Diffusion imaging quality control via entropy of principal direction distribution.

Diffusion MR imaging has received increasing attention in the neuroimaging community, as it yields new insights into the microstructural organization of white matter that are not available with conventional MRI techniques. While the technology has enormous potential, diffusion MRI suffers from a unique and complex set of image quality problems, limiting the sensitivity of studies and reducing the accuracy of findings. Furthermore, the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts, reduced signal-to-noise ratio (SNR), and increased proneness to a wide variety of artifacts, including eddy-current and motion artifacts, "venetian blind" artifacts, as well as slice-wise and gradient-wise inconsistencies. Such artifacts mandate stringent Quality Control (QC) schemes in the processing of diffusion MRI data. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts, often only visible when investigating groups of DWI's or a derived diffusion model, such as the most-employed diffusion tensor imaging (DTI). Here, we propose a novel regional QC measure in the DTI domain that employs the entropy of the regional distribution of the principal directions (PD). The PD entropy quantifies the scattering and spread of the principal diffusion directions and is invariant to the patient's position in the scanner. High entropy value indicates that the PDs are distributed relatively uniformly, while low entropy value indicates the presence of clusters in the PD distribution. The novel QC measure is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Such residual artifacts cause directional bias in the measured PD and here called dominant direction artifacts. Experiments show that our automatic method can reliably detect and potentially correct such artifacts, especially the ones caused by the vibrations of the scanner table during the scan. The results further indicate the usefulness of this method for general quality assessment in DTI studies.

Clinical applications of arterial spin labeling of the intracranial compartment in vascular anomalies-A case-based review.

Arterial spin labeling (ASL) is a magnetic resonance perfusion technique that allows for quantification of cerebral blood flow (CBF) without the use of contrast or radiation. Several applications of ASL have been described in diagnosis of strokes and stroke mimics, intracranial tumors, and other conditions. Various vascular anomalies exhibit specific CBF patterns that correlate with different signal intensities on ASL. In this case-based review, we demonstrate the utility of ASL in diagnosis and surveillance of vascular anomalies in the intracranial compartment.

Disparate participation by gender of conference attendants in scientific discussions.

One important metric of a radiologist's visibility and influence is their ability to participate in discussion within their community. The goal of our study was to compare the participation level of men and women in scientific discussions at the annual meeting of the Radiological Society of North America (RSNA). Eleven volunteers collected participation data by gender in 59 sessions (286 presentations) at the 2018 RSNA meeting. Data was analyzed using a combination of Chi-squared, paired Wilcoxon signed-rank and T-test. Of all RSNA professional attendees at the RSNA, 68% were men and 32% were women. Of the 2869 presentations listed in the program, 65% were presented by men and 35% were presented by women. Of the 286 presentations in our sample, 177 (61.8%) were presented by men and 109 (38.1%) were presented by women. Of these 286 presentations, 81 (63%) were moderated by men and 47 (37%) were moderated by women. From the audience, 190 male attendees participated in 134 question-and-answer (Q&A) sessions following presentations and 58 female attendees participated in 52 Q&A sessions (P<0.001). Female attendees who did participate in Q&A sessions talked for a significantly shorter period of time (mean 7.14 ± 17.7 seconds, median 0) compared to male attendees (28.7 ± 29.6 seconds, median 16; P<0.001). Overall, our findings demonstrate that women participated less than men in the Q&A sessions at RSNA 2018, and talked for a shorter period of time. The fact that women were outnumbered among their male peers may explain the difference in behavior by gender.

Antenatal depression, treatment with selective serotonin reuptake inhibitors, and neonatal brain structure: A propensity-matched cohort study.

The aim of this propensity-matched cohort study was to evaluate the impact of prenatal SSRI exposure and a history of maternal depression on neonatal brain volumes and white matter microstructure. SSRI-exposed neonates (n=27) were matched to children of mothers with no history of depression or SSRI use (n=54). Additionally, neonates of mothers with a history of depression, but no prenatal SSRI exposure (n=41), were matched to children of mothers with no history of depression or SSRI use (n=82). Structural magnetic resonance imaging and diffusion weighted imaging scans were acquired with a 3T Siemens Allegra scanner. Global tissue volumes were characterized using an automatic, atlas-moderated expectation maximization segmentation tool. Local differences in gray matter volumes were examined using deformation-based morphometry. Quantitative tractography was performed using an adaptation of the UNC-Utah NA-MIC DTI framework. SSRI-exposed neonates exhibited widespread changes in white matter microstructure compared to matched controls. Children exposed to a history of maternal depression but no SSRIs showed no significant differences in brain development compared to matched controls. No significant differences were found in global or regional tissue volumes. Additional research is needed to clarify whether SSRIs directly alter white matter development or whether this relationship is mediated by depressive symptoms during pregnancy.

UNC-Utah NA-MIC framework for DTI fiber tract analysis.

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

DTI Quality Control Assessment via Error Estimation From Monte Carlo Simulations.

Diffusion Tensor Imaging (DTI) is currently the state of the art method for characterizing microscopic tissue structure in the white matter in normal or diseased brain in vivo. DTI is estimated from a series of Diffusion Weighted Imaging (DWI) volumes. DWIs suffer from a number of artifacts which mandate stringent Quality Control (QC) schemes to eliminate lower quality images for optimal tensor estimation. Conventionally, QC procedures exclude artifact-affected DWIs from subsequent computations leading to a cleaned, reduced set of DWIs, called DWI-QC. Often, a rejection threshold is heuristically/empirically chosen above which the entire DWI-QC data is rendered unacceptable and thus no DTI is computed. In this work, we have devised a more sophisticated, Monte-Carlo simulation based method for the assessment of resulting tensor properties. This allows for a consistent, error-based threshold definition in order to reject/accept the DWI-QC data. Specifically, we propose the estimation of two error metrics related to directional distribution bias of Fractional Anisotropy (FA) and the Principal Direction (PD). The bias is modeled from the DWI-QC gradient information and a Rician noise model incorporating the loss of signal due to the DWI exclusions. Our simulations further show that the estimated bias can be substantially different with respect to magnitude and directional distribution depending on the degree of spatial clustering of the excluded DWIs. Thus, determination of diffusion properties with minimal error requires an evenly distributed sampling of the gradient directions before and after QC.

Software-based Diffusion MR Human Brain Phantom for Evaluating Fiber-tracking Algorithms.

Fiber tracking provides insights into the brain white matter network and has become more and more popular in diffusion MR imaging. Hardware or software phantom provides an essential platform to investigate, validate and compare various tractography algorithms towards a "gold standard". Software phantoms excel due to their flexibility in varying imaging parameters, such as tissue composition, SNR, as well as potential to model various anatomies and pathologies. This paper describes a novel method in generating diffusion MR images with various imaging parameters from realistically appearing, individually varying brain anatomy based on predefined fiber tracts within a high-resolution human brain atlas. Specifically, joint, high resolution DWI and structural MRI brain atlases were constructed with images acquired from 6 healthy subjects (age 22-26) for the DWI data and 56 healthy subject (age 18-59) for the structural MRI data. Full brain fiber tracking was performed with filtered, two-tensor tractography in atlas space. A deformation field based principal component model from the structural MRI as well as unbiased atlas building was then employed to generate synthetic structural brain MR images that are individually varying. Atlas fiber tracts were accordingly warped into each synthetic brain anatomy. Diffusion MR images were finally computed from these warped tracts via a composite hindered and restricted model of diffusion with various imaging parameters for gradient directions, image resolution and SNR. Furthermore, an open-source program was developed to evaluate the fiber tracking results both qualitatively and quantitatively based on various similarity measures.

UNC-Utah NA-MIC DTI framework: Atlas Based Fiber Tract Analysis with Application to a Study of Nicotine Smoking Addiction.

PURPOSE: The UNC-Utah NA-MIC DTI framework represents a coherent, open source, atlas fiber tract based DTI analysis framework that addresses the lack of a standardized fiber tract based DTI analysis workflow in the field. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. DATA: We illustrate the use of our framework on a 54 directional DWI neuroimaging study contrasting 15 Smokers and 14 Controls. METHODS: At the heart of the framework is a set of tools anchored around the multi-purpose image analysis platform 3D-Slicer. Several workflow steps are handled via external modules called from Slicer in order to provide an integrated approach. Our workflow starts with conversion from DICOM, followed by thorough automatic and interactive quality control (QC), which is a must for a good DTI study. Our framework is centered around a DTI atlas that is either provided as a template or computed directly as an unbiased average atlas from the study data via deformable atlas building. Fiber tracts are defined via interactive tractography and clustering on that atlas. DTI fiber profiles are extracted automatically using the atlas mapping information. These tract parameter profiles are then analyzed using our statistics toolbox (FADTTS). The statistical results are then mapped back on to the fiber bundles and visualized with 3D Slicer. RESULTS: This framework provides a coherent set of tools for DTI quality control and analysis. CONCLUSIONS: This framework will provide the field with a uniform process for DTI quality control and analysis.