGRB), cytokeratin fragment 21‑1 (Cyfra21‑1) and neuron‑specific enolase (NSE), were analyzed in patients with lung cancer. The tumor size was evaluated using CT, and the association between lung serum levels of marker gene protein expression and tumor size was investigated. A total of 328 patients with lung cancer were identified, including 204 adenocarcinoma, 75 large cell carcinoma and 49 squamous cell carcinoma cases. All patients were indicated to have a high serum level of CEA, CA125, TPA, ProGRB, Cyfra21‑1 and NSE, compared with the normal range. Immunohistochemistry demonstrated higher expression levels of CEA, CA125, TPA, ProGRB, Cyfra21‑1 and NSE in lung tumor tissues, compared with the normal range. Results indicated that CT was able to diagnose tumor size for patients with lung cancer. The CEA and CA125 expression levels were associated with CT‑diagnosed adenocarcinoma tumor size. Large cell carcinoma tumor size was associated with serum levels of CEA, TPA and ProGRB. Results indicated that Cyfra21‑1 and NSE were associated with the squamous cell carcinoma cases, as demonstrated using CT. In conclusion, these results indicated that comprehensive analysis of marker gene detection and CT results may be used to diagnose human lung cancer. [ABSTRACT FROM AUTHOR]

Background: Stroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients., Purpose: We aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke., Materials and Methods: A total of 476 patients were enrolled in the study and divided into the training set ( n = 381) and testing set ( n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)-binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients-the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation., Results: In the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781-0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model., Conclusion: The novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency., Summary: Combining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay., Key Results: Using a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800)., Competing Interests: JW, QZ, HY, YS, SL, and FS were employed by Shanghai United Imaging Intelligence Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer, JS, declared a past co-authorship with several of the authors, SL and FS to the handling editor., (Copyright © 2022 Sui, Wu, Zhou, Liu, Lv, Zhang, Yang, Shen, Liao, Shi and Mo.)

Homogeneous and monodisperse GdPO4 ·H2 O nanobundles are successfully synthesized via a solvothermal method. Then, GdPO4 ·H2 O are incorporated into the composite of hydroxyapatite and poly(lactic-co-glycolic acid) to obtain a biodegradable and traceable bone implant. After implanted, the GdPO4 ·H2 O/HA/PLGA implant and the newly formed bone can be easily traced and observed through the combination of magnetic resonance imaging and X-ray imaging., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)