Magnetic Resonance Research Facility

  • Current Research Projects at the MRRF

    The MR Research Facility currently supports more than 40 research projects from ten different departments within the University of Iowa. The primary utilization of the equipment has been for neuroimaging studies. This includes several large studies from the Departments of Psychiatry, Neurology, Anesthesia, and Radiology. The majority of these studies have acquired anatomical brain scans for brain morphology studies. A smaller number of studies have utilized fMRI and diffusion tensor imaging (DTI). Other research projects have utilized MR imaging to study cardiac function, assess cartilage, liver fat content, and have evaluated subcutaneous facial implants. Four of our current research projects are outlined below.

    Imaging of the Lung

    William R. Kearney, Ph.D

    Kearney

    We are developing pulse sequences and methodologies to image lung structure and function. The structure and function of the lung has been investigated using magnetic resonance with hyperpolarized He and Xe. Due to the high cost and complexity of using these agents, we are seeking more versatile and economical alternatives. Fluorine imaging is a promising approach that may replace exotic nuclei in both structural and functional pulmonary studies. Fluorine has a high intrinsic MR sensitivity, and a number of biologically benign reagents are available to investigate properties of both the gas and tissue volumes. Fluorine imaging may also provide information about the gas/tissue interface that is not accessible by any other method.

    Assessment of psychiatric disorders using pH-sensitive imaging methods

    Casey Johnson, PhD, Postdoctoral Fellow

    Casey

    A growing area of interest in psychiatric research is the role of pH in various disorders, for example bipolar and panic. Baseline pH as well as pH dynamics may be fundamental to these disorders. To better understand these mechanisms, methods that can probe brain pH noninvasively are needed. A number of MR methods have been shown to be pH sensitive, including 31P and 1H (lactate) spectroscopy, T1rho imaging, and chemical-exchange saturation transfer (CEST). Spectroscopy techniques are the most common approach to noninvasively assess brain pH. However, these methods have coarse spatial resolution and require long scan times. The methods for pH-sensitive T1rho and CEST imaging are fairly new and have not been extensively demonstrated in vivo. However, these methods have the potential to enable imaging of the entire brain with high spatial and temporal resolution within a reasonable scan time. My project involves the use of these pH-sensitive methods, as well as the development and assessment of improved techniques, to image patients with psychiatric disorders, specifically bipolar and panic. The primary goal of this work is to identify pH differences in these patients versus normal controls. A secondary goal is to develop pH-sensitive MR imaging methods that are practical for human imaging at 3T.
    T1rho Precision Calculator

    Previous Projects

    BrainCSI

    Jeff R. Yager, PhD Student

    Jeff

    Magnetic Resonance Spectroscopy is a technique used in brain imaging to analyze the brain’s chemical content. Spectroscopy data is typically collected with a single voxel (SV) method or a multiple voxel method called Chemical Shift Imaging (CSI). A software tool, BrainCSI, is being developed to help process data obtained in these experiments. The metabolite concentrations are most commonly reported as ratios of two metabolites, but the BrainCSI tool attempts to report absolute concentrations. One current limitation absolute concentrations in spectroscopy is the large voxel size ( ~ 1 cm3). The large voxel size causes multiple tissues types to be contained inside a voxel. Brain metabolites concentrations are known to vary according to the tissue type, most notably that cerebral spinal fluid (CSF) is void of most metabolites of interest. Therefore, it is important to determine each tissue partial volume of the spectroscopy voxel. The BrainCSI tool classifies the anatomical image into different tissue classes and determines the partial volume. The point spread function of the spectroscopy voxel in CSI is also significant and is corrected for in BrainCSI. Finally, in 2D CSI and SVS, there is a chemical shift that is metabolite dependant that needs to be corrected.

    Cerebral Surface Generation and Fully Automated Parcellation

  • Brain pH Changes

    Hye Young Heo, PhD Student

    Young Heo

    Brain pH has been shown to fluctuate as a result of brain activity. It has been difficult to measure such changes in vivo due to lack of methods for non-invasively measuring pH with high spatial and temporal resolution. In this study we performed a series of experiments to access the ability and sensitivity of T1 relaxation in the rotating frame (T1ρ) to measure pH changes in the mouse and human brain evoked by systemically manipulating carbon dioxide or bicarbonate. Furthermore, T1ρ detected a localized acidosis in the human visual cortex induced by a flashing checkerboard. This was confirmed with 31P spectroscopic measurements. In addition, 1H spectroscopy showed a significant elevation of the lactate signal providing a mechanism for the acidosis. These studies will open significant opportunities for understanding the role of pH-dependent signaling in the brain, for understanding the importance of pH in neurological disease, and for novel diagnostic and therapeutic approaches.

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    Figure 1. Human brain T1ρ measurements across a pH spectrum.

    (A) T1ρ maps of the human brain varied with end-tidal CO2 concentration (EtCO2) during the 5% CO2, room air, and hyperventilation conditions. The intensity maps represent T1ρ times ranging between 0ms (basic) to 200ms (acidic). During CO2 inhalation, the widespread increases in T1ρ times are consistent with the expected acidosis as compared to the baseline room air condition. Whereas the reduced T1ρ times during hyperventilation are consistent with the expected alkalosis.

    (B) The change of T1ρ times in brain tissue is clearly seen in the Figure 3B.

    (C) Figure 3C shows the subtracted images between the T1ρ map with hyperventilation and the T1ρ map with room air and between the T1ρ map with 5% CO2 and the T1ρ map with room air. T1ρ times change with respiratory challenges. T1ρ times were calculated in a 5x5 square ROI placed manually in the gray matter as shown in white color square in the Figure 3A.

    Wen Li, PhD

    I recently graduated from the MRI Research Lab with a PhD in Biomedical Engineering. My research was focused primarily on applying a combination of medical image processing tools into a pipeline, which provides an automated labeling to the surface of the human cerebral cortex.

    The cerebral cortex is a highly convolved thin layer neural tissue located at the outermost layer of the human brain. It can be divided into anatomically and functionally meaningful sub-regions. We developed a fast and automated parcellation pipeline, which takes T1 and T2 images of the subject and automatically parcellates the cortical surface of each hemisphere into 24 well defined regions. This tool can be potentially used in studying the brain morphology related with psychiatric and neurological disorders. It will help us understand the underlying pathology that can occur when the brain gets sick. Our parcellation pipeline has been tested to be comparable with manual parcellations. It is a flexible pipeline, which can incorporate various sets of labeling systems and is faster than FreeSurfer (it takes approximately ¼ of time to get similar results).

    The hwelateral (left) and medial (right) surface of the human cerebral cortex parcellated into 24 sub-regionsThe hwelateral (left) and medial (right) surface of the human cerebral cortex parcellated into 24 sub-regions

    The lateral (top) and medial (bottom) surface of the human cerebral cortex parcellated into 24 sub-regions.

    BRAINS3 Software Development

    Steve Dunner, Programmer

    My primary role at MRRF is working on the creation of the BRAINS3 software package, a joint venture with software engineers in the Dept. of Psychiatry. This is a suite of tools to facilitate systematic analysis of MRI datasets. This analysis is performed both dynamically by neuroscientists as well as automatically by the system itself. My work focuses on the latter. I have created several tools for use in this automated processing, including, but not limited to: dynamic generation of Talairach atlases, skull-stripping/brain-masking and surface generation. My current project is a tissue classifier that will allow a user to provide an list of associated pixel value ranges and tissue types, which will then be used to accurately label the entire brain. This can be fine-tuned on a variety of parameters (primarily statistical thresholds). In my spare time I like to volunteer at the hospital and the Knights of Columbus, study topics in computer science and engineering and avoid cleaning my apartment at all costs.

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    Figure (A): Curvature measurement, default coloration, axial view. Note the hemispheric separation in this and the following images. This is calculated by warping hemispheric reference images onto the brain, and using those images to define the regions for cropping. Each hemisphere is a separate surface that are loaded together for this picture.Figure (B): Depth measurement, default coloration, axial view. Figure (C): Cortical depth measurement, blue to red rainbow coloration, axial view. This is a visualization of an auto-calculated statistic.

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    Figure (A): Curvature measurement, default coloration, sagittal view. Figure (B): Depth measurement, default coloration, sagittal view.

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    Figure (A): Axial view of mask. Figure (B): Saggital view of mask. Figure (C): Coronal view of brain mask.

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    Figure (A): Overlay of auto-generated brain mask onto the original image. This allows us to extract the brain image exclusively, which is used later in our surface generation. Figure (B): Talairach atlas, visualized as a rectangular surface. This atlas is uniquely generated for each image, allowing one to specify locations on the brain with a high degree of accuracy. The lines divide the brain into regions that are spaced in such a way as to include certain anatomical regions within each. Figure (C): An image from the in-progress tissue classifier. The blue squares are 'plugs' selected because they contain both the pixel values requested and have an internal variance that is within a certain specified threshold. These plugs are used to build up the labeled regions over multiple iterations, each iteration grabbing successively more plugs, until all the appropriate regions have been captured. Later analysis resolves conflicts and smooths the regions.

    Automated Building Block Assignments for Hexahedral Finite Element Meshing

    Austin J. Ramme, MD/PhD Student

    Finite element analysis has allowed for investigations of the human musculoskeletal system and orthopaedic implant design. IA-FEMesh, an open-source hexahedral meshing software package, provides tools to develop volumetric meshes on a patient-specific basis. Part of the IA-FEMesh meshing protocol relies on manual placement of building blocks around a virtual model. As the complexity of the surface increases (e.g. phalanx versus cervical vertebrae), the number of required blocks increases and hence the time required to create the block structure increases. The Automated Building Block Algorithm incorporates principles from differential geometry, combinatorics, statistical analysis, and computer science to automatically generate a building block structure for a given surface without prior information. The finalized building block structure is imported into IA-FEMesh to generate a hexahedral mesh for finite element analysis. Two metrics were used to judge the quality of the resulting finite element mesh: minimum element Jacobian and minimum element volume. As a final check of mesh quality, simulated material properties, boundary conditions, and initial conditions were applied to the mesh, and the mesh was analyzed using the ABAQUS® finite element solver.

    It is common to perform group analysis of diffusion tensor data based on rotationally invariant scalar indices like fractional anisotropy (FA), relative anisotropy (RA) etc. All of the subjects for the study are mapped to a common coordinate system, often defined by an atlas image. The transformation between the space of the acquired diffusion weighted images and the atlas space is defined via an image registration procedure. The resulting transformation is then applied to the scalar images where the voxel values within the atlas space are interpolated using conventional techniques such as linear interpolation. To perform group analysis of diffusion tensors, it is necessary to interpolate the tensor representation as well as rotate the diffusion tensors to keep the tensors consistent with the tissue reorientation. Higher order diffusion models like the Q-Ball model (QBI) or high order tensor model that are gathered from high angular resolution diffusion imaging (HARDI) data, also need to be reoriented during spatial normalization. This research is focused on developing a generalized me

    The steps of the Automated Building Block Algorithm are illustrated above: (i) reorientation of the bone surface model, (ii) surface feature identification, (iii) point selection filter, (iv) medial axis analysis, (v) surface segmentation, (vi) raw building block definitions, (vii) combinatorial building block reorganization, (viii) building block manipulation, and (ix) finalized building block vertex projection.

    Spatial Normalization of Diffusion Models and Tensor Analysis

    Madhura A. Ingalhalikar, PhD, Biomedical Engineering

    It is common to perform group analysis of diffusion tensor data based on rotationally invariant scalar indices like fractional anisotropy (FA), relative anisotropy (RA) etc. All of the subjects for the study are mapped to a common coordinate system, often defined by an atlas image. The transformation between the space of the acquired diffusion weighted images and the atlas space is defined via an image registration procedure. The resulting transformation is then applied to the scalar images where the voxel values within the atlas space are interpolated using conventional techniques such as linear interpolation. To perform group analysis of diffusion tensors, it is necessary to interpolate the tensor representation as well as rotate the diffusion tensors to keep the tensors consistent with the tissue reorientation. Higher order diffusion models like the Q-Ball model (QBI) or high order tensor model that are gathered from high angular resolution diffusion imaging (HARDI) data, also need to be reoriented during spatial normalization. This research is focused on developing a generalized method for spatial normalization of diffusion models. Following below are the aims of this project:

    1. Develop a generalized tool for spatial normalization of tensors and other higher order diffusion models.
    2. Implement non-linear intersubject registration using a multichannel registration scheme.
    3. Validate the above methods primarily on tensors and then on higher order diffusion models.
    4. Develop tensor analysis tools.

    Expectation Maximization Based Bone Segmentation

    Austin J. Ramme, MD/PhD Student

    Medical imaging technologies have allowed for in vivo exploration and evaluation of the human musculoskeletal system. Three-dimensional bone models generated using image segmentation techniques provide a means to optimize individualized orthopaedic surgical procedures using engineering analyses. Traditionally, manual raters have performed the segmentation of bone from medical images. However these techniques are not clinically practical due to the extensive time and human intervention that is required. This study aims to develop an automated preprocessing and segmentation method to accurately, efficiently, and reliably separate bony regions of interest. Others in our laboratory are working towards producing automatic hexahedral meshes from the surface definitions appropriate for surface contact analysis using the finite element method. Our ultimate goal is to automate the preprocessing, segmentation, and mesh generation procedures to provide a clinically useful means of surgical analysis and planning.

     Figure (A): Surface models of the anterior aspects of twelve phalanx bones generated using the Expectation Maximization algorithm for image segmentation. Figure (B): Euclidean distance map comparison between physical bone surface scans and the Expectation Maximization algorithm based segmentations of the index finger.