Comparative analysis of hepatic fat quantification across 5 T, 3 T and 1.5 T: A study on consistency and feasibility.

OBJECTIVES: Magnetic resonance imaging (MRI) is a critical noninvasive technique for evaluating liver steatosis, with efficient and precise fat quantification being essential for diagnosing liver diseases. This study leverages 5 T ultra-high-field MRI to demonstrate the clinical significance of liver fat quantification, and explores the consistency and accuracy of the Proton Density Fat Fraction (PDFF) in the liver across different magnetic field strengths and measurement methodologies. METHODS: The study involved phantoms with lipid contents ranging from 0 % to 30 % and 35 participants (21 females, 14 males; average age 30.17 ± 13.98 years, body mass index 25.84 ± 4.76, waist-hip ratio 0.84 ± 0.09). PDFF measurements were conducted using chemical shift encoded (CSE) MRI at 5 T, 3 T, and 1.5 T, alongside magnetic resonance spectroscopy (MRS) at 5 T and 1.5 T for both liver and phantoms, analyzed using jMRUI software. The MRS-derived PDFF values served as the reference standard. Repeatability of 5 T MRI measurements was assessed through correlation analysis, while accuracy was evaluated using linear regression analysis against the reference standards. RESULTS: The CSE-PDFF measurements at 5 T demonstrated strong consistency with those at 3 T and 1.5 T, showing high intraclass correlation coefficients (ICC) of 0.988 and 0.980, respectively (all p < 0.001). There was also significant consistency across ROIs within liver lobes, with ICC values ranging from 0.975 to 0.986 (all p < 0.001). MRS-PDFF measurements for both phantoms and liver at 5 T and 1.5 T exhibited substantial agreement, with ICC values of 0.996 and 0.980, respectively (all p < 0.001). Particularly, ICC values for ROIs in the liver ranged from 0.963 to 0.990 (all p < 0.001). Despite overall agreement, statistically significant differences were noted in specific ROIs within the liver lobes (p = 0.004 and 0.012). The CSE and MRS PDFF measurements at 5 T displayed strong consistency, with an ICC of 0.988 (p < 0.001), and significant agreement was also found between 5 T CSE and 1.5 T MRS PDFF measurements, with an ICC of 0.978 (p < 0.001). Agreement was significant within the ROIs of the liver lobes on the same platform at 5 T, with ICC values ranging from 0.986 to 0.991 (all p < 0.001). CONCLUSION: PDFF measurements at 5 T MR imaging exhibited both accuracy and repeatability, indicating that 5 T imaging provides reliable quantification of liver fat content and shows substantial potential for clinical diagnostic applications.

Improvement of sand-washing performance and internal flow field analysis of a novel downhole sand removal device.

With the progression of many shale gas wells in the Sichuan-Chongqing region of China into the middle and late stages of exploitation, the problem of sand production in these wells is a primary factor influencing production. Failure to implement measures to remove sand from the gas wells will lead to a sharp decline in production after a certain period of exploitation. Moreover, As the amount of sand produced in the well increases, the production layer will be potentially buried by sand. To boost the production of shale gas wells in the Sichuan-Chongqing region and improve production efficiency, a novel downhole jet sand-washing device has been developed. Upon analyzing the device's overall structure, it is revealed that the device adopts a structural design integrating a jet pump with an efficient sand- washing nozzle, providing dual capabilities for jet sand- washing and sand conveying via negative pressure. To enhance the sand- washing and unblocking performance of the device, various sand- washing fluids and the structures of different sand- washing nozzles are compared for selection, aiming to elevate the device's sand- washing and unblocking performance from a macro perspective. Subsequently, drawing on simulation and internal flow field analysis of the device's sand- washing and unblocking process through CFD and the control variable method, it is ultimately found that the length diameter ratio of the cylindrical segment of the nozzle outlet, the outlet diameter, and the contraction angle of the nozzle greatly influence the device's sand- washing and unblocking performance. And the optimum ranges for the length-diameter ratio of the cylindrical segment of the nozzle outlet, the outlet diameter, the contraction angle of the nozzle, and the inlet diameter are 2 to 4, 6 mm to 10 mm, 12° to 16°, and 18 mm and 22 mm, respectively. The findings of the research not only provide new insights into existing sand removal processes but also offer a novel structure for current downhole sand removal devices and a specific range for the optimal size of the nozzle.

The predictive value of a computed tomography-based radiomics model for the surgical separability of thymic epithelial tumors from the superior vena cava and the left innominate vein.

Background: The aim of this study was to develop a radiomics machine learning model based on computed tomography (CT) that can predict whether thymic epithelial tumors (TETs) can be separated from veins during surgery and to compare the accuracy of the radiomics model to that of radiologists. Methods: Patients who underwent thymectomy at our hospital from 2009 to 2017 were included in the screening process. After the selection of patients according to the inclusion and exclusion criteria, the cohort was randomly divided into training and testing groups, and CT images of these patients were collected. Subsequently, two-dimensional (2D) and three-dimensional (3D) regions of interest were labelled using ITK-SNAP 3.8.0 software, and Radiomics features were extracted using Python software (Python Software Foundation) and selected through the least absolute shrinkage and selection operator (LASSO) regression model. To construct the classifier, a support vector machine (SVM) was employed, and a nomogram was created using logistic regression to predict vascular inseparable TETs based on the radiomics score (radscore) and image features. To assess the accuracy of these models, area under receiver operating characteristic (ROC) curves of these models were calculated, and differences among the models were identified using the Delong test. Results: In this retrospective study, 204 patients with TETs were included, among whom 21 were diagnosed with surgical vascularly inseparable TETs. The area under ROC curve (AUC) of the 2D model, 3D model, 2D + 3D model, and radiologist diagnoses were 0.94, 0.92, 0.95, and 0.87 in the training cohort and 0.95, 0.92, 0.98, and 0.78 in testing cohort, respectively. The Delong test revealed a significant improvement in the performance of the radiomics models compared to radiologists' diagnoses. The logistic regression selected 3 image features, namely maximum diameter of the tumor, degree of abutment of vessel circumference >50%, and absence of the mediastinal fat layer or space between the tumor and surrounding structures. These features, along with the radscore, were included to develop a nomogram. The AUCs of this nomogram were 0.99 in both the training set and testing set, and the Delong test did not find a significant difference between ROC plots of the nomogram and radiomics models. Conclusions: The proposed radiomics model could accurately predict surgical vascularly inseparable TETs preoperatively and was shown to have a higher predictive value than the radiologists.

The analysis of root canal curvature and direction of maxillary lateral incisors by using cone-beam computed tomography: A retrospective study.

ABSTRACT: The aim of this study was to investigate root canal curvature and direction of maxillary lateral incisors in Shandong, China.Cone beam computed tomography (CBCT) images of 176 maxillary lateral incisors of 88 patients were collected in Shandong Province, China. Software included with CBCT was used to measure the angle of root canal curvature of maxillary lateral incisors on the maximum bending plane. In addition, the direction of each root canal was recorded. The data were statistically analyzed by SPSS 17.0 software package.The results showed that all the samples had a single canal (Vertucci's type I). The incidence of straight root canals, curved root canals, and S-type root canals was 39.2%, 58%, and 2.8%, respectively. The difference in the mean angle of root canal curvature failed to identify any differences between the left and right side (P > .05). The most curved root canal of maxillary lateral incisors oriented in the palato-distal direction.The maxillary lateral incisors were mainly curved root canals of which the proportion of moderate curvature was the largest. Software included with CBCT would provide some valuable information for root canal instrumentation of maxillary lateral incisors.

A deep semantic segmentation correction network for multi-model tiny lesion areas detection.

BACKGROUND: Semantic segmentation of white matter hyperintensities related to focal cerebral ischemia (FCI) and lacunar infarction (LACI) is of significant importance for the automatic screening of tiny cerebral lesions and early prevention of LACI. However, existing studies on brain magnetic resonance imaging lesion segmentation focus on large lesions with obvious features, such as glioma and acute cerebral infarction. Owing to the multi-model tiny lesion areas of FCI and LACI, reliable and precise segmentation and/or detection of these lesion areas is still a significant challenge task. METHODS: We propose a novel segmentation correction algorithm for estimating the lesion areas via segmentation and correction processes, in which we design two sub-models simultaneously: a segmentation network and a correction network. The segmentation network was first used to extract and segment diseased areas on T2 fluid-attenuated inversion recovery (FLAIR) images. Consequently, the correction network was used to classify these areas at the corresponding locations on T1 FLAIR images to distinguish between FCI and LACI. Finally, the results of the correction network were used to correct the segmentation results and achieve segmentation and recognition of the lesion areas. RESULTS: In our experiment on magnetic resonance images of 113 clinical patients, our method achieved a precision of 91.76% for detection and 92.89% for classification, indicating a powerful method to distinguish between small lesions, such as FCI and LACI. CONCLUSIONS: Overall, we developed a complete method for segmentation and detection of WMHs related to FCI and LACI. The experimental results show that it has potential clinical application potential. In the future, we will collect more clinical data and test more types of tiny lesions at the same time.

Brain GABA+ changes in primary hypothyroidism patients before and after levothyroxine treatment: A longitudinal magnetic resonance spectroscopy study.

OBJECTIVE: Increasing evidence indicates the involvement of the GABAergic system in the pathophysiology of hypothyroidism. We aimed to investigate longitudinal changes of brain GABA in primary hypothyroidism before and after levothyroxine (L-T4) treatment. MATERIAL AND METHODS: In 18 patients with hypothyroidism, we used the MEGA-PRESS (Mescher-Garwood point-resolved spectroscopy) editing sequence to measure brain GABA levels from medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) at baseline and after 6-months of L-T4 treatment. Sex- and age-matched healthy controls (n = 18) were scanned at baseline. Thyroid function and neuropsychological tests were also performed. RESULTS: GABA signals were successfully quantified from all participants with fitting errors lower than 15%. GABA signal was labeled as GABA+ due to contamination from co-edited macromoleculars and homocarnosine. In hypothyroid patients, mean GABA+ was significantly lower in the mPFC region compared with controls (p = 0.031), and the mPFC GABA+ measurements were significantly correlated with depressive symptoms and memory function (r = -0.558, p = 0.016; r = 0.522, p = 0.026, respectively). After adequate L-T4 treatment, the mPFC GABA+ in hypothyroid patients increased to normal level, along with relieved neuropsychological impairments. CONCLUSION: The study suggested the decrease of GABA+ may be an important neurobiological factor in the pathophysiology of hypothyroidism. Treatment of L-T4 may reverse the abnormal GABA+ and hypothyroidism-induced neuropsychiatric impairments, indicating the action mode of L-T4 in adjunctive treatment of affective disorders.

Learning-based deformable registration for infant MRI by integrating random forest with auto-context model.

PURPOSE: Accurately analyzing the rapid structural evolution of human brain in the first year of life is a key step in early brain development studies, which requires accurate deformable image registration. However, due to (a) dynamic appearance and (b) large anatomical changes, very few methods in the literature can work well for the registration of two infant brain MR images acquired at two arbitrary development phases, such as birth and one-year-old. METHODS: To address these challenging issues, we propose a learning-based registration method, which can handle the anatomical structures and the appearance changes between the two infant brain MR images with possible time gap. Specifically, in the training stage, we employ a multioutput random forest regression and auto-context model to learn the evolution of anatomical shape and appearance from a training set of longitudinal infant images. To make the learning procedure more robust, we further harness the multimodal MR imaging information. Then, in the testing stage, for registering the two new infant images scanned at two different development phases, the learned model will be used to predict both the deformation field and appearance changes between the images under registration. After that, it becomes much easier to deploy any conventional image registration method to complete the remaining registration since the above-mentioned challenges for state-of-the-art registration methods have been well addressed. RESULTS: We have applied our proposed registration method to intersubject registration of infant brain MR images acquired at 2-week-old, 3-month-old, 6-month-old, and 9-month-old with the images acquired at 12-month-old. Promising registration results have been achieved in terms of registration accuracy, compared with the counterpart nonlearning based registration methods. CONCLUSIONS: The proposed new learning-based registration method have tackled the challenging issues in registering infant brain images acquired from the first year of life, by leveraging the multioutput random forest regression with auto-context model, which can learn the evolution of shape and appearance from a training set of longitudinal infant images. Thus, for the new infant image, its deformation field to the template and also its template-like appearances can be predicted by the learned models. We have extensively compared our method with state-of-the-art deformable registration methods, as well as multiple variants of our method, which show that our method can achieve higher accuracy even for the difficult cases with large appearance and shape changes between subject and template images.

Investigation of brain GABA+ in primary hypothyroidism using edited proton MR spectroscopy.

OBJECTIVE: Evidence indicates that thyroid hormones have effects on the inhibitory GABAergic system. The aim of this study was to investigate whether brain GABA levels are altered in patients with hypothyroidism compared with healthy controls. DESIGN/METHODS: Fifteen patients with primary hypothyroidism and 15 matched healthy controls underwent single-voxel MEGA-PRESS magnetic resonance spectroscopy at 3T, to quantify GABA levels in the median prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). All participants underwent thyroid function test. Neuropsychological performances were evaluated by administration of the Montreal Cognitive Assessment (MoCA) and the 21-item Beck Depression Inventory-II (BDI-II). RESULTS: The patients with hypothyroidism had significantly lower GABA+ levels in the mPFC compared with healthy controls (P = 0·016), whereas no significant difference (P = 0·214) was observed in the PCC. Exploratory analyses revealed that mPFC GABA+ levels were negatively correlated with the BDI-II scores in patient group (r = -0·60, P = 0·018). No correlations were found between GABA+ levels and TSH or fT3 or fT4 levels in either region (all P > 0·05). CONCLUSION: This study suggests that alteration of GABAergic neurotransmission may play an important role in the pathophysiology of primary hypothyroidism, providing intriguing neurochemical clues to understand thyroid-brain interactions.

GABA+ levels in postmenopausal women with mild-to-moderate depression: A preliminary study.

BACKGROUND: It is increasingly being recognized that alterations of the GABAergic system are implicated in the pathophysiology of depression. This study aimed to explore in vivo gamma-aminobutyric acid (GABA) levels in the anterior cingulate cortex/medial prefrontal cortex (ACC/mPFC) and posterior-cingulate cortex (PCC) of postmenopausal women with depression using magnetic resonance spectroscopy (H-MRS). METHODS: Nineteen postmenopausal women with depression and thirteen healthy controls were enrolled in the study. All subjects underwent H-MRS of the ACC/mPFC and PCC using the "MEGA Point Resolved Spectroscopy Sequence" (MEGA-PRESS) technique. The severity of depression was assessed by 17-item Hamilton Depression Scale (HAMD). Quantification of MRS data was performed using Gannet program. Differences of GABA+ levels from patients and controls were tested using one-way analysis of variance. Spearman correlation coefficients were used to evaluate the linear associations between GABA+ levels and HAMD scores, as well as estrogen levels. RESULTS: Significantly lower GABA+ levels were detected in the ACC/mPFC of postmenopausal women with depression compared to healthy controls (P = 0.002). No significant correlations were found between 17-HAMD/14-HAMA and GABA+ levels, either in ACC/mPFC (P = 0.486; r = 0.170/P = 0.814; r = -0.058) or PCC (P = 0.887; r = 0.035/ P = 0.987; r = -0.004) in the patients; there is also no significant correlation between GABA+ levels and estrogen levels in patients group (ACC/mPFC: P = 0.629, r = -0.018; PCC: P = 0.861, r = 0.043). CONCLUSION: Significantly lower GABA+ levels were found in the ACC/mPFC of postmenopausal women with depression, suggesting that the dysfunction of the GABAergic system may also be involved in the pathogenesis of depression in postmenopausal women.

Compression quality prediction model for JPEG2000.

A compression quality prediction model is proposed for grey images coding with JPEG2000. With this model, the compression quality (PSNR) could be estimated according to the given compression ratio (CR) and the image activity measures (IAM) without coding images. The image activity measure is the weighted sum of the IAM values based on the 1-pixel-distance and 2-pixel-distance gradients along horizontal and vertical directions. We have shown that IAM is a function of the image variance and autocorrelation coefficients. Based on Shannon's rate-distortion theorem, a theoretical justification is provided for the correlation of IAM with PSNR. Experimental results show that the prediction error is lower than 1 dB for more than 70% sample images when CR is higher than 15. The prediction error is less than 2 dB for over 90% images. This prediction performance is acceptable for general applications.