CT PHYSICS Registry Review · In CT, µ needs to be calculated for each voxel in the patientpatient in order to obtain a CT number (Hounsfield Unit) which is the ultimate goal of

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CT PHYSICSRegistry Review

Slide # 1

Registry Review

Wil Reddinger M.S. R.T. (R) (CT)

Slide # 2

X-ray tube/Focal spot

Filter (beam filter)Pre-patient Collimator (patient protection and slice thickness)

i

High frequency generator, now shown.

Makes beam more homogeneous.

Generates the radiation.

Slide # 3

Patient

Pre-detector Collimator

ADC

(post patient collimator and re-defines slice thickness)

DetectorDetects attenuated radiation.

Converts analog signal to digital.

• X-rays are produced, emitted, filtered, and collimated

• X-ray penetration occurs - data collection schemes or beam geometries

Data Acquisition

Filter

Tube

Pre-patient collimator

Slide # 4

g

• Transmission measurements from the patient are obtained and converted to digital signals (involves detectors detector electronics)

ADC

Detector collimator

Analog Signal

Digital Signal

Detector

Slide # 5 Slide # 6

2

Slide # 7

Volume (Helical - Spiral) CTAdvantages of Multi-Slice

• Speed of coverage• Slice reconstruction• Improved cooperation• Improved contrast

enhancement• Thinner slices

Slide # 8

Single-Slice & Multi-slice Volume CT Volume CT

Thinner slices Resulting in…“improved “

- MPR - VRdata sets

MPR = Multi-Planar ReformatsVR = volume rendered

Slide # 9

Multi-slice CT, Detector Evolution

Single Dual Quad

Slide # 10

Multi- slice systems = Multi – channel systemsWhereby, 64 channels produce 64 slices per rotation!

Then,8, 16, 32, 40, 64 slice systems

Scan Data to Image Data

• Path x-ray beam travels from the

tube to the detector = RAY

• Detector reads each ray &

Slide # 11

• Detector reads each ray &

measures beam attenuation

• Measurement = Ray Sum

Ray (Projection)

I0 Itissue attenuates the ray

Slide # 12

Each ray produces a single measurement of theEach ray produces a single measurement of thexx--ray attenuation along a path between the ray attenuation along a path between the

source and a detectorsource and a detector

3

View (Profile)Complete set of Ray Sums = VIEW“Like looking at an object from a particular angle.”Many views are needed to create an image

Slide # 13

Obtained from a set of measurements across the object at Obtained from a set of measurements across the object at one angular position. Views are made up of raysone angular position. Views are made up of rays

VIEWVIEW

Slide # 14

ProfileProfile

Image Reconstruction• CT system accounts for the attenuation

properties of each ray and correlates them with the position of the ray

• An ATTENUATION PROFILE is created• resulting shadow (analogy)

Slide # 15

Image Reconstruction• Attenuation Profile is obtained

for each view• All profiles are projected back

into a matrix

Slide # 16

into a matrix• a big rectangle divided into

smaller squares called pixels

Matrix

Slide # 17

6 x 66 x 6

Reconstruction• Describes method for

converting Scan Data to Image Data

• x-ray beam-absorbed x-ray

Slide # 18

• x-ray beam-absorbed x-ray info-to analog signal-converted to digital signal-placed on a matrix in the computer-matrix on TV

4

Back Projection Method• Oldest means of

reconstruction• An image is created by

reflecting attenuation profiles

Slide # 19

reflecting attenuation profiles in the same direction they were obtained

• Produces streak artifacts• 1st attempt at CT imaging

BPBP55

BackBack--ProjectionProjection

BPBP11

PP11

PP33

PP55

Slide # 20

BPBP22BPBP33 BPBP44

PP22 PP44

Back Projection Method• Artifacts are not in the scanned

image• Appear due to the reconstruction

process ( Back Projecting)

Slide # 21

p ( j g)• “placing” information on a matrix

ReconstructionReconstruction

Slide # 22

Scanning

• Movement of the

- Tube and

- Detectors with X-ray transmission

Scanning

Slide # 23

• Tube and detectors are

-Co-linear (in alignment)

- And at the same speed

Back Projection Scanning a “phantom” With two “cups of water”

Within a cube phantom

X-ray Beam

Water

Water

Phantom

X-ray Tube

X-ray Tube

Proj

ectio

n #1

Slide # 24

In CT, the x-ray tube rotates around the “phantom”

In this case the x-ray beam is attenuated by the water in the

phantom, and therefore “projects” a “shadow” within the detectors…

Detectors

When the tube and detectors are in this configuration

This Projection is sampled or “detected” or by the detectors

Projection #2

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Back Projection - Reconstruction

Proj

ectio

n #1•If we draw lines

from the actual phantomto the projections

•Where the lines intersectthere should be a cup of water

Projection #1

Projection #2

Projection #3

•If, however, a third projection Is acquired

•Continue to connect “lines” from the

Slide # 25

Projection #2

•But, since only 2 projections are acquiredin this example

•It appears as thoughthere are 4 cups of water

•Two projections are not enough!

Projection #2 from the actual phantom to the projection

•Now, the locations where the lines intersect, represent the actual cups of water.

•Three projections ARE enough! (For a simple phantom), but many more projections are required for the complex anatomy to be displayed on a CT image!

Image Reconstruction• Artifacts minimized by changing

the shapes of the attenuation profiles before back projection to a matrix.

Slide # 26

• Reconstruction “filter” is applied to the Raw Data or Scan data

• 1st type of “recon” = Filtered back Projection

Filtered Back Projection• Reconstruction filter • Kernel , Algorithm, Math Filter• Process of applying

M h i l fil i h

Slide # 27

Mathematical filtration to the raw data = Convolution

Filtered Filtered BackBack--ProjectionProjection

Slide # 28

ProfileProfile Filtered ProfileFiltered Profile

Image Reconstruction

• Mathematical filtration• Kernel

Al ith

Slide # 29

• Algorithm

Algorithms

• Mathematical method for

solving a problem that

f

Slide # 30

involves repetition of an

operation.

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Types of Reconstruction• 2nd type avail today

• Fourier Reconstruction

• any function or variation of a quantity

Slide # 31

any function or variation of a quantity

in time or space can be expressed as a

sum of sine and cosine waves

• frequencies instead of profiles

Reconstruction

• involves millions of data points• ARRAY PROCESSOR• dedicated to rapid calculations

Slide # 32

involved in reconstruction• large numbers of calculations

needed to convert data to image

Mathematical FiltersAlgorithms

• High Pass (Sharp, Bone)

• primarily used in high contrast regions

Slide # 33

contrast regions (temporal bones)

• areas of sudden “large” drops in CT numbers( extreme tissue density)

High Pass Algorithms

• Optimizes spatial resolution, Edge enhancement.

• Decreases

Slide # 34

• Decreases blurring of edges

• Bone , sharp, detail algorithm

Low Pass filters / algorithms

• Soft tissue algorithms

• standard, smooth• area of gradual

ti h

Slide # 35

tissue change• optimizes contrast

resolution• smoothness of

larger objects

Raw Data• Includes all

measurements obtained from detector array

• Within the SFOV

Slide # 36

Within the SFOV• specialized

reconstruction• attenuation

information

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Image Data• Data displayed on

the monitor• operator chooses

to viewi l t d

Slide # 37

• manipulated using window level and window width

Image DisplayMATRIX

• CT Image is represented by a matrix of numbers

• Rows and Columns of pixels

Slide # 38

Rows and Columns of pixels• 512 X 512 Matrix = 262,144 Pixels• 256, 340, 512, 768, 1024• 80, 160, 180 not used today

THE IMAGE IS MADE OF BLOCKS

Slide # 39

MATRIX• The bigger the matrix, the

smaller the pixel size• reduces partial volume

i

Slide # 40

averaging• effects image quality• effects reconstruction time

VOXELVOXEL PIXEL

Slide # 41

3D3D 2D

Volume Element

• Voxel• Three Dimensional

representation of tissue

Slide # 42

representation of tissue• Pixel Area x Section Thickness• Voxel Depth determined by ST

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Picture Element• Pixel• Two dimensional

representation of the tissue volume

Slide # 43

volume• Pixel Size = FOV divided by

Matrix size

Raw Data• Includes all

measurements obtained from detector arrayWithin the SFOV

Slide # 44

• Within the SFOV• specialized

reconstruction• attenuation

information

Image Data• Data displayed on

the monitor• operator chooses

to view

Slide # 45

• manipulated using window level and window width

THE IMAGE IS MADE OF BLOCKS

Slide # 46

MATRIX• The bigger the matrix, the

smaller the pixel size• reduces partial volume

i

Slide # 47

averaging• effects image quality• effects reconstruction time

Imaging Matrix

• Digital images are created with a matrix

S ll t it

Slide # 48

6 x 66 x 6 3 x 33 x 3

• Smallest unit of the digital image is a pixel

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MATRIX & PIXEL SIZE

Size effects resolutionThe bigger the Matrix, The better

the resolution.The bigger the matri The more

Slide # 49

The bigger the matrix, The more pixels you have, smaller too.

The more “ little blocks” you have to make the image , the more detail…

Calculating Pixel & Voxel Size

PixelPixel SliceSlice

VoxelVoxel• Isotropic voxel

• Pixel sizeFOVMatrixMatrix

FOVMatrixMatrix

FOVMatrixMatrix== ==

ThicknessThickness

FOVMatrixMatrix

FOVMatrixMatrixxx

byby

equalsequals equalsequals

Slide # 50

Slice Slice ThicknessThickness• Area of the Pixel

• Voxel Volume

FOVMatrixMatrix

FOVMatrixMatrix

FOVMatrixMatrixxx = mm= mm22

Thickness = mmThickness = mm33FOVMatrixMatrix

FOVMatrixMatrixxx xx

timestimes

timestimes timestimes

VOXELVOXEL PIXEL

Slide # 51

3D3D 2D

Picture Element• Pixel• Two dimensional

representation of the tissue volume

Slide # 52

volume• Pixel Size = FOV divided by

Matrix size

Volume Element

• Voxel• Three Dimensional

representation of tissue

Slide # 53

representation of tissue• Pixel Area x Section Thickness• Voxel Depth determined by ST

PixelPixel

Voxel

Slide # 54

Slice Slice ThicknessThickness

PixelPixel

Voxel Volume = Pixel Size x Slice ThicknessVoxel Volume = Pixel Size x Slice Thickness

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Pixels & Voxels

• In CT slices are acquired• The voxel is a 3D volume element

Slide # 55

PixelPixel



VoxelVoxel•The face of the voxel is the pixel

FOV, Matrix, Thickness & Voxels

• The size of the area imaged in CT is the field of view (FOV)• The number of pixels ( l ) i th

matrixmatrix FOVFOV

Slide # 56


(rows x columns) is the matrix• The depth is the slice thickness

FOVFOV

matrixmatrix

larger

smaller

How Much “Meat” is in the Box? vs. Signal?

Slide # 57

The larger the pixel (voxel)The more “meat” tissues / protonsThe larger the CT signalBut… since signals get averaged togetherThe lower the spatial resolution

larger

smaller

How Much “Meat” is in the Box? vs. Resolution?

Slide # 58

The larger the pixel (voxel)The more “meat” tissues / protonsThe larger the CT signalBut… since signals get averaged togetherThe lower the spatial resolution

Partial Volume Averaging

Slide # 59

smaller

Signals signals get averaged together in large voxelsThe lower the spatial resolutionPartial Volume Averaging

larger smallest

Calculating Pixel Size

VoxelVoxel

• to calculate the pixel size• to calculate the voxel size FOVFOVmatrixmatrix

Slide # 60


Slice Slice ThicknessThicknessFOV

MatrixMatrix

FOVMatrixMatrix

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Slice Thickness & Voxel Size

Slide # 61

1.25 mm slice thickness 2.5 mm slice thickness

Partial Volume Averaging Artifact• PVA = The inability to distinguish separate structures within a single pixel or voxel …• To reduce PVA (Partial Volume Averaging)

- Reduce FOV- Slice Thickness

Slide # 62

- Increase Matrix

Matrix = 320 x 320 Matrix = 512 x 512FOV = 20 cm – better resolutionLess partial volume averaging

FOV = 40 cm

FOVFOV

Pixels

Slide # 63

6 x 6 Matrix6 x 6 Matrix

Pixel size = FOV / MatrixPixel size = FOV / Matrix

QuickTime™ and aQuickTime™ and a

Matrix and Spatial Resolution

Slide # 64

QuickTime and aPhoto - JPEG decompresso

are needed to see this picture

QuickTime and aPhoto - JPEG decompressor

are needed to see this picture

Scan FOV

Slide # 65

Scan vs. Reconstructed FOV

Reconstructed FOV

Scan FOV Scan FOV

Scan FOV• Large

Slide # 66

Reconstructing smaller FOV increases noise

Recon FOV

FOV• Large acquired FOV • Largereconstructed FOV

• Large acquired FOV • Smallreconstructed FOV

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Scan FOV

Slide # 67

Scan FOV Reconstructed FOV

Large Scan FOV

Slide # 68

Scan FOV Reconstructed FOV

Small Reconstructed FOV

Slide # 69

Magnification vs. Reconstruction

Small FOV•Reconstructed FOV• Reconstructs pixels• Uses raw data• Reformats data• Better resolution

Slide # 70

Magnification• Stretches pixels• Uses Image Data• Poorer resolution

Targeting Vs Zooming

Slide # 71

The reconstruction process assigns

an integer value to each voxel that

is linearly related to the attenuation

CT Numbers

Slide # 72

is linearly related to the attenuation

coefficent (µ) of the tissue(s)

within a voxel

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Linear Attenuation Coefficient - a quantitative measurement of attenuation per cm of an absorber ( i.e. bone or soft tissue)

Represented by the greek letter µ

I CT d t b l l t d f hf h ll i thi th

Key Terms - Linear Attenuation Coefficient

Slide # 73

In CT, µ needs to be calculated for each for each voxelvoxel in the in the patientpatient in order to obtain a CT number (Hounsfield Unit) which is the ultimate goal of reconstruction.

µ = (1/x) * = (1/x) * lnln(I(I00/I)/I)(Algebraic manipulation of the original formula)(Algebraic manipulation of the original formula)

CT NUMBERS• unit for attenuation values within

a pixel (brightness)• brightness value is determined

by the detector signal

Slide # 74

y g• linear attenuation coefficient• absorption measurement• Hounsfield Unit

attenuation > water = +attenuation of water = 0attenuation < water = -

CT Number ValuesCT Number Values

Bone1000

Water0

Slide # 75

A 1% difference in µ = 10 HUHounsfield Units (HU)

Fat- 100

Air-1000

Godfrey N. HounsfieldNobel Prize: Physiology or Medicine 1979

CT NUMBERS• Attenuation coefficients

dependent on photon energy (keV)

• HU values depend on kVp and

Slide # 76

• HU values depend on kVp and filtration

• µPE is energy dependent• µCompton not as energy dependent• As kVp is increased, µPE is decreased• A high kVp is used in CT to diminish the contribution of µPE and

increased the contribution of µCompton

Linear Attenuation Coefficient – Beam Energy & Z

µ = µPE + µComptonBeam Energy

Slide # 77

µCompton

Atomic Number (Z) –The probability of photo-electric effect goes by Z3

Example: Bone Z (13) and soft tissue (8)13 ÷ 8 = 1.625 or roughly 2 and 23 = 8

Therefore, the probability of photoelectric effect is 8 times more for bone

CT Numbers• HU values generated by a CT

scanner are only valid for the effective kVp used to generated the image

Slide # 78

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The voxel values are normalized to the µ of water to obtain CT numbers

µmaterial - µwater

CT Numbers

A 1% difference in µ = 10 HUHounsfield Units (HU)

Bone1000

Water0

Fat- 100

Air-1000

Slide # 79

x 1000CT number =µmaterial µwater

µwater

CT numbers are called HOUNSFIELD UNITS (HU)

Godfrey N. HounsfieldNobel Prize: Physiology or Medicine 1979

CT Numbers

• Comparison of linear attenuation coefficient of tissue to linear attenuation coefficient of water

Slide # 80

coefficient of water• uwater-utissue• uwater• multiplied by 1000

CT Numbers

• Greek letter mu• linear attenuation coefficient• CT numbers

Slide # 81

• Hounsfield Unit• Absorption measurement

Region Of Interest … ROI

512

Slide # 82

Mean 54STD 2.4

attenuation > water = +attenuation > water = +attenuation of water = 0attenuation of water = 0attenuation < water =attenuation < water = --

CT Numbers

Slide # 83

attenuation < water = attenuation < water = --

A 1% difference in µ = 10 HUA 1% difference in µ = 10 HU

Since CT scanners operate at high xSince CT scanners operate at high x--ray ray energies, Compton interaction is energies, Compton interaction is predominate. The probability of a predominate. The probability of a

Compton interaction depends primarilyCompton interaction depends primarily

CT Numbers

Slide # 84

Compton interaction depends primarily Compton interaction depends primarily on the density of the tissue.on the density of the tissue.

CT numbers are closely related CT numbers are closely related to tissue densityto tissue density

15

Since CT scanners operate at high x-ray energies, Compton interaction is predominate.

The probability of a Compton interaction depends primarily on the density of the tissue.

Compton InteractionCompton Interaction

CT numbers are closely related to tissue density

Slide # 85

CT numbers are closely related to tissue density

FYI…“A Compton interaction is one in which only a portion of the energy is absorbed and a photon is

produced with reduced energy.”+

Compton

backscatter

CT Numbers

• Less than water = Negative Number

• Greater than water = Positive Number

Slide # 86

• 0 = water, 1000 = bone

• -100 = fat, -1000 air

Normal RangeNormal Range

WaterWater BoneBoneAirAir

CT Numbers

Slide # 87

--10001000 +1000+100000

The Hounsfield ScaleThe Hounsfield ScaleHounsfield Scale

60

40

Calcified Bone

Gray Matter

Congealed Blood

+1000 Hu

Slide # 88

-1000 Hu

-100 Hu

20

0 Hu

White Matter

Blood

Water

Fat

Air

Extended RangeExtended Range

WaterWater BoneBoneAirAir

CT Numbers

Slide # 89

--10001000 +3000+300000

11 bit (11 bit (--1000 to +2000)1000 to +2000)12 bit (12 bit (--1000 to +3000)1000 to +3000)

Window Width / LevelWindow Width / Level

width

possible range of pixel valuespossible range of pixel values

10001000

Slide # 90

--10001000 +3000+3000

levellevel

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Introduction to “Windowing”WW (Window Width) = Range of Gray Shades

that the user chooses to display for a given clinical situation

WL (Window Level) = Center of Gray Shadesthat the user chooses to display for a given clinical situation

Slide # 91

Wide Narrow

Narrow Window Width

Slide # 92

Narrow Window Width – only two shades of gray (black & white)

Low Window level High Window Level

Wide Window Width

Slide # 93

Wide Window Width – Many shades of gray

Low Window level High Window Level


--10001000 +3000+300000

width = 100

Slide # 94

level37


--10001000 +3000+300000

width = 3000

level

+1875-1143

Slide # 95

357

Window Width and Level

Grayscale with Narrow Window Width (only black and white)

Grayscale with Wide Window Width (lots of grays)

Slide # 96

Low level (darker) settings on an Axial CT image of the chest

High level settings (whiter) on an Axial CT image of the chest

Wide Window Width Wide Window Width

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Window Width & Level

Slide # 97

High Contrast & Density

Slide # 98

You do not have to have raw dataYou do not have to have raw datain order to alter window width / levelin order to alter window width / level

Slide # 99 Slide # 100

Slide # 101 Slide # 102

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CT Numbers• Linear attenuation coefficients

are converted to CT Numbers so conversion to a visual gray scale is possible

Slide # 103

scale is possible

Windowing• Each Pixel is represented by

4096 gray levels• larger than display range of

f

Slide # 104

monitors or film

Signal Numbers

The numbers contained in each matrix pixel are assigned a shade of gray

Slide # 105

Giving each pixel a number allows for visualization of an image on a TV screen

Numbers and Shades

The number or shade of gray that is assigned to each pixel

Slide # 106

is determined and proportional to the electrical signal from the detector

Basics of Digital Images

0 1 2 3 4 4 60 152 120 22 215 34 1 0 1171 3 114 199 134 88 20 60 1992 234 72 65 17 145 185 235 1813 141 214 169 134 85 234 237 684 241 154 141 231 145 236 35 275 45 95 65 127 123 94 47 1665 45 95 65 127 123 94 47 1666 127 98 137 149 67 45 52 297 162 81 83 189 69 195 94 1718 64 123 130 100 58 226 214 34

102 189 174 35 169 203 243 135213 235 219 137 22 195 168 208227 103 192 243 102 220 187 21

1 2

IMAGE MATRIX

Slide # 108

3 4

19

Signals Pixel Brightness

1 2

Slide # 109

1 2

3 4

WINDOWING

Computer can “ see” more gray shades than the human eyemathematically brings density or

Slide # 110

contrast differences into the visual range.WINDOW WIDTH ( WW )WINDOW LEVEL ( WL )

Window Width (WW)

• Range of CT numbers for the gray scale

• Range of gray shades used to

Slide # 111

display the image• Defines contrast of the image

WINDOW WIDTH ( WW )

Range of Gray Shades Displayed

Contrast of the Image

Slide # 112

Contrast of the ImageWW increases, image

contrast decreases

Window Level (WL)

• Center of the gray Scale• Density of the image

Slide # 113

Wide Window Widths• Tissues of

greatly differing attenuation

• Lung air spaces

Slide # 114

Lung air spaces and vessels

• Bone

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Narrow Window Widths• Display soft tissues• within structures containing different

tissues with similar densities• Brain & Liver

Slide # 115

Window Levels• Select Near

average attenuations of tissues of interest

Slide # 116

interest• Not too high,

obscure pathology (brain bleed)

WINDOW LEVEL ( WL )

Center of the Gray ScaleCenter of the Range of gray

shades used for viewing an image.

Slide # 117

Density of the imageWL increases, Density increases.

CT PHYSICS Registry Review · In CT, µ needs to be calculated for each voxel in the patientpatient in order to obtain a CT number (Hounsfield Unit) which is the ultimate goal of

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