Content-Based Image Retrieval Using Spatial Layout Information in Brain Tumor T1-Weighted Contrast-Enhanced MR Images

This study aims to develop content-based image retrieval (CBIR) system for the retrieval of T1-weighted contrast-enhanced MR (CE-MR) images of brain tumors. When a tumor region is fed to the CBIR system as a query, the system attempts to retrieve tumors of the same pathological category. The bag-of-visual-words (BoVW) model with partition learning is incorporated into the system to extract informative features for representing the image contents. Furthermore, a distance metric learning algorithm called the Rank Error-based Metric Learning (REML) is proposed to reduce the semantic gap between low-level visual features and high-level semantic concepts. The effectiveness of the proposed method is evaluated on a brain T1-weighted CE-MR dataset with three types of brain tumors (i.e., meningioma, glioma, and pituitary tumor). Using the BoVW model with partition learning, the mean average precision (mAP) of retrieval increases beyond 4.6% with the learned distance metrics compared with the spatial pyramid BoVW method. The distance metric learned by REML significantly outperforms three other existing distance metric learning methods in terms of mAP. The mAP of the CBIR system is as high as 91.8% using the proposed method, and the precision can reach 93.1% when the top 10 images are returned by the system. These preliminary results demonstrate that the proposed method is effective and feasible for the retrieval of brain tumors in T1-weighted CE-MR Images.     

Increasing the Contrast of the Brain MR FLAIR Images Using Fuzzy Membership Functions and Structural Similarity Indices in Order to Segment MS Lesions

Segmentation is an important step for the diagnosis of multiple sclerosis (MS). This paper presents a new approach to the fully automatic segmentation of MS lesions in Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance (MR) images. With the aim of increasing the contrast of the FLAIR MR images with respect to the MS lesions, the proposed method first estimates the fuzzy memberships of brain tissues (i.e., the cerebrospinal fluid (CSF), the normal-appearing brain tissue (NABT), and the lesion). The procedure for determining the fuzzy regions of their member functions is performed by maximizing fuzzy entropy through Genetic Algorithm. Research shows that the intersection points of the obtained membership functions are not accurate enough to segment brain tissues. Then, by extracting the structural similarity (SSIM) indices between the FLAIR MR image and its lesions membership image, a new contrast-enhanced image is created in which MS lesions have high contrast against other tissues. Finally, the new contrast-enhanced image is used to segment MS lesions. To evaluate the result of the proposed method, similarity criteria from all slices from 20 MS patients are calculated and compared with other methods, which include manual segmentation. The volume of segmented lesions is also computed and compared with Gold standard using the Intraclass Correlation Coefficient (ICC) and paired samples t test. Similarity index for the patients with small lesion load, moderate lesion load and large lesion load was 0.7261, 0.7745 and 0.8231, respectively. The average overall similarity index for all patients is 0.7649. The t test result indicates that there is no statistically significant difference between the automatic and manual segmentation. The validated results show that this approach is very promising.     

A Multi-Methodological MR Resting State Network Analysis to Assess the Changes in Brain Physiology of Children with ADHD

The purpose of this work was to highlight the neurological differences between the MR resting state networks of a group of children with ADHD (pre-treatment) and an age-matched healthy group. Results were obtained using different image analysis techniques. A sample of n = 46 children with ages between 6 and 12 years were included in this study (23 per cohort). Resting state image analysis was performed using ReHo, ALFF and ICA techniques. ReHo and ICA represent connectivity analyses calculated with different mathematical approaches. ALFF represents an indirect measurement of brain activity. The ReHo and ICA analyses suggested differences between the two groups, while the ALFF analysis did not. The ReHo and ALFF analyses presented differences with respect to the results previously reported in the literature. ICA analysis showed that the same resting state networks that appear in healthy volunteers of adult age were obtained for both groups. In contrast, these networks were not identical when comparing the healthy and ADHD groups. These differences affected areas for all the networks except the Right Memory Function network. All techniques employed in this study were used to monitor different cerebral regions which participate in the phenomenological characterization of ADHD patients when compared to healthy controls. Results from our three analyses indicated that the cerebellum and mid-frontal lobe bilaterally for ReHo, the executive function regions in ICA, and the precuneus, cuneus and the clacarine fissure for ALFF, were the “hubs” in which the main inter-group differences were found. These results do not just help to explain the physiology underlying the disorder but open the door to future uses of these methodologies to monitor and evaluate patients with ADHD.     

Fully Automated Segmentation of the Pons and Midbrain Using Human T1 MR Brain Images

Purpose

This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called LABS: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction.

Methods

This method was first tested on morphological T1-weighted MRIs of 30 healthy subjects. Its reliability was further confirmed by including neurological patients (with Alzheimer''s Disease) from the ADNI repository, in whom the presence of volumetric loss within the brainstem had been previously described. Segmentation accuracies were evaluated against expert-drawn manual delineation. To evaluate the quality of LABS segmentation we used volumetric, spatial overlap and distance-based metrics.

Results

The comparison between the quantitative measurements provided by LABS against manual segmentations revealed excellent results in healthy controls when considering either the midbrain (DICE measures higher that 0.9; Volume ratio around 1 and Hausdorff distance around 3) or the pons (DICE measures around 0.93; Volume ratio ranging 1.024–1.05 and Hausdorff distance around 2). Similar performances were detected for AD patients considering segmentation of the pons (DICE measures higher that 0.93; Volume ratio ranging from 0.97–0.98 and Hausdorff distance ranging 1.07–1.33), while LABS performed lower for the midbrain (DICE measures ranging 0.86–0.88; Volume ratio around 0.95 and Hausdorff distance ranging 1.71–2.15).

Conclusions

Our study represents the first attempt to validate a new fully automated method for in vivo segmentation of two anatomically complex brainstem subregions. We retain that our method might represent a useful tool for future applications in clinical practice.     

Voxel-Based Texture Analysis of the Brain

This paper presents a novel voxel-based method for texture analysis of brain images. Texture analysis is a powerful quantitative approach for analyzing voxel intensities and their interrelationships, but has been thus far limited to analyzing regions of interest. The proposed method provides a 3D statistical map comparing texture features on a voxel-by-voxel basis. The validity of the method was examined on artificially generated effects as well as on real MRI data in Alzheimer''s Disease (AD). The artificially generated effects included hyperintense and hypointense signals added to T1-weighted brain MRIs from 30 healthy subjects. The AD dataset included 30 patients with AD and 30 age/sex matched healthy control subjects. The proposed method detected artificial effects with high accuracy and revealed statistically significant differences between the AD and control groups. This paper extends the usage of texture analysis beyond the current region of interest analysis to voxel-by-voxel 3D statistical mapping and provides a hypothesis-free analysis tool to study cerebral pathology in neurological diseases.     

Hepatocellular Carcinoma in Budd-Chiari Syndrome: Enhancement Patterns at Dynamic Gadolinium-Enhanced T1-Weighted MR Imaging

The objective of this study was to analyze the enhancement patterns at dynamic gadolinium-enhanced T1-weighted MR imaging in patients of Hepatocellular Carcinoma (HCC) with associated Budd-Chiari syndrome (BCS). The MR imaging findings in 10 patients of HCC with associated BCS were compared to those of 32 other patients of HCC without BCS. During the arterial phase, significantly more lesions with BCS were hyperintense than lesions without BCS; during the equilibrium phase, significantly more lesions with BCS were slightly hyperintense or isointense than lesions without BCS (P < 0.05 for both). For HCC, contrast enhancement on MRI shows different enhancement patterns between patients of HCC with associated BCS and those without BCS.     

Semi-Automated Detection of Cerebral Microbleeds on 3.0 T MR Images

Cerebral microbleeds are associated with vascular disease and dementia. They can be detected on MRI and receive increasing attention. Visual rating is the current standard for microbleed detection, but is rater dependent, has limited reproducibility, modest sensitivity, and can be time-consuming. The goal of the current study is to present a tool for semi-automated detection of microbleeds that can assist human raters in the rating procedure. The radial symmetry transform is originally a technique to highlight circular-shaped objects in two-dimensional images. In the current study, the three-dimensional radial symmetry transform was adapted to detect spherical microbleeds in a series of 72 patients from our hospital, for whom a ground truth visual rating was made by four raters. Potential microbleeds were automatically identified on T2*-weighted 3.0 T MRI scans and the results were visually checked to identify microbleeds. Final ratings of the radial symmetry transform were compared to human ratings. After implementing and optimizing the radial symmetry transform, the method achieved a high sensitivity, while maintaining a modest number of false positives. Depending on the settings, sensitivities ranged from 65%–84% compared to the ground truth rating. Rating of the processed images required 1–2 minutes per participant, in which 20–96 false positive locations per participant were censored. Sensitivities of individual raters ranged from 39%–86% compared to the ground truth and required 5–10 minutes per participant per rater. The sensitivities that were achieved by the radial symmetry transform are similar to those of individual experienced human raters, demonstrating its feasibility and usefulness for semi-automated microbleed detection.     

4D Segmentation of Brain MR Images with Constrained Cortical Thickness Variation

Segmentation of brain MR images plays an important role in longitudinal investigation of developmental, aging, disease progression changes in the cerebral cortex. However, most existing brain segmentation methods consider multiple time-point images individually and thus cannot achieve longitudinal consistency. For example, cortical thickness measured from the segmented image will contain unnecessary temporal variations, which will affect the time related change pattern and eventually reduce the statistical power of analysis. In this paper, we propose a 4D segmentation framework for the adult brain MR images with the constraint of cortical thickness variations. Specifically, we utilize local intensity information to address the intensity inhomogeneity, spatial cortical thickness constraint to maintain the cortical thickness being within a reasonable range, and temporal cortical thickness variation constraint in neighboring time-points to suppress the artificial variations. The proposed method has been tested on BLSA dataset and ADNI dataset with promising results. Both qualitative and quantitative experimental results demonstrate the advantage of the proposed method, in comparison to other state-of-the-art 4D segmentation methods.     

Stimulation of Brain AMP-Activated Protein Kinase Attenuates Inflammation and Acute Lung Injury in Sepsis

Sepsis and septic shock are enormous public health problems with astronomical financial repercussions on health systems worldwide. The central nervous system (CNS) is closely intertwined in the septic process but the underlying mechanism is still obscure. AMP-activated protein kinase (AMPK) is a ubiquitous energy sensor enzyme and plays a key role in regulation of energy homeostasis and cell survival. In this study, we hypothesized that activation of AMPK in the brain would attenuate inflammatory responses in sepsis, particularly in the lungs. Adult C57BL/6 male mice were treated with 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR, 20 ng), an AMPK activator, or vehicle (normal saline) by intracerebroventricular (ICV) injection, followed by cecal ligation and puncture (CLP) at 30 min post-ICV. The septic mice treated with AICAR exhibited elevated phosphorylation of AMPKα in the brain along with reduced serum levels of aspartate aminotransferase, tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6), compared with the vehicle. Similarly, the expressions of TNF-α, IL-1β, keratinocyte-derived chemokine and macrophage inflammatory protein-2 as well as myeloperoxidase activity in the lungs of AICAR-treated mice were significantly reduced. Moreover, histological findings in the lungs showed improvement of morphologic features and reduction of apoptosis with AICAR treatment. We further found that the beneficial effects of AICAR on septic mice were diminished in AMPKα2 deficient mice, showing that AMPK mediates these effects. In conclusion, our findings reveal a new functional role of activating AMPK in the CNS to attenuate inflammatory responses and acute lung injury in sepsis.     

Statistical Analysis of Multiplex Brain Gene Expression Images

Analysis of variance (ANOVA) was employed to investigate 9,000 gene expression patterns from brains of both normal mice and mice with a pharmacological model of Parkinson's disease (PD). The data set was obtained using voxelation, a method that allows high-throughput acquisition of 3D gene expression patterns through analysis of spatially registered voxels (cubes). This method produces multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems. The ANOVA model was compared to the results from singular value decomposition (SVD) by using the first 42 singular vectors of the data matrix, a number equal to the rank of the ANOVA model. The ANOVA was also compared to the results from non-parametric statistics. Lastly, images were obtained for a subset of genes that emerged from the ANOVA as significant. The results suggest that ANOVA will be a valuable framework for insights into the large number of gene expression patterns obtained from voxelation.     

Acute Brain Inflammation and Oxidative Damage Are Related to Long-Term Cognitive Deficits and Markers of Neurodegeneration in Sepsis-Survivor Rats

Survivors from sepsis present long-term cognitive deficits and some of these alterations resemble the pathophysiological mechanisms of neurodegenerative diseases. For this reason, we analyzed beta-amyloid peptide (Aβ) and synaptophysin levels in the brain of rats that survived from sepsis and their relation to cognitive dysfunction and to acute brain inflammation. Sepsis was induced in rats by cecal ligation and puncture, and 30 days after surgery, the hippocampus and prefrontal cortex were isolated just after cognitive evaluation by the inhibitory avoidance test. The immunocontent of Aβ and synaptophysin were analyzed by Western blot analysis. Aβ increased and synaptophysin decreased in septic animals both in the hippocampus and prefrontal cortex concurrent with the presence of cognitive deficits. Prefrontal levels of synaptophysin correlated to the performance in the inhibitory avoidance. Two different treatments known to decrease brain inflammation and oxidative stress when administered at the acute phase of sepsis decreased Aβ levels both in the prefrontal cortex and hippocampus, increased synaptophysin levels only in the prefrontal cortex, and improved cognitive deficit in sepsis-survivor animals. In conclusion, we demonstrated that brain from sepsis-survivor animals presented an increase in Aβ content and a decrease in synaptophysin levels and cognitive impairment. These alterations can be prevented by treatments aimed to decrease acute brain inflammation and oxidative stress.     

Spred-2 Deficiency Exacerbates Lipopolysaccharide-Induced Acute Lung Inflammation in Mice

Background

Acute respiratory distress syndrome (ARDS) is a severe and life-threatening acute lung injury (ALI) that is caused by noxious stimuli and pathogens. ALI is characterized by marked acute inflammation with elevated alveolar cytokine levels. Mitogen-activated protein kinase (MAPK) pathways are involved in cytokine production, but the mechanisms that regulate these pathways remain poorly characterized. Here, we focused on the role of Sprouty-related EVH1-domain-containing protein (Spred)-2, a negative regulator of the Ras-Raf-extracellular signal-regulated kinase (ERK)-MAPK pathway, in lipopolysaccharide (LPS)-induced acute lung inflammation.

Methods

Wild-type (WT) mice and Spred-2^−/− mice were exposed to intratracheal LPS (50 µg in 50 µL PBS) to induce pulmonary inflammation. After LPS-injection, the lungs were harvested to assess leukocyte infiltration, cytokine and chemokine production, ERK-MAPK activation and immunopathology. For ex vivo experiments, alveolar macrophages were harvested from untreated WT and Spred-2^−/− mice and stimulated with LPS. In in vitro experiments, specific knock down of Spred-2 by siRNA or overexpression of Spred-2 by transfection with a plasmid encoding the Spred-2 sense sequence was introduced into murine RAW264.7 macrophage cells or MLE-12 lung epithelial cells.

Results

LPS-induced acute lung inflammation was significantly exacerbated in Spred-2^−/− mice compared with WT mice, as indicated by the numbers of infiltrating leukocytes, levels of alveolar TNF-α, CXCL2 and CCL2 in a later phase, and lung pathology. U0126, a selective MEK/ERK inhibitor, reduced the augmented LPS-induced inflammation in Spred-2^−/− mice. Specific knock down of Spred-2 augmented LPS-induced cytokine and chemokine responses in RAW264.7 cells and MLE-12 cells, whereas Spred-2 overexpression decreased this response in RAW264.7 cells.

Conclusions

The ERK-MAPK pathway is involved in LPS-induced acute lung inflammation. Spred-2 controls the development of LPS-induced lung inflammation by negatively regulating the ERK-MAPK pathway. Thus, Spred-2 may represent a therapeutic target for the treatment of ALI.     

Hepatitis C Related Chronic Liver Cirrhosis: Feasibility of Texture Analysis of MR Images for Classification of Fibrosis Stage and Necroinflammatory Activity Grade

Purpose

To assess the feasibility of texture analysis for classifying fibrosis stage and necroinflammatory activity grade in patients with chronic hepatitis C on T2-weighted (T2W), T1-weighted (T1W) and Gd-EOB-DTPA-enhanced hepatocyte-phase (EOB-HP) imaging.

Materials and methods

From April 2008 to June 2012, MR images from 123 patients with pathologically proven chronic hepatitis C were retrospectively analyzed. Texture parameters derived from histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model and wavelet transform methods were estimated with imaging software. Fisher, probability of classification error and average correlation, and mutual information coefficients were used to extract subsets of optimized texture features. Linear discriminant analysis in combination with 1-nearest neighbor classifier (LDA/1-NN) was used for lesion classification. In compliance with the software requirement, classification was performed based on datasets from all patients, the patient group with necroinflammatory activity grade 1, and that with fibrosis stage 4, respectively.

Results

Based on all patient dataset, LDA/1-NN produced misclassification rates of 28.46%, 35.77% and 20.33% for fibrosis staging and 34.15%, 25.20% and 28.46% for necroinflammatory activity grading in T2W, T1W and EOB-HP images. In the patient group with necroinflammatory activity grade 1, LDA/1-NN yielded misclassification rates of 5.00%, 0% and 12.50% for fibrosis staging in T2W, T1W and EOB-HP images respectively. In the patient group with fibrosis stage 4, LDA/1-NN yielded misclassification rates of 5.88%, 12.94% and 11.76% for necroinflammatory activity grading in T2W, T1W and EOB-HP images respectively.

Conclusion

Texture quantitative parameters of MR images facilitate classification of the fibrosis stage as well as necroinflammatory activity grade in chronic hepatitis C, especially after categorizing the input dataset according to the activity or fibrosis degree in order to remove the interference between the fibrosis stage and necroinflammatory activity grade on texture features.     

Characterization of Microcirculation in Multiple Sclerosis Lesions by Dynamic Texture Parameter Analysis (DTPA)

Objective

Texture analysis is an alternative method to quantitatively assess MR-images. In this study, we introduce dynamic texture parameter analysis (DTPA), a novel technique to investigate the temporal evolution of texture parameters using dynamic susceptibility contrast enhanced (DSCE) imaging. Here, we aim to introduce the method and its application on enhancing lesions (EL), non-enhancing lesions (NEL) and normal appearing white matter (NAWM) in multiple sclerosis (MS).

Methods

We investigated 18 patients with MS and clinical isolated syndrome (CIS), according to the 2010 McDonald''s criteria using DSCE imaging at different field strengths (1.5 and 3 Tesla). Tissues of interest (TOIs) were defined within 27 EL, 29 NEL and 37 NAWM areas after normalization and eight histogram-based texture parameter maps (TPMs) were computed. TPMs quantify the heterogeneity of the TOI. For every TOI, the average, variance, skewness, kurtosis and variance-of-the-variance statistical parameters were calculated. These TOI parameters were further analyzed using one-way ANOVA followed by multiple Wilcoxon sum rank testing corrected for multiple comparisons.

Results

Tissue- and time-dependent differences were observed in the dynamics of computed texture parameters. Sixteen parameters discriminated between EL, NEL and NAWM (pAVG = 0.0005). Significant differences in the DTPA texture maps were found during inflow (52 parameters), outflow (40 parameters) and reperfusion (62 parameters). The strongest discriminators among the TPMs were observed in the variance-related parameters, while skewness and kurtosis TPMs were in general less sensitive to detect differences between the tissues.

Conclusion

DTPA of DSCE image time series revealed characteristic time responses for ELs, NELs and NAWM. This may be further used for a refined quantitative grading of MS lesions during their evolution from acute to chronic state. DTPA discriminates lesions beyond features of enhancement or T2-hypersignal, on a numeric scale allowing for a more subtle grading of MS-lesions.     

Differential Contribution of Acute and Chronic Inflammation to the Development of Murine Mammary 4T1 Tumors

Based on the notion that inflammation favors tumorigenesis, our experiments comparatively assessed the influence of acute and chronic inflammation on the development of a murine mammary tumor (4T1). In addition, we characterized angiogenic and inflammatory markers in the tumor tissue and systemically. Subcutaneous implantation of polyether-polyurethane sponge discs in Balb/c mice was used to host 4T1 tumor cells (1x10⁶), which were inoculated intraimplant 24h or 10 days post implantation. Flow cytometric analysis of enzyme-digested implants revealed that, after 24 hours, the population of leukocytes was primarily characterized by neutrophils (42.53% +/- 8.45) and monocytes (37.53% +/- 7.48), with some lymphocytes (16.27% +/- 4.0) and a few dendritic cells (1.82% +/- 0.36). At 10 days, macrophages were predominant (37.10% +/- 4.54), followed by lymphocytes (28.1% +/- 4.77), and monocytes (22.33% +/- 3.05), with some dendritic cells (13.60% +/- 0.55) and neutrophils (11.07% +/- 2.27). A mammary tumor grown in a chronic inflammatory environment was 2-fold when compared with one grown in acute inflammation and 5-fold when compared with tumor alone. The levels of pro-angiogenic cytokine (VEGF-Vascular Endothelial Growth Factor) were higher in implant-bearing tumor when 4T1 cells were grown in 10-day old implants as compared to the VEGF levels of the two other groups. Overall, the levels of the inflammatory markers evaluated (NAG -N-acetylglucosaminidase, TNF-α –Tumor Necrosis Factor- α) were higher in both groups of implant-bearing tumors and in serum from those animals when compared with the tumor alone levels. This inflammation-related difference in tumor growth may provide new insights into the contribution of different inflammatory cell populations to cancer progression.     

Role of an Indole-Thiazolidine Molecule PPAR Pan-Agonist and COX Inhibitor on Inflammation and Microcirculatory Damage in Acute Gastric Lesions

The present study aimed to show the in vivo mechanisms of action of an indole-thiazolidine molecule peroxisome-proliferator activated receptor pan-agonist (PPAR pan) and cyclooxygenase (COX) inhibitor, LYSO-7, in an ethanol/HCl-induced (Et/HCl) gastric lesion model. Swiss male mice were treated with vehicle, LYSO-7 or Bezafibrate (p.o.) 1 hour before oral administration of Et/HCl (60%/0.03M). In another set of assays, animals were injected i.p. with an anti-granulocyte antibody, GW9962 or L-NG-nitroarginine methyl ester (L-NAME) before treatment. One hour after Et/HCl administration, neutrophils were quantified in the blood and bone marrow and the gastric microcirculatory network was studied in situ. The gastric tissue was used to quantify the percentage of damaged area, as well as myeloperoxidase (MPO), inducible nitric oxide synthase (iNOS), endothelial nitric oxide synthase (eNOS) protein and PPARγ protein and gene expression. Acid secretion was evaluated by the pylorus ligation model. LYSO-7 or Bezafibrate treatment reduced the necrotic area. LYSO-7 treatment enhanced PPARγ gene and protein expression in the stomach, and impaired local neutrophil influx and stasis of the microcirculatory network caused by Et/HCl administration. The effect seemed to be due to PPARγ agonist activity, as the LYSO-7 effect was abolished in GW9962 pre-treated mice. The reversal of microcirculatory stasis, but not neutrophil influx, was mediated by nitric oxide (NO), as L-NAME pre-treatment abolished the LYSO-7-mediated reestablishment of microcirculatory blood flow. This effect may depend on enhanced eNOS protein expression in injured gastric tissue. The pH and concentration of H⁺ in the stomach were not modified by LYSO-7 treatment. In addition, LYSO-7 may induce less toxicity, as 28 days of oral treatment did not induce weight loss, as detected in pioglitazone treated mice. Thus, we show that LYSO-7 may be an effective treatment for gastric lesions by controlling neutrophil influx and microcirculatory blood flow mediated by NO.     

Assessment of Myocardial Fibrosis in Mice Using a T2*-Weighted 3D Radial Magnetic Resonance Imaging Sequence

Background

Myocardial fibrosis is a common hallmark of many diseases of the heart. Late gadolinium enhanced MRI is a powerful tool to image replacement fibrosis after myocardial infarction (MI). Interstitial fibrosis can be assessed indirectly from an extracellular volume fraction measurement using contrast-enhanced T1 mapping. Detection of short T2* species resulting from fibrotic tissue may provide an attractive non-contrast-enhanced alternative to directly visualize the presence of both replacement and interstitial fibrosis.

Objective

To goal of this paper was to explore the use of a T2*-weighted radial sequence for the visualization of fibrosis in mouse heart.

Methods

C57BL/6 mice were studied with MI (n = 20, replacement fibrosis), transverse aortic constriction (TAC) (n = 18, diffuse fibrosis), and as control (n = 10). 3D center-out radial T2*-weighted images with varying TE were acquired in vivo and ex vivo (TE = 21 μs-4 ms). Ex vivo T2*-weighted signal decay with TE was analyzed using a 3-component model. Subtraction of short- and long-TE images was used to highlight fibrotic tissue with short T2*. The presence of fibrosis was validated using histology and correlated to MRI findings.

Results

Detailed ex vivo T2*-weighted signal analysis revealed a fast (T2*_fast), slow (T2*_slow) and lipid (T2*_lipid) pool. T2*_fast remained essentially constant. Infarct T2*_slow decreased significantly, while a moderate decrease was observed in remote tissue in post-MI hearts and in TAC hearts. T2*_slow correlated with the presence of diffuse fibrosis in TAC hearts (r = 0.82, P = 0.01). Ex vivo and in vivo subtraction images depicted a positive contrast in the infarct co-localizing with the scar. Infarct volumes from histology and subtraction images linearly correlated (r = 0.94, P<0.001). Region-of-interest analysis in the in vivo post-MI and TAC hearts revealed significant T2* shortening due to fibrosis, in agreement with the ex vivo results. However, in vivo contrast on subtraction images was rather poor, hampering a straightforward visual assessment of the spatial distribution of the fibrotic tissue.