Scanner Invariant Representations for Diffusion MRI Harmonization

Purpose: In the present work we describe the correction of diffusion-weighted MRI for site and scanner biases using a novel method based on invariant representation. Theory and Methods: Pooled imaging data from multiple sources are subject to variation between the sources. Correcting for these biases has become very important as imaging studies increase in size and multi-site cases become more common. We propose learning an intermediate representation invariant to site/protocol variables, a technique adapted from information theory-based algorithmic fairness; by leveraging the data processing inequality, such a representation can then be used to create an image reconstruction that is uninformative of its original source, yet still faithful to underlying structures. To implement this, we use a deep learning method based on variational auto-encoders (VAE) to construct scanner invariant encodings of the imaging data. Results: To evaluate our method, we use training data from the 2018 MICCAI Computational Diffusion MRI (CDMRI) Challenge Harmonization dataset. Our proposed method shows improvements on independent test data relative to a recently published baseline method on each subtask, mapping data from three different scanning contexts to and from one separate target scanning context. Conclusion: As imaging studies continue to grow, the use of pooled multi-site imaging will similarly increase. Invariant representation presents a strong candidate for the harmonization of these data

"MASSIVE" Brain Dataset: Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation

PURPOSE: In this work, we present the MASSIVE (Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation) brain dataset of a single healthy subject, which is intended to facilitate diffusion MRI (dMRI) modeling and methodology development. METHODS: MRI data of one healthy subject (female, 25 years) were acquired on a clinical 3 Tesla system (Philips Achieva) with an eight-channel head coil. In total, the subject was scanned on 18 different occasions with a total acquisition time of 22.5 h. The dMRI data were acquired with an isotropic resolution of 2.5 mm(3) and distributed over five shells with b-values up to 4000 s/mm(2) and two Cartesian grids with b-values up to 9000 s/mm(2) . RESULTS: The final dataset consists of 8000 dMRI volumes, corresponding B0 field maps and noise maps for subsets of the dMRI scans, and ten three-dimensional FLAIR, T1 -, and T2 -weighted scans. The average signal-to-noise-ratio of the non-diffusion-weighted images was roughly 35. CONCLUSION: This unique set of in vivo MRI data will provide a robust framework to evaluate novel diffusion processing techniques and to reliably compare different approaches for diffusion modeling. The MASSIVE dataset is made publically available (both unprocessed and processed) on www.massive-data.org. Magn Reson Med, 2016

Simultaneous Matrix Diagonalization for Structural Brain Networks Classification

This paper considers the problem of brain disease classification based on connectome data. A connectome is a network representation of a human brain. The typical connectome classification problem is very challenging because of the small sample size and high dimensionality of the data. We propose to use simultaneous approximate diagonalization of adjacency matrices in order to compute their eigenstructures in more stable way. The obtained approximate eigenvalues are further used as features for classification. The proposed approach is demonstrated to be efficient for detection of Alzheimer's disease, outperforming simple baselines and competing with state-of-the-art approaches to brain disease classification

The importance of correcting for signal drift in diffusion MRI

PURPOSE: To investigate previously unreported effects of signal drift as a result of temporal scanner instability on diffusion MRI data analysis and to propose a method to correct this signal drift. METHODS: We investigated the signal magnitude of non-diffusion-weighted EPI volumes in a series of diffusion-weighted imaging experiments to determine whether signal magnitude changes over time. Different scan protocols and scanners from multiple vendors were used to verify this on phantom data, and the effects on diffusion kurtosis tensor estimation in phantom and in vivo data were quantified. Scalar metrics (eigenvalues, fractional anisotropy, mean diffusivity, mean kurtosis) and directional information (first eigenvectors and tractography) were investigated. RESULTS: Signal drift, a global signal decrease with subsequently acquired images in the scan, was observed in phantom data on all three scanners, with varying magnitudes up to 5% in a 15-min scan. The signal drift has a noticeable effect on the estimation of diffusion parameters. All investigated quantitative parameters as well as tractography were affected by this artifactual signal decrease during the scan. CONCLUSION: By interspersing the non-diffusion-weighted images throughout the session, the signal decrease can be estimated and compensated for before data analysis; minimizing the detrimental effects on subsequent MRI analyses. Magn Reson Med, 2016. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine

Backpropagated Gradient Representations for Anomaly Detection

Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use activation representations from forward propagation while not exploiting gradients from backpropagation to characterize data. Gradients capture model updates required to represent data. Anomalies require more drastic model updates to fully represent them compared to normal data. Hence, we propose the utilization of backpropagated gradients as representations to characterize model behavior on anomalies and, consequently, detect such anomalies. We show that the proposed method using gradient-based representations achieves state-of-the-art anomaly detection performance in benchmark image recognition datasets. Also, we highlight the computational efficiency and the simplicity of the proposed method in comparison with other state-of-the-art methods relying on adversarial networks or autoregressive models, which require at least 27 times more model parameters than the proposed method.Comment: European Conference on Computer Vision (ECCV) 202

Consumer evaluation of complaint handling in the Dutch health insurance market

<p>Abstract</p> <p>Background</p> <p>How companies deal with complaints is a particularly challenging aspect in managing the quality of their service. In this study we test the direct and relative effects of service quality dimensions on consumer complaint satisfaction evaluations and trust in a company in the Dutch health insurance market.</p> <p>Methods</p> <p>A cross-sectional survey design was used. Survey data of 150 members of a Dutch insurance panel who lodged a complaint at their healthcare insurer within the past 12 months were surveyed. The data were collected using a questionnaire containing validated multi-item measures. These measures assess the service quality dimensions consisting of functional quality and technical quality and consumer complaint satisfaction evaluations consisting of complaint satisfaction and overall satisfaction with the company after complaint handling. Respondents' trust in a company after complaint handling was also measured. Using factor analysis, reliability and validity of the measures were assessed. Regression analysis was used to examine the relationships between these variables.</p> <p>Results</p> <p>Overall, results confirm the hypothesized direct and relative effects between the service quality dimensions and consumer complaint satisfaction evaluations and trust in the company. No support was found for the effect of technical quality on overall satisfaction with the company. This outcome might be driven by the context of our study; namely, consumers get in touch with a company to resolve a specific problem and therefore might focus more on complaint satisfaction and less on overall satisfaction with the company.</p> <p>Conclusions</p> <p>Overall, the model we present is valid in the context of the Dutch health insurance market. Management is able to increase consumers' complaint satisfaction, overall satisfaction with the company, and trust in the company by improving elements of functional and technical quality. Furthermore, we show that functional and technical quality do not influence consumer satisfaction evaluations and trust in the company to the same extent. Therefore, it is important for managers to be aware of the type of consumer satisfaction they are measuring when evaluating the handling of complaints within their company.</p

Integrating the Genetic and Physical Maps of Arabidopsis thaliana: Identification of Mapped Alleles of Cloned Essential (EMB) Genes

The classical genetic map of Arabidopsis includes more than 130 genes with an embryo-defective (emb) mutant phenotype. Many of these essential genes remain to be cloned. Hundreds of additional EMB genes have been cloned and catalogued (www.seedgenes.org) but not mapped. To facilitate EMB gene identification and assess the current level of saturation, we updated the classical map, compared the physical and genetic locations of mapped loci, and performed allelism tests between mapped (but not cloned) and cloned (but not mapped) emb mutants with similar chromosome locations. Two hundred pairwise combinations of genes located on chromosomes 1 and 5 were tested and more than 1100 total crosses were screened. Sixteen of 51 mapped emb mutants examined were found to be disrupted in a known EMB gene. Alleles of a wide range of published EMB genes (YDA, GLA1, TIL1, AtASP38, AtDEK1, EMB506, DG1, OEP80) were discovered. Two EMS mutants isolated 30 years ago, T-DNA mutants with complex insertion sites, and a mutant with an atypical, embryo-specific phenotype were resolved. The frequency of allelism encountered was consistent with past estimates of 500 to 1000 EMB loci. New EMB genes identified among mapped T-DNA insertion mutants included CHC1, which is required for chromatin remodeling, and SHS1/AtBT1, which encodes a plastidial nucleotide transporter similar to the maize Brittle1 protein required for normal endosperm development. Two classical genetic markers (PY, ALB1) were identified based on similar map locations of known genes required for thiamine (THIC) and chlorophyll (PDE166) biosynthesis. The alignment of genetic and physical maps presented here should facilitate the continued analysis of essential genes in Arabidopsis and further characterization of a broad spectrum of mutant phenotypes in a model plant