Introduction to Medical Image Analysis
DTU Compute Introduction to Medical Image Analysis Rasmus R. Paulsen DTU Compute [email protected] http://www.compute.dtu.dk/courses/02511 http://www.compute.dtu.dk/courses/02512 Slides adapted from Jens E. Wilhjelms lectures DTU Compute Lecture 8 – X-ray imaging and CT scanning 9.00 Lecture Exercises 2 12.00 – 13.00 Lunch break 13.00 - Exercises DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute What can you do after today? Describe the basic use of X-rays Describe the X-ray as an electromagnetic wave Explain the basic technique in an X-ray tube Estimate how simple materials will look on an X-ray Use Lambert-Beers law to compute material attenuation Compute the attenuation of simple non-homogenous materials Describe the concept of tomographic reconstruction Describe the relation between Hounsfield units and tissue types Compute a CT slice using a simple and idealised setup 3 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute X-ray imaging The most used form of medical imaging Simple Cheap Fast Radiation 4 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute The history of X-ray Discovered by chance 5 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Wilhelm Conrad Röntgen German physics professor Experimented with a Crookes tube Discovered that an unknown ray could be captured on photographic plates Named them X-rays – Other call them Röntgen-rays Crookes tube Had no idea they were dangerous Made an X-ray of his wife's hand – First medical X-ray Wilhelm Röntgen's first medical X-ray, of his wife's hand, taken on 22 December 1895 6 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Quick popularity X-ray became popular extremely fast – Shoe fitting – Examine your bones in coin machines – Wedding pictures X-ray clinics in small normal apartments 7 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Dangers People started to realise that exposure to X-rays could be dangerous Hands of X-ray pioneer Mihran Kassabian 8 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Brief history of X-ray and CT 9 1895 1896 1896 1904 1956 1958 1968 1972 1989 Wilhelm Conrad Röntgen discovered X-ray Within a week X-ray was not known worlwide GE and Siemens begin selling X-ray equipment Dangers of radiation are described First reconstruction algorithm is described First prototype CT scanner without computer Hounsfield’s method for CT patented Hounsfield’s method for CT demonstrated in the US First spiral CT scanner enters the market DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute X-rays as electromagnetic radiation It has a frequency f – (v is used for frequency in notes) – Measured in Hertz [Hz] It has a wavelength λ (lambda) – Measured in meters [m] It has a speed – “The speed of light” c 10 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Wavelength – 10 pm < λ < 10 nm pm = picometer = 1×10−12 m nm = nanometer = 1×10−9 m 11 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Production of X-rays Electrons are accelerated using a cathode Some hit the anode (heavy metal target) Slowed down in the anode material – Generating heat – A small part of the energy is transformed to X-rays The electron comes very close to the nucleus – Electromagnetic interaction causes a deviation of the trajectory – The electron looses energy and an X-ray photon is emitted. 12 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Electron volts 1 eV is the energy increase that an electron experiences, when accelerated over a potential difference of 1 V. In medical imaging – 20 keV < E < 150 keV keV = kilo-electron-volts Cathode 13 DTU Compute, Technical University of Denmark 100 kV + Anode Introduction to Medical Image Analysis 25/3/2015 DTU Compute X-ray tube The anode rotates to avoid over heating 14 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute X-ray tube Jackson X-ray tube, 1896. 15 DTU Compute, Technical University of Denmark Modern rotating anode tube Introduction to Medical Image Analysis 25/3/2015 DTU Compute Full X-ray system 16 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute X-ray film Until recently “real film was used” Being replaced by digital radiography The first step was to use phosphor plates that was later “scanned” 17 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Contrast in X-ray images Some materials absorb more X-rays than others We see the X-rays that “got through” Dark area – high radiation – Air – Soft-tissue – Fat Bright area – low radiation – Metals – Bone Scanned X-ray film 18 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute X-ray attenuation (dæmpning) I0 I (x) x 19 DTU Compute, Technical University of Denmark X-rays hits an object and travels through it I 0 is the intensity at the entrance I (x) is what is left on the other side – after a length of x The rest disappears in several different ways Computed using LambertBeer’s law Introduction to Medical Image Analysis 25/3/2015 DTU Compute Lambert-Beer’s law μ: Linear attenuation coefficient I (x) I0 Materials can be described using a linear attenuation coefficient – How much do the material dampen X-rays High coefficient x – Metal and bone Low coefficient 1 High 0.9 Low – Soft tissue and fat 0.8 0.7 exp( x) 0.6 0.5 0.4 0.3 0.2 0.1 0 20 0 1 2 3 4 5 6 Distance in object (cm) 7 8 DTU Compute, Technical University of Denmark 9 10 Introduction to Medical Image Analysis 25/3/2015 DTU Compute Non-homogeneous material Non-homogeneous – not the same everywhere film I0 x2 x3 x5 I0 I0 x4 I0 x1 21 DTU Compute, Technical University of Denmark I3 ? I2 I1 Introduction to Medical Image Analysis 25/3/2015 DTU Compute X-rays in practise Several parameters to tune – KV – Exposure time – Etc Settings dependent on body part Radiologists are trained for this Still a lot of human knowledge in taking good X-rays 22 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Uhomogent materiale A) 1 B) 2 C) 3 D) 4 E) 5 F) 6 27 0 23 0 0 A D B E 1 0 C F DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute DTU Årsfest Fredag 8. maj 2015 Kæmpe fest – Studerende – Undervisere 3000+ til spisning Billetter til spisning kan være meget svære at få 25 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Polyteknisk Forening – Årets Underviser Hvad går det ud på? Prisen “Årets Underviser” som uddeles til Årsfesten, gives til de undervisere, der er særlige dygtige og engagerede, og samtidig formår at brænde igennem med deres undervisningsform. “Årets Underviser” bliver uddelt for at sætte fokus på undervisningskvalitet. Hvad bliver der lagt vægt på? Kåringen af “Årets Underviser” handler om, hvordan du som studerende oplever undervisningen. Så derfor vil begrundelsen for nomineringen, være afgørende for hvem der løber af med titlen som “Årets Underviser”. http://www.pf.dk/en/aarets/ Årets Underviser kåres i forbindelse med den officielle del af DTU's Årsfest. Blandt underviserne er der stor prestige i ikke bare at vinde prisen, men også at blive nomineret, så din nominering betyder noget! 26 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute CT scanning 27 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Invention of Computed Tomography 1972: G.N. Hounsfield, scientist in Middlesex, England – Announced computed axial transverse scanning – Presented cross-sectional images of the head showing tissues inside the brain as separate structures of gray matter, white matter, cerebrospinal fluid, and bone – Pathologic processes such as blood clots, tumors, and infarcts could be easily seen Dr. Hounsfield's discovery completely revolutionized the practice of medicine: Structures inside the human body that had never been imaged before, could now be visualized. 1979 Nobel Prize in Medicine 28 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Development of CT Early 70’s : Several minutes to acquire single slice Today: Less than a minute for a full body scan 29 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Basic principle of CT – Super Sudoku I0 I0 I0 It is assumed that the object consists of small cubes of homogeneous material We want to find the linear attenuation coefficient in each cube I0 30 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Quiz? A B A+B = 13 C D C+D = 10 A+C = 12 31 B+D = 11 DTU Compute, Technical University of Denmark What is A, B, C, D? Can be formulated as a system of equations – 4 unknowns – 4 equations DISCLAIMER: just an illustration – not solvable… Introduction to Medical Image Analysis 25/3/2015 DTU Compute Basic principle of CT – Super Sudoku I0 I0 I0 I0 32 It is assumed that the object consists of small cubes of homogeneous material We want to find the linear attenuation coefficient in each cube We measure the radiation that comes through DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Basic principle of CT – Super Sudoku I0 I0 What is ? I0 I0 33 x DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Basic principle of CT – Super Sudoku I0 I0 Four equations Four unknowns Can be solved directly I0 I0 34 x DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Real CT More than 4 pixels per slice 512 x 512 pixels Many projections Enormous system of equations Not solvable by direct methods 35 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Hounsfield Units Instead of using a linear attenuation coefficient Dimensionless Calibrated – Air: -1000 – Water: 0 Linear attenuation coefficient in voxel Different equation used in book (eq (1)) 36 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Hounsfield Units 37 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Hounsfield Units 38 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Real CT scanning 39 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Real CT scanning Many projections Advanced reconstruction algorithms – Filtered back projection – Radon transform 40 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Different versions of CT machines Rotating detectors 41 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Rotating source – stationary detectors 42 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Modern CT High Speed Advantage GE Spiral CT 43 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Multi-slice CT 128 slice scanner 44 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute What can be seen on the CT image Photograph of cryosectioned head 45 DTU Compute, Technical University of Denmark CT scan Introduction to Medical Image Analysis 25/3/2015 DTU Compute Todays exercise Automatic algorithm to compute the cross-sectional area of the muscle Musculus erector spinae Several methods – Pixel classification – Morphology – BLOB analysis Training data Validation data – why? 46 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Bonus Challenge The MuscleChallenge I have three “secret” images – What is the left muscle area (in pixels) on these? Upload your Matlab code on CampusNet – “The Muscle Challenge” – Input: file name of DICOM file – Output: area in pixel of the muscle I will run your function on the images and compare the results to the “gold truth” The winner is the algorithm that is closest to the ground truth Give your algorithm a funky name: MuscleMaster, Beefcake2000, Musculus, Musculator, Rasmuscle Deadline 8. April 47 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Spleen Challenge – the competitors of 2011! 48 Splenectomy SpleenSisters AWESOMESpleenFinder Spleenosaurus Har du en milt der skal SpleenSize bortopereres? Så har jeg en MatLab funktion som lige er Spleenelicious sagen. Splenectomy kan lokalisere Splenomegaly milten og gør anatomisk viden overflødig. Det er også lige før den SpleenAreaInPixels DTU Compute, Technical University of Denmark selv kan foretage indgrebet. Tjek den gerne ud! Introduction to Medical Image Analysis 25/3/2015 DTU Compute Highlights from 2012 49 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute 50 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute 51 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute 52 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Epic_SpleenMasterOrz9000Extreme_supre me_premium_Deluxe_Edition 53 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute 54 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute What can you do after today? Describe the basic use of X-rays Describe the X-ray as an electromagnetic wave Explain the basic technique in an X-ray tube Estimate how simple materials will look on an X-ray Use Lambert-Beers law to compute material attenuation Compute the attenuation of simple non-homogenous materials Describe the concept of tomographic reconstruction Describe the relation between Hounsfield units and tissue types Compute a CT slice using a simple and idealised setup 55 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Next week Geometric transformations 56 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015 DTU Compute Exercises ? 57 DTU Compute, Technical University of Denmark Introduction to Medical Image Analysis 25/3/2015
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