MEGN 536 – Computational Biomechanics Prof. Anthony J. Petrella Basics of Medical Imaging Introduction to Simpleware Software

Medical Imaging Used for measuring anatomical structures… size, shape, relative position in body Can reconstruct geometry for modeling purposes X-ray techniques planar x-rays, mammography, chest x-ray, bone fracture CT scans – computed tomography Nuclear imaging, radioactive isotope planar imaging, bone scan positron emission tomography (PET) MRI – magnetic resonance imaging Ultrasound

Medical Imaging Ionizing Non-ionizing Broken molecular Ultrasound (~1mm) Ionizing Non-ionizing Broken molecular bonds, DNA damage May produce heating, induce currents Non-thermal, low induction

X-ray Imaging (Roentgenogram) Wilhelm Röntgen (1845-1923) Nov 1895, announces X-ray discovery Jan 1896, images needle in patient’s hand 1901, receives first Nobel Prize in Physics Röntgen’s wife, 1895

X-ray Imaging X-ray film shows intensity as a negative ( dark areas, high x-ray detection)

X-ray Imaging X-ray film shows intensity as a negative ( dark areas, high x-ray detection) = radiolucency

CT Imaging Computed tomography Tomography – imaging by sections or sectioning, creation of a 2D image by taking a slice through a 3D object 2D images are captured with X-ray techniques X-ray source is rotated through 360° and images are taken at regular intervals CT image is computed from X-ray data

CT Imaging Developed by Sir Godfrey Hounsfield, engineer for EMI PLC 1972 Nobel Prize 1979 (with Alan Cormack) “Pretty pictures, but they will never replace radiographs” –Neuroradiologist 1972 early today

Inhalation Exhalation

How a CT Image is Formed X-ray source is rotated around body for each slice Patient is moved relative to the beam Figure below does not show it well, but the X-ray beam has a thickness  each slice has a thickness Note: slice thickness http://www.sprawls.org/resources/CTIMG/module.htm

How a CT Image is Formed Figures below show only two views 90° apart A process of “back projection” is used to indicate regions where X-ray attenuation is greater – i.e., tissue is more dense

How a CT Image is Formed Example at left w/ only 2 views shows poor image Clinical CT uses several hundred views for each slice Data collected in matrix

CT Image Data Recall that each CT slice has a thickness  each element in the data matrix for a single CT slice represents a measurement of X-ray attenuation for a small volume or “voxel” of tissue X-ray attenuation is expressed in terms of the X-ray attenuation coefficient, which is dependent primarily on tissue density

CT Numbers CT numbers are expressed in Hounsfield units (HU) and normalized to the attenuation coefficient of water (atomic number)

CT Numbers & Viewing a CT Image CT numbers usually recorded as 12-bit binary number, so they have 212 = 4096 possible values Values arranged on a scale from -1024 HU to +3071 HU Scale is callibrated so air gives a value of -1024 HU and water has a CT number of 0 HU Dense cortical bone falls in the +1000 to +2000 HU range 0-2000 HU 1000-2000 HU

MR Imaging Magnetic resonance imaging 1946: Felix Block and Edward Purcell discover magnetic resonance 1975-1977: Richard Ernst and Peter Mansfifield develop MR imaging An object is exposed to a spatially varying magnetic field, causing certain atomic nuclei to spin at their resonant frequencies An electromagnetic signal is generated and varies with spatial position and tissue type Hydrogen is commonly measured – hence, good contrast for soft tissues that contain more water than hard tissues like bone

MR Imaging – 30 Years Later “Interesting images, but will never be as useful as CT” –Neuroradiologist (different), 1982 First brain MR image Contemporary Image

Notes on CT v. MR Images CT image based on X-ray beam attenuation, depends on tissue density CT images generally regarded as better for visualization & contrast in bone imaging Bone density and modulus can be estimated MR image based on resonance of certain atomic nuclei, e.g. hydrogen MR images generally regarded as better for visualization & contrast in imaging soft tissues, which contain more water than bone

3D Reconstruction CT & MR images represent 2D slices through 3D anatomic structures 2D slices can be “stacked” and reconstructed to form an estimate of the original 3D structure

Simpleware Software Simpleware (http://simpleware.com/) is a leading commercial software program for reconstruction of CT & MR image data

What Data Format Does Simpleware Read? Most medical images are saved in the DICOM image format What is DICOM? The standard for Digital Imaging and Communications in Medicine Developed by the National Electrical Manufacturers Association (NEMA) in conjunction with the American College of Radiology (ACR) Covers most image formats for all of medicine Specification for messaging and communication between imaging machines You don’t need to know the details of the format, but software is happiest when reading DICOM images

What If You Don’t Have DICOM Data? DePuy Orthopaedics, Inc. Feb 2005 What If You Don’t Have DICOM Data? You will need to use manual input methods with to read the data You need to know something about the images A CT or MR scan consists of many slices We will be focused on bone modeling, so CT data will be our main interest It is also important to remember how a CT image slice is formed and what data it contains CONFIDENTIAL

Data in an Image File The format of CT numbers in the data file depends on the precision of the binary data For CT numbers, we only need to cover the 12-bit range, -1024 to 3071 short has 2 bytes = 2 × 8 bits/byte = 216 binary values = 65,536 When using unsigned shorts the data is shifted so all CT numbers are positive  0 to 4095

Data in an Image File Recall a single CT slice is a matrix of data 512 x 512 is a common size  262,144 pixels Each element in the matrix represents a pixel value with a binary format of “short”, therefore each pixel contains 2 bytes of data 262,144 x 2 = 524,288 bytes, any additional data is part of the “header”

Data in an Image File Visible Human link on class website Data are available for download Download sample of Visible Human data from today’s Class Notes page These images are 512 x 512 and the data format is unsigned short How large is the header (bytes)?

Data in an Image File 512 x 512 = 262,144 pixels Each element in the matrix represents a pixel value with a binary format of “short”, therefore each pixel contains 2 bytes of data 262,144 x 2 = 524,288 bytes, any additional data is part of the “header” Total file size is 527,704  header is 3416 bytes

Starting Simpleware DO NOT use the search box on the Start menu You should find a ScanIP icon in the EG Apps folder Run Simpleware with the ScanIP icon Since we do not have a DICOM dataset, choose to Import Raw Stack

Importing Vizible Human CT Data Download ZIP file from class notes page and uncompress in a separate folder Use following settings to import raw stack…

Working with Vizible Human Data Now you are ready to work on reconstructing geometry of the knee (next time)

Simpleware Tutorials Find tutorial and reference guide under Welcome tab

Simpleware Tutorials Complete Tutorial Chapter 7 to learn basics of segmentation and meshing