Publications

2019
Filip Szczepankiewicz, Carl-Fredrik Westin, and Markus Nilsson. 10/2019. “Maxwell-compensated Design of Asymmetric Gradient Waveforms for Tensor-valued Diffusion Encoding.” Magn Reson Med, 82, 4, Pp. 1424-37.Abstract
PURPOSE: Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients. METHODS: The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. RESULTS: Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m) ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. CONCLUSION: Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.
Fabio Nery, Filip Szczepankiewicz, Leevi Kerkelä, Matt G Hall, Enrico Kaden, Isky Gordon, David L Thomas, and Chris A Clark. 12/2019. “In vivo Demonstration of Microscopic Anisotropy in the Human Kidney using Multidimensional Diffusion MRI.” Magn Reson Med, 82, 6, Pp. 2160-8.Abstract
PURPOSE: To demonstrate the feasibility of multidimensional diffusion MRI to probe and quantify microscopic fractional anisotropy (µFA) in human kidneys in vivo. METHODS: Linear tensor encoded (LTE) and spherical tensor encoded (STE) renal diffusion MRI scans were performed in 10 healthy volunteers. Respiratory triggering and image registration were used to minimize motion artefacts during the acquisition. Kidney cortex-medulla were semi-automatically segmented based on fractional anisotropy (FA) values. A model-free analysis of LTE and STE signal dependence on b-value in the renal cortex and medulla was performed. Subsequently, µFA was estimated using a single-shell approach. Finally, a comparison of conventional FA and µFA is shown. RESULTS: The hallmark effect of µFA (divergence of LTE and STE signal with increasing b-value) was observed in all subjects. A statistically significant difference between LTE and STE signal was found in the cortex and medulla, starting from b = 750 s/mm and b = 500 s/mm , respectively. This difference was maximal at the highest b-value sampled (b = 1000 s/mm ) which suggests that relatively high b-values are required for µFA mapping in the kidney compared to conventional FA. Cortical and medullary µFA were, respectively, 0.53 ± 0.09 and 0.65 ± 0.05, both respectively higher than conventional FA (0.19 ± 0.02 and 0.40 ± 0.02). CONCLUSION: The feasibility of combining LTE and STE diffusion MRI to probe and quantify µFA in human kidneys is demonstrated for the first time. By doing so, we show that novel microstructure information-not accessible by conventional diffusion encoding-can be probed by multidimensional diffusion MRI. We also identify relevant technical limitations that warrant further development of the technique for body MRI.
Jie Luo, Alireza Sedghi, Karteek Popuri, Dana Cobzas, Miaomiao Zhang, Frank Preiswerk, Matthew Toews, Alexandra Golby, Masashi Sugiyama, William III M Wells, and Sarah Frisken. 10/2019. “On the Applicability of Registration Uncertainty.” In MICCAI 2019, LNCS 11765: Pp. 410-9. Shenzhen, China: Springer.Abstract
Estimating the uncertainty in (probabilistic) image registration enables, e.g., surgeons to assess the operative risk based on the trustworthiness of the registered image data. If surgeons receive inaccurately calculated registration uncertainty and misplace unwarranted confidence in the alignment solutions, severe consequences may result. For probabilistic image registration (PIR), the predominant way to quantify the registration uncertainty is using summary statistics of the distribution of transformation parameters. The majority of existing research focuses on trying out different summary statistics as well as means to exploit them. Distinctively, in this paper, we study two rarely examined topics: (1) whether those summary statistics of the transformation distribution most informatively represent the registration uncertainty; (2) Does utilizing the registration uncertainty always be beneficial. We show that there are two types of uncertainties: the transformation uncertainty, Ut, and label uncertainty Ul. The conventional way of using Ut to quantify Ul is inappropriate and can be misleading. By a real data experiment, we also share a potentially critical finding that making use of the registration uncertainty may not always be an improvement.
Luo MICCAI 2019
Hamza Farooq, Yongxin Chen, Tryphon T Georgiou, Allen Tannenbaum, and Christophe Lenglet. 10/2019. “Network Curvature as a Hallmark of Brain Structural Connectivity.” Nat Commun, 10, 1, Pp. 4937.Abstract
Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies.
Esra Abaci Turk, Jeffrey N Stout, Christopher Ha, Jie Luo, Borjan Gagoski, Filiz Yetisir, Polina Golland, Lawrence L Wald, Elfar Adalsteinsson, Julian N Robinson, Drucilla J Roberts, William H Barth, and Ellen P Grant. 10/2019. “Placental MRI: Developing Accurate Quantitative Measures of Oxygenation.” Top Magn Reson Imaging, 28, 5, Pp. 285-297.Abstract
The Human Placenta Project has focused attention on the need for noninvasive magnetic resonance imaging (MRI)-based techniques to diagnose and monitor placental function throughout pregnancy. The hope is that the management of placenta-related pathologies would be improved if physicians had more direct, real-time measures of placental health to guide clinical decision making. As oxygen alters signal intensity on MRI and oxygen transport is a key function of the placenta, many of the MRI methods under development are focused on quantifying oxygen transport or oxygen content of the placenta. For example, measurements from blood oxygen level-dependent imaging of the placenta during maternal hyperoxia correspond to outcomes in twin pregnancies, suggesting that some aspects of placental oxygen transport can be monitored by MRI. Additional methods are being developed to accurately quantify baseline placental oxygenation by MRI relaxometry. However, direct validation of placental MRI methods is challenging and therefore animal studies and ex vivo studies of human placentas are needed. Here we provide an overview of the current state of the art of oxygen transport and quantification with MRI. We suggest that as these techniques are being developed, increased focus be placed on ensuring they are robust and reliable across individuals and standardized to enable predictive diagnostic models to be generated from the data. The field is still several years away from establishing the clinical benefit of monitoring placental function in real time with MRI, but the promise of individual personalized diagnosis and monitoring of placental disease in real time continues to motivate this effort.
Luca Canalini, Jan Klein, Dorothea Miller, and Ron Kikinis. 10/2019. “Segmentation-based Registration of Ultrasound Volumes for Glioma Resection in Image-guided Neurosurgery.” Int J Comput Assist Radiol Surg, 14, 10, Pp. 1697-1713.Abstract
PURPOSE: In image-guided surgery for glioma removal, neurosurgeons usually plan the resection on images acquired before surgery and use them for guidance during the subsequent intervention. However, after the surgical procedure has begun, the preplanning images become unreliable due to the brain shift phenomenon, caused by modifications of anatomical structures and imprecisions in the neuronavigation system. To obtain an updated view of the resection cavity, a solution is to collect intraoperative data, which can be additionally acquired at different stages of the procedure in order to provide a better understanding of the resection. A spatial mapping between structures identified in subsequent acquisitions would be beneficial. We propose here a fully automated segmentation-based registration method to register ultrasound (US) volumes acquired at multiple stages of neurosurgery. METHODS: We chose to segment sulci and falx cerebri in US volumes, which remain visible during resection. To automatically segment these elements, first we trained a convolutional neural network on manually annotated structures in volumes acquired before the opening of the dura mater and then we applied it to segment corresponding structures in different surgical phases. Finally, the obtained masks are used to register US volumes acquired at multiple resection stages. RESULTS: Our method reduces the mean target registration error (mTRE) between volumes acquired before the opening of the dura mater and during resection from 3.49 mm (± 1.55 mm) to 1.36 mm (± 0.61 mm). Moreover, the mTRE between volumes acquired before opening the dura mater and at the end of the resection is reduced from 3.54 mm (± 1.75 mm) to 2.05 mm (± 1.12 mm). CONCLUSION: The segmented structures demonstrated to be good candidates to register US volumes acquired at different neurosurgical phases. Therefore, our solution can compensate brain shift in neurosurgical procedures involving intraoperative US data.
Karol Miller, Grand R Joldes, George Bourantas, Simon K Warfield, Damon E Hyde, Ron Kikinis, and Adam Wittek. 8/2019. “Biomechanical Modeling and Computer Simulation of the Brain during Neurosurgery.” Int J Numer Method Biomed Eng, Pp. e3250.Abstract
Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine Laboratory and its collaborators to this field, discussing the modeling approaches adopted and the methods developed for obtaining the numerical solutions. We adopt a physics-based modeling approach and describe the brain deformation in mechanical terms (such as displacements, strains, and stresses), which can be computed using a biomechanical model, by solving a continuum mechanics problem. We present our modeling approaches related to geometry creation, boundary conditions, loading, and material properties. From the point of view of solution methods, we advocate the use of fully nonlinear modeling approaches, capable of capturing very large deformations and nonlinear material behavior. We discuss finite element and meshless domain discretization, the use of the total Lagrangian formulation of continuum mechanics, and explicit time integration for solving both time-accurate and steady-state problems. We present the methods developed for handling contacts and for warping 3D medical images using the results of our simulations. We present two examples to showcase these methods: brain shift estimation for image registration and brain deformation computation for neuronavigation in epilepsy treatment.
Inês Machado, Matthew Toews, Elizabeth George, Prashin Unadkat, Walid Essayed, Jie Luo, Pedro Teodoro, Herculano Carvalho, Jorge Martins, Polina Golland, Steve Pieper, Sarah Frisken, Alexandra Golby, William Wells, and Yangming Ou. 8/2019. “Deformable MRI-Ultrasound Registration using Correlation-based Attribute Matching for Brain Shift Correction: Accuracy and Generality in Multi-site Data.” Neuroimage, 202, Pp. 116094.Abstract
Intraoperative tissue deformation, known as brain shift, decreases the benefit of using preoperative images to guide neurosurgery. Non-rigid registration of preoperative magnetic resonance (MR) to intraoperative ultrasound (US) has been proposed as a means to compensate for brain shift. We focus on the initial registration from MR to predurotomy US. We present a method that builds on previous work to address the need for accuracy and generality of MR-iUS registration algorithms in multi-site clinical data. To improve accuracy of registration, we use high-dimensional texture attributes instead of image intensities and propose to replace the standard difference-based attribute matching with correlation-based attribute matching. We also present a strategy that deals explicitly with the large field-of-view mismatch between MR and iUS images. We optimize key parameters across independent MR-iUS brain tumor datasets acquired at three different institutions, with a total of 43 tumor patients and 758 corresponding landmarks to validate the registration algorithm. Despite differences in imaging protocols, patient demographics and landmark distributions, our algorithm was able to reduce landmark errors prior to registration in three data sets (5.37 ± 4.27, 4.18 ± 1.97 and 6.18 ± 3.38 mm, respectively) to a consistently low level (2.28 ± 0.71, 2.08 ± 0.37 and 2.24 ± 0.78 mm, respectively). Our algorithm is compared to 15 other algorithms that have been previously tested on MR-iUS registration and it is competitive with the state-of-the-art on multiple datasets. We show that our algorithm has one of the lowest errors in all datasets (accuracy), and this is achieved while sticking to a fixed set of parameters for multi-site data (generality). In contrast, other algorithms/tools of similar performance need per-dataset parameter tuning (high accuracy but lower generality), and those that stick to fixed parameters have larger errors or inconsistent performance (generality but not the top accuracy). We further characterized landmark errors according to brain regions and tumor types, a topic so far missing in the literature. We found that landmark errors were higher in high-grade than low-grade glioma patients, and higher in tumor regions than in other brain regions.
Paolo Zaffino, Guillaume Pernelle, Andre Mastmeyer, Alireza Mehrtash, Hongtao Zhang, Ron Kikinis, Tina Kapur, and Maria Francesca Spadea. 8/2019. “Fully Automatic Catheter Segmentation in MRI with 3D Convolutional Neural Networks: Application to MRI-guided Gynecologic Brachytherapy.” Phys Med Biol, 64, 16, Pp. 165008.Abstract
External-beam radiotherapy followed by high dose rate (HDR) brachytherapy is the standard-of-care for treating gynecologic cancers. The enhanced soft-tissue contrast provided by magnetic resonance imaging (MRI) makes it a valuable imaging modality for diagnosing and treating these cancers. However, in contrast to computed tomography (CT) imaging, the appearance of the brachytherapy catheters, through which radiation sources are inserted to reach the cancerous tissue later on, is often variable across images. This paper reports, for the first time, a new deep-learning-based method for fully automatic segmentation of multiple closely spaced brachytherapy catheters in intraoperative MRI. Represented in the data are 50 gynecologic cancer patients treated by MRI-guided HDR brachytherapy. For each patient, a single intraoperative MRI was used. 826 catheters in the images were manually segmented by an expert radiation physicist who is also a trained radiation oncologist. The number of catheters in a patient ranged between 10 and 35. A deep 3D convolutional neural network (CNN) model was developed and trained. In order to make the learning process more robust, the network was trained 5 times, each time using a different combination of shown patients. Finally, each test case was processed by the five networks and the final segmentation was generated by voting on the obtained five candidate segmentations. 4-fold validation was executed and all the patients were segmented. An average distance error of 2.0  ±  3.4 mm was achieved. False positive and false negative catheters were 6.7% and 1.5% respectively. Average Dice score was equal to 0.60  ±  0.17. The algorithm is available for use in the open source software platform 3D Slicer allowing for wide scale testing and research discussion. In conclusion, to the best of our knowledge, fully automatic segmentation of multiple closely spaced catheters from intraoperative MR images was achieved for the first time in gynecological brachytherapy.
Jean-Jacques Lemaire, Antonio De Salles, Guillaume Coll, Youssef El Ouadih, Rémi Chaix, Jérôme Coste, Franck Durif, Nikos Makris, and Ron Kikinis. 8/2019. “MRI Atlas of the Human Deep Brain.” Front Neurol, 10, Pp. 851.Abstract
Mastering detailed anatomy of the human deep brain in clinical neurosciences is challenging. Although numerous pioneering works have gathered a large dataset of structural and topographic information, it is still difficult to transfer this knowledge into practice, even with advanced magnetic resonance imaging techniques. Thus, classical histological atlases continue to be used to identify structures for stereotactic targeting in functional neurosurgery. Physicians mainly use these atlases as a template co-registered with the patient's brain. However, it is possible to directly identify stereotactic targets on MRI scans, enabling personalized targeting. In order to help clinicians directly identify deep brain structures relevant to present and future medical applications, we built a volumetric MRI atlas of the deep brain (MDBA) on a large scale (infra millimetric). Twelve hypothalamic, 39 subthalamic, 36 telencephalic, and 32 thalamic structures were identified, contoured, and labeled. Nineteen coronal, 18 axial, and 15 sagittal MRI plates were created. Although primarily designed for direct labeling, the anatomic space was also subdivided in twelfths of AC-PC distance, leading to proportional scaling in the coronal, axial, and sagittal planes. This extensive work is now available to clinicians and neuroscientists, offering another representation of the human deep brain ([https://hal.archives-ouvertes.fr/] [hal-02116633]). The atlas may also be used by computer scientists who are interested in deciphering the topography of this complex region.
Filip Szczepankiewicz, Scott Hoge, and Carl-Fredrik Westin. 7/2019. “Linear, Planar and Spherical Tensor-valued Diffusion MRI Data by Free Waveform Encoding in Healthy Brain, Water, Oil and Liquid Crystals.” Data Brief, 25, Pp. 104208.Abstract
Recently, several biophysical models and signal representations have been proposed for microstructure imaging based on tensor-valued, or multidimensional, diffusion MRI. The acquisition of the necessary data requires non-conventional pulse sequences, and data is therefore not available to the wider diffusion MRI community. To facilitate exploration and development of analysis techniques based on tensor-valued diffusion encoding, we share a comprehensive data set acquired in a healthy human brain. The data encompasses diffusion weighted images using linear, planar and spherical diffusion tensor encoding at multiple b-values and diffusion encoding directions. We also supply data acquired in several phantoms that may support validation. The data is hosted by GitHub: https://github.com/filip-szczepankiewicz/Szczepankiewicz_DIB_2019.
Tonya M Gilbert, Nicole R Zürcher, Mary C Catanese, Chieh-En J Tseng, Maria A Di Biase, Amanda E Lyall, Baileigh G Hightower, Anjali J Parmar, Anisha Bhanot, Christine J Wu, Matthew L Hibert, Minhae Kim, Umar Mahmood, Steven M Stufflebeam, Frederick A Schroeder, Changning Wang, Joshua L Roffman, Daphne J Holt, Douglas N Greve, Ofer Pasternak, Marek Kubicki, Hsiao-Ying Wey, and Jacob M Hooker. 7/2019. “Neuroepigenetic Signatures of Age and Sex in the Living Human Brain.” Nat Commun, 10, 1, Pp. 2945.Abstract
Age- and sex-related alterations in gene transcription have been demonstrated, however the underlying mechanisms are unresolved. Neuroepigenetic pathways regulate gene transcription in the brain. Here, we measure in vivo expression of the epigenetic enzymes, histone deacetylases (HDACs), across healthy human aging and between sexes using [C]Martinostat positron emission tomography (PET) neuroimaging (n = 41). Relative HDAC expression increases with age in cerebral white matter, and correlates with age-associated disruptions in white matter microstructure. A post mortem study confirmed that HDAC1 and HDAC2 paralogs are elevated in white matter tissue from elderly donors. There are also sex-specific in vivo HDAC expression differences in brain regions associated with emotion and memory, including the amygdala and hippocampus. Hippocampus and white matter HDAC expression negatively correlates with emotion regulation skills (n = 23). Age and sex are associated with HDAC expression in vivo, which could drive age- and sex-related transcriptional changes and impact human behavior.
Fan Zhang, Ye Wu, Isaiah Norton, Yogesh Rathi, Alexandra J Golby, and Lauren J O'Donnell. 7/2019. “Test-retest Reproducibility of White Matter Parcellation using Diffusion MRI Tractography Fiber Clustering.” Hum Brain Mapp, 40, 10, Pp. 3041-57.Abstract
There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.
Christian Lepage, Marc Muehlmann, Yorghos Tripodis, Jakob Hufschmidt, Julie Stamm, Katie Green, Pawel Wrobel, Vivian Schultz, Isabelle Weir, Michael L Alosco, Christine M Baugh, Nathan G Fritts, Brett M Martin, Christine Chaisson, Michael J Coleman, Alexander P Lin, Ofer Pasternak, Nikos Makris, Robert A Stern, Martha E Shenton, and Inga K Koerte. 6/2019. “Limbic System Structure Volumes and Associated Neurocognitive Functioning in Former NFL Players.” Brain Imaging Behav, 13, 3, Pp. 725-34.Abstract
Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with exposure to repetitive head impacts. CTE has been linked to disruptions in cognition, mood, and behavior. Unfortunately, the diagnosis of CTE can only be made post-mortem. Neuropathological evidence suggests limbic structures may provide an opportunity to characterize CTE in the living. Using 3 T magnetic resonance imaging, we compared select limbic brain regional volumes - the amygdala, hippocampus, and cingulate gyrus - between symptomatic former National Football League (NFL) players (n = 86) and controls (n = 22). Moreover, within the group of former NFL players, we examined the relationship between those limbic structures and neurobehavioral functioning (n = 75). The former NFL group comprised eighty-six men (mean age = 55.2 ± 8.0 years) with at least 12 years of organized football experience, at least 2 years of active participation in the NFL, and self-reported declines in cognition, mood, and behavior within the last 6 months. The control group consisted of men (mean age = 57.0 ± 6.6 years) with no history of contact-sport involvement or traumatic brain injury. All control participants provided neurobehavioral data. Compared to controls, former NFL players exhibited reduced volumes of the amygdala, hippocampus, and cingulate gyrus. Within the NFL group, reduced bilateral cingulate gyrus volume was associated with worse attention and psychomotor speed (r = 0.4 (right), r = 0.42 (left); both p < 0.001), while decreased right hippocampal volume was associated with worse visual memory (r = 0.25, p = 0.027). Reduced volumes of limbic system structures in former NFL players are associated with neurocognitive features of CTE. Volume reductions in the amygdala, hippocampus, and cingulate gyrus may be potential biomarkers of neurodegeneration in those at risk for CTE.
Björn Lampinen, Filip Szczepankiewicz, Mikael Novén, Danielle van Westen, Oskar Hansson, Elisabet Englund, Johan Mårtensson, Carl-Fredrik Westin, and Markus Nilsson. 6/2019. “Searching for the Neurite Density with Diffusion MRI: Challenges for Biophysical Modeling.” Hum Brain Mapp, 40, 8, Pp. 2529-45.Abstract
In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic ("stick-like") diffusion. Second, the "density" of tissue components may be confounded by non-diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with "b-tensor encoding" and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b-tensor data is associated with myelinated axons but not with dendrites. Furthermore, b-tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data-driven estimates of compartment-specific T2 values. Finally, the "stick" fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment-specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained "index" parameters could be valid within limited domains that should be delineated by future studies.
Ashwati Vipin, Kwun Kei Ng, Fang Ji, Hee Youn Shim, Joseph KW Lim, Ofer Pasternak, Juan Helen Zhou, and Juan Helen Zhou. 5/2019. “Amyloid Burden Accelerates White Matter Degradation in Cognitively Normal Elderly Individuals.” Hum Brain Mapp, 40, 7, Pp. 2065-75.Abstract
Alterations in parietal and temporal white matter microstructure derived from diffusion tensor imaging occur in preclinical and clinical Alzheimer's disease. Amyloid beta (Aβ) deposition and such white matter alterations are two pathological hallmarks of Alzheimer's disease. However, the relationship between these pathologies is not yet understood, partly since conventional diffusion MRI methods cannot distinguish between cellular and extracellular processes. Thus, we studied Aβ-associated longitudinal diffusion MRI changes in Aβ-positive (N = 21) and Aβ-negative (N = 51) cognitively normal elderly obtained from the Alzheimer's Disease Neuroimaging Initiative dataset using linear mixed models. Aβ-positivity was based on Alzheimer's Disease Neuroimaging Initiative amyloid-PET recommendations using a standardized uptake value ratio cut-off of 1.11. We used free-water imaging to distinguish cellular and extracellular changes. We found that Aβ-positive subjects had increased baseline right uncinate fasciculus free-water fraction (FW), associated with worse baseline Alzheimer's disease assessment scale scores. Furthermore, Aβ-positive subjects showed faster decrease in fractional anisotropy (FW-corrected) in the right uncinate fasciculus and faster age-dependent right inferior longitudinal fasciculus FW increases over time. Right inferior longitudinal fasciculus FW increases were associated with greater memory decline. Importantly, these results remained significant after controlling for gray and white matter volume and hippocampal volume. This is the first study to illustrate the influence of Aβ burden on early longitudinal (in addition to baseline) white matter changes in cognitively normal elderly individuals at-risk of Alzheimer's disease, thus underscoring the importance of longitudinal studies in assessing microstructural alterations in individuals at risk of Alzheimer's disease prior to symptoms onset.
Alex V Nguyen, Andras Lasso, Hannah H Nam, Jennifer Faerber, Ahmed H Aly, Alison M Pouch, Adam B Scanlan, Francis X McGowan, Laura Mercer-Rosa, Meryl S Cohen, John Simpson, Gabor Fichtinger, and Matthew A Jolley. 5/2019. “Dynamic Three-Dimensional Geometry of the Tricuspid Valve Annulus in Hypoplastic Left Heart Syndrome with a Fontan Circulation.” J Am Soc Echocardiogr, 32, 5, Pp. 655-66.Abstract
BACKGROUND: Tricuspid regurgitation (TR) is a significant contributor to morbidity and mortality in patients with hypoplastic left heart syndrome. The goal of this study was to characterize the dynamic annular motion of the tricuspid valve in patients with HLHS with a Fontan circulation and assess the relation to tricuspid valve function. METHODS: Tricuspid annuli of 48 patients with HLHS with a Fontan circulation were modeled at end-diastole, mid-systole, end-systole, and mid-diastole using transthoracic three-dimensional echocardiography and custom code in 3D Slicer. The angle of the anterior papillary muscle (APM) relative to the annular plane in each systolic phase was also measured. RESULTS: Imaging was performed 5.0 years (interquartile range, 2-11 years) after Fontan operation. The tricuspid annulus varies in shape significantly throughout the cardiac cycle, changing in sphericity (P < .001) but not in annular height or bending angle. In univariate modeling, patients with significant TR had larger changes in septolateral diameter, lateral quadrant area, and posterior quadrant area (P < .05 for all) as well as lower (more laterally directed) APM angles (P < .001) than patients with mild or less TR. In multivariate modeling, a 1 mm/(body surface area) increase in the maximum change in septolateral diameter was associated with a 1.7-fold increase in having moderate or greater TR, while a 10° decrease in APM angle at mid-systole was associated with an almost 2.5-fold increase in moderate or greater TR (P ≤ .01 for all). CONCLUSIONS: The tricuspid annulus in patients with HLHS with a Fontan circulation changes in shape significantly throughout the cardiac cycle but remains relatively planar. Increased change in septolateral diameter and decreased APM angle are strongly associated with the presence of TR. These findings may inform annuloplasty methods and subvalvular interventions in these complex patients.
Markus D Schirmer, Adrian V Dalca, Ramesh Sridharan, Anne-Katrin Giese, Kathleen L Donahue, Marco J Nardin, Steven JT Mocking, Elissa C McIntosh, Petrea Frid, Johan Wasselius, John W Cole, Lukas Holmegaard, Christina Jern, Jordi Jimenez-Conde, Robin Lemmens, Arne G Lindgren, James F Meschia, Jaume Roquer, Tatjana Rundek, Ralph L Sacco, Reinhold Schmidt, Pankaj Sharma, Agnieszka Slowik, Vincent Thijs, Daniel Woo, Achala Vagal, Huichun Xu, Steven J Kittner, Patrick F McArdle, Braxton D Mitchell, Jonathan Rosand, Bradford B Worrall, Ona Wu, Polina Golland, Natalia S Rost, and Natalia S Rost. 5/2019. “White Matter Hyperintensity Quantification in Large-scale Clinical Acute Ischemic Stroke Cohorts - The MRI-GENIE Study.” Neuroimage Clin, 23, Pp. 101884.Abstract
White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited.
Lauren J O'Donnell, Alessandro Daducci, Demian Wassermann, and Christophe Lenglet. 4/2019. “Advances in Computational and Statistical Diffusion MRI.” NMR Biomed., 32, 4, Pp. e3805.Abstract
Computational methods are crucial for the analysis of diffusion magnetic resonance imaging (MRI) of the brain. Computational diffusion MRI can provide rich information at many size scales, including local microstructure measures such as diffusion anisotropies or apparent axon diameters, whole-brain connectivity information that describes the brain's wiring diagram and population-based studies in health and disease. Many of the diffusion MRI analyses performed today were not possible five, ten or twenty years ago, due to the requirements for large amounts of computer memory or processor time. In addition, mathematical frameworks had to be developed or adapted from other fields to create new ways to analyze diffusion MRI data. The purpose of this review is to highlight recent computational and statistical advances in diffusion MRI and to put these advances into context by comparison with the more traditional computational methods that are in popular clinical and scientific use. We aim to provide a high-level overview of interest to diffusion MRI researchers, with a more in-depth treatment to illustrate selected computational advances.
Fan Zhang, Lipeng Ning, Lauren J O'Donnell, and Ofer Pasternak. 4/2019. “MK-curve - Characterizing the Relation between Mean Kurtosis and Alterations in the Diffusion MRI Signal .” Neuroimage, 196, Pp. 68-80.Abstract
Diffusion kurtosis imaging (DKI) is a diffusion MRI (dMRI) technique to quantify brain microstructural properties. While DKI measures are sensitive to tissue alterations, they are also affected by signal alterations caused by imaging artifacts such as noise, motion and Gibbs ringing. Consequently, DKI often yields output parameter values (e.g. mean kurtosis; MK) that are implausible. These include implausible values that are outside of the range dictated by physics/biology, and visually apparent implausible values that form unexpected discontinuities, being too high or too low comparing with their neighborhood. These implausible values will introduce bias into any following data analyses (e.g. between-population statistical computation). Existing studies have attempted to correct implausible DKI parameter values in multiple ways; however, these approaches are not always effective. In this study, we propose a novel method for detecting and correcting voxels with implausible values to enable improved DKI parameter estimation. In particular, we focus on MK parameter estimation. We first characterize the relation between MK and alterations in the dMRI signal including diffusion weighted images (DWIs) and the baseline (b0) images. This is done by calculating MK for a range of synthetic DWI or b0 for each voxel, and generating curves (MK-curve) representing how alterations to the input dMRI signals affect the resulting output MK. We find that voxels with implausible MK values are more likely caused by artifacts in the b0 images than artifacts in DWIs with higher b-values. Accordingly, two characteristic b0 values, which define a range of synthetic b0 values that generate implausible MK values, are identified on the MK-curve. Based on this characterization, we propose an automatic approach for detection of voxels with implausible MK values by comparing a voxel's original b0 signal to the identified two characteristic b0 values, along with a correction strategy to replace the original b0 in each detected implausible voxel with a synthetic b0 value computed from the MK-curve. We evaluate the method on a DKI phantom dataset and dMRI datasets from the Human Connectome Project (HCP), and we compare the proposed correction method with other previously proposed correction methods. Results show that our proposed method is able to identify and correct most voxels with implausible DKI parameter values as well as voxels with implausible diffusion tensor parameter values.

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