Magnetic Resonance Imaging Image Data Research Articles

Comparison of CT and MRI in the Diagnosis of Liver Tumors: A Retrospective Case-Control Analysis.

To compare the diagnostic value of multi-slice computed tomography (CT) and magnetic resonance imaging (MRI) in liver tumors. Retrospective selection of CT and MRI imaging data from 109 cases of liver tumors treated in our hospital from January 2020 to March 2023. The selection was determined through pathological examination. According to the pathological examination results, 61 cases were benign tumors, and 48 cases were malignant tumors. The hepatic portal flow (HPF), hepatic artery perfusion index (HPI) and hepatic artery perfusion (HAF) of malignant tumors were significantly lower than in benign tumors (P<0.05). The signal enhancement ratio of malignant tumors was significantly higher than in benign tumors, and the peak time was significantly lower than in benign tumors (P<0.05). The sensitivity (97.92%) and accuracy (97.25%) of the combined examination were significantly higher than those of MRI (83.33%, 90.83%) or CT alone (81.25%, 88.99%) (P<0.05). CT and MRI have high application value in the diagnosis and evaluation of liver tumors, and the combination of these two methods can further improve diagnostic sensitivity and accuracy, providing an objective reference for early diagnosis and treatment of liver cancer.</p>.

The relationship between the articular disc in magnetic resonance imaging and the condyle in cone beam computed tomography: A retrospective study

BackgroundThe authors retrospectively studied the relationship between the morphology and position of the articular disc in magnetic resonance imaging (MRI) and the morphology and position of the condyle in cone beam computed tomography (CBCT), with the purpose for providing reference for clinical diagnosis and treatment of temporomandibular disorders (TMD). MethodsPatients with both CBCT and MRI imaging data were studied retrospectively, excluding TMJ tumour, fracture, severe condylar morphological abnormalities, non-intercuspal position, and poor quality images. A total of 744 temporomandibular joints (TMJs) from 372 patients were included, with the mean age of 25.94±11.04Y (75 males and 297 females). T2-weighted image (T2WI) of MRI imagings were used to evaluate disc morphology and disc displacement, while CBCT was obtained to evaluate the condylar bone and sagittal condylar position. Data were analysed by Pearson Chi square test and Spearman correlation coefficient. Results: The distribution of 744 TMJs is as follows: 1) disc morphologycontracture (37.1 %) > biconcave (32.9 %) > irregular (18.5 %) > lengthened (11.4 %); 2) disc position: ADDWoR (48.3 %) > NA (26.9 %) > ADDWR (21.6 %) > PDDWR (2.8 %) > PDDWoR (0.4 %); 3) condylar position: concentric (43.7 %) > posterior (37.6 %) > anterior (18.7 %); 4) condylar bone: normal (63.4 %)> abnormal (36.6 %). There were significant differences in the distribution of disc morphology and disc position between the sex (P < 0.05). There were significant differences in the distribution of disc position and condylar morphology amongst the age groups (P < 0.05). There were significant differences in the distribution of disc position, condylar position and condylar morphology amongst disc morphology (P < 0.05), and there were positive correlation between disc position(r = 0.703, P = 0.000), the score of condyle (r = 0.478, P = 0.000) and disc morphology respectively. There were significant differences in the distribution of disc position and condylar position amongst condylar morphology (P < 0.05). There was a positive correlation between disc position and condyle morphology (r = 0.413, P = 0.000), and a negative correlation between condyle position and condyle morphology (r=-0.152, P = 0.000). There were significant differences in the distribution of disc position amongst condylar position (P < 0.05), but there was no linear correlation (P = 0.159). ConclusionsThe mutual distribution of disc morphology, disc position, condylar morphology and condylar position was statistically significant. Disc displacement did not necessarily lead to condylar bone changes, but 92.7 % TMJs with condylar bone abnormalities had disc displacement. Trial registrationThis study was retrospectively registered on 28/03/2022 and endorsed by the Ethics committee (LCYJ2022014).

TENSOR QUANTILE REGRESSION WITH LOW-RANK TENSOR TRAIN ESTIMATION.

Neuroimaging studies often involve predicting a scalar outcome from an array of images collectively called tensor. The use of magnetic resonance imaging (MRI) provides a unique opportunity to investigate the structures of the brain. To learn the association between MRI images and human intelligence, we formulate a scalar-on-image quantile regression framework. However, the high dimensionality of the tensor makes estimating the coefficients for all elements computationally challenging. To address this, we propose a low-rank coefficient array estimation algorithm based on tensor train (TT) decomposition which we demonstrate can effectively reduce the dimensionality of the coefficient tensor to a feasible level while ensuring adequacy to the data. Our method is more stable and efficient compared to the commonly used, Canonic Polyadic rank approximation-based method. We also propose a generalized Lasso penalty on the coefficient tensor to take advantage of the spatial structure of the tensor, further reduce the dimensionality of the coefficient tensor, and improve the interpretability of the model. The consistency and asymptotic normality of the TT estimator are established under some mild conditions on the covariates and random errors in quantile regression models. The rate of convergence is obtained with regularization under the total variation penalty. Extensive numerical studies, including both synthetic and real MRI imaging data, are conducted to examine the empirical performance of the proposed method and its competitors.

Assessment of cerebral venous sinus: Anatomical and functional diagnostic performance of three-dimensional reconstruction models based on venous sinus MRI and CT images

Venous sinus stenosis is commonly observed in patients presenting with pulsatile tinnitus (PT). While magnetic resonance imaging (MRI) and computed tomography (CT) are commonly used for assessing venous sinus geometries, the preferred modality remains unclear. In this study, we reconstructed the three-dimensional (3D) geometries of the venous sinus using MRI and CT imaging data from 20 PT patients. We conducted comparisons of the anatomical features of the venous sinus through case-wise analysis and anatomic geometrical parameter-wise analysis. Our findings indicate that by taking the geometries from CT as a reference, MRI could provide a better illustration of venous structure, primarily due to a stronger flow signal concentrated in the vascular tree. We observed high agreements in anatomic parameters measured from 3D geometries reconstructed based on CT and MRI in 19 out of 20 cases. Notably, the cross-sectional area of the sinus and segment length displayed the highest consistency, with a mean difference of -5.01% and 6.5% between modalities, respectively. In addition, we noticed that 55% of cases exhibited consistency in analyzing the confluence of the sinus, while variants of connectivity and collateral branching were observed between CT and MRI. Importantly, CT-based geometric reconstruction provided better detail of inflow side branches in the straight sinus, whereas MRI preserved more side branches of outflow in the downstream sinus. It is important to note that CT-based evaluation may be affected by the bone structures surrounding the venous sinus, whereas MRI-based evaluation focuses on blood flow to the segments, potentially indicating both anatomical and functional abnormalities.

Combination of high resolution MRI with 3D-printed needle guides for ex vivo myocardial biopsies

Magnetic resonance imaging (MRI) provides a multitude of techniques to detect and characterize myocardial infarction. To correlate MRI findings with histology, in most cases terminal animal studies are performed; however, precise extraction and spatial correlation of myocardial tissue samples to MRI image data is difficult. In this proof of concept study, we present a 3D-printing technique to facilitate the extraction of tissue samples from myocardial regions. Initially, seven pig hearts embedded in formaldehyde were imaged on a clinical 3 T system to define biopsy targets on high resolution ex vivo images. Magnitude images and R2*-maps acquired with a 3D multi-echo gradient echo sequence and 0.58 mm isotropic resolution were used to create digital models of the cardiac anatomy. Biopsy guides were 3D-printed to steer the extraction of myocardial samples. In total, 61 tissue samples were extracted with an average offset of the tissue sample location from the target location of 0.59 ± 0.36 mm. This offset was not dependent on the distance of the target point to the epicardial surface. Myocardial tissue could be extracted from all samples. The presented method enables extraction of myocardial tissue samples that are selected by ex vivo MRI with submillimeter precision.

Magnetic resonance imaging analysis of embryonal rhabdomyosarcoma of the prostate.

Rhabdomyosarcoma (RMS) is a highly malignant tumor that originates from myogenic progenitor cells. To investigate the magnetic resonance imaging (MRI) characteristics of prostate embryonal rhabdomyosarcoma (ERMS). We retrospectively analyzed the clinical and MRI imaging data of 9 cases of prostate ERMS that were confirmed pathologically. The patients were aged between 14∼49 years with a median age of 27 years, and they all underwent MRI, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced MRI (DCE-MRI). The MRI scan of the lesions showed an irregular shape, mixed signals, uneven equal/long T1 signal and an equal/long T2 signal, cystic necrosis in 9 cases and hemorrhage in 6 cases; DWI and ADC images showed a mixed high/low signal, and the tumor parenchyma showed ADC low signal, with an average ADC value of 0.666 × 10-3 mm2/s. There were 5 cases of DCE-MRI TIC type II and 4 cases of DCE-MRI TIC type I. The average value of Tpeak was 120 s and the average value of MCER was 172.3%. After the enhancement, the signal of tumor enhancement was uneven, and showed patchy and reticular enhancement, however, the cyst degeneration, necrosis area, and hemorrhage focus were not enhanced. There were 3 cases with multiple pelvic lymph nodes and 1 case with multiple bone metastases. The MRI manifestations of prostate ERMS have certain characteristics, and the combination of DWI and DCE-MRI are helpful in the diagnosis.

Diagnostic value of combined magnetic resonance imaging techniques in the evaluation of Parkinson disease.

The incidence of Parkinson disease (PD) has been increasing each year. The development of new magnetic resonance imaging (MRI) technology can help understand its pathogenesis and identify more effective imaging-based biological indicators. The clinical and MRI imaging data of 40 patients with PD and 40 healthy controls were analyzed. All participants underwent susceptibility-weighted imaging (SWI), neuromelanin-sensitive magnetic resonance imaging (NM-MRI), and T2*mapping sequence examination. The diagnostic value of single and combined multiparameter indicators was analyzed using the receiver operating characteristic curve. Compared with the healthy control group, the PD group showed significant differences in the disappearance of bilateral "swallow tail sign", the distribution volume of melanocytes in the substantia nigra and the smaller volume in the bilateral substantia nigra, the maximum signal of the locus coeruleus and the smaller and average volume in the bilateral substantia nigra, and the values of T2* and R2* in the bilateral substantia nigra (P<0.01). The maximum and smaller value and the average value of the bilateral locus coeruleus signal were negatively correlated with the disease course duration (P<0.05), and the smaller distribution volume of the melanin neurons in the bilateral substantia nigra was negatively correlated with Hoehn and Yahr (H-Y) grade (P<0.05). In the joint diagnosis with multiple indicators, some composite parameters were found to be negatively correlated with H-Y grading (P<0.05), while others were negatively correlated with disease course duration (P<0.05). Joint use of multiple parameter indicators greatly improved diagnostic efficacy [area under the curve (AUC) =0.958]. The distribution volume of melanin in substantia nigra and the maximum value of locus coeruleus signal may be the biological imaging indicators for the early diagnosis, severity, and follow-up evaluation of PD. Compared with a single indicator, composite indicators used in combination with multiple techniques have a significantly better diagnostic efficacy for PD.

Prediction of Progression After Cervix Cancer Radiotherapy Using a Machine-Learning Model on Pre-Treatment MRI

MRI may be useful to identify women with cervical cancer at high risk of disease progression to test strategies of treatment intensification. The purpose of this study was to determine the value of a machine-learning model built on pre-treatment MRI for prediction of risk of progression after radiation therapy. MagneticResonance Imaging (MRI) data for women with cervical cancer was collected from The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma Collection (TCGA-CESC) on the Cancer Imaging Archive (TCIA), which reported clinical, treatment, and imaging data from a single institution. 27 patients who had received radiation for cervical cancer were selected for input into a custom 3-D Residual Neural Network (ResNet) model with added custom layers specific to DICOM data in tensorflow python package. One T2 MRI per patient was used to predict recurrence free survival after radiation treatment, where patients were predicted to be "high risk" or "low risk" for disease recurrence as the output of the model. All slices of the T2 MRI were used. The model was validated using five-fold cross validation; 80% of the data was used to train each fold and 20% was used for testing. Final model statistical significance was confirmed through shuffle test at the p < 0.01 level. The clinical outcomes of patients and the model's "low-risk" and "high-risk" prediction were compared. There were 27 patients in the study with mean age of 51 years (range 29-79). 20 patients had squamous cell carcinoma and 7 patients had adenocarcinoma. The stage breakdown consisted of 9 women IB, 2 IIA, 9 IIB, 2 IIIA, 2 IIIB, and 3 stage IV. 10 women were treated with radiation alone and 17 with chemo-radiation. 5 women received surgery in addition to radiation or chemoradiation. 21 patients received brachytherapy. Median follow-up of patients was 29 months (range 3-64). The model predicted 7 patients as "high risk" for recurrence; all 7 developed a recurrence during follow up. None of the 20 patients predicted to be "low risk" developed disease recurrence. Among all patients in the study, the two-year progression free survival (PFS) was 82.0%. Patients identified as "low risk" and "high risk" by model had two-year PFS of 100% and 43%, respectively. Among patients with recurrence, 3 developed local recurrence and 4 developed distant metastases. The ResNet model achieved cross-validated accuracy of 92% for prediction of progression-free survival (p<0.01). A 3-D ResNet machine-learning model using pretreatment MRI image data can accurately predict clinical outcomes for cervical cancer following radiation therapy. Future work to confirm generalizability should focus on validation with a larger clinical dataset.

Ensemble method employing quantitative biomarkers and deep learning approach from structural magnetic resonance imaging to diagnose Alzheimer’s disease

Abstract Introduction: Alzheimer’s disease (AD) is a neurodegenerative condition that impairs activities of daily living and sharply declines gross cognitive ability. Over 152 million individuals worldwide will live with the dreaded consequence of a longer lifespan by the year 2050, making it a pressing public health issue. Magnetic resonance imaging (MRI) provides excellent soft tissue contrast and helps image the brain in vivo, non-invasively. Aims and Objectives: To summarize AD’s anatomical, physiological, and pathophysiological changes and derivation of quantifiable biomarkers from MRI to develop artificial intelligence (AI) based computer-aided detection (CAD) system to classify subjects among AD, mild cognitive impairment (MCI), and cognitively normal (CN). Materials and Methods: This retrospective study uses clinical and standardized, pre-processed, quality-controlled, and quality-checked—structural MRI imaging (diagnosed/labeled) data of 1069 subjects, age, gender, and class matched, taken from Alzheimer’s disease neuroimaging initiative. A pipeline is developed to get quantified biomarkers from the assessment of (1) cortical thickness, (2) volumetric segmentation for whole brain volumes, and (3) region of interest (ROI) areas most affected in AD. A gradient boosting method is used to predict class labels. The second approach implements a convolution neural network (CNN) model comprising 3D ROI. Results: Implemented CAD system using an ensemble gradient boosting approach has demonstrated good receiver operating characteristics characteristic and yielded balanced accuracy (BA) of 82.31%, 78.52%, and 72.73%, and the CNN approach has given better results 88.44%, 82.96%, and 74.34% for classification task AD versus CN, AD versus MCI, and MCI versus CN, respectively. Conclusion: This study has used a substantially large dataset of 1069 subjects. The deep learning-based efficient and optimal CNN model has used significantly large ROI-based 3-Dimentional volume, resulting in impressive performance improvements over comparable methods. The CNN model had given higher accuracy (6.13% for AD vs. CN, 4.44% for AD vs. MCI and 1.61% for MCI vs. CN) over gradient boosting, as the model uses significantly large ROI-based 3D brain volume and an inherent capability of it in learning most discriminative features automatically. However, quantitative biomarkers derived from brain morphometry, which accesses structural changes, yield reasonable estimates over pathophysiological alterations across the brain and augment a clinician with insightful and a holistic view, resulting in higher confidence over predicated class label by CNN and is a step closer to explainable AI. Accuracy for MCI versus CN drops as these classes share similar features and characteristics and can be improved by integrating biomarkers from other MRI modalities.

Development of machine learning models aiming at knee osteoarthritis diagnosing: an MRI radiomics analysis

BackgroundTo develop and assess the performance of machine learning (ML) models based on magnetic resonance imaging (MRI) radiomics analysis for knee osteoarthritis (KOA) diagnosis.MethodsThis retrospective study analysed 148 consecutive patients (72 with KOA and 76 without) with available MRI image data, where radiomics features in cartilage portions were extracted and then filtered. Intraclass correlation coefficient (ICC) was calculated to quantify the reproducibility of features, and a threshold of 0.8 was set. The training and validation cohorts consisted of 117 and 31 cases, respectively. Least absolute shrinkage and selection operator (LASSO) regression method was employed for feature selection. The ML classifiers were logistic regression (LR), K-nearest neighbour (KNN) and support vector machine (SVM). In each algorithm, ten models derived from all available planes of three joint compartments and their various combinations were, respectively, constructed for comparative analysis. The performance of classifiers was mainly evaluated and compared by receiver operating characteristic (ROC) analysis.ResultsAll models achieved satisfying performances, especially the Final model, where accuracy and area under ROC curve (AUC) of LR classifier were 0.968, 0.983 (0.957–1.000, 95% CI) in the validation cohort, and 0.940, 0.984 (0.969–0.995, 95% CI) in the training cohort, respectively.ConclusionThe MRI radiomics analysis represented promising performance in noninvasive and preoperative KOA diagnosis, especially when considering all available planes of all three compartments of knee joints.

Multi-Scale Tumor Localization Based on Priori Guidance-Based Segmentation Method for Osteosarcoma MRI Images

Osteosarcoma is a malignant osteosarcoma that is extremely harmful to human health. Magnetic resonance imaging (MRI) technology is one of the commonly used methods for the imaging examination of osteosarcoma. Due to the large amount of osteosarcoma MRI image data and the complexity of detection, manual identification of osteosarcoma in MRI images is a time-consuming and labor-intensive task for doctors, and it is highly subjective, which can easily lead to missed and misdiagnosed problems. AI medical image-assisted diagnosis alleviates this problem. However, the brightness of MRI images and the multi-scale of osteosarcoma make existing studies still face great challenges in the identification of tumor boundaries. Based on this, this study proposed a prior guidance-based assisted segmentation method for MRI images of osteosarcoma, which is based on the few-shot technique for tumor segmentation and fine fitting. It not only solves the problem of multi-scale tumor localization, but also greatly improves the recognition accuracy of tumor boundaries. First, we preprocessed the MRI images using prior generation and normalization algorithms to reduce model performance degradation caused by irrelevant regions and high-level features. Then, we used a prior-guided feature abdominal muscle network to perform small-sample segmentation of tumors of different sizes based on features in the processed MRI images. Finally, using more than 80,000 MRI images from the Second Xiangya Hospital for experiments, the DOU value of the method proposed in this paper reached 0.945, which is at least 4.3% higher than other models in the experiment. We showed that our method specifically has higher prediction accuracy and lower resource consumption.

Feature Fusion and Detection in Alzheimer's Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data.

Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10−6) and cell adhesion molecules (corrected p-value = 5.44 × 10−4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.

Magnetic resonance imaging characteristics predict pituitary function in non-functional pituitary macro-adenoma undergoing trans-sphenoidal surgery

IntroductionMaintaining the pituitary function after surgery is highly important. The aim of this study was to investigate the relationship between preoperative magnetic resonance imaging (MRI) characteristics and pituitary function after surgery of non-functional pituitary macroadenoma.MethodsThis retrospective study was performed between 2016 and 2018. Preoperative and postoperative MRI imaging data were retrieved from electronic registration system. The relationship between preoperative MRI characteristics and postoperative pituitary function as well as reconstruction of pituitary gland was investigated using regression models.ResultsComplete data were available for 44 patients. Before surgery, invisible normal tissue was observed in 23 patients (53.5%). Suprasellar extension and cavernous sinus invasion were seen in 36 patients (each one 49.1%). There was a significant reverse relationship between preoperative tumor size and postoperative thyroid stimulating hormone (TSH) (odds ratio (OR): − 0.99 (− 0.18, − 0.003), p = 0.04). In addition, we found a significant positive correlation between prolactin level after surgery and tumor size before surgery, (OR: 5.29 (1.65, 8.92), p = 0006). Moreover, postoperative panhypopituitarism was observed in 25% of patients with complete morphologic reconstitution of pituitary tissue. While the rate was 50% in patients with no or partial morphologic reconstruction of pituitary tissue.ConclusionPreoperative MRI characteristics predict TSH and prolactin level after operation. Furthermore, the adenoma size and volume prior to surgery are the main determinants of normal morphologic reconstruction of pituitary gland.

Neuroimaging of Mouse Models of Alzheimer's Disease.

Magnetic resonance imaging (MRI) and positron emission tomography (PET) have made great strides in the diagnosis and our understanding of Alzheimer’s Disease (AD). Despite the knowledge gained from human studies, mouse models have and continue to play an important role in deciphering the cellular and molecular evolution of AD. MRI and PET are now being increasingly used to investigate neuroimaging features in mouse models and provide the basis for rapid translation to the clinical setting. Here, we provide an overview of the human MRI and PET imaging landscape as a prelude to an in-depth review of preclinical imaging in mice. A broad range of mouse models recapitulate certain aspects of the human AD, but no single model simulates the human disease spectrum. We focused on the two of the most popular mouse models, the 3xTg-AD and the 5xFAD models, and we summarized all known published MRI and PET imaging data, including contrasting findings. The goal of this review is to provide the reader with broad framework to guide future studies in existing and future mouse models of AD. We also highlight aspects of MRI and PET imaging that could be improved to increase rigor and reproducibility in future imaging studies.

Aberrant Structure MRI in Parkinson's Disease and Comorbidity with Depression Based on Multinomial Tensor Regression Analysis.

Background: Depression is a prominent and highly prevalent nonmotor feature in patients with Parkinson’s disease (PD). The neural and pathophysiologic mechanisms of PD with depression (DPD) remain unclear. The current diagnosis of DPD largely depends on clinical evaluation. Methods: We proposed a new family of multinomial tensor regressions that leveraged whole-brain structural magnetic resonance imaging (MRI) data to discriminate among 196 non-depressed PD (NDPD) patients, 84 DPD patients, 200 healthy controls (HC), and to assess the special brain microstructures in NDPD and DPD. The method of maximum likelihood estimation coupled with state-of-art gradient descent algorithms was used to predict the individual diagnosis of PD and the development of DPD in PD patients. Results: The results reveal that the proposed efficient approach not only achieved a high prediction accuracy (0.94) with a multi-class AUC (0.98) for distinguishing between NDPD, DPD, and HC on the testing set but also located the most discriminative regions for NDPD and DPD, including cortical regions, the cerebellum, the brainstem, the bilateral basal ganglia, and the thalamus and limbic regions. Conclusions: The proposed imaging technique based on tensor regression performs well without any prior feature information, facilitates a deeper understanding into the abnormalities in DPD and PD, and plays an essential role in the statistical analysis of high-dimensional complex MRI imaging data to support the radiological diagnosis of comorbidity of depression with PD.

Magnetic Resonance Imaging Data Features to Evaluate the Efficacy of Compound Skin Graft for Diabetic Foot.

This study aimed to analyze the role of magnetic resonance imaging (MRI) data characteristics based on the deep learning algorithm in evaluating the treatment of diabetic foot (DF) with composite skin graft. In this study, 78 patients with DF were randomly rolled into the experimental group (composite skin graft) and control group (autologous skin graft) with 39 patients in each group. MRI scans were performed before and after treatment to compare the changes of experimental observation indicators such as healing time, recurrence rate, and scar score. The results showed that T1-weighted imaging (T1WI) of the scanning sequence was considerably increased in the experimental group after treatment. The signal intensity of fat-suppressed T2-weighted imaging (T2WI) and fat-suppressed T1WI enhancement sequences was considerably decreased (P < 0.05). In addition, compared with the control group, the recurrence rate, healing time, and scar score in the experimental group were considerably decreased (P < 0.05). The accuracy, specificity, and sensitivity of MRI imaging information in evaluating the therapeutic effect of DF patients were 85.2%, 89.75%, and 86.47%, respectively. According to the specificity and sensitivity, the subject operating characteristic curve was drawn, and the area under the curve was determined to be 0.838. In summary, MRI image data characteristics based on the deep learning algorithm can provide auxiliary reference information for the efficacy evaluation of compound skin transplantation for DF.

Deep Learning-Based MRI in Diagnosis of Fracture of Tibial Plateau Combined with Meniscus Injury

This study aimed to explore the application value of magnetic resonance imaging (MRI) images based on deep learning algorithms in the diagnosis of tibial plateau fractures combined with meniscus injuries. The original MRI image was input into the deep learning convolutional neural network (CNN), and the knee joint undersampled and fully sampled MRI image data were used for training to obtain a neural network model that can effectively remove the noise and blur of the undersampled image. Then, the image was reconstructed by the Regridding model to obtain an image with less noise and clearer structure. At the same time, all subjects underwent knee MRI examinations, and algorithms were used to analyze the sensitivity, specificity, and accuracy of their images. It was found that of 160 menisci from 80 cases of tibial plateau fractures, 64 were normal meniscus and 88 were injured menisci. The sensitivity, specificity, and accuracy of optimized MRI in diagnosing fracture of tibial plateau combined with meniscus injury were 96.9%, 93.2%, and 95.3%, respectively. In conclusion, the restored MRI images have high sensitivity in the diagnosis of meniscus injury and high consistency with the intraoperative results. It suggests that the optimized MRI image is effective in the diagnosis of meniscus injury.

High-Definition Transcranial Direct Current Stimulation Improves Delayed Memory in Alzheimer's Disease Patients: A Pilot Study Using Computational Modeling to Optimize Electrode Position.

The optimal stimulation parameters when using transcranial direct current stimulation (tDCS) to improve memory performance in patients with Alzheimer's disease (AD) are lacking. In healthy individuals, inter-individual differences in brain anatomy significantly influence current distribution during tDCS, an effect that might be aggravated by variations in cortical atrophy in AD patients. To measure the effect of individualized HD-tDCS in AD patients. Nineteen AD patients were randomly assigned to receive active or sham high-definition tDCS (HD-tDCS). Computational modeling of the HD-tDCS-induced electric field in each patient's brain was analyzed based on magnetic resonance imaging (MRI) scans. The chosen montage provided the highest net anodal electric field in the left dorsolateral prefrontal cortex (DLPFC). An accelerated HD-tDCS design was conducted (2 mA for 3×20 min) on two separate days. Pre- and post-intervention cognitive tests and T1 and T2-weighted MRI and diffusion tensor imaging data at baseline were analyzed. Different montages were optimal for individual patients. The active HD-tDCS group improved significantly in delayed memory and MMSE performance compared to the sham group. Five participants in the active group had higher scores on delayed memory post HD-tDCS, four remained stable and one declined. The active HD-tDCS group had a significant positive correlation between fractional anisotropy in the anterior thalamic radiation and delayed memory score. HD-tDCS significantly improved delayed memory in AD. Our study can be regarded as a proof-of-concept attempt to increase tDCS efficacy. The present findings should be confirmed in larger samples.

Optimized CSMRI Algorithm-Based MRI Image Analysis in the Active Rehabilitation Method for Patients with Acute Cerebral Infarction

This paper combines optimized CSMRI algorithm (CS) and magnetic resonance imaging (MRI) to shorten the scanning time of MRI image data and improve the imaging quality. At the same time, the paper applies functional magnetic resonance imaging (BOLD-fMRI) based on the principle of blood oxygen level dependence to explore the application value of the nerve function reconstruction therapy system for the rehabilitation of active and passive motor functions in patients with acute cerebral infarction. Methods. In this paper, 20 patients with acute cerebral infarction were included. The random drawing method was used to divide them into active group and passive group, each with 10 cases. Both groups were treated with conventional medication and acupuncture. The active group used the active mode of the nerve function reconstruction treatment system to guide the patients’ limb active exercise; all training in the passive group is provided by the nerve function reconstruction treatment system to passively exercise the patients’ limbs; both groups undergo BOLD-fMRI examination before treatment and after 2 weeks of treatment and observe the activated parts of the brain functional area and corresponding parts of the two groups before and after treatment. We observe the activation volume and, at the same time, the ADL score. Results. After treatment, the activation volume and ADL scores of brain functional areas in the two groups were significantly improved compared with those before treatment, and the difference was statistically significant ( P &lt; 0.05 ). Conclusion. The combination of optimized CSMRI algorithm (CS) and magnetic resonance imaging (MRI) can be used to evaluate the early rehabilitation efficacy of patients with acute cerebral infarction and has certain guiding value for clinical treatment.

Three-dimensional MRI-based treatment planning approach for non-invasive ocular proton therapy.

PurposeTo develop a high‐resolution three‐dimensional (3D) magnetic resonance imaging (MRI)‐based treatment planning approach for uveal melanomas (UM) in proton therapy.Materials/methodsFor eight patients with UM, a segmentation of the gross tumor volume (GTV) and organs‐at‐risk (OARs) was performed on T1‐ and T2‐weighted 7 Tesla MRI image data to reconstruct the patient MR‐eye. An extended contour was defined with a 2.5‐mm isotropic margin derived from the GTV. A broad beam algorithm, which we have called πDose, was implemented to calculate relative proton absorbed doses to the ipsilateral OARs. Clinically favorable gazing angles of the treated eye were assessed by calculating a global weighted‐sum objective function, which set penalties for OARs and extreme gazing angles. An optimizer, which we have named OPT’im‐Eye‐Tool, was developed to tune the parameters of the functions for sparing critical‐OARs.ResultsIn total, 441 gazing angles were simulated for every patient. Target coverage including margins was achieved in all the cases (V95% > 95%). Over the whole gazing angles solutions space, maximum dose (Dmax) to the optic nerve and the macula, and mean doses (Dmean) to the lens, the ciliary body and the sclera were calculated. A forward optimization was applied by OPT’im‐Eye‐Tool in three different prioritizations: iso‐weighted, optic nerve prioritized, and macula prioritized. In each, the function values were depicted in a selection tool to select the optimal gazing angle(s). For example, patient 4 had a T2 equatorial tumor. The optimization applied for the straight gazing angle resulted in objective function values of 0.46 (iso‐weighted situation), 0.90 (optic nerve prioritization) and 0.08 (macula prioritization) demonstrating the impact of that angle in different clinical approaches.ConclusionsThe feasibility and suitability of a 3D MRI‐based treatment planning approach have been successfully tested on a cohort of eight patients diagnosed with UM. Moreover, a gaze‐angle trade‐off dose optimization with respect to OARs sparing has been developed. Further validation of the whole treatment process is the next step in the goal to achieve both a non‐invasive and a personalized proton therapy treatment.