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.
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.
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.
High-frequency steady-state asymmetric visual evoked potential (SSaVEP) provides a new paradigm for designing comfortable and practical brain-computer interface (BCI) systems. However, due to the weak amplitude and strong noise of high-frequency signals, it is of great significance to study how to enhance their signal features. In this study, a 30 Hz high-frequency visual stimulus was used, and the peripheral visual field was equally divided into eight annular sectors. Eight kinds of annular sector pairs were selected based on the mapping relationship of visual space onto the primary visual cortex (V1), and three phases (in-phase[0o, 0o], anti-phase [0o, 180o], and anti-phase [180o, 0o]) were designed for each annular sector pair to explore response intensity and signal-to-noise ratio under phase modulation. A total of 8 healthy subjects were recruited in the experiment. The results showed that three annular sector pairs exhibited significant differences in SSaVEP features under phase modulation at 30 Hz high-frequency stimulation. And the spatial feature analysis showed that the two types of features of the annular sector pair in the lower visual field were significantly higher than those in the upper visual field. This study further used the filter bank and ensemble task-related component analysis to calculate the classification accuracy of annular sector pairs under three-phase modulations, and the average accuracy was up to 91.5%, which proved that the phase-modulated SSaVEP features could be used to encode high- frequency SSaVEP. In summary, the results of this study provide new ideas for enhancing the features of high-frequency SSaVEP signals and expanding the instruction set of the traditional steady state visual evoked potential paradigm.
ObjectiveTo compare the effectiveness of T2 weighted image (T2WI) and some compounded MRI techniques, including T2WI combined with magnetic resonance spectroscopy (T2WI+MRS), T2WI combined with diffusion weighted imaging (T2WI+DWI) and T2WI combined with dynamic contrast-enhancement [T2WI+(DCE-MRI)] respectively, with 1.5 T MR scanner in diagnosing prostate cancer through a blinding method.
MethodsBetween March 2011 and April 2013, two observers diagnosed 59 cases with a blinding method. The research direction of radiologist A was to diagnose prostate cancer. The observers diagnosed and scored the cases with T2WI, T2WI+(DCE-MRI), T2WI+MRS, T2WI+DWI and compositive method respectively. The data were statistically analyzed with receiver operating characteristic (ROC) curve.
ResultsAccording to the ROC curve, both observers got the sequence of area under curve (AUC) as T2WI+DWI > T2WI+(DCE-MRI) > T2WI+MRS > T2WI. On the basis of the result from observer A, the AUC from each technique was similar. The AUC of T2+DWI was slightly bigger than others. The specificity of single T2WI was the lowest; the sensitivity of T2WI was slightly higher. The AUC of the compositive method was marginally larger than T2WI+DWI. According to the result from observer B, the AUC of T2WI+DWI was obviously larger than the others. The AUC of single T2WI was much smaller than the other techniques. The single T2WI method had the lowest sensitivity and the highest specificity. The AUC of T2WI+DWI was slightly larger than the compositive method. The AUC of T2WI+(DCE-MRI), T2WI+MRS, single T2WI methods from observer A was obviously higher than those from the score of observer B. The AUC of T2WI+DWI from the two observers was similar.
ConclusionThe method of combined T2WI and functional imaging sequences can improve the diagnosing specificity when a 1.5 T MR scanner is used. T2WI+DWI is the best method in diagnosing prostate cancer with least influence from the experience of observers in this research. The compositive method can improve the diagnosis of prostate cancer effectively, but when there are contradictions between different methods, the T2WI+DWI should be considered as a key factor.
The processing mechanism of the human brain for speech information is a significant source of inspiration for the study of speech enhancement technology. Attention and lateral inhibition are key mechanisms in auditory information processing that can selectively enhance specific information. Building on this, the study introduces a dual-branch U-Net that integrates lateral inhibition and feedback-driven attention mechanisms. Noisy speech signals input into the first branch of the U-Net led to the selective feedback of time-frequency units with high confidence. The generated activation layer gradients, in conjunction with the lateral inhibition mechanism, were utilized to calculate attention maps. These maps were then concatenated to the second branch of the U-Net, directing the network’s focus and achieving selective enhancement of auditory speech signals. The evaluation of the speech enhancement effect was conducted by utilising five metrics, including perceptual evaluation of speech quality. This method was compared horizontally with five other methods: Wiener, SEGAN, PHASEN, Demucs and GRN. The experimental results demonstrated that the proposed method improved speech signal enhancement capabilities in various noise scenarios by 18% to 21% compared to the baseline network across multiple performance metrics. This improvement was particularly notable in low signal-to-noise ratio conditions, where the proposed method exhibited a significant performance advantage over other methods. The speech enhancement technique based on lateral inhibition and feedback-driven attention mechanisms holds significant potential in auditory speech enhancement, making it suitable for clinical practices related to artificial cochleae and hearing aids.
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.
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.
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.
