Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI ImagesRead the full article
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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MRI Image Segmentation Model with Support Vector Machine Algorithm in Diagnosis of Solitary Pulmonary Nodule
This study focused on the application value of MRI images processed by a Support Vector Machine (SVM) algorithm-based model in diagnosis of benign and malignant solitary pulmonary nodule (SPN). The SVM algorithm was constrained by a self-paced regularization item and gradient value to establish the MRI image segmentation model (SVM-L) for lung. Its performance was compared factoring into the Dice index (DI), sensitivity (SE), specificity (SP), and Mean Square Error (MSE). 28 SPN patients who underwent the parallel MRI examination were selected as research subjects and were divided into the benign group (11 patients) and malignant group (17 patients) according to different plans for diagnosis and treatment. The apparent diffusion coefficient (ADC) at different b values was analyzed, and the steepest slope (SS) and washout ratio (WR) values in the two groups were calculated. The result showed that the MSE, DI, SE, SP values, and operation time of the SVM-L model were (0.41 ± 0.02), (0.84 ± 0.13), (0.89 ± 0.04), (0.993 ± 0.004), and (30.69 ± 2.60)s, respectively, apparently superior to those of the other algorithms, but there were no statistic differences () in the WR value between the two groups of patients. The SS values of the time-signal curve in the benign and malignant groups were (2.52 ± 0.69) %/s and (3.34 ± 00.41) %/s, respectively. Obviously, the SS value of the benign group was significantly lower than that of the malignant group (). The ADC value with different b values in the benign group was significantly lower than that of the malignant group (). It suggested that the SVM-L model significantly improved the quality of lung MRI images and increased the accuracy to differentiate benign and malignant SPN, providing reference for the diagnosis and treatment of SPN patients.
Diagnosis of Early Neurological Deterioration after Intravenous Thrombolysis for Patients with Cerebral Ischemic Stroke Using Compressed Sensing-Magnetic Resonance Imaging Algorithm
This study was to explore the risk factors and prognosis of early neurological deterioration (END) after intravenous thrombolysis in patients with cerebral ischemic stroke (CIS) with the guidance of magnetic resonance imaging (MRI) under the compressed sensing-MRI (CSMRI) algorithm. 187 patients with CIS in the hospital were selected and grouped into a deterioration group and a control group according to whether they had END. The CSMRI algorithm was constructed and compared with digital television (DTV) algorithm and Bayesian compressed sensing (BCS) algorithm. It was found that the reconstruction time of CSMRI algorithm in platform I (1134.9 s) and platform II (2615.8 s) was visibly lower than that of DTV algorithm (2634.6 s, 3963.4 s) and BCS algorithm (5631.5 s, 7412.3 s), showing statistically obvious differences (). In addition, the reconstruction efficiency of the CSMRI algorithm was the best. After 4 hours of intravenous thrombolysis, the stroke scale score (12.3 scores) of the deterioration group was much higher than that of the control group (8.4 scores) (). The occlusion of responsible great vessel in the deterioration group (30 cases, 83.33%) was obviously higher in contrast to that in the control group (74 cases, 49%) (). Stroke scale score and occlusion of responsible great vessel were risk factors for EBD after intravenous thrombolysis.
Firework Optimization Algorithm-Based Diagnosis of Hepatocellular Carcinoma and Hepatic Cavernous Hemangioma Using MRI Images
This study was aimed to explore the diagnostic features of magnetic resonance imaging (MRI) on hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HCH). A fireworks algorithm optimization (FAO) was proposed based on the fireworks algorithm (FA), and it was compared with the maximum between-class variance method (OTSU) and the maximum entropy threshold method (KSW) for analysis. In addition, it was applied to the diagnosis of MRI images of 55 HCC patients in the experimental group (group E) and 55 HCH patients in the control group (group C). It was found that the FAO showed a greatly lower difference function (DF) and a shorter running time in contrast to the OTSU and KSW algorithms (); the diagnostic accuracy (DA) of the T1-weighted image (T1WI) for patients in groups E and C was 85.31% and 95.85%, respectively, and the DA of the T2-weighted image (T2WI) was 97.84% (group E) and 89.71% (group C), respectively. In short, FAO showed an excellent performance in segmentation and reconstruction of MRI images for liver tissue, and T1WI and T2WI of MRI images showed high accuracy in diagnosing the HCC and HCH, respectively.
Intelligent Segmentation Algorithm for Diagnosis of Meniere’s Disease in the Inner Auditory Canal Using MRI Images with Three-Dimensional Level Set
This paper aimed to explore segmentation effects of the magnetic resonance imaging (MRI) images of the inner auditory canal of patients with Meniere’s disease under the intelligent segmentation method of the inner ear based on three-dimensional (3D) level set (IS3DLS). The statistical shape model and the level set segmentation algorithm were combined to propose the IS3DLS. First, the shape training samples of the inner ear model were determined, and the results were manually segmented to further obtain region of interest (ROI) of the inner ear. The IS3DLS was employed to accurately segment MRI images of the inner auditory canal of patients with Meniere’s disease. The segmentation performance of IS3DLS was compared with the expert manual segmentation method and the region growth level set-based segmentation algorithm. Results showed that Matthews correlation coefficient (MCC), Dice similarity coefficient (DSC), false positive rate (FPR), and false negative rate (FNR) of this algorithm were 0.9599, 0.9594, 0.0325, and 0.03655, respectively. Therefore, the IS3DLS could achieve good segmentation effect in MRI images of the inner auditory canal of patients with Meniere’s disease, which was helpful for diagnosis and subsequent treatment of Meniere’s disease.
Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators
Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Researchers tend to discuss its early state (early MCI, eMCI) due to its high conversion rate of dementia and poor treatment effect in the middle and late stages. Currently, the research on the disease evolution of the brain functional networks of patients with MCI has gradually become a research hotspot. In this study, we compare the differences in dynamic functional connectivity among eMCI, late MCI (lMCI), and normal control (NC) groups, and their graph theory indicators reveal the integration and segregation of functional connectivity states. Firstly, dynamic functional network windows were constructed based on the sliding time window method, and then these window samples were clustered by k-means to extract the functional connectivity states. The differences in the three groups were compared by analyzing the graph theory indicators, such as the participation coefficient, module degree distribution, clustering coefficient, global efficiency, and local efficiency, which distinguish the functional connectivity states. The results reveal that the NC group has the strongest integration and segregation, followed by the eMCI group, and the lMCI group has the weakest integration and segregation. We conclude that with the aggravation of MCI, the integration and segregation of dynamic functional connectivity states tend to decline. The results also reflect that the lMCI group has significantly more brain functional connections in some states, such as IPL.L-MTG.R and DCG.R-SMG.L, than the eMCI group, while the lMCI group has significantly less OLF.L-SPG.L than the NC group.
Diagnostic Value of Diffusion Tensor Imaging for Infants’ Brain Development Retardation Caused by Pre-Eclampsia
Objective. Pre-eclampsia (PE) can cause brain development delay in infants. This work aims to characterize the pattern differences of brain white matter development in premature infants under PE conditions and those without. Methods. Eighty preterm infants delivered by women with PE were selected as the PE group, and ninety-six preterm infants of the same period born to women without high-risk perinatal factors were used as control. All infants underwent diffusion tensor imaging (DTI) examination. The fractional anisotropy (FA) was measured in five regions of interests (ROIs), including posterior limbs of internal capsule (PLIC), splenium of the corpus callosum (SCC), superior frontal gyrus (SFG), superior parietal lobule (SPL), and superior occipital gyrus (SOG). The relationship between the FA values and postmenstrual age (PMA) was analyzed. Results. After adjusting for the birth weight and gestational ages, in the SCC and PLIC, the PMA and FA values showed a low-to-medium intensity positive correlation in the control group (r = 0.30, ; r = 0.53, ), while no positive relevance was detected in the PE group (r = 0.08, ; r = 0.19, ). In the PE and control groups, in the SPL and SOG, the PMA and FA values showed a near-consistent positive correlation (r = 0.57, r = 0.55 vs. r = 0.31, r = 0.55; all ). In the control group, in SFG, the PMA and FA values had a medium intensity positive correlation (r = 0.47, ), but there was no statistical difference in correlation in PE (r = 0.10, ). Conclusion. PE may cause lagging brain development in the SCC, PLIC, and SFG during infancy. DTI may be an effective and sensitive detection tool.