Computed tomography - outline

Transcription

1 Computed tomography - outline Computed Tomography Systems Jørgen Arendt Jensen and Mikael Jensen (DTU Nutech) October 6, 216 Center for Fast Ultrasound Imaging, Build 349 Department of Electrical Engineering The idea behind computed tomography Simple reconstruction First and second generation systems Fan beam and helical scan systems The gantry and detection system Fast CT systems Image examples Center for Fast Ultrasound Imaging Department of Electrical Engineering Discussion of exercise 4 Advise for the assignments Why use CT? Conventional radiography suffers from the collapsing of 3D structures onto a 2D image Although resolution is lower in CT, it has extremely good low contrast resolution, enabling the detection of very small changes in tissue type CT gives accurate diagnostic information about structures inside the body 1

2 What do we measure? Early CT scanner geometry Intensity measured by detector: I = Io exp( µ x) Conversion to attenuation: µ = 1 ln x I I Attenuation values µ are scaled relative to water: µ HU = µ µ tissue water water 1 Single slice Single projection Single direction Very long scan time! Measurement of attenuation Algebraic reconstruction I o I o I o I o From: W. A. Kalender; Computed Tomography, Publicis, 25 2

3 Algebraic reconstruction I I I I I ln( I I ln( I I ln( I I ln( I = I exp( µ exp( µ 1 = I exp( µ exp( µ 4 = I exp( µ exp( µ 1 = I exp( µ exp( µ ) dx ) dx ) dx ) dx = µ 1 + µ 3 = µ 4 + µ 2 4 equations,4 unknown = µ 1 + µ 2 = µ 3 + µ 4 Back projection For all projections, the measured values are added to all contributing pixels This will be further addressed in two lectures Sir Godfrey N. Hounsfield (Nobel Prize 1979) Obituary in The Telegraph: The invention of the CAT scanner was a remarkable achievement, not least because of the complex algebraic calculations involved in the computer programming. Other research teams with larger resources than EMI had already dismissed such a device as impossible to develop, and one prominent British scientist remarked that Hounsfield's machine used "mathematics I wouldn't pretend to understand now or at any stage of my career". 3

4 First and second generation scans First CT scanner (EMI scanner) From Hounsfield s original patent 8 x 8 pixel matrix 4 min rotation time 8 gray levels Overnight image reconstruction First clinical CT images from Atkinson Morley's Hospital in 1971 Hounsfield units: Intensity measured by detector: I = Io exp( µ x) Conversion to attenuation: 1 I µ = ln x I Attenuation values µ are scaled relative to water: µ HU = µ µ tissue water water 1 4

5 Typical H.U. Values Hounsfield units Windowing 5

6 Modern CT system generations Modern CT scanner The development in image quality 6

7 From: Tube for X-ray generation in CT 7

8 Slip rings in the CT gantry Slip Rings From: Detector types in CT helical Continuous helical scan 8

9 Modern CT system generations Imatron scanner Multi-slice CT system and fast scanning Today: Toshiba Aquilion One: 32 slices.275 s rotation 9

10 Number of detectors Coverage in multi-slice CT Low-end scanners: 4-6 detectors/row High-end scanner: 65-9 detectors/row Multi-slice scanners: 64, 256, 384 rows today Number of projections Low-end scanners: 6-1 per rotation/slice High-end scanner: per rotation/slice Examples of CT images 1

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12 3D reconstruction, segmentation and shading Examples of high-quality CT images with 3D reconstruction, segmentation and rendering 3D reconstruction and segmentation Segmentation 12

13 Cardiac CT image multi-slice and ECG gated Summary CT developed since the 197 ties Several generations have been made Overview of technology used Helical, multi-slice scanning possible today Excellent image quality obtained today Next lecture: mathematical background for 2D image processing Later: filtered backprojection algorithm and other reconstruction methods Exercise 4: Signal processing in pulsed wave system Exercise 4: In-vivo spectrum for portal vein Frequency [Hz] 1. Process receive signal to get complex data (load from file) 2. Divide into overlapping segments 3. Calculate power spectrum (apply compression) 4. Display the spectra as a function of time 5. Compare the spectra for different vessels Time [s] 13

14 Exercise 5: Image processing Shepp-Logan phantom Purpose: show how the manipulation of gray levels in CT images can reveal new details and structures, and how filtration on images can be performed. Equalize gray level values to see details Calculate spectrum and make low pass-filtration High pass filtration 53/x 54/x Clinical images Advise for assignments Get started Get started Get started.. Make a validated Matlab estimator and show its performance Document it in your report with a description and plots of results Remember always to put SI units (or some scaled version) on the axis Validate using exercise 3 data that your estimator works Show performance with different parameters Discuss results also if they are wrong (find the error) Report in searchable pdf should contain all code in appendix 55/x 14

15 Exercise 3 data: Simulated RF signals Velocity spectrum Parameters: Sampling frequency: f s =1 MHz Transducer center frequency: f = 3 MHz Velocity: v z =.15 m/s Pulse repetition frequency: f prf =5 khz Speed of sound: c=15 m/s Expected frequency: 2vz f p = f = 3 1 = 6 Hz c 15 Data from arteria femoralis RF data from one depth in the femoral artery 15