Phase Image Texture Analysis for Motion Detection in Diffusion MRI (PITA-MDD)

Purpose Pronounced spin phase artifacts appear in diffusion-weighted imaging (DWI) with only minor subject motion. While DWI data corruption is often identified as signal drop out in diffusion-weighted (DW) magnitude images, DW phase images may have higher sensitivity for detecting subtle subject motion. Methods This article describes a novel method to return a metric of subject motion, computed using an image texture analysis of the DW phase image. This Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) method is computationally fast and reliably detects subject motion from diffusion-weighted images. A threshold of the motion metric was identified to remove motion-corrupted slices, and the effect of removing corrupted slices was assessed on the reconstructed FA maps and fiber tracts. Results Using a motion-metric threshold to remove the motion-corrupted slices results in superior fiber tracts and fractional anisotropy maps. When further compared to a state-of-the-art magnitude-based motion correction method, PITA-MDD was able to detect comparable corrupted slices in a more computationally efficient manner. Conclusion In this study, we evaluated the use of DW phase images to detect motion corruption. The proposed method can be a robust and fast alternative for automatic motion detection in the brain with multiple applications to inform prospective motion correction or as real-time feedback for data quality control during scanning, as well as after data is already acquired

Application of phase-based motion outlier detection to infant dMRI

Detecting and eliminating motion-corrupted slices is crucial in diffusion MRI (dMRI), and particularly essential in imaging neonates. Conventional magnitude-based outlier rejection methods are intensity-based and can usually detect and correct intra-volume movement but can miss outliers in cases of small continuous motions. Phase-based methods can be used to detect motion independently, regardless of the slice-to-volume location. The phase-based method is reasonably accurate and computationally fast, and may be better suited for real-time detection of motion in dMRI. Combining magnitude and phase methods could produce the best results. Here, we evaluate the phase-based method versus the magnitude-based method in neonatal data

Resting state functional MRI in infants with prenatal opioid exposure-a pilot study

PURPOSE: Exposure to prenatal opioids may adversely impact the developing brain networks. The aim of this pilot study was to evaluate alterations in amygdalar functional connectivity in human infants with prenatal opioid exposure. METHODS: In this prospective IRB approved study, we performed resting state functional MRI (rs-fMRI) in 10 infants with prenatal opioid exposure and 12 infants without prenatal drug exposure at < 48 weeks corrected gestational age. Following standard preprocessing, we performed seed-based functional connectivity analysis with the right and left amygdala as the regions of interest after correcting for maternal depression and infant sex. We compared functional connectivity of the amygdala network between infants with and without prenatal opioid exposure. RESULTS: There were significant differences in connectivity of the amygdala seed regions to the several cortical regions including the medial prefrontal cortex in infants who had prenatal opioid exposure when compared with opioid naïve infants. CONCLUSION: This finding of increased amygdala functional connectivity in infants with in utero opioid exposure suggests a potential role of maternal opioid exposure on infants' altered amygdala function. This association with prenatal exposure needs to be replicated in future larger studies

Longitudinal white-matter abnormalities in sports-related concussion: A diffusion MRI study

Objective To study longitudinal recovery trajectories of white matter after sports-related concussion (SRC) by performing diffusion tensor imaging (DTI) on collegiate athletes who sustained SRC. Methods Collegiate athletes (n = 219, 82 concussed athletes, 68 contact-sport controls, and 69 non–contact-sport controls) were included from the Concussion Assessment, Research and Education Consortium. The participants completed clinical assessments and DTI at 4 time points: 24 to 48 hours after injury, asymptomatic state, 7 days after return-to-play, and 6 months after injury. Tract-based spatial statistics was used to investigate group differences in DTI metrics and to identify white-matter areas with persistent abnormalities. Generalized linear mixed models were used to study longitudinal changes and associations between outcome measures and DTI metrics. Cox proportional hazards model was used to study effects of white-matter abnormalities on recovery time. Results In the white matter of concussed athletes, DTI-derived mean diffusivity was significantly higher than in the controls at 24 to 48 hours after injury and beyond the point when the concussed athletes became asymptomatic. While the extent of affected white matter decreased over time, part of the corpus callosum had persistent group differences across all the time points. Furthermore, greater elevation of mean diffusivity at acute concussion was associated with worse clinical outcome measures (i.e., Brief Symptom Inventory scores and symptom severity scores) and prolonged recovery time. No significant differences in DTI metrics were observed between the contact-sport and non–contact-sport controls. Conclusions Changes in white matter were evident after SRC at 6 months after injury but were not observed in contact-sport exposure. Furthermore, the persistent white-matter abnormalities were associated with clinical outcomes and delayed recovery tim

A Non-invasive Validation Method for Conductivity Imaging

Conductivity imaging is of great potential importance in many biomedical applications, such as in the early diagnosis of breast tumors and the diagnosis and therapy planning for cardiac arrhythmia. With significant progress in conductivity imaging, there is a growing need for a quantitative non-invasive validation method. A new non-invasive validation procedure has been developed to assess the accuracy of any low-frequency conductivity imaging method. This procedure is based on Current Density Imaging, a well-established procedure to measure currents inside an object. A testing current is applied to the object under study. The proposed validation protocol compares measurements of this testing current against that computed using a forward solver. The forward solver computes the current using the conductivity determined by the method under test and the boundary conditions for the measured current. We implemented the proposed procedure on three conductivity imaging methods. The first, Current Density Impedance Imaging (CDII), uses measurements of two currents to determine the isotropic conductivity of the structure under test. The second, Diffusion Tensor Current Density Impedance Imaging (DT-CD-II), was recently developed in our laboratory for imaging anisotropic conductivities and relies on the measurement of the diffusion tensor and of two currents. The third method requires only the magnitude of one current and of the corresponding voltage on the boundary of the object to determine an isotropic conductivity, and will be referred to as MCDII. The validation method was first tested on earlier experimental data for CDII. It was then used to assess the accuracy of DT-CD-II on experimental data for an anisotropic conductivity. We show that the results of the DT-CD-II reconstruction are better than those obtained using CDII on the same current measurements and assuming the conductivity to be isotropic. The validation procedure was also shown to be applicable to a third, and very different conductivity imaging method (MCDII), that requires only the magnitude of one current and the corresponding boundary voltage. The thesis includes the first three-dimensional numerical implementation of this method. The validation yields a quantitative comparison of its accuracy with that of CDII on the same simulated data.Ph.D.2017-07-06 00:00:0

Super-Resolution Diffusion Tensor Imaging using SRCNN: A Feasibility Study *

High-resolution diffusion imaging with submillimeter isotropic voxels requires long scan times that are usually clinically impractical. Even with those long scans, the image quality can still suffer from low signal-to-noise ratio (SNR) and severe geometric distortion due to long echo spacing in echo-planar imaging sequences. In this study, we proposed and validated the efficacy of using a stateof-the-art deep-learning method, super-resolution convolutional neural network (SRCNN), to achieve submillimeter super-resolution diffusion-weighted (DW) images. The 2D-based deep-learning method was validated by comparing with the ground truth using numerical simulations and by studying region-of-interest (ROI) using real human data of three healthy volunteers. Furthermore, we interrogated the proposed method under different real-life SNR conditions. The results demonstrated that the proposed deep- learning method was able to reproduce sufficient details in the anatomy that can only be detected using high-resolution diffusion imaging. The percentage errors in diffusion tensor imaging (DTI) derived metrics were less than 8% when the baseline SNR larger than 20. The ROI results demonstrated that the proposed method produced comparable values of diffusion metrics to the matched high-resolution diffusion metrics of real human data. Particularly, the patterns of distributions of the subjects were similar between the proposed method and real data across wholebrain gray-matter and white-matter ROIs. A deep-learned submillimeter resolution of 0.625 mm diffusion directional image showed high image quality, particularly in the cortical gray matter. We demonstrated the feasibility of using a deep-learning algorithm based on SRCNN in DTI. This approach can be a robust alternative when acquiring the true sub-millimeter diffusion MRI is not available