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2019

Khan, Shadab, Lana Vasung, Bahram Marami, Caitlin Rollins, Onur Afacan, Cynthia Ortinau, Edward Yang, Simon Warfield, and Ali Gholipour. 2019. “Fetal Brain Growth Portrayed by a Spatiotemporal Diffusion Tensor MRI Atlas Computed from in Utero Images”. Neuroimage 185: 593-608. https://doi.org/10.1016/j.neuroimage.2018.08.030.
Altered structural fetal brain development has been linked to neuro-developmental disorders. These structural alterations can be potentially detected in utero using diffusion tensor imaging (DTI). However, acquisition and reconstruction of in utero fetal brain DTI remains challenging. Until now, motion-robust DTI methods have been employed for reconstruction of in utero fetal DTIs. However, due to the unconstrained fetal motion and permissible in utero acquisition times, these methods yielded limited success and have typically resulted in noisy DTIs. Consequently, atlases and methods that could enable groupwise studies, multi-modality imaging, and computer-aided diagnosis from in utero DTIs have not yet been developed. This paper presents the first DTI atlas of the fetal brain computed from in utero diffusion-weighted images. For this purpose an algorithm for computing an unbiased spatiotemporal DTI atlas, which integrates kernel-regression in age with a diffeomorphic tensor-to-tensor registration of motion-corrected and reconstructed individual fetal brain DTIs, was developed. Our new algorithm was applied to a set of 67 fetal DTI scans acquired from healthy fetuses each scanned at a gestational age between 21 and 39 weeks. The neurodevelopmental trends in the fetal brain, characterized by the atlas, were qualitatively and quantitatively compared with the observations reported in prior ex vivo and in utero studies, and with results from imaging gestational-age equivalent preterm infants. Our major findings revealed early presence of limbic fiber bundles, followed by the appearance and maturation of projection pathways (characterized by an age related increase in FA) during late 2nd and early 3rd trimesters. During the 3rd trimester association fiber bundles become evident. In parallel with the appearance and maturation of fiber bundles, from 21 to 39 gestational weeks gradual disappearance of the radial coherence of the telencephalic wall was qualitatively identified. These results and analyses show that our DTI atlas of the fetal brain is useful for reliable detection of major neuronal fiber bundle pathways and for characterization of the fetal brain reorganization that occurs in utero. The atlas can also serve as a useful resource for detection of normal and abnormal fetal brain development in utero.
Wallace, Tess, Onur Afacan, Maryna Waszak, Tobias Kober, and Simon Warfield. 2019. “Head Motion Measurement and Correction Using FID Navigators”. Magn Reson Med 81 (1): 258-74. https://doi.org/10.1002/mrm.27381.
PURPOSE: To develop a novel framework for rapid, intrinsic head motion measurement in MRI using FID navigators (FIDnavs) from a multichannel head coil array. METHODS: FIDnavs encode substantial rigid-body motion information; however, current implementations require patient-specific training with external tracking data to extract quantitative positional changes. In this work, a forward model of FIDnav signals was calibrated using simulated movement of a reference image within a model of the spatial coil sensitivities. A FIDnav module was inserted into a nonselective 3D FLASH sequence, and rigid-body motion parameters were retrospectively estimated every readout time using nonlinear optimization to solve the inverse problem posed by the measured FIDnavs. This approach was tested in simulated data and in 7 volunteers, scanned at 3T with a 32-channel head coil array, performing a series of directed motion paradigms. RESULTS: FIDnav motion estimates achieved mean absolute errors of 0.34 ± 0.49 mm and 0.52 ± 0.61° across all subjects and scans, relative to ground-truth motion measurements provided by an electromagnetic tracking system. Retrospective correction with FIDnav motion estimates resulted in substantial improvements in quantitative image quality metrics across all scans with intentional head motion. CONCLUSIONS: Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach, which represents a practical method for retrospective motion compensation in less cooperative patient populations.
Marami, Bahram, Benoit Scherrer, Shadab Khan, Onur Afacan, Sanjay Prabhu, Mustafa Sahin, Simon Warfield, and Ali Gholipour. 2019. “Motion-Robust Diffusion Compartment Imaging Using Simultaneous Multi-Slice Acquisition”. Magn Reson Med 81 (5): 3314-29. https://doi.org/10.1002/mrm.27613.
PURPOSE: To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI. METHODS: Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction. RESULTS: We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion. CONCLUSION: The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.
Ortinau, Cynthia, Caitlin Rollins, Ali Gholipour, Hyuk Jin Yun, Mackenzie Marshall, Borjan Gagoski, Onur Afacan, et al. 2019. “Early-Emerging Sulcal Patterns Are Atypical in Fetuses With Congenital Heart Disease”. Cereb Cortex 29 (8): 3605-16. https://doi.org/10.1093/cercor/bhy235.
Fetuses with congenital heart disease (CHD) have third trimester alterations in cortical development on brain magnetic resonance imaging (MRI). However, the intersulcal relationships contributing to global sulcal pattern remain unknown. This study applied a novel method for examining the geometric and topological relationships between sulci to fetal brain MRIs from 21-30 gestational weeks in CHD fetuses (n = 19) and typically developing (TD) fetuses (n = 17). Sulcal pattern similarity index (SI) to template fetal brain MRIs was determined for the position, area, and depth for corresponding sulcal basins and intersulcal relationships for each subject. CHD fetuses demonstrated altered global sulcal patterns in the left hemisphere compared with TD fetuses (TD [SI, mean ± SD]: 0.822 ± 0.023, CHD: 0.795 ± 0.030, P = 0.002). These differences were present in the earliest emerging sulci and were driven by differences in the position of corresponding sulcal basins (TD: 0.897 ± 0.024, CHD: 0.878 ± 0.019, P = 0.006) and intersulcal relationships (TD: 0.876 ± 0.031, CHD: 0.857 ± 0.018, P = 0.033). No differences in cortical gyrification index, mean curvature, or surface area were present. These data suggest our methods may be more sensitive than traditional measures for evaluating cortical developmental alterations early in gestation.
Marami, Bahram, Benoit Scherrer, Shadab Khan, Onur Afacan, Sanjay P Prabhu, Mustafa Sahin, Simon K Warfield, and Ali Gholipour. (2019) 2019. “Motion-Robust Diffusion Compartment Imaging Using Simultaneous Multi-Slice Acquisition.”. Magnetic Resonance in Medicine 81 (5): 3314-29. https://doi.org/10.1002/mrm.27613.
Publisher's Version
Publisher's Version

PURPOSE: To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI.

METHODS: Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction.

RESULTS: We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion.

CONCLUSION: The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.

2018

Martinot, Amanda, Peter Abbink, Onur Afacan, Anna Prohl, Roderick Bronson, Jonathan Hecht, Erica Borducchi, et al. 2018. “Fetal Neuropathology in Zika Virus-Infected Pregnant Female Rhesus Monkeys”. Cell 173 (5): 1111-1122.e10. https://doi.org/10.1016/j.cell.2018.03.019.
The development of interventions to prevent congenital Zika syndrome (CZS) has been limited by the lack of an established nonhuman primate model. Here we show that infection of female rhesus monkeys early in pregnancy with Zika virus (ZIKV) recapitulates many features of CZS in humans. We infected 9 pregnant monkeys with ZIKV, 6 early in pregnancy (weeks 6-7 of gestation) and 3 later in pregnancy (weeks 12-14 of gestation), and compared findings with uninfected controls. 100% (6 of 6) of monkeys infected early in pregnancy exhibited prolonged maternal viremia and fetal neuropathology, including fetal loss, smaller brain size, and histopathologic brain lesions, including microcalcifications, hemorrhage, necrosis, vasculitis, gliosis, and apoptosis of neuroprogenitor cells. High-resolution MRI demonstrated concordant lesions indicative of deep gray matter injury. We also observed spinal, ocular, and neuromuscular pathology. Our data show that vascular compromise and neuroprogenitor cell dysfunction are hallmarks of CZS pathogenesis, suggesting novel strategies to prevent and to treat this disease.
Khan, Shadab, Caitlin K Rollins, Cynthia M Ortinau, Onur Afacan, Simon K Warfield, and Ali Gholipour. (2018) 2018. “Tract-Specific Group Analysis in Fetal Cohorts Using in Utero Diffusion Tensor Imaging.”. Medical Image Computing and Computer-Assisted Intervention : MICCAI . International Conference on Medical Image Computing and Computer-Assisted Intervention 11072: 28-35. https://doi.org/10.1007/978-3-030-00931-1_4.
Publisher's Version
Publisher's Version

Diffusion tensor imaging (DTI) based group analysis has helped uncover the impact of white matter injuries in a wide range of studies involving subjects from preterm neonates to adults. The application of these methods to fetal cohorts, however, has been hampered by the challenging nature of in utero fetal DTI caused by unconstrained fetal motion, limited scan times, and limited signal-to-noise ratio. We present a framework that addresses these issues to systematically evaluate group differences in fetal cohorts. A motion-robust DTI computation approach with a new unbiased DTI template construction method is unified with kernel-regression in age and tensor-specific registration to normalize DTI volumes in an unbiased space. A robust statistical approach is used to map region-specific group differences to the medial representation of the tracts of interest. The proposed approach was applied and showed, for the first time, differences in local white matter fractional anisotropy based on in utero DTI of fetuses with congenital heart disease and age-matched healthy controls. This paper suggests the need for fetal-specific pipelines to be used for DTI-based group analysis involving fetal cohorts.

2017

Kurugol, Sila, Bahram Marami, Onur Afacan, Simon Warfield, and Ali Gholipour. (2017) 2017. “Motion-Robust Spatially Constrained Parameter Estimation in Renal Diffusion-Weighted MRI by 3D Motion Tracking and Correction of Sequential Slices”. Mol Imaging Reconstr Anal Mov Body Organs Stroke Imaging Treat (2017) 10555: 75-85. https://doi.org/10.1007/978-3-319-67564-0_8.
In this work, we introduce a novel motion-robust spatially constrained parameter estimation (MOSCOPE) technique for kidney diffusion-weighted MRI. The proposed motion compensation technique does not require a navigator, trigger, or breath-hold but only uses the intrinsic features of the acquired data to track and compensate for motion to reconstruct precise models of the renal diffusion signal. We have developed a technique for physiological motion tracking based on robust state estimation and sequential registration of diffusion sensitized slices acquired within 200ms. This allows a sampling rate of 5Hz for state estimation in motion tracking that is sufficiently faster than both respiratory and cardiac motion rates in children and adults, which range between 0.8 to 0.2Hz, and 2.5 to 1Hz, respectively. We then apply the estimated motion parameters to data from each slice and use motion-compensated data for 1) robust intra-voxel incoherent motion (IVIM) model estimation in the kidney using a spatially constrained model fitting approach, and 2) robust weighted least squares estimation of the diffusion tensor model. Experimental results, including precision of IVIM model parameters using bootstrap-sampling and in-vivo whole kidney tractography, showed significant improvement in precision and accuracy of these models using the proposed method compared to models based on the original data and volumetric registration.
Kurugol, Sila, Moti Freiman, Onur Afacan, Liran Domachevsky, Jeannette Perez-Rossello, Michael Callahan, and Simon Warfield. (2017) 2017. “Motion-Robust Parameter Estimation in Abdominal Diffusion-Weighted MRI by Simultaneous Image Registration and Model Estimation”. Med Image Anal 39: 124-32. https://doi.org/10.1016/j.media.2017.04.006.
Quantitative body DW-MRI can detect abdominal abnormalities as well as monitor response-to-therapy for applications including cancer and inflammatory bowel disease with increased accuracy. Parameter estimates are obtained by fitting a forward model of DW-MRI signal decay to the observed data acquired with several b-values. The DW-MRI signal decay models typically used do not account for respiratory, cardiac and peristaltic motion, however, which may deteriorate the accuracy and robustness of parameter estimates. In this work, we introduce a new model of DW-MRI signal decay that explicitly accounts for motion. Specifically, we estimated motion-compensated model parameters by simultaneously solving image registration and model estimation (SIR-ME) problems utilizing the interdependence of acquired volumes along the diffusion-weighting dimension. To accomplish this, we applied the SIR-ME model to the in-vivo DW-MRI data sets of 26 Crohn's disease (CD) patients and achieved improved precision of the estimated parameters by reducing the coefficient of variation by 8%, 24% and 8% for slow diffusion (D), fast diffusion (D*) and fast diffusion fraction (f) parameters respectively, compared to parameters estimated with independent registration in normal-appearing bowel regions. Moreover, the parameters estimated with the SIR-ME model reduced the error rate in classifying normal and abnormal bowel loops to 12% for D and 10% for f parameter with a reduction in error rate by 13% and 11% for D and f parameters, respectively, compared to the error rate in classifying parameter estimates obtained with independent registration. The experiments in DW-MRI of liver in 20 subjects also showed that the SIR-ME model improved the precision of parameter estimation by reducing the coefficient of variation to 7% for D, 23% for D*, and 8% for the f parameter. Using the SIR-ME model, the coefficient of variation was reduced by 4%, 14% and 6% for D, D* and f parameters, respectively, compared to parameters estimated with independent registration. These results demonstrate that the proposed SIR-ME model improves the accuracy and robustness of quantitative body DW-MRI in characterizing tissue microstructure.
Marami, Bahram, Seyed Sadegh Mohseni Salehi, Onur Afacan, Benoit Scherrer, Caitlin Rollins, Edward Yang, Judy Estroff, Simon Warfield, and Ali Gholipour. 2017. “Temporal Slice Registration and Robust Diffusion-Tensor Reconstruction for Improved Fetal Brain Structural Connectivity Analysis”. Neuroimage 156: 475-88. https://doi.org/10.1016/j.neuroimage.2017.04.033.
Diffusion weighted magnetic resonance imaging, or DWI, is one of the most promising tools for the analysis of neural microstructure and the structural connectome of the human brain. The application of DWI to map early development of the human connectome in-utero, however, is challenged by intermittent fetal and maternal motion that disrupts the spatial correspondence of data acquired in the relatively long DWI acquisitions. Fetuses move continuously during DWI scans. Reliable and accurate analysis of the fetal brain structural connectome requires careful compensation of motion effects and robust reconstruction to avoid introducing bias based on the degree of fetal motion. In this paper we introduce a novel robust algorithm to reconstruct in-vivo diffusion-tensor MRI (DTI) of the moving fetal brain and show its effect on structural connectivity analysis. The proposed algorithm involves multiple steps of image registration incorporating a dynamic registration-based motion tracking algorithm to restore the spatial correspondence of DWI data at the slice level and reconstruct DTI of the fetal brain in the standard (atlas) coordinate space. A weighted linear least squares approach is adapted to remove the effect of intra-slice motion and reconstruct DTI from motion-corrected data. The proposed algorithm was tested on data obtained from 21 healthy fetuses scanned in-utero at 22-38 weeks gestation. Significantly higher fractional anisotropy values in fiber-rich regions, and the analysis of whole-brain tractography and group structural connectivity, showed the efficacy of the proposed method compared to the analyses based on original data and previously proposed methods. The results of this study show that slice-level motion correction and robust reconstruction is necessary for reliable in-vivo structural connectivity analysis of the fetal brain. Connectivity analysis based on graph theoretic measures show high degree of modularity and clustering, and short average characteristic path lengths indicative of small-worldness property of the fetal brain network. These findings comply with previous findings in newborns and a recent study on fetuses. The proposed algorithm can provide valuable information from DWI of the fetal brain not available in the assessment of the original 2D slices and may be used to more reliably study the developing fetal brain connectome.