An Open-Source Tool for Anisotropic Radiation Therapy Planning in Neuro-oncology Using DW-MRI Tractography.

There is evidence from histopathological studies that glioma tumor cells migrate preferentially along large white matter bundles. If the peritumoral white matter structures can be used to predict the likely trajectory of migrating tumor cells outside of the surgical margin, then this information could be used to inform the delineation of radiation therapy (RT) targets. In theory, an anisotropic expansion that takes large white matter bundle anatomy into account may maximize the chances of treating migrating cancer cells and minimize the amount of brain tissue exposed to high doses of ionizing radiation. Diffusion-weighted MRI (DW-MRI) can be used in combination with fiber tracking algorithms to model the trajectory of large white matter pathways using the direction and magnitude of water movement in tissue. The method presented here is a tool for translating a DW-MRI fiber tracking (tractography) dataset into a white matter path length (WMPL) map that assigns each voxel the shortest distance along a streamline back to a specified region of interest (ROI). We present an open-source WMPL tool, implemented in the package Diffusion Imaging in Python (DIPY), and code to convert the resulting WMPL map to anisotropic contours for RT in a commercial treatment planning system. This proof-of-concept lays the groundwork for future studies to evaluate the clinical value of incorporating tractography modeling into treatment planning

Dipy, a library for the analysis of diffusion MRI data

Diffusion Imaging in Python (Dipy) is a free and open source software projectfor the analysis of data from diffusion magnetic resonance imaging (dMRI)experiments. dMRI is an application of MRI that can be used to measurestructural features of brain white matter. Many methods have been developed touse dMRI data to model the local configuration of white matter nerve fiberbundles and infer the trajectory of bundles connecting different parts of thebrain.Dipy gathers implementations of many different methods in dMRI, including:diffusion signal pre-processing; reconstruction of diffusion distributions inindividual voxels; fiber tractography and fiber track post-processing, analysisand visualization. Dipy aims to provide transparent implementations forall the different steps of dMRI analysis with a uniform programming interface.We have implemented classical signal reconstruction techniques, such as thediffusion tensor model and deterministic fiber tractography. In addition,cutting edge novel reconstruction techniques are implemented, such asconstrained spherical deconvolution and diffusion spectrum imaging withdeconvolution, as well as methods for probabilistic tracking and originalmethods for tractography clustering. Many additional utility functions areprovided to calculate various statistics, informative visualizations, as wellas file-handling routines to assist in the development and use of noveltechniques.In contrast to many other scientific software projects, Dipy is not beingdeveloped by a single research group. Rather, it is an open project thatencourages contributions from any scientist/developer through GitHub and opendiscussions on the project mailing list. Consequently, Dipy today has aninternational team of contributors, spanning seven different academic institutionsin five countries and three continents, which is still growing

Quantitative Imaging of Single, Unstained Viruses with Coherent X-rays

Since Perutz, Kendrew and colleagues unveiled the structure of hemoglobin and myoglobin based on X-ray diffraction analysis in the 1950s, X-ray crystallography has become the primary methodology used to determine the 3D structure of macromolecules. However, biological specimens such as cells, organelles, viruses and many important macromolecules are difficult or impossible to crystallize, and hence their structures are not accessible by crystallography. Here we report, for the first time, the recording and reconstruction of X-ray diffraction patterns from single, unstained viruses. The structure of the viral capsid inside a virion was visualized. This work opens the door for quantitative X-ray imaging of a broad range of specimens from protein machineries, viruses and organelles to whole cells. Moreover, our experiment is directly transferable to the use of X-ray free electron lasers, and represents a major experimental milestone towards the X-ray imaging of single macromolecules.Comment: 16 pages, 5 figure

The Modeling of White Matter Architecture and Networks Using Diffusion MRI: Methods, Tools and Applications

Diffusion magnetic resonance imaging (dMRI) allows us to noninvasively investigate the microstructural properties of brain tissue, and reconstruct the axonal pathways that connect distant brain regions. This enables us to infer the biological processes that give rise to thought and consciousness. However, despite significant advances in both imaging technology and computing power, our ability to estimate connectivity in a single subject using dMRI data remains quite limited. Barriers to accurate single subject estimates include poor accuracy and reproducibility of both fiber tracking and diffusion modeling results, and a difficulty in reproducing the methods of other researchers in this field. As a result, studies using different dMRI methods have drawn conflicting conclusions about the same biological systems. To overcome these barriers, I first present a technique to estimate the noise in dMRI data and show that this measure is a strong indicator of the reproducibility of dMRI measurements. Software engineering principles, such as modularization and thorough testing, were implemented and made publicly available in an open source library called Dipy. By providing a single platform where tools and methods from different developers can be implemented using shared constructs and made publicly available to users, Dipy aims to help the community more easily reproduce the findings of other researchers. In the last section of this work, I use these modeling and fiber tracking tools to reconstruct whole brain networks for individual subjects in a large population. The white matter tissue properties projected onto these networks show that regional differences in white matter integrity are strongly associated with body mass index in young, healthy individuals. This association helps explain the reduced cognitive ability in individuals with higher BMI. This study demonstrates the power of using single subject connectivity networks when studying the human brain and its role in health outcomes. In order to fully unlock the potential of dMRI imaging, methods development needs to continue to focus on improving the reproducibility and accuracy of dMRI studies

The Modeling of White Matter Architecture and Networks Using Diffusion MRI: Methods, Tools and Applications

Diffusion magnetic resonance imaging (dMRI) allows us to noninvasively investigate the microstructural properties of brain tissue, and reconstruct the axonal pathways that connect distant brain regions. This enables us to infer the biological processes that give rise to thought and consciousness. However, despite significant advances in both imaging technology and computing power, our ability to estimate connectivity in a single subject using dMRI data remains quite limited. Barriers to accurate single subject estimates include poor accuracy and reproducibility of both fiber tracking and diffusion modeling results, and a difficulty in reproducing the methods of other researchers in this field. As a result, studies using different dMRI methods have drawn conflicting conclusions about the same biological systems. To overcome these barriers, I first present a technique to estimate the noise in dMRI data and show that this measure is a strong indicator of the reproducibility of dMRI measurements. Software engineering principles, such as modularization and thorough testing, were implemented and made publicly available in an open source library called Dipy. By providing a single platform where tools and methods from different developers can be implemented using shared constructs and made publicly available to users, Dipy aims to help the community more easily reproduce the findings of other researchers. In the last section of this work, I use these modeling and fiber tracking tools to reconstruct whole brain networks for individual subjects in a large population. The white matter tissue properties projected onto these networks show that regional differences in white matter integrity are strongly associated with body mass index in young, healthy individuals. This association helps explain the reduced cognitive ability in individuals with higher BMI. This study demonstrates the power of using single subject connectivity networks when studying the human brain and its role in health outcomes. In order to fully unlock the potential of dMRI imaging, methods development needs to continue to focus on improving the reproducibility and accuracy of dMRI studies

Q-ball of inferior fronto-occipital fasciculus and beyond.

The inferior fronto-occipital fasciculus (IFOF) is historically described as the longest associative bundle in the human brain and it connects various parts of the occipital cortex, temporo-basal area and the superior parietal lobule to the frontal lobe through the external/extreme capsule complex. The exact functional role and the detailed anatomical definition of the IFOF are still under debate within the scientific community. In this study we present a fiber tracking dissection of the right and left IFOF by using a q-ball residual-bootstrap reconstruction of High-Angular Resolution Diffusion Imaging (HARDI) data sets in 20 healthy subjects. By defining a single seed region of interest on the coronal fractional anisotropy (FA) color map of each subject, we investigated all the pathways connecting the parietal, occipital and posterior temporal cortices to the frontal lobe through the external/extreme capsule. In line with recent post-mortem dissection studies we found more extended anterior-posterior association connections than the "classical" fronto-occipital representation of the IFOF. In particular the pathways we evidenced showed: a) diffuse projections in the frontal lobe, b) fronto-parietal lobes connections trough the external capsule in almost all the subjects and c) widespread connections in the posterior regions. Our study represents the first consistent in vivo demonstration across a large group of individuals of these novel anterior and posterior terminations of the IFOF detailed described only by post-mortem anatomical dissection. Furthermore our work establishes the feasibility of consistent in vivo mapping of this architecture with independent in vivo methodologies. In conclusion q-ball tractography dissection supports a more complex definition of IFOF, which includes several subcomponents likely underlying specific function

Q-ball of inferior fronto-occipital fasciculus and beyond.

The inferior fronto-occipital fasciculus (IFOF) is historically described as the longest associative bundle in the human brain and it connects various parts of the occipital cortex, temporo-basal area and the superior parietal lobule to the frontal lobe through the external/extreme capsule complex. The exact functional role and the detailed anatomical definition of the IFOF are still under debate within the scientific community. In this study we present a fiber tracking dissection of the right and left IFOF by using a q-ball residual-bootstrap reconstruction of High-Angular Resolution Diffusion Imaging (HARDI) data sets in 20 healthy subjects. By defining a single seed region of interest on the coronal fractional anisotropy (FA) color map of each subject, we investigated all the pathways connecting the parietal, occipital and posterior temporal cortices to the frontal lobe through the external/extreme capsule. In line with recent post-mortem dissection studies we found more extended anterior-posterior association connections than the "classical" fronto-occipital representation of the IFOF. In particular the pathways we evidenced showed: a) diffuse projections in the frontal lobe, b) fronto-parietal lobes connections trough the external capsule in almost all the subjects and c) widespread connections in the posterior regions. Our study represents the first consistent in vivo demonstration across a large group of individuals of these novel anterior and posterior terminations of the IFOF detailed described only by post-mortem anatomical dissection. Furthermore our work establishes the feasibility of consistent in vivo mapping of this architecture with independent in vivo methodologies. In conclusion q-ball tractography dissection supports a more complex definition of IFOF, which includes several subcomponents likely underlying specific function

Q-ball of inferior fronto-occipital fasciculus and beyond.

The inferior fronto-occipital fasciculus (IFOF) is historically described as the longest associative bundle in the human brain and it connects various parts of the occipital cortex, temporo-basal area and the superior parietal lobule to the frontal lobe through the external/extreme capsule complex. The exact functional role and the detailed anatomical definition of the IFOF are still under debate within the scientific community. In this study we present a fiber tracking dissection of the right and left IFOF by using a q-ball residual-bootstrap reconstruction of High-Angular Resolution Diffusion Imaging (HARDI) data sets in 20 healthy subjects. By defining a single seed region of interest on the coronal fractional anisotropy (FA) color map of each subject, we investigated all the pathways connecting the parietal, occipital and posterior temporal cortices to the frontal lobe through the external/extreme capsule. In line with recent post-mortem dissection studies we found more extended anterior-posterior association connections than the "classical" fronto-occipital representation of the IFOF. In particular the pathways we evidenced showed: a) diffuse projections in the frontal lobe, b) fronto-parietal lobes connections trough the external capsule in almost all the subjects and c) widespread connections in the posterior regions. Our study represents the first consistent in vivo demonstration across a large group of individuals of these novel anterior and posterior terminations of the IFOF detailed described only by post-mortem anatomical dissection. Furthermore our work establishes the feasibility of consistent in vivo mapping of this architecture with independent in vivo methodologies. In conclusion q-ball tractography dissection supports a more complex definition of IFOF, which includes several subcomponents likely underlying specific function

Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods.

INTRODUCTION: Diffusion MRI tractography has been increasingly used to delineate white matter pathways in vivo for which the leading clinical application is presurgical mapping of eloquent regions. However, there is rare opportunity to quantify the accuracy or sensitivity of these approaches to delineate white matter fiber pathways in vivo due to the lack of a gold standard. Intraoperative electrical stimulation (IES) provides a gold standard for the location and existence of functional motor pathways that can be used to determine the accuracy and sensitivity of fiber tracking algorithms. In this study we used intraoperative stimulation from brain tumor patients as a gold standard to estimate the sensitivity and accuracy of diffusion tensor MRI (DTI) and q-ball models of diffusion with deterministic and probabilistic fiber tracking algorithms for delineation of motor pathways. METHODS: We used preoperative high angular resolution diffusion MRI (HARDI) data (55 directions, b = 2000 s/mm(2)) acquired in a clinically feasible time frame from 12 patients who underwent a craniotomy for resection of a cerebral glioma. The corticospinal fiber tracts were delineated with DTI and q-ball models using deterministic and probabilistic algorithms. We used cortical and white matter IES sites as a gold standard for the presence and location of functional motor pathways. Sensitivity was defined as the true positive rate of delineating fiber pathways based on cortical IES stimulation sites. For accuracy and precision of the course of the fiber tracts, we measured the distance between the subcortical stimulation sites and the tractography result. Positive predictive rate of the delineated tracts was assessed by comparison of subcortical IES motor function (upper extremity, lower extremity, face) with the connection of the tractography pathway in the motor cortex. RESULTS: We obtained 21 cortical and 8 subcortical IES sites from intraoperative mapping of motor pathways. Probabilistic q-ball had the best sensitivity (79%) as determined from cortical IES compared to deterministic q-ball (50%), probabilistic DTI (36%), and deterministic DTI (10%). The sensitivity using the q-ball algorithm (65%) was significantly higher than using DTI (23%) (p < 0.001) and the probabilistic algorithms (58%) were more sensitive than deterministic approaches (30%) (p = 0.003). Probabilistic q-ball fiber tracks had the smallest offset to the subcortical stimulation sites. The offsets between diffusion fiber tracks and subcortical IES sites were increased significantly for those cases where the diffusion fiber tracks were visibly thinner than expected. There was perfect concordance between the subcortical IES function (e.g. hand stimulation) and the cortical connection of the nearest diffusion fiber track (e.g. upper extremity cortex). DISCUSSION: This study highlights the tremendous utility of intraoperative stimulation sites to provide a gold standard from which to evaluate diffusion MRI fiber tracking methods and has provided an object standard for evaluation of different diffusion models and approaches to fiber tracking. The probabilistic q-ball fiber tractography was significantly better than DTI methods in terms of sensitivity and accuracy of the course through the white matter. The commonly used DTI fiber tracking approach was shown to have very poor sensitivity (as low as 10% for deterministic DTI fiber tracking) for delineation of the lateral aspects of the corticospinal tract in our study. Effects of the tumor/edema resulted in significantly larger offsets between the subcortical IES and the preoperative fiber tracks. The provided data show that probabilistic HARDI tractography is the most objective and reproducible analysis but given the small sample and number of stimulation points a generalization about our results should be given with caution. Indeed our results inform the capabilities of preoperative diffusion fiber tracking and indicate that such data should be used carefully when making pre-surgical and intra-operative management decisions

Quantifying diffusion MRI tractography of the corticospinal tract in brain tumors with deterministic and probabilistic methods.

IntroductionDiffusion MRI tractography has been increasingly used to delineate white matter pathways in vivo for which the leading clinical application is presurgical mapping of eloquent regions. However, there is rare opportunity to quantify the accuracy or sensitivity of these approaches to delineate white matter fiber pathways in vivo due to the lack of a gold standard. Intraoperative electrical stimulation (IES) provides a gold standard for the location and existence of functional motor pathways that can be used to determine the accuracy and sensitivity of fiber tracking algorithms. In this study we used intraoperative stimulation from brain tumor patients as a gold standard to estimate the sensitivity and accuracy of diffusion tensor MRI (DTI) and q-ball models of diffusion with deterministic and probabilistic fiber tracking algorithms for delineation of motor pathways.MethodsWe used preoperative high angular resolution diffusion MRI (HARDI) data (55 directions, b = 2000 s/mm(2)) acquired in a clinically feasible time frame from 12 patients who underwent a craniotomy for resection of a cerebral glioma. The corticospinal fiber tracts were delineated with DTI and q-ball models using deterministic and probabilistic algorithms. We used cortical and white matter IES sites as a gold standard for the presence and location of functional motor pathways. Sensitivity was defined as the true positive rate of delineating fiber pathways based on cortical IES stimulation sites. For accuracy and precision of the course of the fiber tracts, we measured the distance between the subcortical stimulation sites and the tractography result. Positive predictive rate of the delineated tracts was assessed by comparison of subcortical IES motor function (upper extremity, lower extremity, face) with the connection of the tractography pathway in the motor cortex.ResultsWe obtained 21 cortical and 8 subcortical IES sites from intraoperative mapping of motor pathways. Probabilistic q-ball had the best sensitivity (79%) as determined from cortical IES compared to deterministic q-ball (50%), probabilistic DTI (36%), and deterministic DTI (10%). The sensitivity using the q-ball algorithm (65%) was significantly higher than using DTI (23%) (p < 0.001) and the probabilistic algorithms (58%) were more sensitive than deterministic approaches (30%) (p = 0.003). Probabilistic q-ball fiber tracks had the smallest offset to the subcortical stimulation sites. The offsets between diffusion fiber tracks and subcortical IES sites were increased significantly for those cases where the diffusion fiber tracks were visibly thinner than expected. There was perfect concordance between the subcortical IES function (e.g. hand stimulation) and the cortical connection of the nearest diffusion fiber track (e.g. upper extremity cortex).DiscussionThis study highlights the tremendous utility of intraoperative stimulation sites to provide a gold standard from which to evaluate diffusion MRI fiber tracking methods and has provided an object standard for evaluation of different diffusion models and approaches to fiber tracking. The probabilistic q-ball fiber tractography was significantly better than DTI methods in terms of sensitivity and accuracy of the course through the white matter. The commonly used DTI fiber tracking approach was shown to have very poor sensitivity (as low as 10% for deterministic DTI fiber tracking) for delineation of the lateral aspects of the corticospinal tract in our study. Effects of the tumor/edema resulted in significantly larger offsets between the subcortical IES and the preoperative fiber tracks. The provided data show that probabilistic HARDI tractography is the most objective and reproducible analysis but given the small sample and number of stimulation points a generalization about our results should be given with caution. Indeed our results inform the capabilities of preoperative diffusion fiber tracking and indicate that such data should be used carefully when making pre-surgical and intra-operative management decisions