SURFACE RECONSTRUCTION OF EX-VIVO HUMAN V1 THROUGH IDENTIFICATION OF THE STRIA OF GENNARI USING MRI AT 7T

Similar documents
Detecting Cortical Surface Regions in Structural MR Data

ARTICLE IN PRESS. Accurate prediction of V1 location from cortical folds in a surface coordinate system. Introduction

NIH Public Access Author Manuscript Neuroimage. Author manuscript; available in PMC 2009 February 15.

The Intrinsic Shape of Human and Macaque Primary Visual Cortex

The organization of the human cerebral cortex estimated by intrinsic functional connectivity

Lilla Zöllei A.A. Martinos Center, MGH; Boston, MA

Surface-based Analysis: Inter-subject Registration and Smoothing

Automatic segmentation of the cortical grey and white matter in MRI using a Region Growing approach based on anatomical knowledge

Computational Neuroanatomy

Case Study: Interacting with Cortical Flat Maps of the Human Brain

Caret Tutorial Contours and Sections April 17, 2007

THE identification and delineation of brain structures from

Simultaneous Cortical Surface Labeling and Sulcal Curve Extraction

A Design Toolbox to Generate Complex Phantoms for the Evaluation of Medical Image Processing Algorithms

Sereno, M. I., A. M. Dale, et al. (1995). Borders of multiple visual areas in humans revealed by functional magnetic resonance imaging.

User Guide for Sylvius 4 Online An Interactive Atlas and Visual Glossary of Human Neuroanatomy

Supplementary Figure 1

This exercise uses one anatomical data set (ANAT1) and two functional data sets (FUNC1 and FUNC2).

Caret5 Tutorial Segmentation, Flattening, and Registration

ACCURATE NONLINEAR MAPPING BETWEEN MNI152/COLIN27 VOLUMETRIC AND FREESURFER SURFACE COORDINATE SYSTEMS

Acquisition Methods for fmri at 7 Tesla

Detecting Changes In Non-Isotropic Images

Introduction to Neuroimaging Janaina Mourao-Miranda

Chapter 3 Set Redundancy in Magnetic Resonance Brain Images

Computational Medical Imaging Analysis Chapter 4: Image Visualization

Open Topology: A Toolkit for Brain Isosurface Correction

MARS: Multiple Atlases Robust Segmentation

Automatic Registration-Based Segmentation for Neonatal Brains Using ANTs and Atropos

Introduc)on to FreeSurfer h0p://surfer.nmr.mgh.harvard.edu. Jenni Pacheco.

UNC 4D Infant Cortical Surface Atlases, from Neonate to 6 Years of Age

A Novel Contrast for DTI Visualization for Thalamus Delineation

Automated MR Image Analysis Pipelines

arxiv: v2 [q-bio.qm] 16 Oct 2017

Advanced Visual Medicine: Techniques for Visual Exploration & Analysis

Fmri Spatial Processing

NeuroImage (in press) PDF - 6/14/05

Characterizing human retinotopic mapping with conformal geometry: A preliminary study

Multimodal Visualization of DTI and fmri Data using Illustrative Methods

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008

NIH Public Access Author Manuscript IEEE Trans Med Imaging. Author manuscript; available in PMC 2013 September 11.

Diffusion model fitting and tractography: A primer

Electrical Engineering, Vanderbilt University, Nashville, TN, USA b. Computer Science, Vanderbilt University, Nashville, TN, USA c

Brain Warping Via Landmark Points and Curves with a Level Set Representation

ADAPTIVE GRAPH CUTS WITH TISSUE PRIORS FOR BRAIN MRI SEGMENTATION

MARS: Multiple Atlases Robust Segmentation

Reliability in multi-site structural MRI studies: Effects of gradient non-linearity correction on phantom and human data

Subvoxel Segmentation and Representation of Brain Cortex Using Fuzzy Clustering and Gradient Vector Diffusion

An Integrated Software Suite for Surface-based Analyses of Cerebral Cortex

Structural MRI analysis

fmri Image Preprocessing

Edge Tracking of subjective contours in Biomedical Imaging

Modified Normal Vector Voting Estimation in neuroimage

Neural Population Tuning Links Visual Cortical Anatomy to Human Visual Perception

A Model-Independent, Multi-Image Approach to MR Inhomogeneity Correction

Where are we now? Structural MRI processing and analysis

HST.583 Functional Magnetic Resonance Imaging: Data Acquisition and Analysis Fall 2008

(Moon et al., 2007). Animal use was approved by the Institutional Animal Care and Use

Segmenting Glioma in Multi-Modal Images using a Generative Model for Brain Lesion Segmentation

An Introduction To Automatic Tissue Classification Of Brain MRI. Colm Elliott Mar 2014

CHAPTER 2. Morphometry on rodent brains. A.E.H. Scheenstra J. Dijkstra L. van der Weerd

GLIRT: Groupwise and Longitudinal Image Registration Toolbox

NIH Public Access Author Manuscript Proc SPIE. Author manuscript; available in PMC 2013 December 31.

Supplementary methods

Supplementary Methods

Effects of Registration Regularization and Atlas Sharpness on Segmentation Accuracy

A Generative Model for Image Segmentation Based on Label Fusion Mert R. Sabuncu*, B. T. Thomas Yeo, Koen Van Leemput, Bruce Fischl, and Polina Golland

NIH Public Access Author Manuscript Med Image Comput Comput Assist Interv. Author manuscript; available in PMC 2009 December 4.

HHS Public Access Author manuscript Proc IEEE Int Conf Comput Vis. Author manuscript; available in PMC 2015 June 14.

HHS Public Access Author manuscript IEEE Trans Med Imaging. Author manuscript; available in PMC 2015 June 14.

QuickNII Workflow/User guide

The Anatomical Equivalence Class Formulation and its Application to Shape-based Computational Neuroanatomy

Functional MRI data preprocessing. Cyril Pernet, PhD

2 Michael E. Leventon and Sarah F. F. Gibson a b c d Fig. 1. (a, b) Two MR scans of a person's knee. Both images have high resolution in-plane, but ha

Learning Task-Optimal Registration Cost Functions for Localizing Cytoarchitecture and Function in the Cerebral Cortex

Detection of Unique Point Landmarks in HARDI Images of the Human Brain

Automatic MS Lesion Segmentation by Outlier Detection and Information Theoretic Region Partitioning Release 0.00

Video Registration Virtual Reality for Non-linkage Stereotactic Surgery

A Novel Information Theoretic and Bayesian Approach for fmri data Analysis

Generation of Hulls Encompassing Neuronal Pathways Based on Tetrahedralization and 3D Alpha Shapes

Fiber Selection from Diffusion Tensor Data based on Boolean Operators

PROSTATE CANCER DETECTION USING LABEL IMAGE CONSTRAINED MULTIATLAS SELECTION

Section 9. Human Anatomy and Physiology

NIH Public Access Author Manuscript Proc Soc Photo Opt Instrum Eng. Author manuscript; available in PMC 2014 October 07.

Cortical Surface Shape Analysis Based on Spherical Wavelet Transformation

Brain Surface Conformal Parameterization with Algebraic Functions

MB-EPI PCASL. Release Notes for Version February 2015

Using a Statistical Shape Model to Extract Sulcal Curves on the Outer Cortex of the Human Brain

Image Registration Driven by Combined Probabilistic and Geometric Descriptors

Skull Segmentation of MR images based on texture features for attenuation correction in PET/MR

White Pixel Artifact. Caused by a noise spike during acquisition Spike in K-space <--> sinusoid in image space

SPLASh: a software tool for stereotactic planning of recording chamber placement and electrode trajectories

Lightweight, flexible MATLAB-based visualization and analysis of FreeSurfer surfaces

A Multiple-Layer Flexible Mesh Template Matching Method for Nonrigid Registration between a Pelvis Model and CT Images

Chapter 9 Conclusions

What Data to Co-register for Computing Atlases

Effects of Resolution and Registration Algorithm on the Accuracy of EPI vnavs for Real Time Head Motion Correction in MRI

Elastically Deforming a Three-Dimensional Atlas to Match Anatomical Brain Images

Functional MRI in Clinical Research and Practice Preprocessing

BrainPrint : Identifying Subjects by their Brain

Investigating Disease in the Human Brain with Conformal Maps and Conformal Invariants

Transcription:

SURFACE RECONSTRUCTION OF EX-VIVO HUMAN V1 THROUGH IDENTIFICATION OF THE STRIA OF GENNARI USING MRI AT 7T Oliver P. Hinds 1, Jonathan R. Polimeni 2, Megan L. Blackwell 3, Christopher J. Wiggins 3, Graham Wiggins 3, André J.W. van der Kouwe 3, Lawrence L. Wald 3, Eric L. Schwartz 1,2,4, and Bruce Fischl 3,5 1 Department of Cognitive and Neural Systems, Boston University, Boston, MA 02215, USA 2 Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA 3 Department of Radiology, MGH, Athinoula A Martinos Center, Harvard Medical School, Charlestown, MA 02129, USA 4 Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA 5 Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA January 11, 2005 Abstract Objective The stria of Gennari provides a definitive anatomical landmark delineating primary visual cortex (V1). Although the stria has been imaged previously both ex-vivo and in-vivo [1; 2], no surface reconstruction was performed. Here, we detected the stria in structural MR images of ex-vivo human brain and reconstructed the full striate surface. Surfaces computed from intact brain samples eliminate image alignment error, providing more accurate reconstructions than those from serial sections. Computer-flattened reconstructions from MRI data allow quantitative comparison with histologically defined area boundaries from physically flattened cortical tissue. Accurate localization of V1 across ex-vivo samples supplies ground-truth data for probabilistic anatomical atlases used for cross-subject registration. Techniques developed for ex-vivo reconstructions may also be applied in-vivo, allowing direct comparison of functionally and structurally determined V1 in the same subject, and facilitating the testing of human visuotopy models. We present a semi-automated method to reconstruct the striate surface of V1 ex-vivo from structural MRI, which addresses several difficulties associated with surface reconstruction of highresolution serial sections a task which is not well supported by current MRI-based surface reconstruction software. Methods We imaged ex-vivo human occipital cortex at 7T with enhanced contrast between gray and white matter, which enabled identification of the stria of Gennari, a stripe of myelinated tissue characteristic of layer IVb of V1. We developed software aiding the manual identification of stria, and therefore V1, in MR images. The software stores vertices identified as points of the striate surface and uses them as input to a surface tiling algorithm. The output of surface tiling is a two-dimensional, manifold triangular mesh representing the striate surface, which is then flattened quasi-isometrically (see Balasubramanian et al., this meeting) for comparison with physical or computerized flattenings of V1. To increase SNR while reducing MR image distortions due to magnetic field inhomogeneities and susceptibility artifacts, we employed a recently developed high-bandwidth, multiecho FLASH pulse sequence [3]. Results and Discussion Examples of flattened V1 are demonstrated, including statistics such as surface area, perimeter, and per-vertex error in flattening. Comparisons with the flattened shape of V1 defined using fmri are shown. Presented at 11th Annual Meeting of the Organization for Human Brain Mapping. Abstract number 140. Contact info: Oliver P. Hinds, Computer Vision and Computational Neuroscience Lab, 677 Beacon St., Boston, MA 02215. URL: http://eslab.bu.edu, Email: oph@cns.bu.edu 1

Conclusions We present methods for reconstructing the striate surface within V1 in ex-vivo human data using 7T structural MRI. This facilitates comparison of techniques of area identification, development of probabilistic atlases, and testing of two-dimensional models of human visuotopy. References and Acknowledgements [1] V.P. Clark, E. Courchesne, and M. Grafe. In vivo myeloarchitectonic analysis of human striate and extrastriate cortex using magnetic resonance imaging. Cerebral Cortex, 2(5):417 424, 1992. [2] E.L. Barbier, S. Marrett, A. Danek, A. Vortmeyer, P. van Gelderen, J. Duyn, P. Bandettini, J. Grafman, and A. P. Koretsky. Imaging cortical anatomy by high-resolution MR at 3.0T: detection of the stripe of Gennari in visual area 17. Magnetic Resonance in Medicine, 48(4):735 738, 2002. [3] B. Fischl, D.H. Salat, A.J. van der Kouwe, N. Makris, F. Segonne, B.T. Quinn, and A.M. Dale. Sequence-independent segmentation of magnetic resonance images. NeuroImage, 23 Suppl 1:S69 84, 2004. This study was supported by NIH/NIBIB EB001550. 2

Figure 1: Reconstruction of the striate surface of V1 from MR images gathered at 7T acquired using 130 µm isotropic voxels. The stria was manually located, and the surface was reconstructed using a surface tiling algorithm. Four views of a smoothed striate surface are shown. (A) A view of the striate surface looking from medial to lateral into the calcarine sulcus. (B) A view looking down the anteroposterior axis. (C) A view looking ventrally at the dorsal bank of the calcarine sulcus. (D) A view looking posteriorly into the calcarine sulcus. 3

Figure 2: Reconstruction of the striate surface shown embedded in three orthogonal slices from the MR data. The surface is unsmoothed and flat shaded to accentuate ridges orthogonal to the slice direction. These ridges demonstrate the difficulty of locating precise boundaries in high-resolution imaging. 4

Figure 3: (A) Single MR image that intersects the striate surface. (B) The same MR image, showing the points identified as the striate surface. These points become vertices of the final triangular mesh representation. The colors label independent connected components. 5