Objective To explore the differential diagnosis significance of 3.0T MRI united-sequences examination in the diagnosis of benign and malignant breast lesions. Methods A total of 67 breast lesions of 59 patients were collected prospectively, which be treated at the Sichuan Provincial People’s Hospital during July 2015 to January 2017. All patients were underwent bilateral breast 3.0T magnetic resonance plain scan, diffusion weighted imaging, and dynamic enhanced scan successively before surgical operation. Analysis of morphological features of the benign and malignant breast lesions, the time-signal intensity curve (TIC), the apparent diffusion coefficient (ADC), and the combination diagnosis of them were performed. Results Of all 59 patients, 67 lesions were confirmed by histopathology, including 18 benign lesions and 49 malignant lesions. The morphological features (including margin, shape, border, and evenness), the types of TIC of dynamic enhancement, and ADC value between the benign lesions and malignant lesions were statistically significant (P<0.05). The sensitivity and specificity of Fischer scoring system was 89.8% (44/49) and 61.1% (11/18) respectively. The sensitivity and specificity of TIC types was 83.7% (41/49) and 77.8% (14/18) respectively. The diagnostic threshold of ADC value was 1.012×10–3 mm2/s, with the sensitivity and specificity for the diagnosis was 91.8% (45/49) and 83.3% (15/18) respectively. The sensitivity and specificity of the combination of Fischer scoring system and TIC type for diagnosis between benign and malignant breast lesions was 95.9% (47/49) and 72.2% (13/18) respectively. The sensitivity and specificity of the combination of Fischer scoring system, TIC type, and ADC value for benign and malignant breast lesions was 98.0% (48/49) and 83.3% (15/18) respectively. Conclusion The combination of Fischer scoring system, TIC type, and diffusion-weighted imaging for the differential diagnosis between benign lesions and malignant lesions was more effective than single imaging method.
Objective To summarize ultrasonography, CT and (or) MRI imaging features of cystic liver lesions so as to improve its diagnostic accuracy. Methods The literatures relevant imaging studies of different types of cystic liver lesions at home and abroad were searched. Then with the etiology as clue, the imaging fetures of ultrasonography, CT and (or) MRI plain scan and enhancement scan were summarized. Results The cystic liver lesions had many types, their imaging findings were different and existed overlaps. The diagnosis and differential diagnosis of atypical cases were difficult. ① For the simple hepatic cyst, it was a round cystic mass with water-like echo, density and signal. The boundary was clear, and there was no separation in the cyst, without contrast enhancement. The sensitivity and specificity of diagnosing were higher by ultrasonography and MRI as compared with CT. ② For the bile duct hamartoma and Caroli diease, they were manifested as multiple cysts, widely distributed in the whole liver, without enhancement for the most lesions. The multiple cystic lesions without communicating with the bile duct was the key sign of differential diagnosis for these two dieases. ③ Enhancing mural nodules were more common in cystadenocarcinoma than cystadenoma. The accurate diagnosis of biliary cystadenoma depended on combination of ultrasonography, CT, and MRI findings. ④ For the cystic liver metastatic tumor, it was multiple cystic neoplasms in the liver parenchyma or around the liver. CT was the main method for the diagnosis, and which showed that the density was lower than that of the liver parenchyma, peripheral ring-enhanced lesion as enhanced scan. It was easy to distinguish with simple hepatic cyst by MRI. ⑤ For the cystic hepatocellular carcinoma, it presented as a multilocular cystic solid tumor. The presence of tumor thrombus in portal vein could help to the diagnosis. ⑥ For the undifferentiated embryonal sarcoma, CT plain scan showed the cystic low density mass with clear boundary, the edge with calcification, enhanced scan showed that the soft tissue composition presented continuous strengthening sign. There was no specific signal in MRI plain scan, and the periphery of the tumor was slowly strengthening. ⑦ For the liver abscess, it was easy to diagnose because it had different characteristic features in different pathological phase, but it was misdiagnosis of intrahepatic cholangiocarcinoma when its symptoms were atypical. ⑧ The ultrasonography and the CT were the optimal methods for the hepatic cystic echinococcosis and the hepatic alveolar echinococcosis respectively. The significances of imaging were to determine the activity of hydatid cyst and to identify anatomy structure among alveolar echinococcosis, bile duct and blood vessel, and judge invasion or not, MRCP was important for diagnosis. Conclusions Abdominal ultrasonography could be used as the first choice for diagnosis of cystic liver lesions, CT and MRI could be used as effective supplementary methods for it. A combination of various imaging techniques is key to diagnosis. Moreover, number and morphology of lesion, and solid component or not are important imaging features of diagnosis and differential diagnosis of cystic liver lesion.
In order to improve the speech quality and auditory perceptiveness of electronic cochlear implant under strong noise background, a speech enhancement system used for electronic cochlear implant front-end was constructed. Taking digital signal processing (DSP) as the core, the system combines its multi-channel buffered serial port (McBSP) data transmission channel with extended audio interface chip TLV320AIC10, so speech signal acquisition and output with high speed are realized. Meanwhile, due to the traditional speech enhancement method which has the problems as bad adaptability, slow convergence speed and big steady-state error, versiera function and de-correlation principle were used to improve the existing adaptive filtering algorithm, which effectively enhanced the quality of voice communications. Test results verified the stability of the system and the de-noising performance of the algorithm, and it also proved that they could provide clearer speech signals for the deaf or tinnitus patients.
During the automatic reconstruction of panoramic images, the effect of dental arch curve fitting will affect the integrity of the content of the panoramic image. Metal implants in the patient’s mouth usually lead to a decrease in the contrast of the panoramic image, which affects the doctor’s diagnosis. In this paper, an automatic oral panoramic image reconstruction method was proposed. By calculating key image areas and image extraction fusion algorithms, the dental arch curve could be automatically detected and adjusted on a small number of images, and the intensity distribution of teeth, bone tissue and metal implants on the image could be adjusted to reduce the impact of metal on other tissues, to generate high-quality panoramic images. The method was tested on 50 cases of cone beam computed tomography (CBCT) data with good results, which can effectively improve the quality of panoramic images.
Effective medical image enhancement method can not only highlight the interested target and region, but also suppress the background and noise, thus improving the quality of the image and reducing the noise while keeping the original geometric structure, which contributes to easier diagnosis in disease based on the image enhanced. This article carries out research on strengthening methods of subtle structure in medical image nowadays, including images sharpening enhancement, rough sets and fuzzy sets, multi-scale geometrical analysis and differential operator. Finally, some commonly used quantitative evaluation criteria of image detail enhancement are given, and further research directions of fine structure enhancement of medical images are discussed.
Cognitive enhancement refers to the technology of enhancing or expanding the cognitive and emotional abilities of people without psychosis based on relevant knowledge of neurobiology. The common methods of cognitive enhancement include transcranial direct current stimulation (tDCS) and cognitive training (CT). tDCS takes effect quickly, with a short effective time, while CT takes longer to work, requiring several weeks of training, with a longer effective time. In recent years, some researchers have begun to use the method of tDCS combined with CT to regulate the cognitive function. This paper will sort out and summarize this topic from five aspects: perception, attention, working memory, decision-making and other cognitive abilities. Finally, the application prospect and challenges of technology are prospected.
Parkinson’s disease patients have early vocal cord damage, and their voiceprint characteristics differ significantly from those of healthy individuals, which can be used to identify Parkinson's disease. However, the samples of the voiceprint dataset of Parkinson's disease patients are insufficient, so this paper proposes a double self-attention deep convolutional generative adversarial network model for sample enhancement to generate high-resolution spectrograms, based on which deep learning is used to recognize Parkinson’s disease. This model improves the texture clarity of samples by increasing network depth and combining gradient penalty and spectral normalization techniques, and a family of pure convolutional neural networks (ConvNeXt) classification network based on Transfer learning is constructed to extract voiceprint features and classify them, which improves the accuracy of Parkinson’s disease recognition. The validation experiments of the effectiveness of this paper’s algorithm are carried out on the Parkinson’s disease speech dataset. Compared with the pre-sample enhancement, the clarity of the samples generated by the proposed model in this paper as well as the Fréchet inception distance (FID) are improved, and the network model in this paper is able to achieve an accuracy of 98.8%. The results of this paper show that the Parkinson’s disease recognition algorithm based on double self-attention deep convolutional generative adversarial network sample enhancement can accurately distinguish between healthy individuals and Parkinson’s disease patients, which helps to solve the problem of insufficient samples for early recognition of voiceprint data in Parkinson’s disease. In summary, the method effectively improves the classification accuracy of small-sample Parkinson's disease speech dataset and provides an effective solution idea for early Parkinson's disease speech diagnosis.
In order to fully explore the neural oscillatory coupling characteristics of patients with mild cognitive impairment (MCI), this paper analyzed and compared the strength of the coupling characteristics for 28 MCI patients and 21 normal subjects under six different-frequency combinations. The results showed that the difference in the global phase synchronization index of cross-frequency coupling under δ-θ rhythm combination was statistically significant in the MCI group compared with the normal control group (P = 0.025, d = 0.398). To further validate this coupling feature, this paper proposed an optimized convolutional neural network model that incorporated a time-frequency data enhancement module and batch normalization layers to prevent overfitting while enhancing the robustness of the model. Based on this optimized model, with the phase locking value matrix of δ-θ rhythm combination as the single input feature, the diagnostic accuracy of MCI patients was (95.49 ± 4.15)%, sensitivity and specificity were (93.71 ± 7.21)% and (97.50 ± 5.34)%, respectively. The results showed that the characteristics of the phase locking value matrix under the combination of δ-θ rhythms can adequately reflect the cognitive status of MCI patients, which is helpful to assist the diagnosis of MCI.
Arthroscopic rotator cuff repair is widely used clinically, but the phenomenon of re-tear after repair is still common. Due to the special structure of the tendon-bone junction, the promotion of tissue regeneration from the perspective of biological enhancement has attracted attention. Platelet-rich plasma (PRP) is a supraphysiological concentration of autologous platelets, which can promote the healing of rotator cuff injury after repair. However, due to the lack of clinical use standards, not all PRPs are the same, there are clear differences between liquid PRP and solid platelet-rich fibrin, and many studies have not differentiated their properties. This article reviews the research progress of different types of PRP in the repair of rotator cuff injury, aiming to provide some reference for clinical treatment selection.
Speech enhancement methods based on microphone array adopt many microphones to record speech signal simultaneously. As spatial information is increased, these methods can increase speech recognition for cochlear implant in noisy environment. Due to the size limitation, the number of microphones used in the cochlear implant cannot be too large, which limits the design of microphone array beamforming. To balance the size limitation of cochlear implant and the spatial orientation information of the signal acquisition, we propose a speech enhancement and beamforming algorithm based on dual thin uni-directional / omni-directional microphone pairs (TP) in this paper. Each TP microphone contains two sound tubes for signal acquisition, which increase the overall spatial orientation information. In this paper, we discuss the beamforming characteristics with different gain vectors and the influence of the inter-microphone distance on beamforming, which provides valuable theoretical analysis and engineering parameters for the application of dual microphone speech enhancement technology in cochlear implants.