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MEGN 536 – Computational Biomechanics

MEGN 536 – Computational Biomechanics

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MEGN 536 – Computational Biomechanics

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

  2. 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

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

  4. 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

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

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

  7. 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

  8. 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

  9. Inhalation Exhalation

  10. 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

  11. 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

  12. 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

  13. 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

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

  15. 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

  16. 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

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

  18. 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

  19. 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

  20. Mimics Software • Mimics ( is the leading commercial software program for reconstruction of CT & MR image data

  21. What Data Format Does Mimics 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 Mimics is happiest when reading DICOM images

  22. 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

  23. 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

  24. 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”

  25. 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)?

  26. 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

  27. Starting Mimics • You should find a Mimics icon on the desktop… or • Find “Materialise Software” in Start menu… or • Type “Mimics” in the Start menu search box • Run Mimics • The software should ask if you want to reboot…click “No” or “Reboot Later” • If Mimics doesn’t start then on it’s own, attempt to start it again

  28. Mimics Tutorials • Complete Lessons 1 and 2 in Mimics SE Course Book, pages 8-28 • You will need the Mimics SE Course Data, which is posted on the front page of the class website • If you don’t have your Course Book, it is also posted on the class website