Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI

Background

Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).

Methods

155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.

Results

Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.

Conclusion

The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

Considerations on brain age predictions from repeatedly sampled data across time

INTRODUCTION

Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls.

METHODS

We used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model.

RESULTS

We found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality.

CONCLUSION

The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.

Deep whole brain segmentation of 7T structural MRI

7T magnetic resonance imaging (MRI) has the potential to drive our understanding of human brain function through new contrast and enhanced resolution. Whole brain segmentation is a key neuroimaging technique that allows for region-by-region analysis of the brain. Segmentation is also an important preliminary step that provides spatial and volumetric information for running other neuroimaging pipelines. Spatially localized atlas network tiles (SLANT) is a popular 3D convolutional neural network (CNN) tool that breaks the whole brain segmentation task into localized sub-tasks. Each sub-task involves a specific spatial location handled by an independent 3D convolutional network to provide high resolution whole brain segmentation results. SLANT has been widely used to generate whole brain segmentations from structural scans acquired on 3T MRI. However, the use of SLANT for whole brain segmentation from structural 7T MRI scans has not been successful due to the inhomogeneous image contrast usually seen across the brain in 7T MRI. For instance, we demonstrate the mean percent difference of SLANT label volumes between a 3T scan-rescan is approximately 1.73%, whereas its 3T-7T scan-rescan counterpart has higher differences around 15.13%. Our approach to address this problem is to register the whole brain segmentation performed on 3T MRI to 7T MRI and use this information to finetune SLANT for structural 7T MRI. With the finetuned SLANT pipeline, we observe a lower mean relative difference in the label volumes of ~8.43% acquired from structural 7T MRI data. Dice similarity coefficient between SLANT segmentation on the 3T MRI scan and the after finetuning SLANT segmentation on the 7T MRI increased from 0.79 to 0.83 with p<0.01. These results suggest finetuning of SLANT is a viable solution for improving whole brain segmentation on high resolution 7T structural imaging.

Fusion of quantitative susceptibility maps and T1-weighted images improve brain tissue contrast in primates

Recent progress in quantitative susceptibility mapping (QSM) has enabled the accurate delineation of submillimeter-scale subcortical brain structures in humans. However, the simultaneous visualization of cortical, subcortical, and white matter structure remains challenging, utilizing QSM data solely. Here we present TQ-SILiCON, a fusion method that enhances the contrast of cortex and subcortical structures and provides an excellent white matter delineation by combining QSM and conventional T1-weighted (T1w) images. In this study, we first applied QSM in the macaque monkey to map iron-rich subcortical structures. Implementing the same QSM acquisition and analysis methods allowed a similar accurate delineation of subcortical structures in humans. However, the QSM contrast of white and cortical gray matter was not sufficient for appropriate segmentation. Applying automatic brain tissue segmentation to TQ-SILiCON images of the macaque improved the classification of subcortical brain structures as compared to the single T1 contrast by maintaining an excellent white to cortical gray matter contrast. Furthermore, we validated our dual-contrast fusion approach in humans and similarly demonstrated improvements in automated segmentation of the cortex and subcortical structures. We believe the proposed contrast will facilitate translational studies in nonhuman primates to investigate the pathophysiology of neurodegenerative diseases that affect subcortical structures such as the basal ganglia in humans.

Brain MRI dataset of multiple sclerosis with consensus manual lesion segmentation and patient meta information

Magnetic resonance imaging (MRI) provides a significant key to diagnose and monitor the progression of multiple sclerosis (MS) disease. Manual MS-lesion segmentation, expanded disability status scale (EDSS) and patient's meta information can provide a gold standard for research in terms of automated MS-lesion quantification, automated EDSS prediction and identification of the correlation between MS-lesion and patient disability. In this dataset, we provide a novel multi-sequence MRI dataset of 60 MS patients with consensus manual lesion segmentation, EDSS, general patient information and clinical information. On this dataset, three radiologists and neurologist experts segmented and validated the manual MS-lesion segmentation for three MRI sequences T1-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR). The dataset can be used to study the relationship between MS-lesion, EDSS and patient clinical information. Furthermore, it also can be used for the development of automated MS-lesion segmentation, patient disability prediction using MRI and correlation analysis between patient disability and MRI brain abnormalities include MS lesion location, size, number and type.

Evolution of acute "black hole" lesions in patients with relapsing-remitting multiple sclerosis

OBJECTIVE

Gadolinium-enhanced T1-weighted lesions are a well-established marker of areas with acute inflammatory activity. A majority of these gadolinium-enhanced T1 lesions are isointense relative to the surrounding white matter, but 20-40% of such active lesions will evolve during one year into areas of low signal ("black hole"). This study sought to characterize evolution of "black hole" lesions in patients with relapsing-remitting multiple sclerosis (MS) using the magnetic resonance imaging (MRI), which measures active lesions via the count of new or enlarged T2 and gadolinium-enhanced T1-weighted lesions.

MATERIALS AND METHODS

This was a prospective, observational case-series study which utilized pre- and post-gadolinium contrast T1-weighted and Proton density MRI scans. Twenty-nine patients (8 males and 21 females) with average age of 38.86 ± 6.58 years and disease duration of 5.75 ± 7.00 years were used to analyze 196 acute demyelinating plaques detected on MRI images during the 24-month follow-up of post-gadolinium signal intensity enhancement of MS plaques.

RESULTS

Significant difference in black hole development was found between the shapes of acute and chronic "black holes". Ring-shaped and patchy plaques were 4.09 (1.87-8.91) times more likely and 1.49 (0.71-3.12) times less likely to develop an acute "black holes" than homogeneous plaques, respectively. Acute plaques with higher lesion-to-CSF SI ratio and larger surface area showed a greater tendency to develop into acute and chronic "black holes".

CONCLUSIONS

The value of lesion-to-CSF SI ratio and surface area were found as the predictors of the "black hole" formation.

Development and evaluation of a high resolution 0.5mm isotropic T1-weighted template of the older adult brain

Investigating the structure of the older adult brain at high spatial resolution is of high significance, and a dedicated older adult structural brain template with sub-millimeter resolution is currently lacking. Therefore, the purpose of this work was twofold: (A) to develop a 0.5mm isotropic resolution standardized T1-weighted template of the older adult brain by applying principles of super resolution to high quality MRI data from 222 older adults (65-95 years of age), and (B) to systematically compare the new template to other standardized and study-specific templates in terms of image quality and performance when used as a reference for alignment of older adult data. The new template exhibited higher spatial resolution and improved visualization of fine structural details of the older adult brain compared to a template constructed using a conventional template building approach and the same data. In addition, the new template exhibited higher image sharpness and did not contain image artifacts observed in some of the other templates considered in this work. Due to the above enhancements, the new template provided higher inter-subject spatial normalization precision for older adult data compared to the other templates, and consequently enabled detection of smaller inter-group morphometric differences in older adult data. Finally, the new template was among those that were most representative of older adult brain data. Overall, the new template constructed here is an important resource for studies of aging, and the findings of the present work have important implications in template selection for investigations on older adults.

Ultra-high-resolution Mapping of Cortical Layers 3T-Guided 7T MRI

7T MRI provides unprecedented resolution for examining human brain anatomy in vivo. For example, 7T MRI enables deep thickness measurement of laminar subdivisions in the right fusiform area. Existing laminar thickness measurement on 7T is labor intensive, and error prone since the visual inspection of the image is typically along one of the three orthogonal planes (axial, coronal, or sagittal view). To overcome this, we propose a new analytics tool that allows flexible quantification of cortical thickness on a 2D plane that is orthogonal to the cortical surface (beyond axial, coronal, and sagittal views) based on the 3D computational surface reconstruction. The proposed method further distinguishes high quality 2D planes and the low-quality ones by automatically inspecting the angles between the surface normals and slice direction. In our approach, we acquired a pair of 3T and 7T scans (same subject). We extracted the brain surfaces from the 3T scan using MaCRUISE and projected the surface to the 7T scan's space. After computing the angles between the surface normals and axial direction vector, we found that 18.58% of surface points were angled at more than 80° with the axial direction vector and had 2D axial planes with visually distinguishable cortical layers. 15.12% of the surface points with normal vectors angled at 30° or lesser with the axial direction, had poor 2D axial slices for visual inspection of the cortical layers. This effort promises to dramatically extend the area of cortex that can be quantified with ultra-high resolution in-plane imaging methods.

Simultaneous T1 -weighted and T2 -weighted 3D MRI using RF phase-modulated gradient echo imaging

PURPOSE

T₁ -weighted and T₂ -weighted (T1w and T2w) imaging are essential sequences in routine clinical practice to detect and characterize a wide variety of pathologies. Many approaches have been proposed to obtain T1w and T2w contrast, although many challenges still remain, including long acquisition time and limitations that favor 2D imaging. In this study, we propose a novel method for simultaneous T1w and T2w imaging using RF phase-modulated 3D gradient-echo imaging.

THEORY

Configuration theory is used to derive closed-form equations for the steady state of RF phase-modulated gradient-echo signal. These equations suggest the use of small RF phase increments to provide orthogonal signal contrast with T2w and T1w in the real and imaginary components, respectively. Background phase can be removed using a two-pass acquisition with opposite RF phase increments.

METHODS

Simulation and phantom experiments were performed to validate our proposed method. Volunteer images of the brain and knee were acquired to demonstrate the clinical feasibility. The proposed method was compared with T1w and T2w fast spin-echo imaging.

RESULTS

The relative signal intensity of images acquired using the proposed method agreed closely with simulations and fast spin-echo imaging in phantoms. Images from volunteer imaging showed very similar contrast compared to conventional fast spin-echo imaging.

CONCLUSION

Radiofrequency phase-modulated gradient-echo with small RF phase increments is an alternative method that provides simultaneous T1w and T2w contrast in short scan times with 3D volumetric coverage.

Free-breathing radial stack-of-stars three-dimensional Dixon gradient echo sequence in abdominal magnetic resonance imaging in sedated pediatric patients

BACKGROUND

There is a strong need for improvements in motion robust T1-weighted abdominal imaging sequences in children to enable high-quality, free-breathing imaging.

OBJECTIVE

To compare imaging time and quality of a radial stack-of-stars, free-breathing T1-weighted gradient echo acquisition (volumetric interpolated breath-hold examination [VIBE]) three-dimensional (3-D) Dixon sequence in sedated pediatric patients undergoing abdominal magnetic resonance imaging (MRI) against conventional Cartesian T1-weighed sequences.

MATERIALS AND METHODS

This study was approved by the institutional review board with informed consent obtained from all subjects. Study subjects included 31 pediatric patients (19 male, 12 female; median age: 5 years; interquartile range: 5 years) undergoing abdominal MRI at 3 tesla with a free-breathing T1-weighted radial stack-of-stars 3-D VIBE Dixon prototype sequence, StarVIBE Dixon (radial technique), between October 2018 and June 2019 with previous abdominal MR imaging using conventional Cartesian T1-weighed imaging (traditional technique). MRI component times were recorded as well as the total number of non-contrast T1-weighted sequences. Two radiologists independently rated images for quality using a scale from 1 to 5 according to the following metrics: overall image quality, hepatic edge sharpness, hepatic vessel clarity and respiratory motion robustness. Scores were compared between the groups.

RESULTS

Mean T1-weighted imaging times for all subjects were 3.63 min for radial exams and 8.01 min for traditional exams (P<0.001), and total non-contrast imaging time was 32.7 min vs. 43.9 min (P=0.002). Adjusted mean total MRI time for all subjects was 60.2 min for radial exams and 65.7 min for traditional exams (P=0.387). The mean number of non-contrast T1-weighted sequences performed in radial MRI exams was 1.0 compared to 1.9 (range: 0-6) in traditional exams (P<0.001). StarVIBE Dixon outperformed Cartesian methods in all quality metrics. The mean overall image quality (scale 1-5) was 3.95 for radial exams and 3.31 for traditional exams (P<0.001).

CONCLUSION

Radial stack-of-stars 3-D VIBE Dixon during free-breathing abdominal MRI in pediatric patients offers improved image quality compared to Cartesian T1-weighted imaging techniques with decreased T1-weighted and total non-contrast imaging time. This has important implications for children undergoing sedation for imaging.

Classifying MRI motion severity using a stacked ensemble approach

Motion artifacts are a common occurrence in Magnetic Resonance Imaging exam. Motion during acquisition has a profound impact on workflow efficiency, often requiring a repeat of sequences. Furthermore, motion artifacts may escape notice by technologists, only to be revealed at the time of reading by the radiologists, affecting their diagnostic quality. There is a paucity of clinical tools to identify and quantitatively assess the severity of motion artifacts in MRI. An image with subtle motion may still have diagnostic value, while severe motion may be uninterpretable by radiologists and requires the exam to be repeated. Therefore, a tool for the automatic identification of motion artifacts would aid in maintaining diagnostic quality, while potentially driving workflow efficiencies. Here we aim to quantify the severity of motion artifacts from MRI images using deep learning. Impact of subject movement parameters like displacement and rotation on image quality is also studied. A state-of-the-art, stacked ensemble model was developed to classify motion artifacts into five levels (no motion, slight, mild, moderate and severe) in brain scans. The stacked ensemble model is able to robustly predict rigid-body motion severity across different acquisition parameters, including T1-weighted and T2-weighted slices acquired in different anatomical planes. The ensemble model with XGBoost metalearner achieves 91.6% accuracy, 94.8% area under the curve, 90% Cohen's Kappa, and is observed to be more accurate and robust than the individual base learners.

A Spatio-Temporal Model for Longitudinal Image-on-Image Regression

Neurologists and radiologists often use magnetic resonance imaging (MRI) in the management of subjects with multiple sclerosis (MS) because it is sensitive to inflammatory and demyelinative changes in the white matter of the brain and spinal cord. Two conventional modalities used for identifying lesions are T1-weighted (T1) and T2-weighted fluid-attenuated inversion recovery (FLAIR) imaging, which are used clinically and in research studies. Magnetization transfer ratio (MTR), which is available only in research settings, is an advanced MRI modality that has been used extensively for measuring disease-related demyelination both in white matter lesions as well across normal-appearing white matter. Acquiring MTR is not standard in clinical practice, due to the increased scan time and cost. Hence, prediction of MTR based on the modalities T1 and FLAIR could have great impact on the availability of these promising measures for improved patient management. We propose a spatio-temporal regression model for image response and image predictors that are acquired longitudinally, with images being co-registered within the subject but not across subjects. The model is additive, with the response at a voxel being dependent on the available covariates not only through the current voxel but also on the imaging information from the voxels within a neighboring spatial region as well as their temporal gradients. We propose a dynamic Bayesian estimation procedure that updates the parameters of the subject-specific regression model as data accummulates. To bypass the computational challenges associated with a Bayesian approach for high-dimensional imaging data, we propose an approximate Bayesian inference technique. We assess the model fitting and the prediction performance using longitudinally acquired MRI images from 46 MS patients.

Contributions of Magnetic Resonance Imaging to Gastroenterological Practice: MRIs for GIs

MRI has transformed from the theoretical, investigative realm to mainstream clinical medicine over the past four decades and has become a core component of the diagnostic toolbox in the practice of gastroenterology (GI). Its success is attributable to exquisite contrast and the ability to isolate specific proton species through the use of different pulse sequences (i.e., T1-weighted, T2-weighted, diffusion-weighted) and exploiting extracellular and hepatobiliary contrast agents. Consequently, MRI has gained preeminence in various GI clinical applications: liver and pancreatic lesion evaluation and detection, liver transplantation evaluation, pancreatitis evaluation, Crohn's disease evaluation (using MR enterography) rectal cancer staging and perianal fistula evaluation. MR elastography, in concert with technical innovations allowing for fat and iron quantification, provides a noninvasive approach, or "MRI virtual liver biopsy" for diagnosis and management of chronic liver diseases. In the future, the arrival of ultra-high-field MR systems (7 T) and the ability to perform magnetic resonance spectroscopy in the abdomen promise even greater diagnostic insight into chronic liver disease.

Rathke's Cleft Cyst or Pituitary Apoplexy: A Case Report and Literature Review

BACKGROUND:

During the examination of the sellar region by magnetic resonance imaging, hyperintensity in T1 weighted is a common finding. This signal intensity has different sources, and its significance depends on the clinical context. Pathologic variations in T1 signal hyperintensity may be related to clotting of blood (pituitary apoplexy) or the presence of a high concentration of protein (Rathke cleft cyst). The purpose of this study is to describe the significance of intracystic nodule, a diagnostic characteristic found in Rathke’s cleft cyst, on MRI.

CASE REPORT:

We will present the case of a 20–year-old girl which referral to our hospital for head examination with magnetic resonance imaging because she has a post-traumatic headache. Pathological findings presented in T1-weighted hyperintensity intrasellar which persist even in T1 weighted-Fat suppression. These changes signal the presence of methemoglobin imposes. The patient is a referral to laboratory tests which result in rate except for slight value increase of prolactin. Recommended controller examination after a month but finding the same results which exclude the presence of methemoglobin.

CONCLUSION:

Morphological characteristics and signal intensity can impose the presence of high concentration of protein (Rathke cleft cyst).

Can T1 w/T2 w ratio be used as a myelin-specific measure in subcortical structures? Comparisons between FSE-based T1 w/T2 w ratios, GRASE-based T1 w/T2 w ratios and multi-echo GRASE-based myelin water fractions

Given the growing popularity of T₁ -weighted/T₂ -weighted (T₁ w/T₂ w) ratio measurements, the objective of the current study was to evaluate the concordance between T₁ w/T₂ w ratios obtained using conventional fast spin echo (FSE) versus combined gradient and spin echo (GRASE) sequences for T₂ w image acquisition, and to compare the resulting T₁ w/T₂ w ratios with histologically validated myelin water fraction (MWF) measurements in several subcortical brain structures. In order to compare these measurements across a relatively wide range of myelin concentrations, whole-brain T₁ w magnetization prepared rapid acquisition gradient echo (MPRAGE), T₂ w FSE and three-dimensional multi-echo GRASE data were acquired from 10 participants with multiple sclerosis at 3 T. Then, after high-dimensional, non-linear warping, region of interest (ROI) analyses were performed to compare T₁ w/T₂ w ratios and MWF estimates (across participants and brain regions) in 11 bilateral white matter (WM) and four bilateral subcortical grey matter (SGM) structures extracted from the JHU_MNI_SS 'Eve' atlas. Although the GRASE sequence systematically underestimated T₁ w/T₂ w values compared to the FSE sequence (revealed by Bland-Altman and mountain plots), linear regressions across participants and ROIs revealed consistently high correlations between the two methods (r² = 0.62 for all ROIs, r² = 0.62 for WM structures and r² = 0.73 for SGM structures). However, correlations between either FSE-based or GRASE-based T₁ w/T₂ w ratios and MWFs were extremely low in WM structures (FSE-based, r² = 0.000020; GRASE-based, r² = 0.0014), low across all ROIs (FSE-based, r² = 0.053; GRASE-based, r² = 0.029) and moderate in SGM structures (FSE-based, r² = 0.20; GRASE-based, r² = 0.17). Overall, our findings indicated a high degree of correlation (but not equivalence) between FSE-based and GRASE-based T₁ w/T₂ w ratios, and low correlations between T₁ w/T₂ w ratios and MWFs. This suggests that the two T₁ w/T₂ w ratio approaches measure similar facets of subcortical tissue microstructure, whereas T₁ w/T₂ w ratios and MWFs appear to be sensitized to different microstructural properties. On this basis, we conclude that multi-echo GRASE sequences can be used in future studies to efficiently elucidate both general (T₁ w/T₂ w ratio) and myelin-specific (MWF) tissue characteristics.

Segmenting and validating brain tissue definitions in the presence of varying tissue contrast

We propose a method for segmenting brain tissue as either gray matter or white matter in the presence of varying tissue contrast, which can derive from either differential changes in tissue water content or increasing myelin content of white matter. Our method models the spatial distribution of intensities as a Markov Random Field (MRF) and estimates the parameters for the MRF model using a maximum likelihood approach. Although previously described methods have used similar models to segment brain tissue, accurate model of the conditional probabilities of tissue intensities and adaptive estimates of tissue properties to local intensities generates tissue definitions that are accurate and robust to variations in tissue contrast with age and across illnesses. Robustness to variations in tissue contrast is important to understand normal brain development and to identify the brain bases of neurological and psychiatric illnesses. We used simulated brains of varying tissue contrast to compare both visually and quantitatively the performance of our method with the performance of prior methods. We assessed validity of the cortical definitions by associating cortical thickness with various demographic features, clinical measures, and medication use in our three large cohorts of participants who were either healthy or who had Bipolar Disorder (BD), Autism Spectrum Disorder (ASD), or familial risk for Major Depressive Disorder (MDD). We assessed validity of the tissue definitions using synthetic brains and data for three large cohort of individuals with various neuropsychiatric disorders. Visual inspection and quantitative analyses showed that our method accurately and robustly defined the cortical mantle in brain images with varying contrast. Furthermore, associating the thickness with various demographic and clinical measures generated findings that were novel and supported by histological analyses or were supported by previous MRI studies, thereby validating the cortical definitions generated by the proposed method: (1) Although cortical thickness decreased with age in adolescents, in adults cortical thickness did not correlate significantly with age. Our synthetic data showed that the previously reported thinning of cortex in adults is likely due to decease in tissue contrast, thereby suggesting that the method generated cortical definitions in adults that were invariant to tissue contrast. In adolescents, cortical thinning with age was preserved likely due to widespread dendritic and synaptic pruning, even though the effects of decreasing tissue contrast were minimized. (3) The method generated novel finding of both localized increases and decreases in thickness of males compared to females after controlling for the differing brain sizes, which are supported by the histological analyses of brain tissue in males and females. (4) The proposed method, unlike prior methods, defined thicker cortex in BD individuals using lithium. The novel finding is supported by the studies that showed lithium treatment increased dendritic arborization and neurogenesis, thereby leading to thickening of cortex. (5) In both BD and ASD participants, associations of more severe symptoms with thinner cortex showed that correcting for the effects of tissue contrast preserved the biological consequences of illnesses. Therefore, consistency of the findings across the three large cohorts of participants, in images acquired on either 1.5T or 3T MRI scanners, and with findings from prior histological analyses provides strong evidence that the proposed method generated valid and accurate definitions of the cortex while controlling for the effects of tissue contrast.

Development of Timd2 as a reporter gene for MRI

PURPOSE

To assess the potential of an MRI gene reporter based on the ferritin receptor Timd2 (T-cell immunoglobulin and mucin domain containing protein 2), using T1- and T2-weighted imaging.

METHODS

Pellets of cells that had been modified to express the Timd2 transgene, and incubated with either iron-loaded or manganese-loaded ferritin, were imaged using T1- and T2-weighted MRI. Mice were also implanted subcutaneously with Timd2-expressing cells and the resulting xenograft tissue imaged following intravenous injection of ferritin using T2-weighted imaging.

RESULTS

Timd2-expressing cells, but not control cells, showed a large increase in both R2 and R1 in vitro following incubation with iron-loaded and manganese-loaded ferritin, respectively. Expression of Timd2 had no effect on cell viability or proliferation; however, manganese-loaded ferritin, but not iron-loaded ferritin, was toxic to Timd2-expressing cells. Timd2-expressing xenografts in vivo showed much smaller changes in R2 following injection of iron-loaded ferritin than the same cells incubated in vitro with iron-loaded ferritin.

CONCLUSION

Timd2 has demonstrated potential as an MRI reporter gene, producing large increases in R2 and R1 with ferritin and manganese-loaded ferritin respectively in vitro, although more modest changes in R2 in vivo. Manganese-loaded apoferritin was not used in vivo due to the toxicity observed in vitro. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.

Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI

Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4-82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation.

The Effect of Lipid Modification on Peripheral Artery Disease after Endovascular Intervention Trial (ELIMIT)

METHODS

A total of 102 patients were randomized to either mono-therapy with simvastatin (40 mg daily) or triple-therapy with simvastatin (40 mg daily), extended-release niacin (1500 mg daily), and ezetimibe (10 mg daily). MRI was performed at baseline and 6, 12, and 24 months. SFA wall, lumen, and total vessel volumes were quantified. MRI-derived SFA parameters and lipids were analyzed with multilevel models and nonparametric tests, respectively.

RESULTS

Baseline characteristics did not differ between mono and triple-therapy groups, except for ethnicity (p = 0.02). SFA wall, lumen, and total vessel volumes increased non-significantly for both groups between baseline and 24-months. Non-high-density lipoprotein cholesterol was significantly reduced at 12 months with triple-therapy compared with mono-therapy (p = 0.01).

CONCLUSION

No significant differences were observed between mono-therapy using simvastatin and triple-therapy with simvastatin, extended-release niacin, and ezetimibe for 24-month changes in SFA wall, lumen, and total vessel volumes.

CLINICAL TRIAL REGISTRATION INFORMATION

NCT00687076; Link: http://clinicaltrials.gov/ct2/show/NCT00687076.