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Contrast Media & Molecular Imaging is an exciting journal in the area of contrast agents and molecular imaging, covering all areas of imaging technologies with a special emphasis on MRI and PET.
Chief Editor, Professor Zimmer, focuses on the development and use of PET radiotracers for new applications of PET/MRI imaging in neuroscience and pharmacology.
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Magnetic Resonance Imaging Feature Analysis and Evaluation of Tubal Patency under Convolutional Neural Network in the Diagnosis of Infertility
To explore the diagnostic value of MRI image features based on convolutional neural network for tubal unobstructed infertility, 30 infertile female patients were first selected as the research objects, who admitted to the hospital from May 2018 to January 2020. They all underwent routine MRI examinations and CNN-based MR-hysteron-salpingography (HSG) examinations, in order to discuss the diagnostic accuracy of the two examinations. In the research, it was necessary to observe the patients’ imaging results, calculate the diagnosis rate of the two examination results, and analyze the application effect of the CNN algorithm, thereby selecting the best reconstruction method. In this study, the analysis was conducted on the basis of no statistical difference in the baseline data of the included patients. The results of undersampling reconstruction at 2-fold, 4-fold, and 6-fold showed that CNN for data consistency layer (CNN_DC) had a better effect, and its peak signal-to-noise ratio (PSNR) was lower sharply than that of the other two reconstruction methods, while the normalized mean square error (NMSE) and structural similarity index measure (SSIM) were higher markedly than the values of the other two reconstruction methods. The diagnostic rate of routine MRI examination of the fallopian tube and other parts of the uterus was lower than or equal to that of MR-HSG examination by CNN. Routine MRI examinations of fallopian tube imaging artifacts were large, and the definition was reduced, which increased the difficulty of identification. However, MR-HSG examination by CNN indicated that the imaging artifacts were low, the clarity was high, and the influence of noise was small, which was conducive to clinical diagnosis and identification. For endometriosis, the accuracy of MR-HSG was 33.33% and the accuracy of MRI was 46.67%. CNN MR-HSG inspection method was significantly better than the conventional MRI inspection method . Therefore, the results of this study revealed that MR-HSG examination by CNN had a clear imaging effect and obvious inhibition effect on background signals and rapid image generation without the need for reconstruction with the same spatial resolution, which improved the imaging quality and could provide a reference value for clinical diagnosis and subsequent related studies.
Intelligent Algorithm-Based Picture Archiving and Communication System of MRI Images and Radiology Information System-Based Medical Informatization
Objective. The study aimed to explore the application value of picture archiving and communication system (PCAS) of MRI images based on radial basis function (RBF) neural network algorithm combined with the radiology information system (RIS). Methods. 551 patients who required MRI examination in a hospital from May 2016 to May 2021 were selected as research subjects. Patients were divided into two groups according to their own wishes. Those who were willing to use the RBF neural network algorithm-based PCAS of MRI images combined with RIS were set as the combined group, involving a total of 278 cases; those who were unwilling were set as the regular group, involving a total of 273 cases. The RBF neural network algorithm-based PCAS of MRI images combined with RIS was trained and tested for classification performance and then used for comparison analysis. Result. The actual output (0.031259–0.038515) of all test samples was almost the same as the target output (0.000000) ( > 0.05). In the first 50,000 learnings, the iteration error of the RBF neural network dropped rapidly and finally stabilized at 0.038. The classification accuracy of the RBF neural network algorithm-based PCAS of MRI images combined with RIS for the head was 94.28%, that of abdomen was 97.22%, and it was 93.10% for knee joint, showing no statistically significant differences ( > 0.05), and the total classification accuracy was as high as 95%. The time spent in the examination in the combined group was about 2 hours, and that in the regular group was about 4 hours ( > 0.05). The satisfaction of the combined group (96.76%) was significantly higher than that of the control group (46.89%) ( > 0.05). Conclusion. The RBF neural network has good classification performance for MRI images. To incorporate intelligent algorithms into the medical information system can optimize the system. RBF has good application prospects in the medical information system, and it is worthy of continuous exploration.
Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories
The rates of skin cancer (SC) are rising every year and becoming a critical health issue worldwide. SC’s early and accurate diagnosis is the key procedure to reduce these rates and improve survivability. However, the manual diagnosis is exhausting, complicated, expensive, prone to diagnostic error, and highly dependent on the dermatologist’s experience and abilities. Thus, there is a vital need to create automated dermatologist tools that are capable of accurately classifying SC subclasses. Recently, artificial intelligence (AI) techniques including machine learning (ML) and deep learning (DL) have verified the success of computer-assisted dermatologist tools in the automatic diagnosis and detection of SC diseases. Previous AI-based dermatologist tools are based on features which are either high-level features based on DL methods or low-level features based on handcrafted operations. Most of them were constructed for binary classification of SC. This study proposes an intelligent dermatologist tool to accurately diagnose multiple skin lesions automatically. This tool incorporates manifold radiomics features categories involving high-level features such as ResNet-50, DenseNet-201, and DarkNet-53 and low-level features including discrete wavelet transform (DWT) and local binary pattern (LBP). The results of the proposed intelligent tool prove that merging manifold features of different categories has a high influence on the classification accuracy. Moreover, these results are superior to those obtained by other related AI-based dermatologist tools. Therefore, the proposed intelligent tool can be used by dermatologists to help them in the accurate diagnosis of the SC subcategory. It can also overcome manual diagnosis limitations, reduce the rates of infection, and enhance survival rates.
Convolutional Neural Network-Processed MRI Images in the Diagnosis of Plastic Bronchitis in Children
Objective. The study focused on the features of the convolutional neural networks- (CNN-) processed magnetic resonance imaging (MRI) images for plastic bronchitis (PB) in children. Methods. 30 PB children were selected as subjects, including 19 boys and 11 girls. They all received the MRI examination for the chest. Then, a CNN-based algorithm was constructed and compared with Active Appearance Model (AAM) algorithm for segmentation effects of MRI images in 30 PB children, factoring into occurring simultaneously than (OST), Dice, and Jaccard coefficient. Results. The maximum Dice coefficient of CNN algorithm reached 0.946, while that of active AAM was 0.843, and the Jaccard coefficient of CNN algorithm was also higher (0.894 vs. 0.758, ). The MRI images showed pulmonary inflammation in all subjects. Of 30 patients, 14 (46.66%) had complicated pulmonary atelectasis, 9 (30%) had the complicated pleural effusion, 3 (10%) had pneumothorax, 2 (6.67%) had complicated mediastinal emphysema, and 2 (6.67%) had complicated pneumopericardium. Also, of 30 patients, 19 (63.33%) had lung consolidation and atelectasis in a single lung lobe and 11 (36.67%) in both two lung lobes. Conclusion. The algorithm based on CNN can significantly improve the segmentation accuracy of MRI images for plastic bronchitis in children. The pleural effusion was a dangerous factor for the occurrence and development of PB.
Intelligent Algorithm-Based Magnetic Resonance Imaging in Radical Gastrectomy under Laparoscope
The study focused on the influence of intelligent algorithm-based magnetic resonance imaging (MRI) on short-term curative effects of laparoscopic radical gastrectomy for gastric cancer. A convolutional neural network- (CNN-) based algorithm was used to segment MRI images of patients with gastric cancer, and 158 subjects admitted at hospital were selected as research subjects and randomly divided into the 3D laparoscopy group and 2D laparoscopy group, with 79 cases in each group. The two groups were compared for operation time, intraoperative blood loss, number of dissected lymph nodes, exhaust time, time to get out of bed, postoperative hospital stay, and postoperative complications. The results showed that the CNN-based algorithm had high accuracy with clear contours. The similarity coefficient (DSC) was 0.89, the sensitivity was 0.93, and the average time to process an image was 1.1 min. The 3D laparoscopic group had shorter operation time (86.3 ± 21.0 min vs. 98 ± 23.3 min) and less intraoperative blood loss (200 ± 27.6 mL vs. 209 ± 29.8 mL) than the 2D laparoscopic group, and the difference was statistically significant (). The number of dissected lymph nodes was 38.4 ± 8.5 in the 3D group and 36.1 ± 6.0 in the 2D group, showing no statistically significant difference (). At the same time, no statistically significant difference was noted in postoperative exhaust time, time to get out of bed, postoperative hospital stay, and the incidence of complications (). It was concluded that the algorithm in this study can accurately segment the target area, providing a basis for the preoperative examination of gastric cancer, and that 3D laparoscopic surgery can shorten the operation time and reduce intraoperative bleeding, while achieving similar short-term curative effects to 2D laparoscopy.
Preclinical Molecular PET-CT Imaging Targeting CDCP1 in Colorectal Cancer
Colorectal cancer (CRC) is the third most common malignancy in the world, with 22% of patients presenting with metastatic disease and a further 50% destined to develop metastasis. Molecular imaging uses antigen-specific ligands conjugated to radionuclides to detect and characterise primary cancer and metastases. Expression of the cell surface protein CDCP1 is increased in CRC, and here we sought to assess whether it is a suitable molecular imaging target for the detection of this cancer. CDCP1 expression was assessed in CRC cell lines and a patient-derived xenograft to identify models suitable for evaluation of radio-labelled 10D7, a CDCP1-targeted, high-affinity monoclonal antibody, for preclinical molecular imaging. Positron emission tomography-computed tomography was used to compare zirconium-89 (89Zr)-10D7 avidity to a nonspecific, isotype control 89Zr-labelled IgGκ1 antibody. The specificity of CDCP1-avidity was further confirmed using CDCP1 silencing and blocking models. Our data indicate high avidity and specificity for of 89Zr-10D7 in CDCP1 expressing tumors at. Significantly higher levels than normal organs and blood, with greatest tumor avidity observed at late imaging time points. Furthermore, relatively high avidity is detected in high CDCP1 expressing tumors, with reduced avidity where CDCP1 expression was knocked down or blocked. The study supports CDCP1 as a molecular imaging target for CRC in preclinical PET-CT models using the radioligand 89Zr-10D7.