Towards Longitudinal Glioma Segmentation: Evaluating combined pre- and post-treatment MRI training data for automated tumor segmentation using nnU-Net

Identification of key phenotypic regions such as necrosis, contrast enhancement, and edema on magnetic resonance imaging (MRI) is important for understanding disease evolution and treatment response in patients with glioma. Manual delineation is time intensive and not feasible for a clinical workflow. Automating phenotypic region segmentation overcomes many issues with manual segmentation, however, current glioma segmentation datasets focus on pre-treatment, diagnostic scans, where treatment effects and surgical cavities are not present. Thus, existing automatic segmentation models are not applicable to post-treatment imaging that is used for longitudinal evaluation of care. Here, we present a comparison of three-dimensional convolutional neural networks (nnU-Net architecture) trained on large temporally defined pre-treatment, post-treatment, and mixed cohorts. We used a total of 1563 imaging timepoints from 854 patients curated from 13 different institutions as well as diverse public data sets to understand the capabilities and limitations of automatic segmentation on glioma images with different phenotypic and treatment appearance. We assessed the performance of models using Dice coefficients on test cases from each group comparing predictions with manual segmentations generated by trained technicians. We demonstrate that training a combined model can be as effective as models trained on just one temporal group. The results highlight the importance of a diverse training set, that includes images from the course of disease and with effects from treatment, in the creation of a model that can accurately segment glioma MRIs at multiple treatment time points.

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

Live predator stress in adolescence results in distinct adult behavioral consequences and dorsal diencephalic brain activation patterns

Exposure to traumatic events during childhood increases the risk of adult psychopathology, including anxiety, depression, alcohol use disorders and their co-morbidity. Early life trauma also results in increased symptom complexity, treatment resistance and poor treatment outcomes. The purpose of this study was to establish a novel rodent model of adolescent stress, based on an ethologically relevant life-threatening event, live predator exposure. Rats were exposed to a live predator for 10 min. at three different time points (postnatal day (PND)31, 46 and 61). Adult depression-, anxiety-like behaviors and ethanol consumption were characterized well past the last acute stress event (two weeks). Behavioral profiles across assessments were developed to characterize individual response to adolescent stress. CNS activation patterns in separate groups of subjects were characterized after the early (PND31) and last predator exposure (PND61). Subjects exposed to live-predator adolescent stress generally exhibited less exploratory behavior, less propensity to venture into open spaces, a decreased preference for sweet solutions and decreased ethanol consumption in a two-bottle preference test. Additional studies demonstrated blunted cortisol response and CNS activation patterns suggestive of habenula, rostromedial tegmental (RMTg), dorsal raphe and central amygdala involvement in mediating the adult consequences of adolescent stress. Thus, adolescent stress in the form of live-predator exposure results in significant adult behavioral and neurobiological disturbances. Childhood trauma, its impact on neurodevelopment and the subsequent development of mood disorders is a pervasive theme in mental illness. Improving animal models and our neurobiological understanding of the symptom domains impacted by trauma could significantly improve treatment strategies.

Integration of machine learning and mechanistic models accurately predicts variation in cell density of glioblastoma using multiparametric MRI

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.

Accurate Patient-Specific Machine Learning Models of Glioblastoma Invasion Using Transfer Learning

BACKGROUND AND PURPOSE

MR imaging-based modeling of tumor cell density can substantially improve targeted treatment of glioblastoma. Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a transfer learning method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient's own histologic data.

MATERIALS AND METHODS

We recruited patients with primary glioblastoma undergoing image-guided biopsies and preoperative imaging, including contrast-enhanced MR imaging, dynamic susceptibility contrast MR imaging, and diffusion tensor imaging. We calculated relative cerebral blood volume from DSC-MR imaging and mean diffusivity and fractional anisotropy from DTI. Following image coregistration, we assessed tumor cell density for each biopsy and identified corresponding localized MR imaging measurements. We then explored a range of univariate and multivariate predictive models of tumor cell density based on MR imaging measurements in a generalized one-model-fits-all approach. We then implemented both univariate and multivariate individualized transfer learning predictive models, which harness the available population-level data but allow individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized transfer learning and generalized one-model-fits-all models.

RESULTS

Tumor cell density significantly correlated with relative CBV (r = 0.33, P < .001), and T1-weighted postcontrast (r = 0.36, P < .001) on univariate analysis after correcting for multiple comparisons. With single-variable modeling (using relative CBV), transfer learning increased predictive performance (r = 0.53, mean absolute error = 15.19%) compared with one-model-fits-all (r = 0.27, mean absolute error = 17.79%). With multivariate modeling, transfer learning further improved performance (r = 0.88, mean absolute error = 5.66%) compared with one-model-fits-all (r = 0.39, mean absolute error = 16.55%).

CONCLUSIONS

Transfer learning significantly improves predictive modeling performance for quantifying tumor cell density in glioblastoma.

Brain MR Radiomics to Differentiate Cognitive Disorders

OBJECTIVE

Subtle and gradual changes occur in the brain years before cognitive impairment due to age-related neurodegenerative disorders. The authors examined the utility of hippocampal texture analysis and volumetric features extracted from brain magnetic resonance (MR) data to differentiate between three cognitive groups (cognitively normal individuals, individuals with mild cognitive impairment, and individuals with Alzheimer's disease) and neuropsychological scores on the Clinical Dementia Rating (CDR) scale.

METHODS

Data from 173 unique patients with 3-T T₁-weighted MR images from the Alzheimer's Disease Neuroimaging Initiative database were analyzed. A variety of texture and volumetric features were extracted from bilateral hippocampal regions and were used to perform binary classification of cognitive groups and CDR scores. The authors used diagonal quadratic discriminant analysis in a leave-one-out cross-validation scheme. Sensitivity, specificity, and area under the receiver operating characteristic curve were used to assess the performance of models.

RESULTS

The results show promise for hippocampal texture analysis to distinguish between no impairment and early stages of impairment. Volumetric features were more successful at differentiating between no impairment and advanced stages of impairment.

CONCLUSIONS

MR radiomics may be a promising tool to classify various cognitive groups.

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health, and will revolutionize radiologists' workflows. The Canadian Association of Radiologists (CAR) is the national voice of radiology committed to promoting the highest standards in patient-centered imaging, lifelong learning, and research. The CAR has created an AI working group with the mandate to discuss and deliberate on practice, policy, and patient care issues related to the introduction and implementation of AI in imaging. This white paper provides recommendations for the CAR derived from deliberations between members of the AI working group. This white paper on AI in radiology will inform CAR members and policymakers on key terminology, educational needs of members, research and development, partnerships, potential clinical applications, implementation, structure and governance, role of radiologists, and potential impact of AI on radiology in Canada.

Retrospective Validation of a Computer-Assisted Quantification Model of Intracerebral Hemorrhage Volume on Accuracy, Precision, and Acquisition Time, Compared with Standard ABC/2 Manual Volume Calculation

BACKGROUND AND PURPOSE

Intracerebral hemorrhage accounts for 6.5%-19.6% of all acute strokes. Initial intracerebral hemorrhage volume and expansion are both independent predictors of clinical outcomes and mortality. Therefore, a rapid, unbiased, and precise measurement of intracerebral hemorrhage volume is a key component of clinical management. The most commonly used method, ABC/2, results in overestimation. We developed an interactive segmentation program, SegTool, using a novel graphic processing unit, level set algorithm. Until now, the speed, bias, and precision of SegTool had not been validated.

MATERIALS AND METHODS

In a single stroke academic center, 2 vascular neurologists and 2 neuroradiologists independently performed a test-retest experiment that involved repeat measurements of static, unchanging intracerebral hemorrhage volumes on CT from 76 intracerebral hemorrhage cases. Measurements were made with SegTool and ABC/2. True intracerebral hemorrhage volumes were estimated from a consensus of repeat manual tracings by 2 operators. These data allowed us to estimate measurement bias, precision, and speed.

RESULTS

The measurements with SegTool were not significantly different from the true intracerebral hemorrhage volumes, while ABC/2 overestimated volume by 45%. The interrater measurement variability with SegTool was 50% less than that with ABC/2. The average measurement times for ABC/2 and SegTool were 35.7 and 44.6 seconds, respectively.

CONCLUSIONS

SegTool appears to have attributes superior to ABC/2 in terms of accuracy and interrater reliability with a 9-second delay in measurement time (on average); hence, it could be useful in clinical trials and practice.

MRI-Based Texture Analysis to Differentiate Sinonasal Squamous Cell Carcinoma from Inverted Papilloma

BACKGROUND AND PURPOSE

Because sinonasal inverted papilloma can harbor squamous cell carcinoma, differentiating these tumors is relevant. The objectives of this study were to determine whether MR imaging-based texture analysis can accurately classify cases of noncoexistent squamous cell carcinoma and inverted papilloma and to compare this classification performance with neuroradiologists' review.

MATERIALS AND METHODS

Adult patients who had inverted papilloma or squamous cell carcinoma resected were eligible (coexistent inverted papilloma and squamous cell carcinoma were excluded). Inclusion required tumor size of >1.5 cm and preoperative MR imaging with axial T1, axial T2, and axial T1 postcontrast sequences. Five well-established texture analysis algorithms were applied to an ROI from the largest tumor cross-section. For a training dataset, machine-learning algorithms were used to identify the most accurate model, and performance was also evaluated in a validation dataset. On the basis of 3 separate blinded reviews of the ROI, isolated tumor, and entire images, 2 neuroradiologists predicted tumor type in consensus.

RESULTS

The inverted papilloma (n = 24) and squamous cell carcinoma (n = 22) cohorts were matched for age and sex, while squamous cell carcinoma tumor volume was larger (P = .001). The best classification model achieved similar accuracies for training (17 squamous cell carcinomas, 16 inverted papillomas) and validation (7 squamous cell carcinomas, 6 inverted papillomas) datasets of 90.9% and 84.6%, respectively (P = .537). For the combined training and validation cohorts, the machine-learning accuracy (89.1%) was better than that of the neuroradiologists' ROI review (56.5%, P = .0004) but not significantly different from the neuroradiologists' review of the tumors (73.9%, P = .060) or entire images (87.0%, P = .748).

CONCLUSIONS

MR imaging-based texture analysis has the potential to differentiate squamous cell carcinoma from inverted papilloma and may, in the future, provide incremental information to the neuroradiologist.

Abstract A08: Histologic evidence for a bio-mathematical model of glioblastoma invasion

Purpose: Investigate the utility of patient-specific spatial predictions of tumor cell density from a bio-mathematical model.

Introduction: Glioblastomas (GBMs) are the most malignant of all primary brain tumors. While it is known there is always a non-detectable portion of the tumor, current techniques of monitoring GBM progression, imaging and initial histological assessment, are not able to reliably estimate the tumor invasion past the enhancing region on T2-Weighted (T2W) imaging. Over the last two decades, a large effort has been made to create a simple patient-specific mathematical model of gliomas. The resulting model, referred to as the Proliferation-Invasion (PI) model, is based on two key parameters, the net growth rate, ρ, and the dispersal coefficient, D. In this model, the ratio of D/ρ is related to degree of invasion and the product D*ρ, is related to the speed of growth.

The intuitive understanding provided by this model has been able to provide patient-specific understanding of disease kinetics enabling prediction of outcomes following surgical resection, radiation and the development of a prognostic response metric. Previous literature utilizing this model has been based on the assumption that what is seen on the pretreatment T1-Weighted contrast-enhanced (T1Gd) and T2W, images correspond to an 80% and 16% tumor cell density threshold respectively. This assumption allows for an estimate of D/ρ from a single time point of imaging. While these values were based on extensive experience, for ethical and technical reasons, they have never been rigorously investigated histologically. Recent technological advances have made it possible for surgeons to use an MRI to guide the acquisition of tissue making it possible to know with a good degree of accuracy where on the MR image the histological specimen comes from.

Methods: Model Calibration : To estimate D/ρ for each patient, we assume abnormalities on the T1Gd and T2W images correspond to an 80% and 16% tumor cell density threshold respectively. We then utilize a Bayesian calibration approach based on adaptive grid refinement while holding the velocity constant to find the most likely value of D/ρ to match the observed radial measurements. Three-Dimensional Density Maps : Given a gray/white segmentation and an estimate for D/ρ, we can build a tumor cell density prediction in the patient's anatomy using the Eikonal equations and the modified Fast Marching Method (FMM) algorithm presented by Konukoglu et al. Patient Cohort : Eighteen patients were recruited with clinically suspected GBM undergoing preoperative stereotactic MRI for surgical resection with IRB approval Barrow Neurological Institute and Mayo Clinic in Arizona. Surgical Biopsy : Pre-operative conventional MRI, including T1Gd and T2W, was utilized to guide stereotactic biopsies. An average of 5–6 tissue specimens were acquired from each tumor by using stereotactic surgical localization, following the smallest possible diameter craniotomies to minimize brain shift. Histological Analysis : 4 μm tissue sections were stained with hematoxylin and eosin (H&E) for neuropathology review. H&E slides were reviewed by a neuropathologist. The percent tumor nuclei in the field was estimated.

Results and Conclusion: Our analysis showed that the intuitive ordering of diffuse to nodular tumor profiles from the PI model is consistent with the histologic data. Further, utilizing the histologic data, limitations to a universal threshold cutoff of the T2/FLAIR region are specified and a better threshold cutoff value is suggested. We hope that this paper will lead to more studies of this kind resulting in more accurate ways to predict the spatial distribution of GBM tumor cells from medical imaging. Such advances could revolutionize surgical procedures and radiation therapy and may enable additional insights into tumor kinetic differences meaningful for treatment.

Citation Format: Andrea Hawkins-Daarud, Lauren DeGirolamo, Joshua Jacobs, Kamala Clark-Swanson, Jennifer M. Eschbacher, Kris A. Smith, Peter Nakaji, Leslie C. Baxter, John P. Karis, Teresa Wu, J. Ross Mitchell, Jing Li, Leland Hu, Kristin R. Swanson. Histologic evidence for a bio-mathematical model of glioblastoma invasion. [abstract]. In: Proceedings of the AACR Special Conference on Engineering and Physical Sciences in Oncology; 2016 Jun 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2017;77(2 Suppl):Abstract nr A08.

Radiogenomics to characterize regional genetic heterogeneity in glioblastoma

BACKGROUND

Glioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.

METHODS

We collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).

RESULTS

We collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).

CONCLUSION

MRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.

Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma

Background

Genetic profiling represents the future of neuro-oncology but suffers from inadequate biopsies in heterogeneous tumors like Glioblastoma (GBM). Contrast-enhanced MRI (CE-MRI) targets enhancing core (ENH) but yields adequate tumor in only ~60% of cases. Further, CE-MRI poorly localizes infiltrative tumor within surrounding non-enhancing parenchyma, or brain-around-tumor (BAT), despite the importance of characterizing this tumor segment, which universally recurs. In this study, we use multiple texture analysis and machine learning (ML) algorithms to analyze multi-parametric MRI, and produce new images indicating tumor-rich targets in GBM.

Methods

We recruited primary GBM patients undergoing image-guided biopsies and acquired pre-operative MRI: CE-MRI, Dynamic-Susceptibility-weighted-Contrast-enhanced-MRI, and Diffusion Tensor Imaging. Following image coregistration and region of interest placement at biopsy locations, we compared MRI metrics and regional texture with histologic diagnoses of high- vs low-tumor content (≥80% vs <80% tumor nuclei) for corresponding samples. In a training set, we used three texture analysis algorithms and three ML methods to identify MRI-texture features that optimized model accuracy to distinguish tumor content. We confirmed model accuracy in a separate validation set.

Results

We collected 82 biopsies from 18 GBMs throughout ENH and BAT. The MRI-based model achieved 85% cross-validated accuracy to diagnose high- vs low-tumor in the training set (60 biopsies, 11 patients). The model achieved 81.8% accuracy in the validation set (22 biopsies, 7 patients).

Conclusion

Multi-parametric MRI and texture analysis can help characterize and visualize GBM’s spatial histologic heterogeneity to identify regional tumor-rich biopsy targets.

Adaptive noise correction of dual-energy computed tomography images

PURPOSE

Noise reduction in material density images is a necessary preprocessing step for the correct interpretation of dual-energy computed tomography (DECT) images. In this paper we describe a new method based on a local adaptive processing to reduce noise in DECT images

METHODS

An adaptive neighborhood Wiener (ANW) filter was implemented and customized to use local characteristics of material density images. The ANW filter employs a three-level wavelet approach, combined with the application of an anisotropic diffusion filter. Material density images and virtual monochromatic images are noise corrected with two resulting noise maps.

RESULTS

The algorithm was applied and quantitatively evaluated in a set of 36 images. From that set of images, three are shown here, and nine more are shown in the online supplementary material. Processed images had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than the raw material density images. The average improvements in SNR and CNR for the material density images were 56.5 and 54.75%, respectively.

CONCLUSION

We developed a new DECT noise reduction algorithm. We demonstrate throughout a series of quantitative analyses that the algorithm improves the quality of material density images and virtual monochromatic images.

Blood pressure-lowering effect of the sodium glucose co-transporter-2 inhibitor ertugliflozin, assessed via ambulatory blood pressure monitoring in patients with type 2 diabetes and hypertension

This study compared the blood pressure-lowering effect of ertugliflozin (1, 5, 25 mg), hydrochlorothiazide (HCTZ; 12.5 mg) and placebo in 194 patients with type 2 diabetes mellitus and hypertension for 4 weeks using ambulatory blood pressure monitoring. Endpoints (change from baseline to week 4) were: 24-h mean systolic blood pressure (SBP; primary); daytime, night-time, seated predose SBP, 24-h, daytime, night-time, seated predose diastolic blood pressure, 24-h urinary glucose excretion and fasting plasma glucose (FPG; secondary). Safety and tolerability were monitored. Significant decreases in placebo-corrected 24-h mean SBP (-3.0 to -4.0 mmHg) were recorded for all doses of ertugliflozin (for HCTZ, this was -3.2 mmHg). Daytime, but not night-time SBP was consistently reduced. Ertugliflozin produced dose-dependent significant decreases in FPG and increases in urinary glucose excretion. No notable changes in plasma renin activity or urinary aldosterone were seen. The most common adverse events were urinary tract infection, genital fungal infection, upper respiratory tract infection and musculoskeletal pain.

Pilot study: Evaluation of dual-energy computed tomography measurement strategies for positron emission tomography correlation in pancreatic adenocarcinoma

We sought to determine whether dual-energy computed tomography (DECT) measurements correlate with positron emission tomography (PET) standardized uptake values (SUVs) in pancreatic adenocarcinoma, and to determine the optimal DECT imaging variables and modeling strategy to produce the highest correlation with maximum SUV (SUVmax). We reviewed 25 patients with unresectable pancreatic adenocarcinoma seen at Mayo Clinic, Scottsdale, Arizona, who had PET-computed tomography (PET/CT) and enhanced DECT performed the same week between March 25, 2010 and December 9, 2011. For each examination, DECT measurements were taken using one of three methods: (1) average values of three tumor regions of interest (ROIs) (method 1); (2) one ROI in the area of highest subjective DECT enhancement (method 2); and (3) one ROI in the area corresponding to PET SUVmax (method 3). There were 133 DECT variables using method 1, and 89 using the other methods. Univariate and multivariate analysis regression models were used to identify important correlations between DECT variables and PET SUVmax. Both R2 and adjusted R2 were calculated for the multivariate model to compensate for the increased number of predictors. The average SUVmax was 5 (range, 1.8-12.0). Multivariate analysis of DECT imaging variables outperformed univariate analysis (r = 0.91; R2 = 0.82; adjusted R2 = 0.75 vs. r < 0.58; adjusted R2 < 0.34). Method 3 had the highest correlation with PET SUVmax (R2 = 0.82), followed by method 1 (R2 = 0.79) and method 2 R2 = 0.57). DECT thus has clinical potential as a surrogate for, or as a complement to, PET in patients with pancreatic adenocarcinoma.

MRI texture analysis predicts p53 status in head and neck squamous cell carcinoma

BACKGROUND AND PURPOSE

Head and neck cancer is common, and understanding the prognosis is an important part of patient management. In addition to the Tumor, Node, Metastasis staging system, tumor biomarkers are becoming more useful in understanding prognosis and directing treatment. We assessed whether MR imaging texture analysis would correctly classify oropharyngeal squamous cell carcinoma according to p53 status.

MATERIALS AND METHODS

A cohort of 16 patients with oropharyngeal squamous cell carcinoma was prospectively evaluated by using standard clinical, histopathologic, and imaging techniques. Tumors were stained for p53 and scored by an anatomic pathologist. Regions of interest on MR imaging were selected by a neuroradiologist and then analyzed by using our 2D fast time-frequency transform tool. The quantified textures were assessed by using the subset-size forward-selection algorithm in the Waikato Environment for Knowledge Analysis. Features found to be significant were used to create a statistical model to predict p53 status. The model was tested by using a Bayesian network classifier with 10-fold stratified cross-validation.

RESULTS

Feature selection identified 7 significant texture variables that were used in a predictive model. The resulting model predicted p53 status with 81.3% accuracy (P < .05). Cross-validation showed a moderate level of agreement (κ = 0.625).

CONCLUSIONS

This study shows that MR imaging texture analysis correctly predicts p53 status in oropharyngeal squamous cell carcinoma with ∼80% accuracy. As our knowledge of and dependence on tumor biomarkers expand, MR imaging texture analysis warrants further study in oropharyngeal squamous cell carcinoma and other head and neck tumors.

Active inflammation increases the heterogeneity of MRI texture in mice with relapsing experimental allergic encephalomyelitis

Inflammation modulates tissue damage in relapsing-remitting multiple sclerosis (MS) both acutely and chronically, but its severity is difficult to evaluate with conventional MRI analysis. In mice with experimental allergic encephalomyelitis (EAE, a model of MS), we administered ultra small particles of iron oxide to track macrophage-mediated inflammation during the onset (relapse) and recovery (remission) of disease activity using high field MRI. We performed MRI texture analysis, a sensitive measure of tissue regularity, and T2 assessment both in EAE lesions and the control tissue, and measured spinal cord volume. We found that inflammation was 3 times more remarkable at onset than at recovery of EAE in histology yet demyelination appeared similar across animals and disease course. In MRI, lesion texture was more heterogeneous; T2 was lower; and spinal cord volume was greater in EAE than in controls, but only MRI texture was worse at relapse than at remission of EAE. Moreover, MRI texture correlated with spinal cord volume and tended to correlate with the extent of disability in EAE. While subject to further confirmation, our findings may suggest the sensitivity of MRI texture analysis for accessing inflammation.

Validation study of a fast, accurate, and precise brain tumor volume measurement

Precision and accuracy are sometimes sacrificed to ensure that medical image processing is rapid. To address this, our lab had developed a novel level set segmentation algorithm that is 16× faster and >96% accurate on realistic brain phantoms.

METHODS

This study reports speed, precision and estimated accuracy of our algorithm when measuring MRIs of meningioma brain tumors and compares it to manual tracing and modified MacDonald (MM) ellipsoid criteria. A repeated-measures study allowed us to determine measurement precisions (MPs) - clinically relevant thresholds for statistically significant change.

RESULTS

Speed: the level set, MM, and trace methods required 1:20, 1:35, and 9:35 (mm:ss) respectively on average to complete a volume measurement (p<0.05). Accuracy: the level set was not statistically different to the estimated true lesion volumes (p>0.05). Precision: the MM's within-operator and between-operator MPs were significantly higher (worse) than the other methods (p<0.05). The observed difference in MP between the level set and trace methods did not reach statistical significance (p>0.05).

CONCLUSION

Our level set is faster on average than MM, yet has accuracy and precision comparable to manual tracing.

A smartphone client-server teleradiology system for primary diagnosis of acute stroke

Background

Recent advances in the treatment of acute ischemic stroke have made rapid acquisition, visualization, and interpretation of images a key factor for positive patient outcomes. We have developed a new teleradiology system based on a client-server architecture that enables rapid access to interactive advanced 2-D and 3-D visualization on a current generation smartphone device (Apple iPhone or iPod Touch, or an Android phone) without requiring patient image data to be stored on the device. Instead, a server loads and renders the patient images, then transmits a rendered frame to the remote device.

Objective

Our objective was to determine if a new smartphone client-server teleradiology system is capable of providing accuracies and interpretation times sufficient for diagnosis of acute stroke.

Methods

This was a retrospective study. We obtained 120 recent consecutive noncontrast computed tomography (NCCT) brain scans and 70 computed tomography angiogram (CTA) head scans from the Calgary Stroke Program database. Scans were read by two neuroradiologists, one on a medical diagnostic workstation and an iPod or iPhone (hereafter referred to as an iOS device) and the other only on an iOS device. NCCT brain scans were evaluated for early signs of infarction, which includes early parenchymal ischemic changes and dense vessel sign, and to exclude acute intraparenchymal hemorrhage and stroke mimics. CTA brain scans were evaluated for any intracranial vessel occlusion. The interpretations made on an iOS device were compared with those made at a workstation. The total interpretation times were recorded for both platforms. Interrater agreement was assessed. True positives, true negatives, false positives, and false negatives were obtained, and sensitivity, specificity, and accuracy of detecting the abnormalities on the iOS device were computed.

Results

The sensitivity, specificity, and accuracy of detecting intraparenchymal hemorrhage were 100% using the iOS device with a perfect interrater agreement (kappa = 1). The sensitivity, specificity, and accuracy of detecting acute parenchymal ischemic change were 94.1%, 100%, and 98.09% respectively for reader 1 and 97.05%, 100%, and 99.04% for reader 2 with nearly perfect interrater agreement (kappa = .8). The sensitivity, specificity, and accuracy of detecting dense vessel sign were 100%, 95.4%, and 96.19% respectively for reader 1 and 72.2%, 100%, and 95.23% for reader 2 using the iOS device with a good interrater agreement (kappa = .69). The sensitivity, specificity, and accuracy of detecting vessel occlusion on CT angiography scans were 94.4%, 100%, and 98.46% respectively for both readers using the iOS device, with perfect interrater agreement (kappa = 1). No significant difference (P < .05) was noted in the interpretation time between the workstation and iOS device.

Conclusion

The smartphone client-server teleradiology system appears promising and may have the potential to allow urgent management decisions in acute stroke. However, this study was retrospective, involved relatively few patient studies, and only two readers. Generalizing conclusions about its clinical utility, especially in other diagnostic use cases, should not be made until additional studies are performed.

AnalyzeNNLS: magnetic resonance multiexponential decay image analysis

Exponential decays are fundamental to magnetic resonance imaging, yet adequately sampling and analyzing multiexponential decays is rarely attempted. The advantage of multiexponential analysis is the quantification of sub-voxel structure caused by water compartmentalization, with application as a non-invasive imaging biomarker for myelin. We have developed AnalyzeNNLS, software designed specifically for multiexponential decay image analysis that has a user-friendly graphical user interface and can analyze data from many MR manufacturers. AnalyzeNNLS is a simple, platform independent analysis tool that was created using the extensive mathematical and visualization libraries in Matlab, and released as open source code allowing scientists to evaluate, scrutinize, improve, and expand.

An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging

In glioblastoma (GBM), promoter methylation of the DNA repair gene O(6)-methylguanine-DNA methyltransferase (MGMT) is associated with benefit from chemotherapy. Correlations between MGMT promoter methylation and visually assessed imaging features on magnetic resonance (MR) have been reported suggesting that noninvasive detection of MGMT methylation status might be possible. Our study assessed whether MGMT methylation status in GBM could be predicted using MR imaging. We conducted a retrospective analysis of MR images in patients with newly diagnosed GBM. Tumor texture was assessed by two methods. First, we analyzed texture by expert consensus describing the tumor borders, presence or absence of cysts, pattern of enhancement, and appearance of tumor signal in T2-weighted images. Then, we applied space-frequency texture analysis based on the S-transform. Tumor location within the brain was determined using automatized image registration and segmentation techniques. Their association with MGMT methylation was analyzed. We confirmed that ring enhancement assessed visually is significantly associated with unmethylated MGMT promoter status (P=0.006). Texture features on T2-weighted images assessed by the space-frequency analysis were significantly different between methylated and unmethylated cases (P<0.05). However, blinded classification of MGMT promoter methylation status reached an accuracy of only 71%. There were no significant differences in the locations of methylated and unmethylated GBM tumors. Our results provide further evidence that individual MR features are associated with MGMT methylation but better algorithms for predicting methylation status are needed. The relevance of this study lies on the application of novel techniques for the analysis of anatomical MR images of patients with GBM allowing the evaluation of subtleties not seen by an observer and facilitating the standardization of the methods, decreasing the potential for interobserver bias.

Multiexponential T2 and magnetization transfer MRI of demyelination and remyelination in murine spinal cord

Identification of remyelination is important in the evaluation of potential treatments of demyelinating diseases such as multiple sclerosis. Local injection of lysolecithin into the brain or spinal cord provides a murine model of demyelination with spontaneous remyelination. The aim of this study was to determine if quantitative, multicomponent T(2) (qT(2)) analysis and magnetization transfer ratio (MTR), both indicative of myelin content, could detect changes in myelination, particularly remyelination, of the cervical spinal cord in mice treated with lysolecithin. We found that the myelin water fraction and geometric mean T(2) value of the intra/extracellular water significantly decreased at 14 days then returned to control levels by 28 days after injury, corresponding to clearance of myelin debris and remyelination which was shown by eriochrome cyanine and oil red O staining of histological sections. The MTR was significantly decreased 14 days after lysolecithin injection, and remained low over the time course studied. Evidence of demyelination shown by both qT(2) and MTR lagged behind the histological evidence of demyelination. Myelin water fraction increased with remyelination, however MTR remained lower after 28 days. The difference between qT(2) and MTR may identify early remyelination.

Image texture characterization using the discrete orthonormal S-transform

We present a new efficient approach for characterizing image texture based on a recently published discrete, orthonormal space-frequency transform known as the DOST. We develop a frequency-domain implementation of the DOST in two dimensions for the case of dyadic frequency sampling. Then, we describe a rapid and efficient approach to obtain local spatial frequency information for an image and show that this information can be used to characterize the horizontal and vertical frequency patterns in synthetic images. Finally, we demonstrate that DOST components can be combined to obtain a rotationally invariant set of texture features that can accurately classify a series of texture patterns. The DOST provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture features in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods.

The clinical response to minocycline in multiple sclerosis is accompanied by beneficial immune changes: a pilot study

Minocycline has immunomodulatory and neuroprotective activities in vitro and in an animal model of multiple sclerosis (MS). We have previously reported that minocycline decreased gadolinium-enhancing activity over six months in a small trial of patients with active relapsing-remitting MS (RRMS). Here we report the impact of oral minocycline on clinical and magnetic resonance imaging (MRI) outcomes and serum immune molecules in this cohort over 24 months of open-label minocycline treatment. Despite a moderately high pretreatment annualized relapse rate (1.3/year pre-enrolment; 1.2/year during a three-month baseline period) prior to treatment, no relapses occurred between months 6 and 24. Also, despite very active MRI activity pretreatment (19/40 scans had gadolinium-enhancing activity during a three-month run-in), the only patient with gadolinium-enhancing lesions on MRI at 12 and 24 months was on half-dose minocycline. Levels of the p40 subunit of interleukin (IL)-12, which at high levels might antagonize the proinflammatory IL-12 receptor, were elevated over 18 months of treatment, as were levels of soluble vascular cell adhesion molecule-1. The activity of matrix metalloproteinase-9 was decreased by treatment. Thus, clinical and MRI outcomes are supported by systemic immunological changes and call for further investigation of minocycline in MS.

A Novel MRI Texture Analysis of Demyelination and Inflammation in Relapsing-Remitting Experimental Allergic Encephalomyelitis

We have developed a novel multiscale localized image texture analysis technique, based upon the polar Stockwell Transform (PST). In this paper we characterized image texture in vivo using the PST in histologically verified lesion areas in T2-weighted MRI of an animal model of multiple sclerosis. Both high and low frequency signals, representing inflammation and demyelination, were significantly increased in pathological regions compared to normal control tissue. This suggests that this new local spatial-frequency measure of image texture may provide a sensitive and precise indication of disease activity.

MR perfusion and diffusion in acute ischemic stroke: human gray and white matter have different thresholds for infarction

It is thought that gray and white matter (GM and WM) have different perfusion and diffusion thresholds for cerebral infarction in humans. We sought to determine these thresholds with voxel-by-voxel, tissue-specific analysis of co-registered acute and follow-up magnetic resonance (MR) perfusion- and diffusion-weighted imaging. Quantitative cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and apparent diffusion coefficient (ADC) maps were analyzed from nine acute stroke patients (imaging acquired within 6 h of onset). The average values of each measure were calculated for GM and WM in normally perfused tissue, the region of recovered tissue and in the final infarct. Perfusion and diffusion thresholds for infarction were determined on a patient-by-patient basis in GM and WM separately by selecting thresholds with equal sensitivities and specificities. Gray matter has higher thresholds for infarction than WM (P<0.009) for CBF (20.0 mL/100 g min in GM and 12.3 mL/100 g min in WM), CBV (2.4 mL/100 g in GM and 1.7 mL/100 g in WM), and ADC (786 x 10(-6) mm(2)/s in GM and 708 x 10(-6) mm(2)/s in WM). The MTT threshold for infarction in GM is lower (P=0.014) than for WM (6.8 secs in GM and 7.1 secs in WM). A single common threshold applied to both tissues overestimates tissue at risk in WM and underestimates tissue at risk in GM. This study suggests that tissue-specific analysis of perfusion and diffusion imaging is required to accurately predict tissue at risk of infarction in acute ischemic stroke.

A novel method to derive separate gray and white matter cerebral blood flow measures from MR imaging of acute ischemic stroke patients

Perfusion-weighted imaging (PWI) measures can predict tissue outcome in acute ischemic stroke. Accuracy might be improved if differential tissue susceptibility to ischemia is considered. We present a novel voxel-by-voxel analysis to characterize cerebral blood flow (CBF) separately in gray (GM) and white matter (WM). Ten patients were scanned with inversion-recovery spin-echo EPI (IRSEPI), diffusion-weighted imaging (DWI), PWI<6 h from onset and fluid attenuated inversion-recovery (FLAIR) at 30 days. Image processing included coregistration to PWI, automatic segmentation of IRSEPI into GM, WM and CSF and semiautomatic segmentation of DWI/FLAIR to derive the acute and 30-day lesions. Five tissue compartments were defined: (1) 'Core' (abnormal acutely and at 30 days), (2) 'Growth' (or 'infarcted penumbra', abnormal only at 30 days), (3) 'Reversed' (abnormal acutely but normal at 30 days), (4) 'MTT-Delayed ' (tissue with delayed mean transit time but not part of the acute or 30-day lesion), and (5) 'Normal' brain. Cerebral blood flow in GM and WM of each compartment was obtained from quantitative maps. Gray matter and WM mean CBF in the growth region differed by 5.5 mL/100 g min (P=0.015). Mean CBF also differed significantly within normal and MTT-Delayed compartments. The difference in the reversed region approached statistical significance. In core, GM and WM CBF did not differ. The results suggest separate ischemic thresholds for GM and WM in stroke penumbra.

Resolution-dependent estimates of multiple sclerosis lesion loads

BACKGROUND

Changes in brain lesion loads assessed with magnetic resonance imaging obtained at 1.5 Telsa (T) are used as a measure of disease evolution in natural history studies and treatment trials of multiple sclerosis.

METHODS

A comparison was made between the total lesion volume and individual lesions observed on 1.5 T images and on high-resolution 4 T images. Lesions were quantified using a computer-assisted segmentation tool.

RESULTS

There was a 46% increase in the total number of lesions detected with 4 T versus 1.5 T imaging (p < 0.005). The 4 T also showed a 60% increase in total lesion volume when compared with the 1.5 T (p < 0.005). In several instances, the 1.5 T scans showed individual lesions that coalesced into larger areas of abnormality in the 4 T scans. The relationship between individual lesion volumes was linear (slope 1.231) showing that the lesion volume observed at 4 T increased with the size of the lesion detected at 1.5 T. The 4 T voxels were less than one quarter the size of those used at 1.5 T and there were no consistent differences between their signal-to-noise ratios.

CONCLUSIONS

The increase in signal strength that accompanied the increase in field strength compensated for the loss in signal amplitude produced by the use of smaller voxels. This enabled the acquisition of images with improved resolution, resulting in increased lesion detection at 4 T and larger lesion volumes.

Increased High-frequency Oscillations Precede in vitro Low-Mg2+ Seizures

PURPOSE

High-frequency oscillations (HFOs) in the range of > or = 80 Hz have been recorded in neocortical and hippocampal brain structures in vitro and in vivo and have been associated with physiologic and epileptiform neuronal population activity. Frequencies in the fast-ripple range (> 200 Hz) are believed to be exclusive to epileptiform activity and have been recorded in vitro, in vivo, and in epilepsy patients. Although the presence of HFOs is well characterized, their temporal evolution in the context of transition to seizure activity is not well understood.

METHODS

With an in vitro low-magnesium model of spontaneous seizures, we obtained extracellular field recordings (hippocampal regions CA1 and CA3) of interictal, preictal, and ictal activity. Recordings were subjected to power-frequency analysis, in time, by using a local multiscale Fourier transform. The power spectrum was computed continuously and was quantified for each epileptiform discharge into four frequency ranges spanning subripple, ripple, and two fast-ripple frequency bands.

RESULTS

A statistically significant increasing trend was observed in the subripple (0-100 Hz), ripple (100-200 Hz), and fast-ripple 1 (200-300 Hz) frequency bands during the epoch corresponding to the transition to seizure (preictal to ictal).

CONCLUSIONS

Temporal patterns of HFOs during epileptiform activity are indicative of dynamic changes in network behavior, and their characterization may offer insights into pathophysiologic processes underlying seizure initiation.

Distributed vector processing of a new local multiscale Fourier transform for medical imaging applications

The recently developed S-transform (ST) combines features of the Fourier and Wavelet transforms; it reveals frequency variation over both space and time. It is a potentially powerful tool that can be applied to medical image processing including texture analysis and noise filtering. However, calculation of the ST is computationally intensive, making conventional implementations too slow for many medical applications. This problem was addressed by combining parallel and vector computations to provide a 25-fold reduction in computation time. This approach could help accelerate many medical image processing algorithms.

Has your patient's multiple sclerosis lesion burden or brain atrophy actually changed?

Changes in mean magnetic resonance imaging (MRI)-derived measurements between patient groups are often used to determine outcomes in therapeutic trials and other longitudinal studies of multiple sclerosis (MS). However, in day-to-day clinical practice the changes within individual patients may also be of interest In this paper, we estimated the measurement error of an automated brain tissue quantification algorithm and determined the thresholds for statistically significant change of MRI-derived T2 lesion volume and brain atrophy in individual patients. Twenty patients with MS were scanned twice within 30 min. Brain tissue volumes were measured using the computer algorithm. Brain atrophy was estimated by calculation of brain parenchymal fraction. The threshold of change between repeated scans that represented statistically significant change beyond measurement error with 95% certainty was 0.65 mL for T2 lesion burden and 0.0056 for brain parenchymal fraction. Changes in lesion burden and brain atrophy below these thresholds can be safely (with 95% certainty) explained by measurement variability alone. These values provide clinical neurologists with a useful reference to interpret MRI-derived measures in individual patients.

Relationship between supra-annual trends in influenza rates and stroke occurrence

BACKGROUND

Stroke occurrence appears to be a random event, yet annual and supra-annual periodicity is observed. Recent attention in atherosclerotic disease etiology has focused on infectious and inflammatory mechanisms. Influenza is one such infection that may influence stroke occurrence.

METHODS

We explored population-based time series data on stroke occurrence and influenza activity. Using Fourier transformation to isolate low-frequency signals in the data, the inverse transformed time series were regressed using Prais-Winsten regression to correct for serially auto-correlated residuals, to assess the relationship between influenza rates and stroke occurrence rates.

RESULTS

Changes in the low-frequency components of influenza activity predicted the changes in low-frequency components of the stroke occurrence data with a delay of about 20 weeks. The delay between changes in influenza activity and subsequent stroke activity was different for different stroke types. Overall, the effect size was small with a tripling of the influenza rate associated with about a 6% change in stroke occurrence rate.

CONCLUSIONS

A small proportion of the patterns of stroke occurrence may be explained by variation in influenza activity. Further evaluation of influenza as a triggering agent in stroke is needed.

Fitting of the beat pattern observed in NMR free-induction decay signals of concentrated carbohydrate-water solutions

A series of mathematical functions has been used to fit the proton free-induction decays (FIDs) of concentrated carbohydrate-water samples. For the solid protons, these functions included a sinc function, as well as the Fourier transforms of single and multiple Pake functions multiplied by a Gaussian broadening. The NMR signal from the mobile protons is described by an exponential function. It is found that in most cases the sinc function gives a satisfactory result and provides valuable information about the second moment M(2) and the ratio of solid to mobile protons (f(s) / f(m)). A good indication for using the sinc function is the presence of a beat in the FID. For high temperatures this approach breaks down, and a biexponential fit is more appropriate. If a clear dipolar splitting is observable in the NMR spectra, the Pake function (or a multiple Pake fit) should be used. In this case information about M(2) and f(s) / f(m) can also be obtained.

Removal of phase artifacts from fMRI data using a Stockwell transform filter improves brain activity detection

A novel and automated technique is described for removing fMRI image artifacts resulting from motion outside of the imaging field of view. The technique is based on the Stockwell transform (ST), a mathematical operation that provides the frequency content at each time point within a time-varying signal. Using this technique, 1D Fourier transforms (FTs) are performed on raw image data to obtain phase profiles. The time series of phase magnitude for each and every point in the phase profile is then subjected to the ST to obtain a time-frequency spectrum. The temporal location of an artifact is identified based on the magnitude of a frequency component relative to the median magnitude of that frequency's occurrence over all time points. After each artifact frequency is removed by replacing its magnitude with the median magnitude, an inverse ST is applied to regain the MR signal. Brain activity detection within fMRI datasets is improved by significantly reducing image artifacts that overlap anatomical regions of interest. The major advantage of ST-filtering is that artifact frequencies may be removed within a narrow time-window, while preserving the frequency information at all other time points.

Posterior fossa lesion volume and slowed information processing in multiple sclerosis

The relationship between performance on information processing efficiency measures and MRI-derived lesion volume including global and regional T2 and T1 lesion volumes was investigated in 20 patients with relapsing-remitting multiple sclerosis (RRMS) and secondary progressive multiple sclerosis (SPMS). Processing speed, as measured by the Sternberg Memory Scanning Test, was significantly correlated with posterior fossa lesion volume and slowed reaction time in seven out of eight patients (six out of seven with SPMS) with any lesion volume in the posterior fossa suggesting a 'threshold effect'. Processing capacity as measured by the Salthouse Keeping Track Test was not significantly correlated with the MRI measures. Cognitive performance did not correlate with Expanded Disability Status Scale score, depression or fatigue, and patients performed within normal limits on tests of attention/concentration ability. The significant relationship between posterior fossa lesion volume and memory scanning speed in this study suggests that pathological damage in the posterior fossa may contribute to slowed cognitive processing and may be an important direction for future studies of cognitive function in multiple sclerosis. Lack of correlation of cognitive measures with the other MRI measures may be due to low lesion volume relative to other studies, sample composition, and limited pathological specificity of the MRI measures.

In vivo 4.0-T magnetic resonance investigation of spinal cord inflammation, demyelination, and axonal damage in chronic-progressive experimental allergic encephalomyelitis

PURPOSE

To image and dissect the lumbar spinal cord of guinea pigs with chronic-progressive experimental allergic encephalomyelitis (CP-EAE) and directly correlate the pathology to the magnetic resonance (MR) image data obtained at 4 T and determine if these MR contrasts can accurately differentiate a specific type of pathology from control tissue.

MATERIALS AND METHODS

The amount of inflammation, demyelination, and axonal pathology were quantified in the whole cord cross sections. The signal intensities (SIs) for 228 individual regions of interest (ROIs) (normal-appearing white matter (NAWM) and tissue containing inflammation with or without demyelination) were measured directly from the corresponding area on the MR images.

RESULTS

Conventional MR contrast SIs and magnetization transfer ratio (MTR) were related to the degree of demyelination and presence of inflammation. MTR and proton density-weighted (PDw) SIs were both moderately related to axonal density. The SIs for NAWM and in lesions containing both cellular infiltrates and demyelination in all conventional MR contrast images were also increased, whereas the MTR was decreased when compared to control tissue.

CONCLUSION

The SIs from the conventional MR contrasts and MTR at 4 T were sensitive to the presence of disease within CP-EAE spinal cord, but were not specific to the underlying pathology.

A comparison of images generated from diffusion-weighted and diffusion-tensor imaging data in hyper-acute stroke

PURPOSE

To compare isotropic (combined diffusion-weighted image [CMB], apparent diffusion coefficient [ADC], TRACE, exponential ADC [eADC], and isotropically-weighted diffusion image [isoDWI]) and anisotropic (relative anisotropy [RA], fractional anisotropy [FA], and volume ratio [VR]) diffusion images collected with fast magnetic resonance (MR) diffusion-weighted (DWI) and diffusion-tensor (DTI) acquisition strategies (each less than one minute) in hyper-acute stroke.

MATERIALS AND METHODS

Twenty-one patients suffering from ischemic stroke-imaged within six hours of symptom onset using both DWI and DTI-were analyzed. Regions of interest were placed in the ischemic lesion and in normal contralateral tissue and the percent difference in image intensity was calculated for all nine generated images.

RESULTS

The average absolute percent changes for the isotropic strategies were all > 38%, with isoDWI found to have a difference of 50.7% +/- 7.9% (mean +/- standard error, P < 0.001). The ADC maps had the most significant difference (-42.4% +/- 2.0%, P < 0.001, coefficient of variation = 0.22). No anisotropic images had significant differences.

CONCLUSION

Anisotropic maps do not consistently show changes in the first six hours of ischemic stroke; therefore, isotropic maps, such as those obtained using DWI, are more appropriate for detecting hyper-acute stroke. Anisotropic images, however, may be useful to differentiate hyper-acute stroke from acute and sub-acute stroke.