PRINCIPLES AND APPROACHES 3D Medical Imaging

Introduction (I) – Purpose and Sources of Medical Imaging Purpose  Given a set of multidimensional images, output qualitative / quantitative information about the object/object system under study in these images. Sources of Images  2D: digital radiography  Computerized tomography (CT)  Magnetic resonance imaging (MRI)  Positron emission tomography (PET)  Single Photon emission computed tomography (SPECT)  Ultrasound (US)  Functional MRI (fMRI)  3D: A time sequence of 2D images or a volume of tomography  4D: A sequence of 3D images of a dynamic object  5D and up  Among tomographic modalities, CT, MRI, and US provide structural/anatomical information; PET, SPECT, and fMRI as well as doppler US provide functional information 2

Introduction (II) – Objects and Classification of Study Objects of Study  Rigid (e.g., bones) vs. deformable (e.g., soft-tissue structures)  Static (e.g., skull) vs. dynamic (e.g., heart, joints)  Mixed characteristics, such as MRI 3D study of the head: white matter, gray matter, and cerebrospinal fluid  Qualitative (e.g., visually) vs. quantitative information (e.g., statistically) Classification  Operations: preprocessing, visualization, manipulation, analysis  Viewing medium: computer monitor, holography, head-mounted display  Systems  physician display console (by imaging device vendors)  Image processing/visualization workstations supplied by workstation vendors  3D imaging software (commercial products)  University-based 3D imaging software (often freely available) 3

Introduction (III) – Schematic Representation of 3D Imaging Systems 4

Introduction (IV) – Basics and Terminology 5

Introduction (V) – Basics and Terminology Object, Object system (a collection of objects) Body region Imaging device Pixel, voxel Scene, scene domain, intensity, binary scene K-th slice, pixel size, slice thickness, slice location, slice spacing Structure, structure system Rendition of a scene/structure/structure system Coordinate systems: imaging device, scene, structure, display (viewing) 6

Introduction (VI) – Object Characteristics Graded composition  Voxels constituting the femur have a gradation of density values; however, they “hang together” to form the femur Hanging-Togetherness (Gestalt)  A configuration, pattern, or organized field having specific properties that cannot be derived from the summation of its component parts; a unified whole 7

Preprocessing – ROI/VOI Region of Interest (ROI)/Volume of Interest (VOI) A sub-scene with reduced sized of the scene domain and/or the intensity ROI/VOI operations may  Specify a rectangle/rectangular volume, or  Drawing and painting, or  Specify ROI/VOI loosely, indicate a region containing ROI but exclude unwanted regions with similar property [Figure; from left to right, top to bottom] A region of interest specified by a rectangular box in the scene (a); the output is shown in (b); region of arbitrary shape by drawing (c) and painting (d) 8

Preprocessing – Filtering (Enhancing) Filtering operations convert a given scene into another scene to enhance wanted (object) information and to suppress unwanted (noise, background) information  Edge Enhancing  Gradient 9 8-connectivity in 2D 6-connectivity in 3D

Preprocessing – Edge Enhancing A slice of a 3D MR scene of a patient’s head (a) and its edge-enhancing filtered output with a 2D (b) and a 3D neighborhood (c). 10

Preprocessing – Filtering (Suppressing)  Edge suppressing, smoothing, or averaging – low-pass filtering Illustration of a smoothing 2D Gaussian Filter (b), a 3D Gaussian filter (c), and a median filter (d) for the scene in (a) Interpolation  Scene-Based Interpolation Methods  Object-Based Interpolation Methods 11

Preprocessing – Diffusion Intensity gradients in a given scene are considered to cause a “flow” within the scene whose functional dependence on gradient is controlled through a parameter K. 12

Preprocessing Registration  Scene-Based Registration Methods  Rigid  Deformable  Object-Based Registration Methods  Rigid  Deformable 13

Preprocessing Segmentation  Hard, Boundary-Based, Automatic Methods  Iso-Surfacing Methods  Gradient-Based Methods  Fuzzy, Boundary-Based, Automatic Methods  Hard, Boundary-Based, Assisted Methods  Active Contours  Live Wire/Lane  Hard, Region-Based, Automatic Methods  Thresholding  Clustering  Fuzzy, Region-Based, Automatic Methods  Hard, Region-Based, Assisted Methods  Fuzzy, Region-Based, Assisted Methods 14

Visualization Scene-Based Visualization Methods  Slice Mode  Volume Mode  Maximum Intensity Projection (MIP)  Surface Rendering  Volume Rendering Object-Based Visualization Methods  Maximum Intensity Projection  Surface Rendering  Volume Rendering Misconceptions and Challenges in Visualization 15

Further Topics Manipulation  Rigid model  Deformable model Analysis  Scene-Based  Object-Based Sources of difficulty in 3D imaging  Qualitative validation  Quantitative validation 16