Dynamic Magnetic Resonance Imaging Research Articles

Comparison of arterial spin labeling and dynamic susceptibility contrast perfusion MR imaging in pediatric brain tumors: A systematic review and meta-analysis.

Brain tumors are a leading cause of mortality in children. Accurate tumor grading is essential to plan treatment and for prognostication. Perfusion imaging has been shown to correlate well with tumor grade in adults, however there are fewer studies in pediatric patients. Moreover, there is no consensus regarding which MR perfusion technique demonstrates the highest accuracy in the latter population. To compare the diagnostic test accuracy of dynamic-susceptibility contrast and arterial spin-labelling, in their ability to differentiate between low-and high-grade pediatric brain tumors at first presentation. Articles were retrieved from online electronic databases: MEDLINE (Ovid), Web of Science Core Collection and SCOPUS. Studies in pediatric patients with a treatment naïve diagnosed brain tumor and imaging including either ASL or DSC or both, together with a histological diagnosis were included. Studies involving adult patient or mixed age populations, studies with incomplete data and those which used dynamic contrast enhanced perfusion were excluded. The sensitivities and specificities obtained from each study were used to calculate the true-positive, true-negative, false-positive, and false-negative count. A case was defined as a histologically proven high-grade tumor. The random-effect model was used to merge statistics. Significance level was set at p < 0.05. Forest plots showing pairs of sensitivity and specificity, with their 95% confidence intervals, were constructed for each study. The bivariate model was applied in order to account for between-study variability. The SROC plots were constructed from the obtained data-sets. The AUC for the SROC of all studies was estimated to determine the overall diagnostic test accuracy of perfusion MRI, followed by a separate comparison of the SROC of ASL versus DSC studies. Small and heterogenous sample size. The diagnostic accuracy of ASL was found to be comparable and not inferior to DSC, thus its use in the diagnostic assessment of pediatric patients should continue to be supported. ASL = arterial spin labelling, DSC = dynamic susceptibility contrast, DCE = dynamic contrast-enhanced, rCBF = relative cerebral blood flow, rCBV = relative cerebral blood volume, MTT = mean transfer time, TR = repetition time, TE = echo time, SROC = summary receiver operating characteristics, HG= high-grade, LG = low-grade, AUC = area under the curve, PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Relationships of Jugular Bulb Parameters with Cerebral Perfusion and Metabolism After Resuscitation from Cardiac Arrest: A Post-Hoc Analysis of Experimental Studies Using a Minipig Model.

Cerebral blood flow (CBF) decreases in the first few hours or days following resuscitation from cardiac arrest, increasing the risk of secondary cerebral injury. Using data from experimental studies performed in minipigs, we investigated the relationships of parameters derived from arterial and jugular bulb blood gas analyses and lactate levels (jugular bulb parameters), which have been used as indicators of cerebral perfusion and metabolism, with CBF and the cerebral lactate to creatine ratio measured with dynamic susceptibility contrast magnetic resonance imaging and proton magnetic resonance spectroscopy, respectively. We retrospectively analyzed 36 sets of the following data obtained during the initial hours following resuscitation from cardiac arrest: percent of measured CBF relative to that at the prearrest baseline (%CBF), cerebral lactate to creatine ratio, and jugular bulb parameters, including jugular bulb oxygen saturation, jugular bulb lactate, arterial-jugular bulb oxygen content difference, cerebral extraction of oxygen, jugular bulb-arterial lactate content difference, lactate oxygen index, estimated respiratory quotient, and arterial-jugular bulb hydrogen ion content difference. Linear mixed-effects models were constructed to examine the effects of each jugular bulb parameter on the %CBF and cerebral lactate to creatine ratio. The arterial-jugular bulb oxygen content difference (P = 0.047) and cerebral extraction of oxygen (P = 0.030) had a significant linear relationship with %CBF, but they explained only 12.0% (95% confidence interval [CI] 0.002-0.371) and 14.2% (95% CI 0.005-0.396) of the total %CBF variance, respectively. The arterial-jugular bulb hydrogen ion content difference had a significant linear relationship with cerebral lactate to creatine ratio (P = 0.037) but explained only 13.8% (95% CI 0.003-0.412) of the total variance in the cerebral lactate to creatine ratio. None of the other jugular bulb parameters were related to the %CBF or cerebral lactate to creatine ratio. In conclusion, none of the jugular bulb parameters appeared to provide sufficient information on cerebral perfusion and metabolism in this setting.

In-silico heart model phantom to validate cardiac strain imaging.

The quantification of cardiac strains as structural indices of cardiac function has a growing prevalence in clinical diagnosis. However, the highly heterogeneous four-dimensional (4D) cardiac motion challenges accurate "regional" strain quantification and leads to sizable differences in the estimated strains depending on the imaging modality and post-processing algorithm, limiting the translational potential of strains as incremental biomarkers of cardiac dysfunction. There remains a crucial need for a feasible benchmark that successfully replicates complex 4D cardiac kinematics to determine the reliability of strain calculation algorithms. In this study, we propose an in-silico heart phantom derived from finite element (FE) simulations to validate the quantification of 4D regional strains. First, as a proof-of-concept exercise, we created synthetic magnetic resonance (MR) images for a hollow thick-walled cylinder under pure torsion with an exact solution and demonstrated that "ground-truth" values can be recovered for the twist angle, which is also a key kinematic index in the heart. Next, we used mouse-specific FE simulations of cardiac kinematics to synthesize dynamic MR images by sampling various sectional planes of the left ventricle (LV). Strains were calculated using our recently developed non-rigid image registration (NRIR) framework in both problems. Moreover, we studied the effects of image quality on distorting regional strain calculations by conducting in-silico experiments for various LV configurations. Our studies offer a rigorous and feasible tool to standardize regional strain calculations to improve their clinical impact as incremental biomarkers.

Perfusion parameter map generation from TOF-MRA in stroke using generative adversarial networks

PurposeTo generate perfusion parameter maps from Time-of-flight magnetic resonance angiography (TOF-MRA) images using artificial intelligence to provide an alternative to traditional perfusion imaging techniques. Materials and methodsThis retrospective study included a total of 272 patients with cerebrovascular diseases; 200 with acute stroke (from 2010 to 2018), and 72 with steno-occlusive disease (from 2011 to 2014). For each patient the TOF MRA image and the corresponding Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) were retrieved from the datasets. The authors propose an adapted generative adversarial network (GAN) architecture, 3D pix2pix GAN, that generates common perfusion maps (CBF, CBV, MTT, TTP, Tmax) from TOF-MRA images. The performance was evaluated by the structural similarity index measure (SSIM). For a subset of 20 patients from the acute stroke dataset, the Dice coefficient was calculated to measure the overlap between the generated and real hypoperfused lesions with a time-to-maximum (Tmax) > 6 s. ResultsThe GAN model exhibited high visual overlap and performance for all perfusion maps in both datasets: acute stroke (mean SSIM 0.88–0.92, mean PSNR 28.48–30.89, mean MAE 0.02–0.04 and mean NRMSE 0.14–0.37) and steno-occlusive disease patients (mean SSIM 0.83–0.98, mean PSNR 23.62–38.21, mean MAE 0.01–0.05 and mean NRMSE 0.03–0.15). For the overlap analysis for lesions with Tmax>6 s, the median Dice coefficient was 0.49. ConclusionOur AI model can successfully generate perfusion parameter maps from TOF-MRA images, paving the way for a non-invasive alternative for assessing cerebral hemodynamics in cerebrovascular disease patients. This method could impact the stratification of patients with cerebrovascular diseases. Our results warrant more extensive refinement and validation of the method.

Characterizing Lung Parenchymal Aeration via Standardized Signal Intensity from Free-breathing 4D Dynamic MRI in Phantoms, Healthy Children, and Pediatric Patients with Thoracic Insufficiency Syndrome.

Purpose To investigate free-breathing thoracic bright-blood four-dimensional (4D) dynamic MRI (dMRI) to characterize aeration of parenchymal lung tissue in healthy children and patients with thoracic insufficiency syndrome (TIS). Materials and Methods All dMR images in patients with TIS were collected from July 2009 to June 2017. Standardized signal intensity (sSI) was investigated, first using a lung aeration phantom to establish feasibility and sensitivity and then in a retrospective research study of 40 healthy children (16 male, 24 female; mean age, 9.6 years ± 2.1 [SD]), 20 patients with TIS before and after surgery (11 male, nine female; mean age, 6.2 years ± 4.2), and another 10 healthy children who underwent repeated dMRI examinations (seven male, three female; mean age, 9 years ± 3.6). Individual lungs in 4D dMR images were segmented, and sSI was assessed for each lung at end expiration (EE), at end inspiration (EI), preoperatively, postoperatively, in comparison to normal lungs, and in repeated scans. Results Air content changes of approximately 6% were detectable in phantoms via sSI. sSI within phantoms significantly correlated with air occupation (Pearson correlation coefficient = -0.96 [P < .001]). For healthy children, right lung sSI was significantly lower than that of left lung sSI (at EE: 41 ± 6 vs 47 ± 6 and at EI: 39 ± 6 vs 43 ± 7, respectively; P < .001), lung sSI at EI was significantly lower than that at EE (P < .001), and left lung sSI at EE linearly decreased with age (r = -0.82). Lung sSI at EE and EI decreased after surgery for patients (although not statistically significantly, with P values of sSI before surgery vs sSI after surgery, left and right lung separately, in the range of 0.13-0.51). sSI varied within 1.6%-4.7% between repeated scans. Conclusion This study demonstrates the feasibility of detecting change in sSI in phantoms via bright-blood dMRI when air occupancy changes. The observed reduction in average lung sSI after surgery in pediatric patients with TIS may indicate postoperative improvement in parenchymal aeration. Keywords: MR Imaging, Thorax, Lung, Pediatrics, Thoracic Surgery, Lung Parenchymal Aeration, Free-breathing Dynamic MRI, MRI Intensity Standardization, Thoracic Insufficiency Syndrome Supplemental material is available for this article. © RSNA, 2024.

Can dynamic contrast-enhanced MR imaging based on radiomics improve the diagnostic efficiency of clinically significant prostate cancer?

Aims/Background Prostate cancer stands out as one of the most prevalent malignant tumours among males. The non-invasive identification of clinically significant prostate cancer via magnetic resonance imaging plays a critical role in circumventing unnecessary biopsies and determining suitable treatment strategies for patients. Our study aimed to evaluate the potential improvement in predictive accuracy for clinically significant prostate cancer by incorporating perfusion data obtained from dynamic contrast-enhanced magnetic resonance imaging acquisition protocols into multiparametric magnetic resonance imaging parameters. Methods Radiomics extracted from perfusion parameters (Ktrans, Kep, Ve) of dynamic contrast-enhanced magnetic resonance imaging were analysed in patients suspected of prostate cancer who underwent 3T multiparametric magnetic resonance imaging between January 2017 and June 2023 in this retrospective study. The pathological findings obtained from biopsy or therapy were categorised into groups based on the Gleason sum score as either clinically significant prostate cancer (Gleason sum score > 7) or non-clinically significant prostate cancer (Gleason sum score ≤ 6). Diagnostic models were constructed using logistic regression analysis, incorporating prostate imaging reporting and data system V2.1 scores and clinical data, with or without radiomics extracted from dynamic contrast-enhanced. The area under curve (AUC) values were compared using the DeLong test. Results Overall, 214 men (clinically significant prostate cancer [n=78] and non-clinically significant prostate cancer [n=136]) were included. The clinical-prostate imaging reporting and data system model demonstrated an AUC of 0.89 (95% confidence interval: 0.84-0.95) in the training cohort and 0.91 (95% confidence interval: 0.84-0.98) in the test cohort. For the clinical-prostate imaging reporting and data system-radscore model, the AUC values were 0.97 (95% confidence interval: 0.95-0.99) for Ktrans, 0.98 (95% confidence interval: 0.96-1.00) for Ve, and 0.96 (95% confidence interval: 0.93-0.98) for Kep in the training cohort, and 0.97 (95% confidence interval: 0.94-1.00) for Ktrans, 0.95 (95% confidence interval: 0.91-1.00) for Ve, and 0.97 (95% confidence interval: 0.94-1.00) for Kep in the test cohort. Radiomics based on perfusion parameters exhibited good diagnostic performance in predicting clinically significant prostate cancer. The clinical-prostate imaging reporting and data system-radscore model demonstrated superior diagnostic capability compared to perfusion-based radiomics or clinical-prostate imaging reporting and data system models alone. Conclusion The application of radiomics, which involves extracting perfusion parameters from dynamic contrast-enhanced imaging, has the potential to enhance diagnostic accuracy for clinically significant prostate cancer.

Diagnosing osteomyelitis in diabetic foot by diffusion-weighted imaging and dynamic contrast material-enhanced magnetic resonance imaging: a systematic review and meta-analysis

To evaluate the diagnostic performance of diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE), for diagnosing osteomyelitis in the diabetic foot. A thorough search was carried out to identify suitable studies published up to September 2023. The quality of the studies involved was evaluated using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The diagnostic sensitivity and specificity of each imaging modality/method for each specific cut point were summarized. The summary receiver operating characteristic (SROC) curve was calculated using bivariate mixed effects models. Five studies investigating 187 patients and 234 bone lesions with 110 diagnosed osteomyelitis were enrolled. Four studies used DWI (172 lesions), three studies used DCE techniques (140 lesions) and two studies presented results of conventional MRI (66 lesions). The sensitivity ranges using conventional MRI, DWI and DCE were 65%-100%, 65%-100% and 64%-100%, respectively. The specificity ranges were 50%-61%, 56%-95%, and 66%-93%, respectively. The SROC curve of DWI and DCE was 0.89 (95% CI, 0.86-0.92) and 0.90 (95% CI, 0.87-0.92), respectively. Combining DWI and DCE methods, alongside conventional MRI, can improve the reliability and accuracy of diabetic foot osteomyelitis diagnosis. However, the study recognizes result variability due to varying protocols and emphasizes the need for well-designed studies with standardized approaches. To optimize diagnostic performance, the study recommends considering low ADC values, Ktrans or rapid wash-in rate from DCE such as iAUC60, along with using large ROIs that cover the entire lesion while excluding normal bone marrow.

An unrolled neural network for accelerated dynamic MRI based on second-order half-quadratic splitting model

The reconstruction of dynamic magnetic resonance images from incomplete k-space data has sparked significant research interest due to its potential to reduce scan time. However, traditional iterative optimization algorithms fail to faithfully reconstruct images at higher acceleration factors and incur long reconstruction time. Furthermore, end-to-end deep learning-based reconstruction algorithms suffer from large model parameters and lack robustness in the reconstruction results. Recently, unrolled deep learning models, have shown immense potential in algorithm stability and applicability flexibility. In this paper, we propose an unrolled deep learning network based on a second-order Half-Quadratic Splitting(HQS) algorithm, where the forward propagation process of this framework strictly follows the computational flow of the HQS algorithm. In particular, we propose a degradation-sense module by associating random sampling patterns with intermediate variables to guide the iterative process. We introduce the Information Fusion Transformer(IFT) to extract both local and non-local prior information from image sequences, thereby removing aliasing artifacts resulting from random undersampling. Finally, we impose low-rank constraints within the HQS algorithm to further enhance the reconstruction results. The experiments demonstrate that each component module of our proposed model contributes to the improvement of the reconstruction task. Our proposed method achieves comparably satisfying performance to the state-of-the-art methods and it exhibits excellent generalization capabilities across different sampling masks. At the low acceleration factor, there is a 0.7% enhancement in the PSNR. Furthermore, when the acceleration factor reached 8 and 12, the PSNR achieves an improvement of 3.4% and 5.8% respectively.

Glioma Type Prediction with Dynamic Contrast-Enhanced MR Imaging and Diffusion Kurtosis Imaging-A Standardized Multicenter Study.

The aim was to explore the performance of dynamic contrast-enhanced (DCE) MRI and diffusion kurtosis imaging (DKI) in differentiating the molecular subtypes of adult-type gliomas. A multicenter MRI study with standardized imaging protocols, including DCE-MRI and DKI data of 81 patients with WHO grade 2-4 gliomas, was performed at six centers. The DCE-MRI and DKI parameter values were quantitatively evaluated in ROIs in tumor tissue and contralateral normal-appearing white matter. Binary logistic regression analyses were performed to differentiate between high-grade (HGG) vs. low-grade gliomas (LGG), IDH1/2 wildtype vs. mutated gliomas, and high-grade astrocytic tumors vs. high-grade oligodendrogliomas. Receiver operating characteristic (ROC) curves were generated for each parameter and for the regression models to determine the area under the curve (AUC), sensitivity, and specificity. Significant differences between tumor groups were found in the DCE-MRI and DKI parameters. A combination of DCE-MRI and DKI parameters revealed the best prediction of HGG vs. LGG (AUC = 0.954 (0.900-1.000)), IDH1/2 wildtype vs. mutated gliomas (AUC = 0.802 (0.702-0.903)), and astrocytomas/glioblastomas vs. oligodendrogliomas (AUC = 0.806 (0.700-0.912)) with the lowest Akaike information criterion. The combination of DCE-MRI and DKI seems helpful in predicting glioma types according to the 2021 World Health Organization's (WHO) classification.

Value of multiparametric magnetic resonance imaging in distinguishing sinonasal lymphoma from sinonasal carcinoma: a case control study

BackgroundThe study aimed to evaluate the diagnostic efficacy of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) parameters in distinguishing sinonasal lymphoma from sinonasal carcinoma.MethodsForty-two participants with histologically confirmed sinonasal lymphomas and fifty-two cases of sinonasal carcinoma underwent imaging with a 3.0T MRI scanner. DCE-MRI and DWI were conducted, and various parameters including type of time-intensity curve(TIC), time to peak, peak enhancement, peak contrast enhancement, washout rate, apparent diffusion coefficient (ADC), and relative ADC were measured. Binary logistic regression and receiver operating characteristic (ROC) curve analysis were employed to assess the diagnostic capability of individual and combined indices for differentiating nasal sinus lymphoma from nasal sinus carcinoma.ResultsSinonasal lymphoma predominantly exhibited type II TIC(n = 20), whereas sinonasal carcinoma predominantly exhibited type III TIC(n = 23). Significant differences were observed in all parameters except washout ratio (p < 0.05), and ADC value emerged as the most reliable diagnostic tool in single parameter. Combined DCE-MRI parameters demonstrated superior diagnostic efficacy compared to individual parameters, with the highest efficiency (area under curve = 0.945) achieved when combining all parameters of DCE-MRI and DWI.ConclusionsMultiparametric evaluation involving contrast-enhanced dynamic MRI and DWI holds considerable diagnostic value in distinguishing sinonasal lymphoma from sinonasal carcinoma.

Estimating Palatal and Pharyngeal Muscle Contraction in Hindi Syllable Pronunciation using Computational Modeling

Abstract Introduction Speech, one of the main functions affected by cleft palate, involves a complex orchestra of sound deformation by various organs including the larynx, pharynx, epiglottis, palate, tongue, lip, and other structures complementing them. Although the effects of palatoplasty are traditionally evaluated subjectively, objective parameters have seldom been described to compare the pre- and postrepair functions. The study tries to extract the palatal and pharyngeal muscles while uttering various Hindi syllables and tries to find the percentage contraction of these structures as an objective assessment. Materials and Methods Digital Imaging and Communications in Medicine (DICOM) images while uttering each syllable of the Hindi syllable are obtained by subjecting a healthy volunteer to a dynamic magnetic resonance imaging (MRI). Using freeform geomagic software, 3D models of the structure of the pharynx and palate were created, which were then used to create a finite element model. Different anatomical constraints were applied to the muscles obtained. The finite element model was tested for convergence and a suitably fine mesh was used to obtain the results. The percentage of contraction of the palate and pharynx for uttering different syllables was thus evaluated. Results The palate and the posterior pharyngeal walls yielded different contractions for different syllables independent of each other. The highest contraction for the palate and posterior pharyngeal wall was for the syllable /k/ and the lowest regarding the palate was for /h/ and /eː/ for the pharyngeal wall. Conclusion Using computational modeling, quantification of speech in terms of percentage contraction of the palate and pharynx has been attempted for the Hindi language. Once validated with a larger population, the database may be used to quantify speech deformities due to structural pathologies in terms of palatal and pharyngeal contractions and help us assess the effectiveness of corrective surgeries for them.

Computational fluid dynamics of bladder voiding using 3D dynamic MRI.

Over the last couple of decades, image-based computational fluid dynamics (CFD) has revolutionized cardiovascular research by uncovering hidden features of wall strain, impact of vortices, and its use in treatment planning, as examples, that were simply not evident in the gold-standard catheterization studies done previously. In the work presented here, we have applied magnetic resonance imaging (MRI)-based CFD to study bladder voiding and to demonstrate the feasibility and potential of this approach. We used 3D dynamic MRI to image the bladder and urethra during voiding. A surface mesh processing tool was developed to process the bladder wall prior to executing a wall-motion driven CFD simulation of the bladder and urethra. The obtained flow rate and pressure were used to calculate urodynamic nomograms, which are currently used in the clinical setting to assess bladder voiding dysfunction. These nomograms concluded that our healthy volunteer has an unobstructed bladder and normal contractility. We calculated the work done to void the bladder and propose this as an additional quantitative metric to comprehensively assess bladder function. Further, we discuss the areas that would improve this relatively new methodology of image-based CFD in urodynamics.

Intraindividual comparison of CT and MRI for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma.

To establish and validate scoring models for predicting vessels encapsulating tumor clusters (VETC) in hepatocellular carcinoma (HCC) using computed tomography (CT) and magnetic resonance imaging (MRI), and to intra-individually compare the predictive performance between the two modalities. We retrospectively included 324 patients with surgically confirmed HCC who underwent preoperative dynamic CT and MRI with extracellular contrast agent between June 2019 and August 2020. These patients were then divided into a discovery cohort (n = 227) and a validation cohort (n = 97). Imaging features and Liver Imaging Reporting and Data System (LI-RADS) categories of VETC-positive HCCs were evaluated. Logistic regression analyses were conducted on the discovery cohort to identify clinical and imaging predictors associated with VETC-positive cases. Subsequently, separate CT-based and MRI-based scoring models were developed, and their diagnostic performance was compared using generalized estimating equations. On both CT and MRI, VETC-positive HCCs exhibited a higher frequency of size > 5.0 cm, necrosis or severe ischemia, non-smooth tumor margin, targetoid appearance, intratumor artery, and heterogeneous enhancement with septations or irregular ring-like structure compared to VETC-negative HCCs (all p < 0.05). Regarding LI-RADS categories, VETC-positive HCCs were more frequently categorized as LR-M than VETC-negative cases (all p < 0.05). In the validation cohort, the CT-based model showed similar sensitivity (76.7% vs. 86.7%, p = 0.375), specificity (83.6% vs. 74.6%, p = 0.180), and area under the curve value (0.80 vs. 0.81, p = 0.910) to the MRI-based model in predicting VETC-positive HCCs. Preoperative CT and MRI demonstrated comparable performance in the identification of VETC-positive HCCs, thus displaying promising predictive capabilities. Both computed tomography and magnetic resonance imaging demonstrated promise in preoperatively identifying the vessel-encapsulating tumor cluster pattern in hepatocellular carcinoma, with no statistically significant difference between the two modalities, potentially adding additional prognostic value. Computed tomography (CT) and magnetic resonance imaging (MRI) show promise in the preoperative identification of vessels encapsulating tumor clusters-positive hepatocellular carcinoma (HCC). HCC with vessels encapsulating tumor cluster patterns were more frequently LR-M compared to those without. These CT and MRI models showed comparable ability in identifying vessels encapsulating tumor clusters-positive HCC.

Cerebral hemodynamics as biomarkers for neuropathic pain in rats: a longitudinal study using a spinal nerve ligation model.

Neuropathic pain is one of the most challenging types of pain to diagnose and treat, a problem exacerbated by the lack of a quantitative biomarker. Recently, several clinical and preclinical studies have shown that neuropathic pain induces cerebral hemodynamic changes as a result of neuroplasticity in the brain. Our hypothesis in this study is that neuropathic pain leads to cerebral hemodynamic changes over postoperative time in a spinal nerve ligation (SNL) rat model, which has not been longitudinally explored previously. Furthermore, by identifying multiple regional hemodynamic features that are the most distinct between SNL and sham groups, where the sham group underwent only an incision without SNL, it may be possible to classify the SNL group regardless of when the onset of pain occurs. We investigate cerebral hemodynamic changes using dynamic susceptibility contrast magnetic resonance imaging in a rat model up to 28 days after ligating L5/L6 spinal nerves. We trained a linear support vector machine with relative cerebral blood volume data from different brain regions and found that the prediction model trained on the nucleus accumbens, motor cortex, pretectal area, and thalamus classified the SNL group and sham group at a 79.27% balanced accuracy, regardless of when the onset of pain occurred (SNL/sham: 60/45 data points). From the use of the SNL model without prior knowledge of the onset time of pain, the current findings highlight the potential of relative cerebral blood volume in the 4 highlighted brain regions as a biomarker for neuropathic pain.

Hydrodynamics in a randomly packed bed of cylindrical particles: A comparison between PR-CFD simulations and MRI experiments

Slender catalytic packed bed reactors are widely used in the chemical industry for chemical processes accompanied with significant heat liberation necessitating efficient heat removal. The employed catalyst particles are often cylindrical shaped, because these are easily manufactured via extrusion. The packing of these cylindrical particles results in a random structure of the bed causing flow maldistribution which reduces the reactor efficiency. The goal of this work is to capture the flow maldistribution with particle-resolved Computational Fluid Dynamics simulations and Magnetic Resonance Imaging experiments. By utilising the packing configuration reconstructed from the experiments, we are able to study the average fluid flow profiles and flow distribution in the bed as well as the local velocity profiles. It can be concluded that there is an overall good correspondence between the experiments and simulations. The minor deviations in the experimental and computed velocity distributions can be attributed to experimental error and slight differences in the experimental and reconstructed packing.

PET/CT-Based Absorbed Dose Maps in 90Y Selective Internal Radiation Therapy Correlate with Spatial Changes in Liver Function Derived from Dynamic MRI.

Functional liver parenchyma can be damaged from treatment of liver malignancies with 90Y selective internal radiation therapy (SIRT). Evaluating functional parenchymal changes and developing an absorbed dose (AD)-toxicity model can assist the clinical management of patients receiving SIRT. We aimed to determine whether there is a correlation between 90Y PET AD voxel maps and spatial changes in the nontumoral liver (NTL) function derived from dynamic gadoxetic acid-enhanced MRI before and after SIRT. Methods: Dynamic gadoxetic acid-enhanced MRI scans were acquired before and after treatment for 11 patients undergoing 90Y SIRT. Gadoxetic acid uptake rate (k1) maps that directly quantify spatial liver parenchymal function were generated from MRI data. Voxel-based AD maps, derived from the 90Y PET/CT scans, were binned according to AD. Pre- and post-SIRT k1 maps were coregistered to the AD map. Absolute and percentage k1 loss in each bin was calculated as a measure of loss of liver function, and Spearman correlation coefficients between k1 loss and AD were evaluated for each patient. Average k1 loss over the patients was fit to a 3-parameter logistic function based on AD. Patients were further stratified into subgroups based on lesion type, baseline albumin-bilirubin scores and alanine transaminase levels, dose-volume effect, and number of SIRT treatments. Results: Significant positive correlations (ρ = 0.53-0.99, P < 0.001) between both absolute and percentage k1 loss and AD were observed in most patients (8/11). The average k1 loss over 9 patients also exhibited a significant strong correlation with AD (ρ ≥ 0.92, P < 0.001). The average percentage k1 loss of patients across AD bins was 28%, with a logistic function model demonstrating about a 25% k1 loss at about 100 Gy. Analysis between patient subgroups demonstrated that k1 loss was greater among patients with hepatocellular carcinoma, higher alanine transaminase levels, larger fractional volumes of NTL receiving an AD of 70 Gy or more, and sequential SIRT treatments. Conclusion: Novel application of multimodality imaging demonstrated a correlation between 90Y SIRT AD and spatial functional liver parenchymal degradation, indicating that a higher AD is associated with a larger loss of local hepatocyte function. With the developed response models, PET-derived AD maps can potentially be used prospectively to identify localized damage in liver and to enhance treatment strategies.

Role of Α-L-Fucosidase as a Diagnostic Marker versus Α-Fetoprotein in HCC Post Viral Cirrhotic Patients

Abstract Background Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third cause of cancer-related mortality world- wide. The incidence of HCC is on the increase, and it is a major threat to our modern life. Epidemiological studies have indicated that HCC are related to chronic hepatitis B virus (HBV) and chronic hepatitis C virus (HCV) infection. Aim of the Work To evaluate the role of AFU as a diagnostic marker versus AFP in HCC post viral cirrhotic Egyptian patients. Patients and Methods This study had been carried out on 90 subjects, age range 21-75 year selected from Internal medicine and Hepatology outpatient clinics and inpatient wards at Ain shams university hospitals from January 2022 to June 2022. Results We found that serum levels of AFU were highest in patients of group A with HCC (129.867 ± 32.997 μl U/ml) compared to those with liver cirrhosis (33.433± 14.818 μl U/ml) with P value &amp;lt; 0.001 and the control groups (3.177 ± 1.199 μl U/ml) with P value &amp;lt; 0.001. Similarly alpha fetoprotein its serum levels were much higher in the HCC group (4688.223±11490.009 ng/ml) compared to the cirrhotic patients (4.407±3.107 ng/ml) with P value 0.02 Also, compared to healthy individuals (2.623±1.566 ng/ml) with p value 1.00. We found that there is No significant statistical correlation between serum AFP and AFU in our studied HCC and cirrhotic patients (R 0.332, P 0.073 for HCC and R 0.222, P 0.238 for cirrhotic). Regarding the negative correlation between serum AFU level and AFP level in order to predict the diagnostic value, sensitivity and specificity of both of them in term of tumor size, our study show that for AFU when the best cut off value was &amp;gt;52μl U/ml the sensitivity was 100% and specificity was 90% while for AFP when the best cut off value was &amp;gt; 10.5 ng/ml the sensitivity was 96.67% and specificity 96.67%. AFU is a promising serum tumor marker for HCC diagnosis and can be combined with AFP. Conclusion We concluded from this study that: Serum AFP was highly significant increase in HCC patients compared to cirrhotic and healthy controls, this is in agreement with many several reports that consider AFP in high serum levels as diagnostic for HCC combined with triphasic CT abdomen or dynamic MRI. Serum AFU levels were significantly higher in patients with HCC and mildly elevated in patients with liver cirrhosis compared to the control group. So it can be used as a tumor marker for HCC diagnosis. No correlation between AFU and laboratory data and child score. AFU was not influenced by clinical condition of the liver itself but with HCC. Around 40% of HCC patients have normal or low AFP level so AFU may be useful as a diagnostic marker in those patients. Serum AFP is the standard tumor marker used in clinical practice for diagnosis and surveillance of HCC in cirrhotic patients in spite of normal and low AFP HCC patients that may reach 40%. Sensitivity and specificity of AFU was nearly similar to sensitivity and specificity of AFP. Combined use of AFU with AFP or possibly other markers will improve the diagnostic field and can help in early detection of HCC in surveillance programms.

3D Observation of Pelvic Organs with Dynamic MRI Segmentation: A Bridge Toward Patient-Specific Models.

Female pelvic organ prolapses are common, but their treatment is challenging. Notably, diagnosis and understanding of these troubles remain incomplete. Tridimensional observations of displacement and deformation of the pelvic organs during a strain could support a better understanding and help to develop comprehensive tools for preoperative planning. The present feasibility study evaluates tridimensional dynamic MRI in 12 healthy volunteers. Tridimensional acquisitions were approximated using five intersecting slices, each recorded twice per second. MRI was performed during rest and strain, with intrarectal and intravaginal contrast gel. Subject-specific dynamic 3D models were built for each volunteer through segmentation. For each volunteer, pelvic organs could be segmented in three dimensions with a rate of acquisition of two cycles per second on five slices, allowing for a fluid observation of displacements and deformations during strain. Manual segmentation of a full strain required 2 h and 33 min on average. The upper limit of the rectum and the pelvic floor were the most difficult structures to identify. This technique is limited by its time-consuming manual segmentation, which impedes its implantation for routine clinical use. This method must be tried in patients with pelvic organ prolapse. This multi-planar acquisition technique applied during a dynamic MRI allows for observation of displacement and deformations of pelvic organs during a strain.

Post chemoembolization of hepatocellular carcinoma: a role of computed tomography and MRI in prediction of treatment response

Background Hepatocellular carcinoma (HCC) is the most prevalent and lethal form of primary liver cancer on a global scale. Aim To assess the tumor response to transarterial chemoembolization (TACE) by comparing computed tomography (CT) and magnetic resonance imaging (MRI) findings in patients with HCC. Patients and methods This prospective cohort research included 30 patients, aged from 40 to 77 years, with HCC, had TACE, good renal and liver functions, and were divided into two groups: the responders’ group and the nonresponders’ group. Patients had pre- and post-TACE examinations. Results There was a significant difference regarding the lesion criteria such as multiplicity, size, margins, involvement of both hepatic lobes, and pattern of arterial phase enhancement in both triphasic CT and dynamic MRI. Agreement between triphasic CT results and MRI results was in 26 patients. Disagreement as: two patients were categorized as complete response by triphasic CT versus partial response (PR) by MRI, one was categorized as PR by triphasic CT versus stable disease by MRI, and one was categorized as PR by triphasic CT versus PD by MRI. MRI was considered as the gold standard. Regarding the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of Triphasic CT in the studied patients, it was 89, 91, 90, 80, and 95%, respectively. Conclusion Triphasic CT is considered as the most used imaging techniques for predicting and evaluating the therapeutic response in HCC patients after TACE as referenced to dynamic MRI which is considered as the best imaging modality with high diagnostic standard in assessment of HCC before and after TACE.

Enhancing ovarian tumour evaluation with dynamic magnetic resonance imaging and diffusion weighted imaging

Enhancing ovarian tumour evaluation with dynamic magnetic resonance imaging and diffusion weighted imaging