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Register a new userQuantifying Residual Motion Artifacts in Fetal fMRI Data
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Fetal functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful tool for investigating brain development in utero, holding promise for generating developmental disease biomarkers and supporting prenatal diagnosis. However, to date its clinical applications have been limited by unpredictable fetal and maternal motion during image acquisition. Even after spatial realigment, these cause spurious signal fluctuations confounding measures of functional connectivity and biasing statistical inference of relationships between connectivity and individual differences. As there is no ground truth for the brains functional structure, especially before birth, quantifying the quality of motion correction is challenging. In this paper, we propose evaluating the efficacy of different regression based methods for removing motion artifacts after realignment by assessing the residual relationship of functional connectivity with estimated motion, and with the distance between areas. Results demonstrate the sensitivity of our evaluations criteria to reveal the relative strengths and weaknesses among different artifact removal methods, and underscore the need for greater care when dealing with fetal motion.
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Head motion is inevitable in the acquisition of diffusion-weighted images, especially for certain motion-prone subjects and for data gathering of advanced diffusion models with prolonged scan times. Deficient accuracy of motion correction cause deterioration in the quality of diffusion model reconstruction, thus affecting the derived measures. This results in either loss of data, or introducing bias in outcomes from data of different motion levels, or both. Hence minimizing motion effects and reutilizing motion-contaminated data becomes vital to quantitative studies. We have previously developed a 3-dimensional hierarchical convolution neural network (3D H-CNN) for robust diffusion kurtosis mapping from under-sampled data. In this study, we propose to extend this method to motion-contaminated data for robust recovery of diffusion model-derived measures with a process of motion assessment and corrupted volume rejection. We validate the proposed pipeline in two in-vivo datasets. Results from the first dataset of individual subjects show that all the diffusion tensor and kurtosis tensor-derived measures from the new pipeline are minimally sensitive to motion effects, and are comparable to the motion-free reference with as few as eight volumes retained from the motion-contaminated data. Results from the second dataset of a group of children with attention deficit hyperactivity disorder demonstrate the ability of our approach in ameliorating spurious group differences due to head motion. This method shows great potential for exploiting some valuable but motion-corrupted DWI data which are likely to be discarded otherwise, and applying to data with different motion level thus improving their utilization and statistic power.
The estimation of causal network architectures in the brain is fundamental for understanding cognitive information processes. However, access to the dynamic processes underlying cognition is limited to indirect measurements of the hidden neuronal activity, for instance through fMRI data. Thus, estimating the network structure of the underlying process is challenging. In this article, we embed an adaptive importance sampler called Adaptive Path Integral Smoother (APIS) into the Expectation-Maximization algorithm to obtain point estimates of causal connectivity. We demonstrate on synthetic data that this procedure finds not only the correct network structure but also the direction of effective connections from random initializations of the connectivity matrix. In addition--motivated by contradictory claims in the literature--we examine the effect of the neuronal timescale on the sensitivity of the BOLD signal to changes in the connectivity and on the maximum likelihood solutions of the connectivity. We conclude with two warnings: First, the connectivity estimates under the assumption of slow dynamics can be extremely biased if the data was generated by fast neuronal processes. Second, the faster the time scale, the less sensitive the BOLD signal is to changes in the incoming connections to a node. Hence, connectivity estimation using realistic neural dynamics timescale requires extremely high-quality data and seems infeasible in many practical data sets.
Monte Carlo (MC) methods provide the most accurate to-date dose calculations in heterogeneous media and complex geometries, and this spawns increasing interest in incorporating MC calculations into treatment planning quality assurance process. This involves MC dose calculations for clinically produced treatment plans. To perform these calculations, a number of treatment plan parameters specifying radiation beam and patient geometries need to be transferred to MC codes, such as BEAMnrc and DOSXYZnrc. Extracting these parameters from DICOM files is not a trivial task, one that has previously been performed mostly using Matlab-based software. This paper describes the DICOM tags that contain information required for MC modeling of conformal and IMRT plans, and reports the development of an in-house DICOM interface, through a library (named Vega) of platform-independent, object-oriented C++ codes. The Vega library is small and succinct, offering just the fundamental functions for reading/modifying/writing DICOM files in a C++ program. The library, however, is flexible enough to extract all MC required data from DICOM files, and write MC produced dose distributions into DICOM files that can then be processed in a treatment planning system environment. The library can be made available upon request to the authors.
In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28-31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20-34 weeks GA range.
Astronomical images from optical photometric surveys are typically contaminated with transient artifacts such as cosmic rays, satellite trails and scattered light. We have developed and tested an algorithm that removes these artifacts using a deep, artifact free, static sky coadd image built up through the median combination of point spread function (PSF) homogenized, overlapping single epoch images. Transient artifacts are detected and masked in each single epoch image through comparison with an artifact free, PSF-matched simulated image that is constructed using the PSF-corrected, model fitting catalog from the artifact free coadd image together with the position variable PSF model of the single epoch image. This approach works well not only for cleaning single epoch images with worse seeing than the PSF homogenized coadd, but also the traditionally much more challenging problem of cleaning single epoch images with better seeing. In addition to masking transient artifacts, we have developed an interpolation approach that uses the local PSF and performs well in removing artifacts whose widths are smaller than the PSF full width at half maximum, including cosmic rays, the peaks of saturated stars and bleed trails. We have tested this algorithm on Dark Energy Survey Science Verification data and present performance metrics. More generally, our algorithm can be applied to any survey which images the same part of the sky multiple times.
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