目的：分析口咽部B细胞来源非霍奇金淋巴瘤(NHL)的CT表现、特征，初步探讨不同病理类型B细胞来源NHL的CT表现特点，为临床诊断和治疗提供更为准确的信息。方法：对18例经病理证实的口咽部B细胞来源非霍奇金淋巴瘤的CT表现进行回顾性分析。结果：18例中，弥漫大B细胞淋巴瘤13例，占72.2%(13/18)，滤泡性淋巴瘤3例，占16.7%(3/18)，套细胞淋巴瘤1例，占5.6%(1/18)，结外边缘区淋巴瘤(MALT淋巴瘤)1例，占5.6%(1/18)。病变分布为：扁桃体NHL9例(弥漫大B细胞淋巴瘤8例、套细胞淋巴瘤1例)；舌根8例(弥漫大B细胞淋巴瘤5例、滤泡性淋巴瘤3例)；软腭1例，为结外边缘区淋巴瘤(MALT淋巴瘤)。18例病变均表现为肿块型。同时有淋巴结受累者12例(66.7%)，其中双侧受累者3例。结论：口咽B细胞来源NHL多发生于扁桃体及舌根。病理类型以弥漫大B细胞淋巴瘤为主，主要表现为肿块。 CT对于B细胞来源NHL的鉴别诊断和病变范围的判断具有重要作用。
Post-traumatic stress disorder (PTSD) is a mental disorder causing great distress to individuals, families and even society, and there is not yet effective way of unified prevention and treatment up till now. Lots of neuroimaging techniques, however, such as the magnetic resonance imaging, are widely used to the study of the pathogenesis of PTSD with the development of medical imaging. Functional magnetic resonance imaging (fMRI) can be applied to detect the abnormalities not only of the brain morphology but also of the function of various cerebral areas and neural circuit, and plays an important role in studying the pathogenesis of psychiatric diseases. In this paper, we mainly review the task-related and resting-state functional magnetic resonance imaging studies of the PTSD, and finally suggest possible directions for future research.
The automatic detection of arrhythmia is of great significance for the early prevention and diagnosis of cardiovascular diseases. Traditional arrhythmia diagnosis is limited by expert knowledge and complex algorithms, and lacks multi-dimensional feature representation capabilities, which is not suitable for wearable electrocardiogram (ECG) monitoring equipment. This study proposed a feature extraction method based on autoregressive moving average (ARMA) model fitting. Different types of heartbeats were used as model inputs, and the characteristic of fast and smooth signal was used to select the appropriate order for the arrhythmia signal to perform coefficient fitting, and complete the ECG feature extraction. The feature vectors were input to the support vector machine (SVM) classifier and K-nearest neighbor classifier (KNN) for automatic ECG classification. MIT-BIH arrhythmia database and MIT-BIH atrial fibrillation database were used to verify in the experiment. The experimental results showed that the feature engineering composed of the fitting coefficients of the ARMA model combined with the SVM classifier obtained a recall rate of 98.2% and a precision rate of 98.4%, and the F1 index was 98.3%. The algorithm has high performance, meets the needs of clinical diagnosis, and has low algorithm complexity. It can use low-power embedded processors for real-time calculations, and it’s suitable for real-time warning of wearable ECG monitoring equipment.
Cochlear implant (CI) is the only method for efficacious treatment of congenital severe deafness at present. However, for children with congenital severe deafness after CI, the mechanism of the structural and functional changes of their cerebral cortex is not clear. This study was based on the cross-modal reorganization of deaf patients. Event related potential (ERP) and source localization technique were used to visualize the change of cortical activity in children with congenital severe deafness during 1-year period (0, 1, 3, 6, 9 and 12 months after CI). We aimed to investigate the association between hearing restoration and cross-modal reorganization in children with congenital severe deafness after CI. The results showed that the cross-modal reorganization exists in children with congenital severe deafness. During hearing restoration, the function of the cross-modal reorganization reversed to the normal state. The method and conclusions of this study may be of significance in guiding the training and evaluation of hearing rehabilitation after CI in patients.
To realize the accurate positioning and quantitative volume measurement of tumor in head and neck tumor CT images, we proposed a level set method based on augmented gradient. With the introduction of gradient information in the edge indicator function, our proposed level set model is adaptive to different intensity variation, and achieves accurate tumor segmentation. The segmentation result has been used to calculate tumor volume. In large volume tumor segmentation, the proposed level set method can reduce manual intervention and enhance the segmentation accuracy. Tumor volume calculation results are close to the gold standard. From the experiment results, the augmented gradient based level set method has achieved accurate head and neck tumor segmentation. It can provide useful information to computer aided diagnosis.
Electrocardiogram (ECG) can visually reflect the physiological electrical activity of human heart, which is important in the field of arrhythmia detection and classification. To address the negative effect of label imbalance in ECG data on arrhythmia classification, this paper proposes a nested long short-term memory network (NLSTM) model for unbalanced ECG signal classification. The NLSTM is built to learn and memorize the temporal characteristics in complex signals, and the focal loss function is used to reduce the weights of easily identifiable samples. Then the residual attention mechanism is used to modify the assigned weights according to the importance of sample characteristic to solve the sample imbalance problem. Then the synthetic minority over-sampling technique is used to perform a simple manual oversampling process on the Massachusetts institute of technology and Beth Israel hospital arrhythmia (MIT-BIH-AR) database to further increase the classification accuracy of the model. Finally, the MIT-BIH arrhythmia database is applied to experimentally verify the above algorithms. The experimental results show that the proposed method can effectively solve the issues of imbalanced samples and unremarkable features in ECG signals, and the overall accuracy of the model reaches 98.34%. It also significantly improves the recognition and classification of minority samples and has provided a new feasible method for ECG-assisted diagnosis, which has practical application significance.