Current Issue : July - September Volume : 2020 Issue Number : 3 Articles : 5 Articles
Objectives: To measure phosphorus metabolites in human parotid glands by\n31P-MRS using three-dimensional chemical-shift imaging (3D-CSI), and ascertain\nwhether this method can capture changes in adenosine triphosphate\n(ATP) and phosphocreatine (PCr) levels due to saliva secretion. Study Design:\nThe parotid glands of 20 volunteers were assessed by 31P-MRS using\n3D-CSI on 3T MRI. After obtaining a first (baseline) measurement, the participants\ntook vitamin-C tablets and measurements were obtained twice\nmore, in a continuous manner....................
Automated detection of lung lesions on Chest X-ray images shows good performance to\nreduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well\nand truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper,\na multi-label classification model combining attention-based neural networks and association-specific\ncontexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional\nneural network and a long short-term memory network are first aligned by an attention mechanism to\ntake advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM\n(CAL) network. In addition, a mining method of implicit association strength to obtain an association\nnetwork of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA\nnetwork provides possible clinical relationships between lesions to help the CAL network obtain\nbetter predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms\nsome state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and\nF-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method\nmay be useful in the computer-aided detection of multiple lesions on chest X-ray images....
The classification of brain tumors is performed by biopsy, which is not usually conducted\nbefore definitive brain surgery. The improvement of technology and machine learning can help\nradiologists in tumor diagnostics without invasive measures. A machine-learning algorithm that\nhas achieved substantial results in image segmentation and classification is the convolutional neural\nnetwork (CNN).We present a new CNN architecture for brain tumor classification of three tumor\ntypes. The developed network is simpler than already-existing pre-trained networks, and it was tested\non T1-weighted contrast-enhanced magnetic resonance images. The performance of the network was\nevaluated using four approaches: combinations of two 10-fold cross-validation methods and two\ndatabases..........................
Convolutional neural networks (CNNs) demonstrate excellent performance when employed\nto reconstruct the images obtained by compressed-sensing magnetic resonance imaging (CS-MRI).\nOur study aimed to enhance image quality by developing a novel iterative reconstruction approach\nthat utilizes image-based CNNs and k-space correction to preserve original k-space data. In the\nproposed method, CNNs represent a priori information concerning image spaces. First, the CNNs are\ntrained to map zero-filling images onto corresponding full-sampled images. Then, they recover the\nzero-filled part of the k-space data. Subsequently, k-space corrections, which involve the replacement\nof unfilled regions by original k-space data, are implemented to preserve the original k-space data.\nThe above-mentioned processes are used iteratively. The performance of the proposed method was\nvalidated using a T2-weighted brain-image dataset, and experiments were conducted with several\nsampling masks. Finally, the proposed method was compared with other noniterative approaches\nto demonstrate its effectiveness. The aliasing artifacts in the reconstructed images obtained using\nthe proposed approach were reduced compared to those using other state-of-the-art techniques.\nIn addition, the quantitative results obtained in the form of the peak signal-to-noise ratio and structural\nsimilarity index demonstrated the effectiveness of the proposed method. The proposed CS-MRI\nmethod enhanced MR image quality with high-throughput examinations....
Early detection of lung nodule is of great importance for the successful diagnosis\nand treatment of lung cancer. Many researchers have tried with diverse\nmethods, such as thresholding, computer-aided diagnosis system, pattern recognition\ntechnique, backpropagation algorithm, etc. Recently, convolutional\nneural network (CNN) finds promising applications in many areas. In this\nresearch, we investigated 3D CNN to detect early lung cancer using LUNA 16\ndataset. At first, we preprocessed raw image using thresholding technique.\nThen we used Vanilla 3D CNN classifier to determine whether the image is\ncancerous or non-cancerous. The experimental results show that the proposed\nmethod can achieve a detection accuracy of about 80% and it is a satisfactory\nperformance compared to the existing technique....
Loading....