Electrical impedance tomography (EIT) is a new non-invasive functional imaging technology, which has the advantages of non-invasion, non-radiation, low cost, fast response, portability and visualization. In recent years, more and more studies have shown that EIT has great potential in the detection of lung diseases and has been applied to early diagnosis and treatment of some diseases. This paper introduced the basic principle of EIT, discussed the research and clinical application of EIT in the detection of acute respiratory distress syndrome, chronic obstructive pulmonary disease, pneumothorax and pulmonary embolism, and focused on the summary and introduction of indicators and functional images of EIT related to the detection of lung diseases. This review will help medical workers understand and use EIT, and promote the further development of EIT in lung diseases as well as other fields.
Quantitative magnetic susceptibility imaging (QSM) is an imaging method based on magnetic resonance imaging (MRI) phase signal processing and inversion to obtain tissue magnetic susceptibility distribution, which can generate images reflecting the magnetic characteristics of tissues. QSM reconstruction process is complex, in which dipole inversion stage is the most challenging and decisive link, and traditional methods are easily affected by pathological conditions at this stage, resulting in artifacts and deviations. With the development of deep learning and machine vision technology, using U-network (U-Net) model to improve dipole inversion process can effectively avoid the shortcomings of traditional algorithms. In this paper, the application of the improved model based on U-Net architecture in dipole inversion from 2020 to now is summarized. Firstly, the theoretical concept of QSM is introduced. Secondly, the existing improved models based on U-Net architecture are divided into three categories: improved U-Net based on structural optimization, improved U-Net based on physical constraints and improved U-Net based on improving generalization ability, and their main characteristics and design starting points are sorted out. Finally, the development trend of the future model is prospected and summarized. To sum up, it is expected that the difficulties and challenges of dipole inversion will be solved, the accuracy of QSM images will be improved, and support for disease-aided diagnosis will be provided by summarizing and comparing different improved U-Net models in this paper.
Early screening of skin cancer is crucial to the survival rate of patients. Although deep learning has made significant progress in dermoscopic image analysis, the blurred edge of the lesion, the vulnerability to noise interference, and the limited computing resources at the time of model deployment are still the main bottlenecks. To this end, this paper proposes a frequency-space collaborative enhancement network (FSC-Net) based on lightweight classification. Aiming at the problem of blurred lesion edge and noise interference, the network first constructs a learning frequency enhancement module. Through the dynamic selective enhancement of frequency domain features, the lesion edge is finely characterized while suppressing high-frequency artifacts. Secondly, aiming at the scale heterogeneity of lesion morphology, this paper proposes a multi-scale aggregation module, which uses multi-branch pooling to reduce the loss of deep semantic features in the lightweight network. Finally, in order to solve the problem of difficult localization of complex lesion areas, this paper introduces a directional spatial calibration mechanism, which realizes accurate localization of lesion features through orthogonal decoupling coding and asymmetry factors. The experimental results on the 2019 international skin image collaboration challenge (ISIC2019) and the human against machine with 10000 training images (HAM10000) dataset show that FSC-Net achieves 93.41% eight-classification accuracy and 95.84% seven-classification accuracy with a lower number of parameters. Compared with the existing advanced models, the proposed method achieves a better balance between computational overhead and diagnostic performance, and provides a robust and efficient solution for auxiliary diagnosis in resource-constrained environments.