Staging liver fibrosis with various diffusion-weighted magnetic resonance imaging models

BACKGROUND Diffusion-weighted imaging(DWI)has been developed to stage liver fibrosis.However,its diagnostic performance is inconsistent among studies.Therefore,it is worth studying the diagnostic value of various diffusion models for liver fibrosis in one cohort.AIM To evaluate the clinical potential of six diffusion-weighted models in liver fibrosis staging and compare their diagnostic performances.METHODS This prospective study enrolled 59 patients suspected of liver disease and scheduled for liver biopsy and 17 healthy participants.All participants underwent multi-b value DWI.The main DWI-derived parameters included Mono-apparent diffusion coefficient(ADC)from mono-exponential DWI,intravoxel incoherent motion model-derived true diffusion coefficient(IVIM-D),diffusion kurtosis imaging-derived apparent diffusivity(DKI-MD),stretched exponential model-derived distributed diffusion coefficient(SEM-DDC),fractional order calculus(FROC)model-derived diffusion coefficient(FROC-D)and FROC model-derived microstructural quantity(FROC-μ),and continuous-time random-walk(CTRW)model-derived anomalous diffusion coefficient(CTRW-D)and CTRW model-derived temporal diffusion heterogeneity index(CTRW-α).The correlations between DWI-derived parameters and fibrosis stages and the parameters’diagnostic efficacy in detecting significant fibrosis(SF)were assessed and compared.RESULTS CTRW-D(r=-0.356),CTRW-α(r=-0.297),DKI-MD(r=-0.297),FROC-D(r=-0.350),FROC-μ(r=-0.321),IVIM-D(r=-0.251),Mono-ADC(r=-0.362),and SEM-DDC(r=-0.263)were significantly correlated with fibrosis stages.The areas under the ROC curves(AUCs)of the combined index of the six models for distinguishing SF(0.697-0.747)were higher than each of the parameters alone(0.524-0.719).The DWI models’ability to detect SF was similar.The combined index of CTRW model parameters had the highest AUC(0.747).CONCLUSION The DWI models were similarly valuable in distinguishing SF in patients with liver disease.The combined index of CTRW parameters had the highest AUC.

Transient elastography and diffusion-weighted magnetic resonance imaging for assessment of liver fibrosis in children with chronic hepatitis C

BACKGROUND Chronic hepatitis C(CHC)is a health burden with consequent morbidity and mortality.Liver biopsy is the gold standard for evaluating fibrosis and assessing disease severity and prognostic purposes post-treatment.Noninvasive altern-atives for liver biopsy such as transient elastography(TE)and diffusion-weighted magnetic resonance imaging(DW-MRI)are critical needs.AIM To evaluate TE and DW-MRI as noninvasive tools for predicting liver fibrosis in children with CHC.METHODS This prospective cross-sectional study initially recruited 100 children with CHC virus infection.Sixty-four children completed the full set of investigations including liver stiffness measurement(LSM)using TE and measurement of apparent diffusion coefficient(ADC)of the liver and spleen using DW-MRI.Liver biopsies were evaluated for fibrosis using Ishak scoring system.LSM and liver and spleen ADC were compared in different fibrosis stages and correlation analysis was performed with histopathological findings and other laboratory parameters.RESULTS Most patients had moderate fibrosis(73.5%)while 26.5%had mild fibrosis.None had severe fibrosis or cirrhosis.The majority(68.8%)had mild activity,while only 7.8%had moderate activity.Ishak scores had a significant direct correlation with LSM(P=0.008)and were negatively correlated with both liver and spleen ADC but with no statistical significance(P=0.086 and P=0.145,respectively).Similarly,histopatho-logical activity correlated significantly with LSM(P=0.002)but not with liver or spleen ADC(P=0.84 and 0.98 respectively).LSM and liver ADC were able to significantly discriminate F3 from lower fibrosis stages(area under the curve=0.700 and 0.747,respectively)with a better performance of liver ADC.CONCLUSION TE and liver ADC were helpful in predicting significant fibrosis in children with chronic hepatitis C virus infection with a better performance of liver ADC.

Epileptic brain network mechanisms and neuroimaging techniques for the brain network

Epilepsy can be defined as a dysfunction of the brain network,and each type of epilepsy involves different brain-network changes that are implicated diffe rently in the control and propagation of interictal or ictal discharges.Gaining more detailed information on brain network alterations can help us to further understand the mechanisms of epilepsy and pave the way for brain network-based precise therapeutic approaches in clinical practice.An increasing number of advanced neuroimaging techniques and electrophysiological techniques such as diffusion tensor imaging-based fiber tra ctography,diffusion kurtosis imaging-based fiber tractography,fiber ball imagingbased tra ctography,electroencephalography,functional magnetic resonance imaging,magnetoencephalography,positron emission tomography,molecular imaging,and functional ultrasound imaging have been extensively used to delineate epileptic networks.In this review,we summarize the relevant neuroimaging and neuroelectrophysiological techniques for assessing structural and functional brain networks in patients with epilepsy,and extensively analyze the imaging mechanisms,advantages,limitations,and clinical application ranges of each technique.A greater focus on emerging advanced technologies,new data analysis software,a combination of multiple techniques,and the construction of personalized virtual epilepsy models can provide a theoretical basis to better understand the brain network mechanisms of epilepsy and make surgical decisions.

Contrast Normalization Strategies in Brain Tumor Imaging:From Preprocessing to Classification

Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries.Magnetic resonance imaging(MRI)and computed tomography(CT)are utilized to capture brain images.MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders.Typically,manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention.However,early diagnosis of brain tumors is intricate,necessitating the use of computerized methods.This research introduces an innovative approach for the automated segmentation of brain tumors and a framework for classifying different regions of brain tumors.The proposed methods consist of a pipeline with several stages:preprocessing of brain images with noise removal based on Wiener Filtering,enhancing the brain using Principal Component Analysis(PCA)to obtain well-enhanced images,and then segmenting the region of interest using the Fuzzy C-Means(FCM)clustering technique in the third step.The final step involves classification using the Support Vector Machine(SVM)classifier.The classifier is applied to various types of brain tumors,such as meningioma and pituitary tumors,utilizing the Contrast-Enhanced Magnetic Resonance Imaging(CE-MRI)database.The proposed method demonstrates significantly improved contrast and validates the effectiveness of the classification framework,achieving an average sensitivity of 0.974,specificity of 0.976,accuracy of 0.979,and a Dice Score(DSC)of 0.957.Additionally,this method exhibits a shorter processing time of 0.44 s compared to existing approaches.The performance of this method emphasizes its significance when compared to state-of-the-art methods in terms of sensitivity,specificity,accuracy,and DSC.To enhance the method further in the future,it is feasible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.

Altered spontaneous brain activity patterns in hypertensive retinopathy using fractional amplitude of low-frequency fluctuations:a functional magnetic resonance imaging study

AIM:To study functional brain abnormalities in patients with hypertensive retinopathy(HR)and to discuss the pathophysiological mechanisms of HR by fractional amplitude of low-frequency fluctuations(fALFFs)method.METHODS:Twenty HR patients and 20 healthy controls(HCs)were respectively recruited.The age,gender,and educational background characteristics of the two groups were similar.After functional magnetic resonance imaging(fMRI)scanning,the subjects’spontaneous brain activity was evaluated with the fALFF method.Receiver operating characteristic(ROC)curve analysis was used to classify the data.Further,we used Pearson’s correlation analysis to explore the relationship between fALFF values in specific brain regions and clinical behaviors in patients with HR.RESULTS:The brain areas of the HR group with lower fALFF values than HCs were the right orbital part of the middle frontal gyrus(RO-MFG)and right lingual gyrus.In contrast,the values of fALFFs in the left middle temporal gyrus(MTG),left superior temporal pole(STP),left middle frontal gyrus(MFG),left superior marginal gyrus(SMG),left superior parietal lobule(SPL),and right supplementary motor area(SMA)were higher in the HR group.The results of a t-test showed that the average values of fALFFs were statistically significantly different in the HR group and HC group(P<0.001).The fALFF values of the left middle frontal gyrus in HR patients were positively correlated with anxiety scores(r=0.9232;P<0.0001)and depression scores(r=0.9682;P<0.0001).CONCLUSION:fALFF values in multiple brain regions of HR patients are abnormal,suggesting that these brain regions in HR patients may be dysfunctional,which may help to reveal the pathophysiological mechanisms of HR.

Is it a normal phenomenon for pediatric patients to have brain leptomeningeal contrast enhancement on 3-tesla magnetic resonance imaging?

Determining whether sevoflurane sedation in children leads to“pseudo”prominent leptomeningeal contrast enhancement(pLMCE)on 3 Tesla magnetic resonance imaging will help reduce overdiagnosis by radiologists and clarify the pathophysiological changes of pLMCE.

A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

Recent progress in the applications of presynaptic dopaminergic positron emission tomography imaging in parkinsonism

Nowadays,presynaptic dopaminergic positron emission tomography,which assesses deficiencies in dopamine synthesis,storage,and transport,is widely utilized for early diagnosis and differential diagnosis of parkinsonism.This review provides a comprehensive summary of the latest developments in the application of presynaptic dopaminergic positron emission tomography imaging in disorders that manifest parkinsonism.We conducted a thorough literature search using reputable databases such as PubMed and Web of Science.Selection criteria involved identifying peer-reviewed articles published within the last 5 years,with emphasis on their relevance to clinical applications.The findings from these studies highlight that presynaptic dopaminergic positron emission tomography has demonstrated potential not only in diagnosing and differentiating various Parkinsonian conditions but also in assessing disease severity and predicting prognosis.Moreover,when employed in conjunction with other imaging modalities and advanced analytical methods,presynaptic dopaminergic positron emission tomography has been validated as a reliable in vivo biomarker.This validation extends to screening and exploring potential neuropathological mechanisms associated with dopaminergic depletion.In summary,the insights gained from interpreting these studies are crucial for enhancing the effectiveness of preclinical investigations and clinical trials,ultimately advancing toward the goals of neuroregeneration in parkinsonian disorders.

Structural and functional alterations in the brains of patients with anisometropic and strabismic amblyopia:a systematic review of magnetic resonance imaging studies

Amblyopia is the most common cause of vision loss in children and can persist into adulthood in the absence of effective intervention.Previous clinical and neuroimaging studies have suggested that the neural mechanisms underlying strabismic amblyopia and anisometropic amblyopia may be different.Therefore,we performed a systematic review of magnetic resonance imaging studies investigating brain alterations in patients with these two subtypes of amblyopia;this study is registered with PROSPERO(registration ID:CRD42022349191).We searched three online databases(PubMed,EMBASE,and Web of Science) from inception to April 1,2022;39 studies with 633 patients(324patients with anisometropic amblyo pia and 309 patients with strabismic amblyopia) and 580 healthy controls met the inclusion criteria(e.g.,case-control designed,pee r-reviewed articles) and were included in this review.These studies highlighted that both strabismic amblyopia and anisometropic amblyopia patients showed reduced activation and distorted topological cortical activated maps in the striate and extrastriate co rtices during tas k-based functional magnetic resonance imaging with spatial-frequency stimulus and retinotopic representations,respectively;these may have arisen from abnormal visual experiences.Compensations for amblyopia that are reflected in enhanced spontaneous brain function have been reported in the early visual cortices in the resting state,as well as reduced functional connectivity in the dorsal pathway and structural connections in the ventral pathway in both anisometro pic amblyopia and strabismic amblyopia patients.The shared dysfunction of anisometro pic amblyopia and strabismic amblyopia patients,relative to controls,is also chara cterized by reduced spontaneous brain activity in the oculomotor co rtex,mainly involving the frontal and parietal eye fields and the cerebellu m;this may underlie the neural mechanisms of fixation instability and anomalous saccades in amblyopia.With regards to specific alterations of the two forms of amblyo pia,anisometropic amblyo pia patients suffer more microstructural impairments in the precortical pathway than strabismic amblyopia patients,as reflected by diffusion tensor imaging,and more significant dysfunction and structural loss in the ventral pathway.Strabismic amblyopia patients experience more attenuation of activation in the extrastriate co rtex than in the striate cortex when compared to anisometropic amblyopia patients.Finally,brain structural magnetic resonance imaging alterations tend to be lateralized in the adult anisometropic amblyopia patients,and the patterns of brain alterations are more limited in amblyopic adults than in childre n.In conclusion,magnetic resonance imaging studies provide important insights into the brain alterations underlying the pathophysiology of amblyopia and demonstrate common and specific alte rations in anisometropic amblyo pia and strabismic amblyopia patients;these alterations may improve our understanding of the neural mechanisms underlying amblyopia.

Transformation of MRI Images to Three-Level Color Spaces for Brain Tumor Classification Using Deep-Net

In the domain ofmedical imaging,the accurate detection and classification of brain tumors is very important.This study introduces an advanced method for identifying camouflaged brain tumors within images.Our proposed model consists of three steps:Feature extraction,feature fusion,and then classification.The core of this model revolves around a feature extraction framework that combines color-transformed images with deep learning techniques,using the ResNet50 Convolutional Neural Network(CNN)architecture.So the focus is to extract robust feature fromMRI images,particularly emphasizingweighted average features extracted fromthe first convolutional layer renowned for their discriminative power.To enhance model robustness,we introduced a novel feature fusion technique based on the Marine Predator Algorithm(MPA),inspired by the hunting behavior of marine predators and has shown promise in optimizing complex problems.The proposed methodology can accurately classify and detect brain tumors in camouflage images by combining the power of color transformations,deep learning,and feature fusion via MPA,and achieved an accuracy of 98.72%on a more complex dataset surpassing the existing state-of-the-art methods,highlighting the effectiveness of the proposed model.The importance of this research is in its potential to advance the field ofmedical image analysis,particularly in brain tumor diagnosis,where diagnoses early,and accurate classification are critical for improved patient results.

Does sevoflurane sedation in pediatric patients lead to“pseudo”leptomeningeal enhancement in the brain on 3 Tesla magnetic resonance imaging?

BACKGROUND Prominent leptomeningeal contrast enhancement(LMCE)in the brain is observed in some pediatric patients during sedation for imaging.However,based on clinical history and cerebrospinal fluid analysis,the patients are not acutely ill and do not exhibit meningeal signs.Our study determined whether sevoflurane inhalation in pediatric patients led to this pattern of‘pseudo’LMCE(pLMCE)on 3 Tesla magnetic resonance imaging(MRI).AIM To highlight the significance of pLMCE in pediatric patients undergoing enhanced brain MRI under sedation to avoid misinterpretation in reports.METHODS A retrospective cross-sectional evaluation of pediatric patients between 0-8 years of age was conducted.The patients underwent enhanced brain MRI under inhaled sevoflurane.The LMCE grade was determined by two radiologists,and interobserver variability of the grade was calculated using Cohen’s kappa.The LMCE grade was correlated with duration of sedation,age and weight using the Spearman rho rank correlation.RESULTS A total of 63 patients were included.Fourteen(22.2%)cases showed mild LMCE,48(76.1%)cases showed moderate LMCE,and 1 case(1.6%)showed severe LMCE.We found substantial agreement between the two radiologists in detection of pLMCE on post-contrast T1 imaging(kappa value=0.61;P<0.001).Additionally,we found statistically significant inverse and moderate correlations between patient weight and age.There was no correlation between duration of sedation and pLMCE.CONCLUSION pLMCE is relatively common on post-contrast spin echo T1-weighted MRI of pediatric patients sedated by sevoflurane due to their fragile and immature vasculature.It should not be misinterpreted for meningeal pathology.Knowing pertinent clinical history of the child is an essential prerequisite to avoid radiological overcalling and the subsequent burden of additional investigations.

Early brainstem hemorrhage progression:multi-sequence magnetic resonance imaging and histopathology

According to clinical statistics,the mortality of patients with early brainstem hemorrhage is high.In this study,we established rat models of brainstem hemorrhage by injecting type Ⅶ collagenase into the right basotegmental pontine and investigated the pathological changes of early brainstem hemorrhage using multi-sequence magnetic resonance imaging and histopathological methods.We found that brainstem hematoma gradually formed in the injured rats over the first 3 days and then reduced after 7 days.The edema that occurred was mainly of the vasogenic type.No complete myelin sheath structure was found around the focus of the brainstem hemorrhage.The integrity and continuity of nerve fibers gradually deteriorated over the first 7 days.Neuronal degeneration was mild in the first 3 days and then obviously aggravated on the 7^(th)day.Inflammatory cytokines,interleukin-1β,and tumor necrosis factorαappeared on the 1st day after intracerebral hemorrhage,reached peak levels on the 3^(rd)day,and decreased from the 7^(th)day.Our findings show the characteristics of the progression of early brainstem hemorrhage.

Quantitative ultrasound brain imaging with multiscale deconvolutional waveform inversion

High-resolution images of human brain are critical for monitoring the neurological conditions in a portable and safe manner.Sound speed mapping of brain tissues provides unique information for such a purpose.In addition,it is particularly important for building digital human acoustic models,which form a reference for future ultrasound research.Conventional ultrasound modalities can hardly image the human brain at high spatial resolution inside the skull due to the strong impedance contrast between hard tissue and soft tissue.We carry out numerical experiments to demonstrate that the time-domain waveform inversion technique,originating from the geophysics community,is promising to deliver quantitative images of human brains within the skull at a sub-millimeter level by using ultra-sound signals.The successful implementation of such an approach to brain imaging requires the following items:signals of sub-megahertz frequencies transmitting across the inside of skull,an accurate numerical wave equation solver simulating the wave propagation,and well-designed inversion schemes to reconstruct the physical parameters of targeted model based on the optimization theory.Here we propose an innovative modality of multiscale deconvolutional waveform inversion that improves ultrasound imaging resolution,by evaluating the similarity between synthetic data and observed data through using limited length Wiener filter.We implement the proposed approach to iteratively update the parametric models of the human brain.The quantitative imaging method paves the way for building the accurate acoustic brain model to diagnose associated diseases,in a potentially more portable,more dynamic and safer way than magnetic resonance imaging and x-ray computed tomography.

Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging

Central nervous system abnormalities in fetuses are fairly common,happening in 0.1%to 0.2%of live births and in 3%to 6%of stillbirths.So initial detection and categorization of fetal Brain abnormalities are critical.Manually detecting and segmenting fetal brain magnetic resonance imaging(MRI)could be timeconsuming,and susceptible to interpreter experience.Artificial intelligence(AI)algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems,improving the diagnosis process and follow-up procedures.The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper.Using AI,anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically.All gestation age weeks(17-38 wk)and different AI models(mainly Convolutional Neural Network and U-Net)have been used.Some models'accuracy achieved 95%and more.AI could help preprocess and postprocess fetal images and reconstruct images.Also,AI can be used for gestational age prediction(with one-week accuracy),fetal brain extraction,fetal brain segmentation,and placenta detection.Some fetal brain linear measurements,such as Cerebral and Bone Biparietal Diameter,have been suggested.Classification of brain pathology was studied using diagonal quadratic discriminates analysis,Knearest neighbor,random forest,naive Bayes,and radial basis function neural network classifiers.Deep learning methods will become more powerful as more large-scale,labeled datasets become available.Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available.Also,physicians should be aware of AI's function in fetal brain MRI,particularly neuroradiologists,general radiologists,and perinatologists.

3D Kronecker Convolutional Feature Pyramid for Brain Tumor Semantic Segmentation in MR Imaging

Brain tumor significantly impacts the quality of life and changes everything for a patient and their loved ones.Diagnosing a brain tumor usually begins with magnetic resonance imaging(MRI).The manual brain tumor diagnosis from the MRO images always requires an expert radiologist.However,this process is time-consuming and costly.Therefore,a computerized technique is required for brain tumor detection in MRI images.Using the MRI,a novel mechanism of the three-dimensional(3D)Kronecker convolution feature pyramid(KCFP)is used to segment brain tumors,resolving the pixel loss and weak processing of multi-scale lesions.A single dilation rate was replaced with the 3D Kronecker convolution,while local feature learning was performed using the 3D Feature Selection(3DFSC).A 3D KCFP was added at the end of 3DFSC to resolve weak processing of multi-scale lesions,yielding efficient segmentation of brain tumors of different sizes.A 3D connected component analysis with a global threshold was used as a post-processing technique.The standard Multimodal Brain Tumor Segmentation 2020 dataset was used for model validation.Our 3D KCFP model performed exceptionally well compared to other benchmark schemes with a dice similarity coefficient of 0.90,0.80,and 0.84 for the whole tumor,enhancing tumor,and tumor core,respectively.Overall,the proposed model was efficient in brain tumor segmentation,which may facilitate medical practitioners for an appropriate diagnosis for future treatment planning.

Machine Learning-Based Models for Magnetic Resonance Imaging(MRI)-Based Brain Tumor Classification

In the medical profession,recent technological advancements play an essential role in the early detection and categorization of many diseases that cause mortality.The technique rising on daily basis for detecting illness in magnetic resonance through pictures is the inspection of humans.Automatic(computerized)illness detection in medical imaging has found you the emergent region in several medical diagnostic applications.Various diseases that cause death need to be identified through such techniques and technologies to overcome the mortality ratio.The brain tumor is one of the most common causes of death.Researchers have already proposed various models for the classification and detection of tumors,each with its strengths and weaknesses,but there is still a need to improve the classification process with improved efficiency.However,in this study,we give an in-depth analysis of six distinct machine learning(ML)algorithms,including Random Forest(RF),Naïve Bayes(NB),Neural Networks(NN),CN2 Rule Induction(CN2),Support Vector Machine(SVM),and Decision Tree(Tree),to address this gap in improving accuracy.On the Kaggle dataset,these strategies are tested using classification accuracy,the area under the Receiver Operating Characteristic(ROC)curve,precision,recall,and F1 Score(F1).The training and testing process is strengthened by using a 10-fold cross-validation technique.The results show that SVM outperforms other algorithms,with 95.3%accuracy.

Role of diffusion-weighted magnetic resonance imaging in the differential diagnosis of focal hepatic lesions

AIM： To evaluate the utility of diffusion-weighted imaging （DWl） in screening and differential diagnosis of benign and malignant focal hepatic lesions. METHODS： Magnetic resonance imaging （MRI） examinations were performed using the Signa Excite Xl Twin Speed 1.5T system （GE Healthcare, Milwaukee, Wl, USA）. Seventy patients who had undergone MRI of the liver [29 hepatocellular carcinomas （HCC）, four cholangiocarcinomas, 34 metastatic liver cancers, 10 hemangiomas, and eight cysts] between April 2004 and August 2008 were retrospectively evaluated. Visualization of lesions, relative contrast ratio （RCR）, and apparent diffusion coefficient （ADC） were compared between benign and malignant lesions on DWl. Su- perparamagnetic iron oxide （SPIO） was administered to 59 patients, and RCR was compared pre- and postadministration.RESULTS： DWI showed higher contrast between malignant lesions （especially in multiple small metastatic cancers） and surrounding liver parenchyma than did contrast-enhanced computed tomography. ADCs （mean±SD × 10^-3 mm2/s） were significantly lower （P 〈 0.05） in malignant lesions （HCC： 1.31 ± 0.28 and liver metastasis： 1.11 ± 0.22） and were significantly higher in benign lesions （hemangioma： 1.84 ± 0.37 and cyst： 2.61 ± 0.45） than in the surrounding hepatic tissues. RCR between malignant lesions and surrounding he- patic tissues significantly improved after SPIO administration, but RCRs in benign lesions were not improved.CONCLUSION： DWI is a simple and sensitive method for screening focal hepatic lesions and is useful for differential diagnosis.

Acute pancreatitis successfully diagnosed by diffusion-weighted imaging: A case report

Diffusion-weighted imaging (DWI) is an established diagnostic method of acute stroke. The latest advances in magnetic resonance imaging (MRI) technology have greatly expanded the utility of DWI in the examination of various organs. Recent studies have revealed the usefulness of DWI for imaging of the liver, kidney, ovary, and breast. We report a patient with acute pancreatitis detected by DWI and discussed the efficacy of DWI in diagnosing acute pancreatitis. A 50-year old man presented with a primary complaint of abdominal pain. We performed both DWI and computed tomography (CT) for this patient. The signal intensity in a series of DWI was measured and the apparent diffusion coefficient (ADC) values were calculated to differentiate inflammation from normal tissue. Two experienced radiologists evaluated the grade of acute pancreatitis by comparing the CT findings. Initially, the pancreas and multiple ascites around the pancreas produced a bright signal and ADC values were reduced on DWI. As the inflammation decreased, the bright signal faded to an iso-signal and the ADC values returned to their normal level. There was no difference in the abilities of DWI and CT images to detect acute pancreatitis. However, our case indicates that DWI can evaluate the manifestations of acute pancreatitis using no enhancement material andhas the potential to replace CT as a primary diagnostic strategy for acute pancreatitis.

Hepatocellular carcinoma: Can LI-RADS v2017 with gadoxetic-acid enhancement magnetic resonance and diffusion-weighted imaging improve diagnostic accuracy?

BACKGROUND The Liver Imaging Reporting and Data System(LI-RADS), supported by the American College of Radiology(ACR), has been developed for standardizing the acquisition, interpretation, reporting, and data collection of liver imaging examinations in patients at risk for hepatocellular carcinoma(HCC). Diffusionweighted imaging(DWI), which is described as an ancillary imaging feature of LI-RADS, can improve the diagnostic efficiency of LI-RADS v2017 with gadoxetic acid-enhanced magnetic resonance imaging(MRI) for HCC.AIM To determine whether the use of DWI can improve the diagnostic efficiency of LIRADS v2017 with gadoxetic acid-enhanced magnetic resonance MRI for HCC.METHODS In this institutional review board-approved study, 245 observations of high risk of HCC were retrospectively acquired from 203 patients who underwent gadoxetic acid-enhanced MRI from October 2013 to April 2018. Two readers independently measured the maximum diameter and recorded the presence of each lesion and assigned scores according to LI-RADS v2017. The test was used to determine the agreement between the two readers with or without DWI. In addition, the sensitivity(SE), specificity(SP), accuracy(AC), positive predictive value(PPV), and negative predictive value(NPV) of LI-RADS were calculated.Youden index values were used to compare the diagnostic performance of LIRADS with or without DWI.RESULTS Almost perfect interobserver agreement was obtained for the categorization of observations with LI-RADS(kappa value: 0.813 without DWI and 0.882 with DWI). For LR-5, the diagnostic SE, SP, and AC values were 61.2%, 92.5%, and71.4%, respectively, with or without DWI; for LR-4/5, they were 73.9%, 80%, and75.9% without DWI and 87.9%, 80%, and 85.3% with DWI; for LR-4/5/M, they were 75.8%, 58.8%, and 70.2% without DWI and 87.9%, 58.8%, and 78.4% with DWI; for LR-4/5/TIV, they were 75.8%, 75%, and 75.5% without DWI and 89.7%,75%, and 84.9% with DWI. The Youden index values of the LI-RADS classification without or with DWI were as follows: LR-4/5: 0.539 vs 0.679; LR-4/5/M: 0.346 vs 0.467; and LR-4/5/TIV: 0.508 vs 0.647.CONCLUSION LI-RADS v2017 has been successfully applied with gadoxetate-enhanced MRI for patients at high risk for HCC. The addition of DWI significantly increases the diagnostic efficiency for HCC.

Characteristics and pathological mechanism on magnetic resonance diffusion-weighted imaging after chemoembolization in rabbit liver VX-2 tumor model

AIM: To investigate dynamic characteristics and pathological mechanism of signal in rabbit VX-2 tumor model on diffusion-weighted imaging (DWI) after chemoembolization. METHODS: Forty New Zealand rabbits were included in the study and forty-seven rabbit VX-2 tumor models were raised by implanting directly and intrahepatically after abdominal cavity opened. Forty VX-2 tumor models from them were divided into four groups. DWI was performed periodically and respectively for each group after chemoembolization. All VX-2 tumor samples of each group were studied by pathology. The distinction of VX-2 tumors on DWI was assessed by their apparent diffusion coefficient (ADC) values. The statistical significance between different time groups, different area groups or different b-value groups was calculated by using SPSS12.0 software. RESULTS: Under b-value of 100 s/mm2, ADC values were lowest at 16 h after chemoembolization in area of VX-2 tumor periphery, central, and normal liver parenchyma around tumor, but turned to increase with further elongation of chemoembolization treatment. The distinction of ADC between different time groups was significant respectively (F = 7.325, P < 0.001; F = 2.496, P < 0.048; F = 6.856, P < 0.001). Cellular edema in the area of VX-2 tumor periphery or normal liver parenchyma around tumor, increased quickly in sixteen h after chemoembolization but, from the 16th h to the 48th h, cellular edema in the area of normal liver parenchyma around tumor decreased gradually and that in the area of VX-2 tumor periphery decreased lightly at, and then increased continually. After chemoembolization, Cellular necrosis in the area of VX-2 tumor periphery was more significantly high than that before chemoembolization. The areas of dead cells in VX-2 tumors manifested low signal and high ADC value, while the areas of viable cells manifested high signal and low ADC value. CONCLUSION: DWI is able to detect and differentiate tumor necrotic areas from viable cellular areas before and after chemoembolization. ADC of normal liver parenchyma and VX-2 tumor are influenced by intracellular edema, tissue cellular death and microcirculation disturbance after chemoembolization.