In order to bridge microscopic molecular motion with macroscopic diffusion MR signal in complex structures, we propose a general stochastic model for molecular motion in a magnetic field. The Fokker-Planck equation of this model governs the probability density function describing the diffusion-magnetization propagator. From the propagator we derive a generalized version of the Bloch-Torrey equation and the relation to the random phase approach. This derivation does not require assumptions such as a spatially constant diffusion coefficient, or ad hoc selection of a propagator. In particular, the boundary conditions that implicitly incorporate the microstructure into the diffusion MR signal can now be included explicitly through a spatially varying diffusion coefficient. While our generalization is reduced to the conventional Bloch-Torrey equation for piecewise constant diffusion coefficients, it also predicts scenarios in which an additional term to the equation is required to fully describe the MR signal.
The hypothesis that brain pathways form 2D sheet-like structures layered in 3D as "pages of a book" has been a topic of debate in the recent literature. This hypothesis was mainly supported by a qualitative evaluation of "path neighborhoods" reconstructed with diffusion MRI (dMRI) tractography. Notwithstanding the potentially important implications of the sheet structure hypothesis for our understanding of brain structure and development, it is still considered controversial by many for lack of quantitative analysis. A means to quantify sheet structure is therefore necessary to reliably investigate its occurrence in the brain. Previous work has proposed the Lie bracket as a quantitative indicator of sheet structure, which could be computed by reconstructing path neighborhoods from the peak orientations of dMRI orientation density functions. Robust estimation of the Lie bracket, however, is challenging due to high noise levels and missing peak orientations. We propose a novel method to estimate the Lie bracket that does not involve the reconstruction of path neighborhoods with tractography. This method requires the computation of derivatives of the fiber peak orientations, for which we adopt an approach called normalized convolution. With simulations and experimental data we show that the new approach is more robust with respect to missing peaks and noise. We also demonstrate that the method is able to quantify to what extent sheet structure is supported for dMRI data of different species, acquired with different scanners, diffusion weightings, dMRI sampling schemes, and spatial resolutions. The proposed method can also be used with directional data derived from other techniques than dMRI, which will facilitate further validation of the existence of sheet structure.
Implant placement has been widely used in various kinds of surgery. However, accurate intraoperative drilling performance is essential to avoid injury to adjacent structures. Although some commercially-available surgical navigation systems have been approved for clinical applications, these systems are expensive and the source code is not available to researchers. 3D Slicer is a free, open source software platform for the research community of computer-aided surgery. In this study, a loadable module based on Slicer has been developed and validated to support surgical navigation. This research module allows reliable calibration of the surgical drill, point-based registration and surface matching registration, so that the position and orientation of the surgical drill can be tracked and displayed on the computer screen in real time, aiming at reducing risks. In accuracy verification experiments, the mean target registration error (TRE) for point-based and surface-based registration were 0.31±0.06mm and 1.01±0.06mm respectively, which should meet clinical requirements. Both phantom and cadaver experiments demonstrated the feasibility of our surgical navigation software module.
We consider diffusion within pores with general shapes in the presence of spatially linear magnetic field profiles. The evolution of local magnetization of the spin bearing particles can be described by the Bloch-Torrey equation. We study the diffusive process in the eigenbasis of the non-Hermitian Bloch-Torrey operator. It is possible to find expressions for some special temporal gradient waveforms employed to sensitize the nuclear magnetic resonance (NMR) signal to diffusion. For more general gradient waveforms, we derive an efficient numerical solution by introducing a novel matrix formalism. Compared to previous methods, this new approach requires a fewer number of eigenfunctions to achieve the same accuracy. This shows that these basis functions are better suited to the problem studied. The new framework could provide new important insights into the fundamentals of diffusion sensitization, which could further the development of the field of NMR.
The basal ganglia is part of a complex system of neuronal circuits that play a key role in the integration and execution of motor, cognitive and emotional function in the human brain. Parkinson's disease is a progressive neurological disorder of the motor circuit characterized by tremor, rigidity, and slowness of movement. Deep brain stimulation (DBS) of the subthalamic nucleus and the globus pallidus pars interna provides an efficient treatment to reduce symptoms and levodopa-induced side effects in Parkinson's disease patients. While the underlying mechanism of action of DBS is still unknown, the potential modulation of white matter tracts connecting the surgical targets has become an active area of research. With the introduction of advanced diffusion MRI acquisition sequences and sophisticated post-processing techniques, the architecture of the human brain white matter can be explored in vivo. The goal of this study is to investigate the white matter connectivity between the subthalamic nucleus and the globus pallidus. Two multi-fiber tractography methods were used to reconstruct pallido-subthalamic, subthalamo-pallidal and pyramidal fibers in five healthy subjects datasets of the Human Connectome Project. The anatomical accuracy of the tracts was assessed by four judges with expertise in neuroanatomy, functional neurosurgery, and diffusion MRI. The variability among subjects was evaluated based on the fractional anisotropy and mean diffusivity of the tracts. Both multi-fiber approaches enabled the detection of complex fiber architecture in the basal ganglia. The qualitative evaluation by experts showed that the identified tracts were in agreement with the expected anatomy. Tract-derived measurements demonstrated relatively low variability among subjects. False-negative tracts demonstrated the current limitations of both methods for clinical decision-making. Multi-fiber tractography methods combined with state-of-the-art diffusion MRI data have the potential to help identify white matter tracts connecting DBS targets in functional neurosurgery intervention.
INTRODUCTION: Huntington's disease (HD) is a genetic neurodegenerative disorder that primarily affects striatal neurons. Striatal volume loss is present years before clinical diagnosis; however, white matter degradation may also occur prior to diagnosis. Diffusion-weighted imaging (DWI) can measure microstructural changes associated with degeneration that precede macrostructural changes. DWI derived measures enhance understanding of degeneration in prodromal HD (pre-HD). METHODS: As part of the PREDICT-HD study, N = 191 pre-HD individuals and 70 healthy controls underwent two or more (baseline and 1-5 year follow-up) DWI, with n = 649 total sessions. Images were processed using cutting-edge DWI analysis methods for large multicenter studies. Diffusion tensor imaging (DTI) metrics were computed in selected tracts connecting the primary motor, primary somato-sensory, and premotor areas of the cortex with the subcortical caudate and putamen. Pre-HD participants were divided into three CAG-Age Product (CAP) score groups reflecting clinical diagnosis probability (low, medium, or high probabilities). Baseline and longitudinal group differences were examined using linear mixed models. RESULTS: Cross-sectional and longitudinal differences in DTI measures were present in all three CAP groups compared with controls. The high CAP group was most affected. CONCLUSIONS: This is the largest longitudinal DWI study of pre-HD to date. Findings showed DTI differences, consistent with white matter degeneration, were present up to a decade before predicted HD diagnosis. Our findings indicate a unique role for disrupted connectivity between the premotor area and the putamen, which may be closely tied to the onset of motor symptoms in HD.
The Open Anatomy Browser (OABrowser) is an open source, web-based, zero-installation anatomy atlas viewer based on current web browser technologies and evolving anatomy atlas interoperability standards. OABrowser displays three-dimensional anatomical models, image cross-sections of labeled structures and source radiological imaging, and a text-based hierarchy of structures. The viewer includes novel collaborative tools: users can save bookmarks of atlas views for later access and exchange those bookmarks with other users, and dynamic shared views allow groups of users can participate in a collaborative interactive atlas viewing session. We have published several anatomy atlases (an MRI-derived brain atlas and atlases of other parts of the anatomy) to demonstrate OABrowser's functionality. The atlas source data, processing tools, and the source for OABrowser are freely available through GitHub and are distributed under a liberal open source license.
PURPOSE: To investigate the heating of EEG electrodes during magnetic resonance imaging (MRI) scans and to better understand the underlying physical mechanisms with a focus on the antenna effect. MATERIALS AND METHODS: Gold cup and conductive plastic electrodes were placed on small watermelons with fiberoptic probes used to measure electrode temperature changes during a variety of 1.5T and 3T MRI scans. A subset of these experiments was repeated on a healthy human volunteer. RESULTS: The differences between gold and plastic electrodes did not appear to be practically significant. For both electrode types, we observed heating below 4°C for straight wires whose lengths were multiples of ½ the radiofrequency (RF) wavelength and stronger heating (over 15°C) for wire lengths that were odd multiples of ¼ RF wavelength, consistent with the antenna effect. CONCLUSIONS: The antenna effect, which has received little attention so far in the context of EEG-MRI safety, can play as significant a role as the loop effect (from electromagnetic induction) in the heating of EEG electrodes, and therefore wire lengths that are odd multiples of ¼ RF wavelength should be avoided. These results have important implications for the design of EEG electrodes and MRI studies as they help to minimize the risk to patients undergoing MRI with EEG electrodes in place.
Inferring the microstructure of complex media from the diffusive motion of molecules is a challenging problem in diffusion physics. In this paper, we introduce a novel representation of diffusion MRI (dMRI) signal from tissue with spatially-varying diffusivity using a diffusion disturbance function. This disturbance function contains information about the (intra-voxel) spatial fluctuations in diffusivity due to restrictions, hindrances and tissue heterogeneity of the underlying tissue substrate. We derive the short- and long-range disturbance coefficients from this disturbance function to characterize the tissue structure and organization. Moreover, we provide an exact relation between the disturbance coefficients and the time-varying moments of the diffusion propagator, as well as their relation to specific tissue microstructural information such as the intra-axonal volume fraction and the apparent axon radius. The proposed approach is quite general and can model dMRI signal for any type of gradient sequence (rectangular, oscillating, etc.) without using the Gaussian phase approximation. The relevance of the proposed PICASO model is explored using Monte-Carlo simulations and in-vivo dMRI data. The results show that the estimated disturbance coefficients can distinguish different types of microstructural organization of axons.
In schizophrenia, abnormalities in structural connectivity between brain regions known to contain mirror neurons and their relationship to negative symptoms related to a domain of social cognition are not well understood. Diffusion tensor imaging (DTI) scans were acquired in 16 patients with first episode schizophrenia and 16 matched healthy controls. FA and Trace of the tracts interconnecting regions known to be rich in mirror neurons, i.e., anterior cingulate cortex (ACC), inferior parietal lobe (IPL) and premotor cortex (PMC) were evaluated. A significant group effect for Trace was observed in IPL-PMC white matter fiber tract (F (1, 28) = 7.13, p = .012), as well as in the PMC-ACC white matter fiber tract (F (1, 28) = 4.64, p = .040). There were no group differences in FA. In addition, patients with schizophrenia showed a significant positive correlation between the Trace of the left IPL-PMC white matter fiber tract, and the Ability to Feel Intimacy and Closeness score (rho = .57, p = 0.034), and a negative correlation between the Trace of the left PMC-ACC and the Relationships with Friends and Peers score (rho = remove -.54, p = 0.049). We have demonstrated disrupted white mater microstructure within the white matter tracts subserving brain regions containing mirror neurons. We further showed that such structural disruptions might impact negative symptoms and, more specifically, contribute to the inability to feel intimacy (a measure conceptually related to theory of mind) in first episode schizophrenia. Further studies are needed to understand the potential of our results for diagnosis, prognosis and therapeutic interventions.
PURPOSE: Surgical navigation systems rely on a monitor placed in the operating room to relay information. Optimal monitor placement can be challenging in crowded rooms, and it is often not possible to place the monitor directly beside the situs. The operator must split attention between the navigation system and the situs. We present an approach for needle-based interventions to provide navigational feedback directly on the instrument and close to the situs by mounting a small display onto the needle. METHODS: By mounting a small and lightweight smartwatch display directly onto the instrument, we are able to provide navigational guidance close to the situs and directly in the operator's field of view, thereby reducing the need to switch the focus of view between the situs and the navigation system. We devise a specific variant of the established crosshair metaphor suitable for the very limited screen space. We conduct an empirical user study comparing our approach to using a monitor and a combination of both. RESULTS: Results from the empirical user study show significant benefits for cognitive load, user preference, and general usability for the instrument-mounted display, while achieving the same level of performance in terms of time and accuracy compared to using a monitor. CONCLUSION: We successfully demonstrate the feasibility of our approach and potential benefits. With ongoing technological advancements, instrument-mounted displays might complement standard monitor setups for surgical navigation in order to lower cognitive demands and for improved usability of such systems.
Neurosurgery makes use of preoperative imaging to visualize pathology, inform surgical planning, and evaluate the safety of selected approaches. The utility of preoperative imaging for neuronavigation, however, is diminished by the well-characterized phenomenon of brain shift, in which the brain deforms intraoperatively as a result of craniotomy, swelling, gravity, tumor resection, cerebrospinal fluid (CSF) drainage, and many other factors. As such, there is a need for updated intraoperative information that accurately reflects intraoperative conditions. Since 1982, intraoperative ultrasound has allowed neurosurgeons to craft and update operative plans without ionizing radiation exposure or major workflow interruption. Continued evolution of ultrasound technology since its introduction has resulted in superior imaging quality, smaller probes, and more seamless integration with neuronavigation systems. Furthermore, the introduction of related imaging modalities, such as 3-dimensional ultrasound, contrast-enhanced ultrasound, high-frequency ultrasound, and ultrasound elastography, has dramatically expanded the options available to the neurosurgeon intraoperatively. In the context of these advances, we review the current state, potential, and challenges of intraoperative ultrasound for brain tumor resection. We begin by evaluating these ultrasound technologies and their relative advantages and disadvantages. We then review three specific applications of these ultrasound technologies to brain tumor resection: (1) intraoperative navigation, (2) assessment of extent of resection, and (3) brain shift monitoring and compensation. We conclude by identifying opportunities for future directions in the development of ultrasound technologies.
We combined diffusion tension imaging (DTI) of prefrontal white matter integrity and neuropsychological measures to examine the functional neuroanatomy of human intelligence. Healthy participants completed the Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) along with neuropsychological tests of attention and executive control, as measured by Trail Making Test (TMT) and Wisconsin Card Sorting Test (WCST). Stochastic tractography, considered the most effective DTI method, quantified white matter integrity of the medial orbital frontal cortex (mOFC) and rostral anterior cingulate cortex (rACC) circuitry. Based on prior studies, we hypothesized that posterior mOFC-rACC connections may play a key structural role linking attentional control processes and intelligence. Behavioral results provided strong support for this hypothesis, specifically linking attentional control processes, measured by Trails B and WCST perseverative errors, to intelligent quotient (IQ). Hierarchical regression results indicated left posterior mOFC-rACC fractional anisotropy (FA) and Trails B performance time, but not WCST perseverative errors, each contributed significantly to IQ, accounting for approximately 33.95-51.60% of the variance in IQ scores. These findings suggested that left posterior mOFC-rACC white matter connections may play a key role in supporting the relationship of executive functions of attentional control and general intelligence in healthy cognition.
Angela Albi, Ofer Pasternak, Ludovico Minati, Moira Marizzoni, David Bartrés-Faz, Núria Bargalló, Beatriz Bosch, Paolo Maria Rossini, Camillo Marra, Bernhard Müller, Ute Fiedler, Jens Wiltfang, Luca Roccatagliata, Agnese Picco, Flavio Mariano Nobili, Oliver Blin, Julien Sein, Jean-Philippe Ranjeva, Mira Didic, Stephanie Bombois, Renaud Lopes, Régis Bordet, Hélène Gros-Dagnac, Pierre Payoux, Giada Zoccatelli, Franco Alessandrini, Alberto Beltramello, Antonio Ferretti, Massimo Caulo, Marco Aiello, Carlo Cavaliere, Andrea Soricelli, Lucilla Parnetti, Roberto Tarducci, Piero Floridi, Magda Tsolaki, Manos Constantinidis, Antonios Drevelegas, Giovanni Frisoni, Jorge Jovicich, and Consortium PharmaCog. 1/2017. “Free Water Elimination Improves Test-Retest Reproducibility of Diffusion Tensor Imaging Indices in the Brain: A Longitudinal Multisite Study of Healthy Elderly Subjects.” Hum Brain Mapp, 38, 1, Pp. 12-26.Abstract
Hypoplastic left heart syndrome (HLHS) is a single-ventricle congenital heart disease that is fatal if left unpalliated. In HLHS patients, the tricuspid valve is the only functioning atrioventricular valve, and its competence is therefore critical. This work demonstrates the first automated strategy for segmentation, modeling, and morphometry of the tricuspid valve in transthoracic 3D echocardiographic (3DE) images of pediatric patients with HLHS. After initial landmark placement, the automated segmentation step uses multi-atlas label fusion and the modeling approach uses deformable modeling with medial axis representation to produce patient-specific models of the tricuspid valve that can be comprehensively and quantitatively assessed. In a group of 16 pediatric patients, valve segmentation and modeling attains an accuracy (mean boundary displacement) of 0.8 ± 0.2 mm relative to manual tracing and shows consistency in annular and leaflet measurements. In the future, such image-based tools have the potential to improve understanding and evaluation of tricuspid valve morphology in HLHS and guide strategies for patient care.
The Neuroimage Analysis Center's Computational Clinical Anatomy Core and the Surgical Planning Laboratory at Brigham and Women's Hospital is pleased to make available a multi-modality MRI-based atlas of the brain. Data was acquired at the Martinos Center for Biomedical Imaging (courtesy Dr. Lawrence Wald) on a Siemens 3T scanner, using a multi-array head coil, in a healthy, 42 year old male. The data set consists of : 1. a volumetric whole head MPRAGE series (voxel size 0.75 mm isotropic). 2. a volumetric whole head T2-weighted series (voxel size 0.75 mm isotropic). 3. a downsampled version of both acquisitions at 1mm isotropic resolution. 4. a per voxel labeling of the structures based on the 1mm volumes. 5. a color file mapping label values to RadLex-ontology derived names and colors suitable for display. 6. MRML files for displaying the volumes in 3D Slicer version 3.6 or greater, available for download. The atlas data is made available under terms of the 3D Slicer License section B. The Slicer4 version also consists of 1. hypotalamic parcellation (courtesy Nikos Makris [Neuroimage. 2013]) 2. cerebellar parcellation (courtesy Nikos Makris [J Cogn Neurosci. 2003], [Neuroimage. 2005]) 3.head and neck muscles segmentation 4. anatomical model hierarchy 5. several pre-defined Scene Views (“anatomy teaching files”). All in a mrb (Medical Reality Bundle) archive file that contains the mrml scene file and all data for loading into Slicer 4 for displaying the volumes in 3D Slicer version 4.0 or greater, available for download. This work is funded as part of the Neuroimaging Analysis Center, grant number P41 RR013218, by the NIH's National Center for Research Resources (NCRR) and grant number P41 EB015902, by the NIH's National Institute of Biomedical Imaging and Bioengineering (NIBIB) and the Google Faculty Research Award. Contributors: Ilwoo Lyu and Martin Styner: Sulcal Curves, Samira Farough: Ventricular System, Ibraheem Naeem and Maria Naeem: Head and Neck Muscles, George Papadimitriou: Cerebellar Parcellation, Madiha Tahir: White Matter. This atlas maybe viewed with our Open Anatomy Browser.
More than half of all cancer patients receive radiotherapy in their treatment process. However, our understanding of abnormal transcriptional responses to radiation remains poor. In this study, we employ an extended definition of Ollivier-Ricci curvature based on LI-Wasserstein distance to investigate genes and biological processes associated with ionizing radiation (IR) and ultraviolet radiation (UV) exposure using a microarray dataset. Gene expression levels were modeled on a gene interaction topology downloaded from the Human Protein Reference Database (HPRD). This was performed for IR, UV, and mock datasets, separately. The difference curvature value between IR and mock graphs (also between UV and mock) for each gene was used as a metric to estimate the extent to which the gene responds to radiation. We found that in comparison of the top 200 genes identified from IR and UV graphs, about 20~30% genes were overlapping. Through gene ontology enrichment analysis, we found that the metabolic-related biological process was highly associated with both IR and UV radiation exposure.
We have developed a novel method to describe human white matter anatomy using an approach that is both intuitive and simple to use, and which automatically extracts white matter tracts from diffusion MRI volumes. Further, our method simplifies the quantification and statistical analysis of white matter tracts on large diffusion MRI databases. This work reflects the careful syntactical definition of major white matter fiber tracts in the human brain based on a neuroanatomist's expert knowledge. The framework is based on a novel query language with a near-to-English textual syntax. This query language makes it possible to construct a dictionary of anatomical definitions that describe white matter tracts. The definitions include adjacent gray and white matter regions, and rules for spatial relations. This novel method makes it possible to automatically label white matter anatomy across subjects. After describing this method, we provide an example of its implementation where we encode anatomical knowledge in human white matter for ten association and 15 projection tracts per hemisphere, along with seven commissural tracts. Importantly, this novel method is comparable in accuracy to manual labeling. Finally, we present results applying this method to create a white matter atlas from 77 healthy subjects, and we use this atlas in a small proof-of-concept study to detect changes in association tracts that characterize schizophrenia.
We propose a method for the automated identification of key white matter fiber tracts for neurosurgical planning, and we apply the method in a retrospective study of 18 consecutive neurosurgical patients with brain tumors. Our method is designed to be relatively robust to challenges in neurosurgical tractography, which include peritumoral edema, displacement, and mass effect caused by mass lesions. The proposed method has two parts. First, we learn a data-driven white matter parcellation or fiber cluster atlas using groupwise registration and spectral clustering of multi-fiber tractography from healthy controls. Key fiber tract clusters are identified in the atlas. Next, patient-specific fiber tracts are automatically identified using tractography-based registration to the atlas and spectral embedding of patient tractography. Results indicate good generalization of the data-driven atlas to patients: 80% of the 800 fiber clusters were identified in all 18 patients, and 94% of the 800 fiber clusters were found in 16 or more of the 18 patients. Automated subject-specific tract identification was evaluated by quantitative comparison to subject-specific motor and language functional MRI, focusing on the arcuate fasciculus (language) and corticospinal tracts (motor), which were identified in all patients. Results indicate good colocalization: 89 of 95, or 94%, of patient-specific language and motor activations were intersected by the corresponding identified tract. All patient-specific activations were within 3mm of the corresponding language or motor tract. Overall, our results indicate the potential of an automated method for identifying fiber tracts of interest for neurosurgical planning, even in patients with mass lesions.
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.
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.
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.
This study determines the impact of change in aeration in sinonasal cavities on the robustness of passive-scattering proton therapy plans in patients with sinonasal and nasopharyngeal malignancies. Fourteen patients, each with one planning CT and one CT acquired during radiotherapy were studied. Repeat and planning CTs were rigidly aligned and contours were transferred using deformable registration. The amount of air, tumor, and fluid within the cavity containing the tumor were measured on both CTs. The original plans were recalculated on the repeat CT. Dosimetric changes were measured for the targets and critical structures. Median decrease in gross tumor volume (GTV) was 19.8% and correlated with the time of rescan. The median change in air content was 7.1% and correlated with the tumor shrinkage. The median of the mean dose D change was +0.4% for GTV and +0.3% for clinical target volume. Median change in the maximum dose D of the critical structures were as follows: optic chiasm +0.66%, left optic nerve +0.12%, right optic nerve +0.38%, brainstem +0.6%. The dose to the GTV decreased by more than 5% in 1 case, and the dose to critical structure(s) increased by more than 5% in three cases. These four patients had sinonasal cancers and were treated with anterior proton fields that directly transversed through the involved sinus cavities. The change in dose in the replanning was strongly correlated with the change in aeration (P = 0.02). We found that the change in aeration in the vicinity of the target and the arrangement of proton beams affected the robustness of proton plan.
In the repeatability analysis, when the measurement is the mean value of a parametric map within a region of interest (ROI), the ROI size becomes important as by increasing the size, the measurement will have a smaller variance. This is important in decision-making in prospective clinical studies of brain when the ROI size is variable, e.g., in monitoring the effect of treatment on lesions by quantitative MRI, and in particular when the ROI is small, e.g., in the case of brain lesions in multiple sclerosis. Thus, methods to estimate repeatability measures for arbitrary sizes of ROI are desired. We propose a statistical model of the values of parametric map within the ROI and a method to approximate the model parameters, based on which we estimate a number of repeatability measures including repeatability coefficient, coefficient of variation, and intraclass correlation coefficient for an ROI with an arbitrary size. We also show how this gives an insight into related problems such as spatial smoothing in voxel-wise analysis. Experiments are conducted on simulated data as well as on scan-rescan brain MRI of healthy subjects. The main application of this study is the adjustment of the decision threshold based on the lesion size in treatment monitoring.