Current Issue : October-December Volume : 2021 Issue Number : 4 Articles : 5 Articles
To explore the application value of MRI in the diagnosis of brain glioma (BG), in the study, a deep learning-based multimodal feature fusion model was established, which was then applied in BG classification. 60 BG patients who came to our hospital for treatment were selected as research subjects. (ey all accepted the MRI scan and the enhanced scan, and the MRI results were compared with the pathological results. (e results showed that the sensitivity of the algorithm was above 90%, and the sensitivity to diagnose grade IV glioma was as high as 98.28%; the specificity was above 78%, and the specificity to diagnose grade IV glioma was as high as 95.85%; the detection accuracy was above 95%. (e relative fractional anisotropy (rFA) values of the tumor body were smaller than those of peritumoral edema in both the high-grade group and low-grade group, and the difference was notable (P < 0.05); the relative apparent diffusion coefficients (rADC) values of the peritumoral edema were greater than those of tumor bodies of the same grade in both the high-grade group and the low-grade group, and the difference was notable (P < 0.05); notable differences were noted in the rADC values of tumor bodies between the high-grade group and the low-grade group (P < 0.05) and in the rADC values of the glioma peritumoral edema between the high-grade group and the low-grade group (P < 0.05). In summary, MRI based on deep learning raises the sensitivity, specificity, and accuracy to diagnose BG and can more accurately classify BG pathologically, providing reference for clinical treatment of BG....
A middle ear infection is a prevalent inflammatory disease most common in the pediatric population, and its financial burden remains substantial. Current diagnostic methods are highly subjective, relying on visual cues gathered by an otoscope. To address this shortcoming, optical coherence tomography (OCT) has been integrated into a handheld imaging probe. This system can non-invasively and quantitatively assess middle ear effusions and identify the presence of bacterial biofilms in the middle ear cavity during ear infections. Furthermore, the complete OCT system is housed in a standard briefcase to maximize its portability as a diagnostic device. Nonetheless, interpreting OCT images of the middle ear more often requires expertise in OCT as well as middle ear infections, making it difficult for an untrained user to operate the system as an accurate standalone diagnostic tool in clinical settings. Here, we present a briefcase OCT system implemented with a real-time machine learning platform for middle ear infections. A random forest-based classifier can categorize images based on the presence of middle ear effusions and biofilms. This study demonstrates that our briefcase OCT system coupled with machine learning can provide user-invariant classification results of middle ear conditions, which may greatly improve the utility of this technology for the diagnosis and management of middle ear infections....
When using ultrahigh-field MR systems (7T), the variations in the RF magnetic field can lead to significant loss in image uniformity. To optimize the overall MR image quality, the image region is divided into multiple smaller regions of interest (the ROIs), which can be independently optimized using transmit array optimization techniques including RF shimming, to improve RF magnetic fields and image intensity. Electromagnetic numerical simulations and corresponding transverse magnetization (| Mt|) acquired using the Bloch equation-based MRI simulator are used to evaluate the proposed method. Compared to the simulation results of quadrature driving method, mean and standard deviation (SD) of |Mt| in the full image (an inner diameter of 500 mm) were improved 47% (mean) and 48% (SD), whereas 94% (max) and 97% (mean) improved in the unaveraged SAR using the proposed method. ,e uniformity of |Mt| acquired using the method was especially improved in the peripheral region of the selected phantom image compared to that of other methods. ,e proposed method using multiple independently optimized ROIs and numerical simulations significantly improved the uniformity of |Mt| body images at 7T. ,is technique would be generally applicable to any high-field strength MR systems, which generate short RF wavelengths compared to the field of view....
The novel coronavirus disease 2019 (COVID-19) is a contagious disease that has caused thousands of deaths and infected millions worldwide. Thus, various technologies that allow for the fast detection of COVID-19 infections with high accuracy can offer healthcare professionals much-needed help. This study is aimed at evaluating the effectiveness of the state-of-the-art pretrained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pretrained deep learning algorithms and fine-tuned to maximise detection accuracy to identify the best algorithms. The results showed that deep learning with X-ray imaging is useful in collecting critical biological markers associated with COVID-19 infections. VGG16 and MobileNet obtained the highest accuracy of 98.28%. However, VGG16 outperformed all other models in COVID-19 detection with an accuracy, F1 score, precision, specificity, and sensitivity of 98.72%, 97.59%, 96.43%, 98.70%, and 98.78%, respectively. The outstanding performance of these pretrained models can significantly improve the speed and accuracy of COVID-19 diagnosis. However, a larger dataset of COVID-19 X-ray images is required for a more accurate and reliable identification of COVID-19 infections when using deep transfer learning. This would be extremely beneficial in this pandemic when the disease burden and the need for preventive measures are in conflict with the currently available resources....
Cardiac magnetic resonance imaging (CMR) is considered the gold standard for measuring cardiac function. Further, in a single CMR exam, information about cardiac structure, tissue composition, and blood flow could be obtained. Nevertheless, CMR is underutilized due to long scanning times, the need for multiple breath-holds, use of a contrast agent, and relatively high cost. In this work, we propose a rapid, comprehensive, contrast-free CMR exam that does not require repeated breath-holds, based on recent developments in imaging sequences. Time-consuming conventional sequences have been replaced by advanced sequences in the proposed CMR exam. Specifically, conventional 2D cine and phase-contrast (PC) sequences have been replaced by optimized 3D-cine and 4D-flow sequences, respectively. Furthermore, conventional myocardial tagging has been replaced by fast strain-encoding (SENC) imaging. Finally, T1 and T2 mapping sequences are included in the proposed exam, which allows for myocardial tissue characterization. The proposed rapid exam has been tested in vivo. The proposed exam reduced the scan time from >1 hour with conventional sequences to <20 minutes. Corresponding cardiovascular measurements from the proposed rapid CMR exam showed good agreement with those from conventional sequences and showed that they can differentiate between healthy volunteers and patients. Compared to 2D cine imaging that requires 12-16 separate breath-holds, the implemented 3D-cine sequence allows for whole heart coverage in 1-2 breath-holds. The 4D-flow sequence allows for whole-chest coverage in less than 10 minutes. Finally, SENC imaging reduces scan time to only one slice per heartbeat. In conclusion, the proposed rapid, contrast-free, and comprehensive cardiovascular exam does not require repeated breath-holds or to be supervised by a cardiac imager. These improvements make it tolerable by patients and would help improve cost effectiveness of CMR and increase its adoption in clinical practice....
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