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.

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.

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.

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.

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.

Diffusion-weighted magnetic resonance imaging to predict response of hepatocellular carcinoma to chemoembolization

AIM: To investigate whether intra-procedural diffusion- weighted magnetic resonance imaging can predict response of hepatocellular carcinoma (HCC) during trans- catheter arterial chemoembolization (TACE). METHODS: Sixteen patients (15 male), aged 59 ±11 years (range: 42-81 years) underwent a total of 21 separate treatments for unresectable HCC in a hybrid magnetic resonance/interventional radiology suite. Ana- tomical imaging and diffusion-weighted imaging (b = 0, 500 s/mm2) were performed on a 1.5-T unit. Tumor enhancement and apparent diffusion coefficient (ADC, mm2/s) values were assessed immediately before and at 1 and 3 mo after TACE. We calculated the percent change (PC) in ADC values at all time points. We compared follow-up ADC values to baseline values using a paired t test (α = 0.05). RESULTS: The intra-procedural sensitivity, specificity, and positive and negative predictive values (%) for detecting a complete or partial 1-mo tumor response using ADC PC thresholds of ±5%, ±10%, and ±15% were 77, 67, 91, and 40; 54, 67, 88, and 25; and 46, 100, 100, and 30, respectively. There was no clear predictive value for the 3-mo follow-up. Compared to baseline, the immediate post-procedure and 1-mo mean ADC values both increased; the latter obtaining statistical significance (1.48 ± 0.29 mm2/s vs 1.65 ± 0.35 × 10-3 mm2/s, P < 0.014). CONCLUSION: Intra-procedural ADC changes of > 15% predicted 1-mo anatomical HCC response with the greatest accuracy, and can provide valuable feedback at the time of TACE.

Diffusion-weighted magnetic resonance imaging for predicting the response of rectal cancer to neoadjuvant concurrent chemoradiation

AIM:To evaluate the clinical value of diffusion-weighted magnetic resonance imaging(DW-MRI)in predicting the response of rectal cancer to neoadjuvant chemoradiation.METHODS:This prospective study was approved by our institutional review board,and informed consent was obtained from each patient.Fifteen patients(median age 56 years)with locally advanced rectal cancer were treated in our hospital from June 2006 to December 2007.All patients were stageⅢB-C according to the results of MRI and endorectal ultrasound examinations.All patients underwent pelvic irradiation with 45 Gy/25 fx per 35 days.The concurrent chemotherapy regimen consisted of capecitabine 625mg/m2,bid(Monday-Friday),and oxaliplatin 50 mg/m2,weekly.The patients underwent surgery 5-8 wk after the completion of neoadjuvant therapy.T downstaging was defined as the downstaging of the tumor from cT3to ypT0-2 or from cT4 to ypT0-3.Good regression was defined as TRG 3-4,and poor regression was defined as TRG 0-2.Diffusion-weighted magnetic resonance images were obtained prior to and weekly during the course of neoadjuvant chemoradiation,and the apparent diffusion coefficient(ADC)values were calculated from the acquired tumor images.RESULTS:Comparison with the mean pretreatment tumor ADC revealed an increase in the mean tumor ADC during the course of neoadjuvant chemoradiation,especially at the 2ndweek(P=0.004).We found a strong negative correlation between the mean pretreatment tumor ADC and tumor regression after neoadjuvant chemoradiation(P=0.021).In the T downstage and tumor regression groups,we found a significant increase in the mean ADC at the 2ndweek of neoadjuvant therapy(P=0.011;0.004).CONCLUSION:DW-MRI might be a valuable clinical tool to help predict or assess the response of rectal cancer to neoadjuvant chemoradiation at an early timepoint.

Abdominal applications of diffusion-weighted magnetic resonance imaging: Where do we stand?

Diffusion-weighted imaging (DWI) is one of the magnetic resonance imaging (MRI) sequences providing qualitative as well as quantitative information at a cellular level. It has been widely used for various applications in the central nervous system. Over the past decade, various extracranial applications of DWI have been increasingly explored, as it may detect changes even before signal alterations or morphological abnormalities become apparent on other pulse sequences. Initial results from abdominal MRI applications are promising, particularly in oncological settings and for the detection of abscesses. The purpose of this article is to describe the clinically relevant basic concepts of DWI, techniques to perform abdominal DWI, its analysis and applications in abdominal visceral MR imaging, in addition to a brief overview of whole body DWI MRI.

Apparent diffusion coefficient by diffusion-weighted magnetic resonance imaging as a sole biomarker for staging and prognosis of gastric cancer

Objective： To investigate the role of apparent diffusion coefficient （ADC） from diffusion-weighted magnetic resonance imaging （DW-MRI） when applied to the 7th TNM classification in the staging and prognosis of gastric cancer （GC）. Methods： Between October 2009 and May 2014, a total of 89 patients with non-metastatic, biopsy proven GC underwent 1.5T DW-MRI, and then treated with radical surgery. Tumor ADC was measured retrospectively and compared with final histology following the 7th TNM staging （local invasion, nodal involvement and according to the different groups -- stage Ⅰ, Ⅱ and Ⅲ）. Kaplan-Meier curves were also generated. The follow-up period is updated to May 2016. Results： Median follow-up period was 33 months and 45/89 （51%） deaths from GC were observed. ADC was significantly different both for local invasion and nodal involvement （P〈0.001）. Considering final histology as the reference standard, a preoperative ADC cut-offof 1.80×10-3 mm^2/s could distinguish between stages I and Ⅱ and an ADC value of ≤1.36-10-3 mm^2/s was associated with stage Ⅲ（P〈0.001）. Kaplan-Meier curves demonstrated that the survival rates for the three prognostic groups were significantly different according to final histology and ADC cut-offs （P〈0.001）. Conclusions： ADC is different according to local invasion, nodal involvement and the 7th TNM stage groups for GC, representing a potential, additional prognostic biomarker. The addition of DW-MRI could aid in the staging and risk stratification of GC.

Therapeutic effect of nerve growth factor on cerebral infarction in dogs using the hemisphere anomalous volume ratio of diffusion-weighted magnetic resonance imaging

A model of focal cerebral ischemic infarction was established in dogs through middle cerebral artery occlusion of the right side.Thirty minutes after occlusion,models were injected with nerve growth factor adjacent to the infarct locus.The therapeutic effect of nerve growth factor against cerebral infarction was assessed using the hemisphere anomalous volume ratio,a quantitative index of diffusion-weighted MRI.At 6 hours,24 hours,7 days and 3 months after modeling,the hemisphere anomalous volume ratio was significantly reduced after treatment with nerve growth factor. Hematoxylin-eosin staining,immunohistochemistry,electron microscopy and neurological function scores showed that infarct defects were slightly reduced and neurological function significantly improved after nerve growth factor treatment.This result was consistent with diffusion-weighted MRI measurements.Experimental findings indicate that nerve growth factor can protect against cerebral infarction,and that the hemisphere anomalous volume ratio of diffusion-weighted MRI can be used to evaluate the therapeutic effect.

Diffusion-weighted magnetic resonance imaging without bowel preparation for detection of ulcerative colitis

AIM: To evaluate the accuracy of diffusion-weighted imaging(DWI) without bowel preparation,the optimal b value and the changes in apparent diffusion coefficient(ADC) in detecting ulcerative colitis(UC).METHODS: A total of 20 patients who underwent 3T magnetic resonance imaging(MRI) without bowel preparation and colonoscopy within 24 h were recruited.Biochemical indexes,including C-reactive protein(CRP),erythrocyte sedimentation rate,hemoglobin,leucocytes,platelets,serum iron and albumin,were determined.Biochemical examinations were then performed within 24 h before or after MR colonography was conducted.DWI was performed at various b values(b = 0,400,600,800,and 1000 s/mm2).Two radiologists independently and blindly reviewed conventional- and contrast-enhanced MR images,DWI and ADC maps; these radiologists also determined ADC in each intestinal segment(rectum,sigmoid,left colon,transverse colon,and right colon).Receiver operating characteristic(ROC) analysis was performed to assess the diagnostic performance of DWI hyperintensity from various b factors,ADC values and different radiological signs to detect endoscopic inflammation in the corresponding bowel segment.Optimal ADC threshold was estimated by maximizing the combination of sensitivity and specificity.MRfindings were correlated with endoscopic results and clinical markers; these findings were then estimated by ROC analysis.RESULTS: A total of 100 segments(71 with endoscopic colonic inflammation; 29 normal) were included.The proposed total magnetic resonance score(MR-score-T) was correlated with the total modified Baron score(Baron-T; r = 0.875,P < 0.0001); the segmental MR score(MR-score-S) was correlated with the segmental modified Baron score(Baron-S; r = 0.761,P < 0.0001).MR-score-T was correlated with clinical and biological markers of disease activity(r = 0.445 to 0.831,P < 0.05).MR-score-S > 1 corresponded to endoscopic colonic inflammation with a sensitivity of 85.9%,a specificity of 82.8% and an area under the curve(AUC) of 0.929(P < 0.0001).The accuracy of DWI hyperintensity was significantly greater at b = 800 than at b = 400,600,or 1000 s/mm2(P < 0.05) when endoscopic colonic inflammation was detected.DWI hyperintensity at b = 800 s/mm2 indicated endoscopic colonic inflammation with a sensitivity of 93.0%,a specificity of 79.3% and an AUC of 0.867(P < 0.0001).Quantitative analysis results revealed that ADC values at b = 800 s/mm2 differed significantly between endoscopic inflamed segment and normal intestinal segment(1.56 ± 0.58 mm2/s vs 2.63 ± 0.46 mm2/s,P < 0.001).The AUC of ADC values was 0.932(95% confidence interval: 0.881-0.983) when endoscopic inflammation was detected.The threshold ADC value of 2.18 × 10-3 mm2/s indicated that endoscopic inflammation differed from normal intestinal segment with a sensitivity of 89.7% and a specificity of 80.3%.CONCLUSION: DWI combined with conventional MRI without bowel preparation provides a quantitative strategy to differentiate actively inflamed intestinal segments from the normal mucosa to detect UC.