T1 Datasets Research Articles

Artificial neural network for Slice Encoding for Metal Artifact Correction (SEMAC) MRI.

To develop new artificial neural networks (ANNs) to accelerate slice encoding for metal artifact correction (SEMAC) MRI. Eight titanium phantoms and 77 patients after brain tumor surgery involving metallic neuro-plating instruments were scanned using SEMAC at a 3T Skyra scanner. For the phantoms, proton-density, T1-, and T2-weighted images were acquired for developing both multilayer perceptron (MLP) and convolutional neural network (CNN). For the patients, T2-weighted images were acquired for developing CNN. All networks were trained with the SEMAC factor 4 or 6 as input and the factor 12 as label, yielding an acceleration factor of 3 or 2. Performance of the CNN model was compared against parallel imaging and compressed sensing on the phantom datasets. Two extra T1-weighted in vivo sets were acquired to investigate generalizability of the models to different contrasts. Both multilayer perceptron and CNN provided artifact-suppressed images better than the input images and comparable to the label images visually and quantitatively, a trend observable regardless of input SEMAC factor and image type (P < .01). CNN suppressed the artifacts better than multilayer perceptron, parallel imaging, and compressed sensing (P < .01). Tests on the patient datasets demonstrated clear metal artifact suppression visually and quantitatively (P < .01). Tests on T1 datasets also demonstrated clear visual metal artifact suppression. Our study introduced a new effective way of artificial neural networks to accelerate SEMAC MRI while maintaining the comparable quality of metal artifact suppression. Application on the preliminary patient datasets proved the feasibility in clinical usage, which warrants further investigation.

T1 and extracellular volume fraction mapping in cardiac magnetic resonance: estimation of accuracy and precision of a novel algorithm

Magnetic resonance imaging is effective for non-invasive detection of myocardial diseases by extracellular volume fraction (ECV) estimation. A new methodology for T1 and ECV mapping is tested in this work, comparing results with other well-consolidated methods. The associated level of uncertainty for data was also estimated, to assess the reliability of the technique. A phantom with known T1 values was used for reference, and 60 subjects (40 controls, 20 diseased patients) were examined, using the modified look-locker inversion-recovery (MOLLI) method. Obtained T1 data were studied in terms of accuracy (difference with reference T1), paired comparisons with other methods, and Gamma-tool analysis with tolerances criteria of 4.21 mm for distance-to-agreement, and between 2%–5% for T1 difference. Accuracy and precision of the T1 mapping was analysed by phantom measurements, and the uncertainty of the ECV was estimated by T1 error propagation. Differences (in paired comparisons) of T1 datasets were not significant neither for statistical tests, nor for Bland–Altman analysis. T1 accuracy was between −12% and −1% across methods, slightly better for the tested method (mean accuracy in the T1 range of interest better than 2%). The Gamma analysis confirm slightly better results for the tested method than other methodologies. The new method improves the computational efficiency by a factor of 25 (at least), revealing to be also more suitable for Big Data-related applications.

EP 76. Quality control of high resolution T1 images in the global repository of the neuroimaging society in amyotrophic lateral sclerosis (NiSALS)

Introduction Pathology in ALS spreads within the brain along tracts which are connected to the primary motor cortex, eventually causing atrophy in several cortical areas. Grey and white matter signals in high resolution T1 MRI can be analyzed using i.e. cortical thickness analyses, voxel-based morphometry (VBM), voxel-based intensitometry (VBI), deformation based mapping and other T1 tools. These tools are capable of detecting ALS related pathology in groups of patients. In order to utilize these capabilities as a monitoring and exploration tool in large multicenter cohorts, the quality of T1 datasets must be comparable between different scanners and centers. Causes of low quality scans must be identified and corrected for, by changing acquisition, by using software correction, or both. Objective (i) to quantify the systematic differences in high resolution T1 scans in a large cross sectional data set uploaded to the central MRI repository of the Neuroimaging Society in ALS (NiSALS); (ii) to identify the major components contributing to the quality of the scans; (iii) to correct for scanner specific systematic distortions and (iv) define a set of quality markers to pass to allow pooling of data for large multicenter trials. Methods T1 data sets ( n = 500) were uploaded from 20 centers in Europe, the US and Canada. Data sets were analyzed using preprocessing from the VBM12 package within SPM on the Matlab platform. A custom developed set of quality markers (Dahnke&Gaser) was used to describe geometrical and noise distortions in a quantitative manner and correct for these distortions on a per scanner basis. Mahalanovis distance analyses were used to describe the relative quality of the scans within the NISALS cohort, and in comparison to other freely available cohorts. Results Data from 15 centers passed the minimum requirements for pooling using stringent criteria, data from three more centers were allowed to enter when correction measures were finely adjusted. The quality of the NISALS data set was comparable to the ADNI data set which imposed strict measures during data acquisition. Conclusion Quality control of T1 data sets in a centralized repository allows the pooling of large MRI data sets from many different centers, the quantification of scanner specific distortions, the software correction of some of these distortions, and the monitoring of the quality of T1 data as it would be used during a clinical trial. Acknowledgement This research is supported by BMBF (Bundesministerium fur Bildung and Forschung) in the framework of the E-RARE programme (PYRAMID) and JPND(SOPHIA) of the European Union.

Fiber Visualization with LIC Maps Using Multidirectional Anisotropic Glyph Samples.

Line integral convolution (LIC) is used as a texture-based technique in computer graphics for flow field visualization. In diffusion tensor imaging (DTI), LIC bridges the gap between local approaches, for example directionally encoded fractional anisotropy mapping and techniques analyzing global relationships between brain regions, such as streamline tracking. In this paper an advancement of a previously published multikernel LIC approach for high angular resolution diffusion imaging visualization is proposed: a novel sampling scheme is developed to generate anisotropic glyph samples that can be used as an input pattern to the LIC algorithm. Multicylindrical glyph samples, derived from fiber orientation distribution (FOD) functions, are used, which provide a method for anisotropic packing along integrated fiber lines controlled by a uniform random algorithm. This allows two- and three-dimensional LIC maps to be generated, depicting fiber structures with excellent contrast, even in regions of crossing and branching fibers. Furthermore, a color-coding model for the fused visualization of slices from T1 datasets together with directionally encoded LIC maps is proposed. The methodology is evaluated by a simulation study with a synthetic dataset, representing crossing and bending fibers. In addition, results from in vivo studies with a healthy volunteer and a brain tumor patient are presented to demonstrate the method's practicality.

Multidimensional morphometric 3D MRI analyses for detecting brain abnormalities in children: impact of control population.

Automated morphometric approaches are used to detect epileptogenic structural abnormalities in 3D MR images in adults, using the variance of a control population to obtain z-score maps in an individual patient. Due to the substantial changes the developing human brain undergoes, performing such analyses in children is challenging. This study investigated six features derived from high-resolution T1 datasets in four groups: normal children (1.5T or 3T data), normal clinical scans (3T data), and patients with structural brain lesions (3T data), with each n = 10. Normative control data were obtained from the NIH study on normal brain development (n = 401). We show that control group size substantially influences the captured variance, directly impacting the patient's z-scores. Interestingly, matching on gender does not seem to be beneficial, which was unexpected. Using data obtained at higher field scanners produces slightly different base rates of suprathreshold voxels, as does using clinically derived normal studies, suggesting a subtle but systematic effect of both factors. Two approaches for controlling suprathreshold voxels in a multidimensional approach (combining features and requiring a minimum cluster size) were shown to be substantial and effective in reducing this number. Finally, specific strengths and limitations of such an approach could be demonstrated in individual cases.

Neuroanatomical Correlates of Depression in Friedreich’s Ataxia: a Voxel-Based Morphometry Study

Affective disorders have been increasingly recognized in neurodegenerative diseases and often result in poor quality of life. However, the frequency, clinical relevance, and anatomical substrate of depression in Friedreich's ataxia were not yet evaluated. We assessed 22 patients with Friedreich's ataxia for major depression using Beck Depression Inventory and cerebral 3T MRI scans. We then employed whole-brain voxel-based morphometry analyses on volumetric T1 datasets to compare tissue loss between patients with and without major depression. Patients (36.3%) fulfilled criteria for major depression (8/22). Mean Beck Depression Inventory (BDI) score was 9.63 ± 8.95 and the depressive group had significantly higher score compared to non-depressive group (18.5 ± 8.6 vs 4.4 ± 2.9, p < 0.001). There was no correlation between Beck Depression Inventory score and age of patients, ataxia severity, age at onset, or duration of the disease. The comparison between patient groups found no significant differences of white matter volumes. In contrast, we found reduction of gray matter volumes in the depressive group in medial and orbital region of frontal lobe and anterior cingulate gyri (p < 0.001). Regression analyses have shown that BDI scores were inversely correlated with gray matter volume at right superior frontal gyrus. Major depression is frequent in Friedreich's ataxia and possibly under recognized. Our results strongly suggest that this may not be a simply reactive phenomenon, but rather associated to structural abnormalities.

Comparative study of standard space and real space analysis of quantitative MR brain data

To compare the robustness of region of interest (ROI) analysis of magnetic resonance imaging (MRI) brain data in real space with analysis in standard space and to test the hypothesis that standard space image analysis introduces more partial volume effect errors compared to analysis of the same dataset in real space. Twenty healthy adults with no history or evidence of neurological diseases were recruited; high-resolution T(1)-weighted, quantitative T(1), and B(0) field-map measurements were collected. Algorithms were implemented to perform analysis in real and standard space and used to apply a simple standard ROI template to quantitative T(1) datasets. Regional relaxation values and histograms for both gray and white matter tissues classes were then extracted and compared. Regional mean T(1) values for both gray and white matter were significantly lower using real space compared to standard space analysis. Additionally, regional T(1) histograms were more compact in real space, with smaller right-sided tails indicating lower partial volume errors compared to standard space analysis. Standard space analysis of quantitative MRI brain data introduces more partial volume effect errors biasing the analysis of quantitative data compared to analysis of the same dataset in real space.

Quantification of white matter and gray matter volumes from T1 parametric images using fuzzy classifiers.

White matter (WM) and gray matter (GM) were accurately measured using a technique based on a single standardized fuzzy classifier (FC) for each tissue. Fuzzy classifier development was based on experts' visual assessments of WM and GM boundaries from a set of T1 parametric MR images. The fuzzy classifier method's accuracy was validated and optimized by a set of T1 phantom images that were based on hand-detailed human brain cryosection images. Nine sets of axial T1 images of varying thickness equally distributed throughout the brain were simulated. All T1 data sets were mapped to the standardized FCs and rapidly segmented into WM and GM voxel fraction images. Resulting volumes revealed that, in most cases, the difference between measured and actual volumes was less than 5%. This was consistent throughout most of the brain, and as expected, the accuracy improved to generally less than 2% for the 1-mm simulated brain slices.