# Basic Physics of Nuclear Medicine/X-Ray CT in Nuclear Medicine

The need for attenuation compensation in SPECT imaging has been
understood for many years but it has only been in recent times that
effective techniques have been applied in commercial systems. Gamma rays
get attenuated by the medium through which they pass, as we\'ve seen
earlier,
via photoelectric absorption and Compton scattering. The projections
measured in SPECT imaging therefore contain impressions of this
attenuation which reduce their fidelity for tomographic reconstruction,
as illustrated in the following figure:

!Tomographic slices from a cardiac study reconstructed without
attenuation correction on the top row and with attenuation correction on
the bottom row. Note the more uniform distribution of radioactivity in
the cardiac muscle depicted in the attenuation corrected
images.{width="280"}

Patient specific **attenuation maps** can be used to correct the
measured projections. These can be produced using the gamma camera to
generate a transmission scan of the patient using a radioisotope source
-- see the following figure for an example:

!A posterior-anterior transmission image of the author\'s chest
acquired using an uncollimated gamma camera and an 18.5 MBq point source
of ^99m^Tc at a focal distance of two
metres.{width="160"}

The transmission scan contains the attenuation information for the
volume of interest within the patient and a map of this attenuation
pattern is subsequently applied to each SPECT projection prior to
filtered back projection. A more powerful approach is to use an X-ray CT
scanner to generate the attenuation maps -- see the following figure.
Numerous *hybrid* scanners have been produced as a result which combine
nuclear medicine with CT imaging, as in **SPECT/CT** and **PET/CT** --
see the following figure for an example:

!A SPECT/CT system, with relevant components labelled in the photograph
on the
right.{width="280"}

An additional benefit of these developments is the simultaneous
capability, with sufficient computing power, of blending the nuclear
medicine and CT images so as to generate physiological images
colocalized with images of patient anatomy -- as shown in the following
figure -- and hence improve the diagnostic utility of nuclear medicine
procedures.

!A SPECT image blended with a CT scan. Blending, indicated by the +
sign, in this case involved sub-processes which included image
magnification, contrast enhancement, application of a colour look-up
table and layering of one
image on top of the other using an appropriate
transparency.{width="280"}

Hybrid scanners generally have a single patient couch with the gamma
camera gantry moulded to the front of a CT unit so that the patient can
be transported into the X-ray beam before or after the SPECT
acquisition. Some CT units are of a low resolution design and generate
crude, single slice CT images which are sufficiently accurate for SPECT
attenuation correction but are of insufficient quality on their own for
diagnostic purposes -- see the example below. There are also higher
resolution CT units in use for hybrid scanning with capabilities of
acquiring, for instance, 16-slice helical scans which can compete
directly with the diagnostic capabilities of dedicated CT scanners.

!A low resolution CT scan generated for attenuation correction
applications in a SPECT/CT
system.

This chapter provides an overview of the physical aspects of X-ray CT
scanning from a nuclear medicine perspective. We begin with various
scanner designs and will explore a number of computational techniques
relevant to this form of imaging. The intention is to provide a
foundation for understanding the application of X-ray CT in nuclear
medicine.

## Conventional Radiography

The attenuation of an X-ray beam in the body is used in conventional
radiography to project a shadow onto an image receptor (as shown in the
figure below). These shadowgraphs record a two-dimensional
representation of a three-dimensional object. Small lesions are
therefore not readily identified because of overlapping and underlying
anatomy, image distortion occurs because of unequal magnification
effects and low contrast masses are poorly delineated since scatter
contributes substantially to the image data.

!Bird\'s eye view of a conventional radiographic procedure on the left,
with a chest radiograph displayed on the right using a nuclear
medicine-oriented look-up table -- where low counts are assigned a dark
shade on a grey scale and high counts as whiter
shades.{width="380"}

We can consider that there is a **degeneracy** introduced into such
images, i.e. two anatomically quite different objects may produce the
same film density because the effective attenuation they each produce is
identical, as illustrated in the following figure:

!The origin of Image Degeneracy in conventional radiography. The
attenuation of each element may be characterised by
`<span style="color:red;">`{=html}μ`</span>`{=html}, the linear
attenuation coefficient as in panel (a). The total attenuation of each
of the two columns of elements in panel (b) is identical so that the
image density is the same for quite different anatomical
detail.. The total attenuation of each of the two columns of elements in panel (b) is identical so that the image density is the same for quite different anatomical detail.")

Panel (a) shows a pencil beam of X-radiation of incident intensity,
I~o~, attenuated by the body, represented here for simplicity by four
volume elements each of thickness, Δx, and of different linear
attenuation coefficients, μ~1~ thru μ~4~. The transmitted intensity,
when no scattered radiation is detected, is given by I, which is
dependent on the sum of these linear attenuation coefficients, according
to the exponential attenuation equation. Panel (b) illustrates that two
adjacent columns of volume elements can generate identical values of the
transmitted intensity, I, even though the two columns contain volume
elements with different linear attenuation coefficients. In other words,
what is detected in the integrated contribution of the linear
attenuation coefficients without any information about the contribution
from any individual volume element.

One way to improve this situation is to use X-Ray Computed Tomography
(**CT**). Note that the image reconstruction algorithms developed for
X-ray CT can also be applied to gamma camera images (as in **SPECT**) to
overcome limitations of planar nuclear imaging and to images generated
using positron-emitting radioisotopes (as in **PET**).

## Back Projection

We have considered this method of computerized image reconstruction in a
previous chapter in the context of SPECT. Our intention here is to
consider this reconstruction method in the context of X-Ray CT.

The following figure is a representation of how back projection works.
The basic premise is that any attenuation of the X-ray beam is assumed
to have occurred uniformly along the entire ray path.

!Back projection
reconstruction.{width="280"}

Thus, the result of the back projection of the first profile, P~1~ is to
put the values 7 and 9 in both elements of the first and second rows,
respectively. The second profile, P~2~ adds a
`<span style="color:green;">`{=html}4`</span>`{=html} to the top right
element, 1 to the bottom left element and 11 to the other two elements
when back projected. The other profiles are treated in similar fashion.
Following subtraction of an offset (16 in this case) and renormalisation
(division by 3 in this case) of the data set, the final image is
obtained. You should confirm for yourself that this final matrix
satisfies all projections.

The principle is demonstrated in more detail in the following figure:

!A demonstration of simple back projection: (a) An X-ray tube scans a
phantom, consisting of a radio-dense object in an otherwise uniform
container, and generates the profile as shown for the back projection
process. (b) Four profiles generated by scanning at slightly different
angles around the phantom. (c) The image reconstructed from just four
projections. An X-ray tube scans a phantom, consisting of a radio-dense object in an otherwise uniform container, and generates the profile as shown for the back projection process. (b) Four profiles generated by scanning at slightly different angles around the phantom. (c) The image reconstructed from just four projections."){width="380"}

A single profile is back projected to give a dark stripe across the
entire image plane \[panel (a)\]. As we scan the phantom from many
directions and back project the ray profiles onto the image plane
\[panel (b)\], an image of the radio-dense dot, albeit a poor one,
begins to resolve \[panel (c)\]. As the number of projections increases,
the quality improves but some blurring will always remain in the image.

This blurring can be removed using what is known as Filtered Back
Projection.

## Filtered Back Projection

The blurring inherent in the simple back projection process can be
suppressed mathematically using filtering techniques and the overall
reconstruction process is then called **Filtered** Back Projection. In
this technique (see the following figure), the attenuation profile is
first filtered to counteract the effect of sudden density changes which
cause the blurring in simple back projection. Note that the graphical
representation in the figure is very crude as the filtered profile may
be a quite complicated function of distance as illustrated in panel (c).
The filter is also referred to as a **convolution filter**, or
**convolution kernel**. After completion of the filtration process, the
Inverse Fourier Transform is applied to the data for each profile before
the back projection process is undertaken. Notice that the reconstructed
image \[panel (d)\] is free of the **star** artefact generated in
unfiltered back projection.

!A demonstration of filtered back projection: (a) An X-ray tube scans a
phantom and generates the profile which is filtered using the Fourier
transform. (b) Four filtered profiles generated by scanning at slightly
different angles around the phantom. (c) A detailed view of the effect
of the filtration process on one ray profile. (d) The reconstructed
image is free of the **star**
artefact. An X-ray tube scans a phantom and generates the profile which is filtered using the Fourier transform. (b) Four filtered profiles generated by scanning at slightly different angles around the phantom. (c) A detailed view of the effect of the filtration process on one ray profile. (d) The reconstructed image is free of the star artefact."){width="380"}

Most CT scanners offer a choice of filters which may be selected by the
operator to enhance either soft tissue features or bone detail in
images. Indeed, the image can be post-processed using a different filter
after the scan has been completed if so desired. Images can be generated
to enhance bone detail on the one hand and to observe subtle low
contrast masses on the other, without the need to re-scan the patient.
Thus, the filter choice will have a major impact on image quality. Two
common filters used in X-ray CT are those due to Ramachandran &
Lakshminarayanan (Ram-Lak) and to Shepp & Logan. In SPECT, Butterworth,
Hanning and Metz are used, as you will
remember.
The ramp filter on its own compensates for artefacts introduced by the
simple back projection process but does not compensate for the
increasing noise content of the data with increasing spatial frequency.
The Ram-Lak is a ramp filter which leaves all frequencies unchanged
except those above a cut-off, which are eliminated. Most of the other
filters suppress higher frequencies to varying degrees. Sometimes these
filters are referred to as **algorithms**. The term **kernel** is also
used. Images of an axial tomogram reconstructed with a soft tissue and
with a bone algorithm are shown below as examples:

!A tomogram reconstructed using (a) a soft tissue filter, and (b) using
a bone filter. Notice the subtle edge enhancement effect generated using
the bone filter and the more smooth nature of the soft tissue
filtration. a soft tissue filter, and (b) using a bone filter. Notice the subtle edge enhancement effect generated using the bone filter and the more smooth nature of the soft tissue filtration."){width="280"}

There\'s an excellent online CT reconstruction simulator available via
the Biomedical Imaging
Group at the EPFL.
As an exercise, you might like to explore your understanding of filtered
back projection by varying the type of filter used prior to
reconstruction. You might also like to explore the influence of the
number of angles used for profile acquisition on the quality of the
reconstructed image.

## CT Scanning Geometries

!First generation CT scanner
design.

Most CT scanner designs use some form of mechanical gantry which allows
movement of the X-ray tube and the detector around the patient. The
X-ray beam was collimated to a pencil beam in early designs and consists
of a wide fan beam in modern systems. The thickness of the slice,
typically 1 to 10 mm, is generally defined by pre-patient collimation
using motor driven adjustable wedges external to the X-ray tube. Even
with relatively large fan beams the volume of tissue irradiated is still
small quite compared with projection radiography and scatter is a less
serious problem. The detectors have collimators placed in front of them
to minimise the impact of out of slice scatter. To minimise the effects
of beam hardening CT scanners use a heavily filtered beam (the Siemens
Somatom Plus uses 2.7 mm Al plus 0.2 mm Cu, for instance) operating at
120 -- 140 kVp. Note that even with the best radiographic geometry, such
as in the third generation scanners, object magnification is substantial
and the finite size of the focal spot, typically 1 mm, may well limit
the achievable spatial resolution.

The **first generation** of CT scanner used what is referred to as
**Translate-Rotate** geometry. The original EMI Mk 1 scanner (see the
figure on the right), for instance, used a pencil X-ray beam and a
single detector (a NaI:Tl scintillator coupled to a photomultiplier
tube).
During the translational motion of the gantry, the transmitted X-ray
beam was sampled 160 times to produce a single profile. A rotation
through 1 degree with the X-ray beam off then followed before a new
profile was obtained. This procedure was completed until the patient\'s
head was scanned from 180 different angles.

!Second generation CT scanner
design.

The whole procedure took about 5 minutes and depended on the patient
remaining completely still. The patient\'s head was usually clamped to
minimise patient motion. In addition, their head was surrounded by a
water bag which helped to overcome problems associated with afterglow in
the NaI(Tl) scintillator and the very substantial signal dynamic range
that would otherwise be present at the detectors. Subsequent generations
of scanners have sought to decrease the scan time to a few seconds to
minimise the movement artifact.

The **second generation** of scanner addressed this issue by using a
small fan beam (see the next figure) with multiple detectors -- up to 30
in some designs. The detectors collect multiple data readings during a
translation so that fewer translations and rotations are required. Each
of the detectors collects its own limited profile during each
translation. Typically, a rotation of 30 degrees was employed between
translational movements so that only six rotational movements were
required to obtain the data for one slice.

Scan times of between 5 to 90 seconds were achieved with this design.
The detector elements were scintillators but the water bag was dispensed
with and body scans could be performed for the first time. The use of
multiple detectors gave a quantum leap in performance.

The **third generation** of CT scanner decreased scan times even further
by using a **Rotate-Rotate** geometry (see the figure on the right).
Most scanners today are of the third generation type. A typical machine
employs a large fan beam such that the patient is completely encompassed
by the fan. The number of detector elements is typically in the
hundreds, e.g.

:\* the GE Hispeed Advantage has 852 elements 1.0 mm apart;

:\* the Siemens Somatom Plus 4 has 768 elements 1.1 mm apart; and

:\* the Toshiba Xpress/SX has 896 elements 1.03 mm apart.

!Third generation CT scanner
design.

The detector elements are aligned along the arc of a circle centred on
the focus of the X-ray tube. The X-ray tube and detector array rotate as
one through 360 degrees during which time several hundred discrete
profiles are obtained. Both solid state detectors and pressurised xenon
gas detectors are used for data acquisition.

Because the fan beam totally encompasses the patient, translational
motion is not needed and the tube and detector array rotate as one about
the patient. The number of profiles recorded depends on the chosen scan
parameters but is at least a few hundred and can be in excess of a
thousand. Scan times can be as low as a second. Although the dynamic
range of the detectors is very high, some manufacturers control
excessive variations in signal strength by using bow-tie shaped filters
chosen to suit the body or head shape. Such filters generally attenuate
the peripheral part of the fan beam to a greater extent than the central
part. It also helps overcome the effects of beam hardening and to
minimise patient skin dose in the peripheral part of the field of view.

A number of variants on this geometry have been developed, which include
those based on offsetting the centre of rotation and the use of a
**flying focus** X-ray tube.

!Fourth generation CT scanner
design.

The **fourth generation** of CT scanner uses a **Rotate-Fixed Ring**
geometry where a ring of fixed detectors completely surrounds the
patient. The X-ray tube rotates inside the detector ring through a full
360 degrees with a wide fan beam. Only those detectors which see the
beam are activated at any one time which means that only a small
fraction of thousands of detector elements (e.g. the Picker PQ-5000V has
4800 detector elements 1.1 mm apart) are in use at any given time. Scan
time is comparable with third generation scanners but the radiographic
geometry is poor because the X-ray tube must be closer to the patient
than the detectors, i.e. the geometric magnification is large.

The disadvantages of poor geometry noted above has been alleviated very
neatly by the so called **nutating** geometry. The X-ray tube is
external to the detector ring but slightly out of the detector plane.
Scan times as low as 0.6 s can be achieved using machines of this type
with interscan delays of as little as 100 ms. Excellent resolution (0.35
mm) can be obtained by using as many several thousand detector elements.

## Helical Scanning

Innovations in slip-ring technology, beginning with the Siemens Somatom
Plus and the Toshiba TCT 900S, have enabled the X-ray tube to rotate
continuously in the same direction which overcomes problems of interscan
delays. When the continuous motion of the X-ray tube is combined with a
continuous advance of the patient table along the axis of the scanner we
have helical (also called spiral) scanning, as illustrated the following
figure. Typical table velocities are 1 -- 10 mm/s; a complete 360 degree
rotation can be achieved in 0.5 s and the nominal fan beam thickness is
1 mm or greater.

!Illustration of helical
scanning.

In the context of helical scanning a parameter called **pitch** is
defined as the:

```{=html}
<div class="center">
```
`<span style="color:green;">`{=html}ratio of the distance that the
patient couch moves in one rotation to the slice
thickness.`</span>`{=html}

```{=html}
</div>
```
In other words, for a couch advance of 10 mm and a nominal slice width
of 10 mm, the pitch is 1. Pitch values are typically in the range of 1
to 2 depending on the required spatial resolution in the direction of
the couch motion. Its a *coverage* indicator, in other words.

A significant reduction in scan times results from helical scanning
because large volumes can be scanned contiguously without any gaps and
with little influence from patient motion. The Somatom Plus-4A, for
instance, allows up to 80 slices to be scanned contiguously in 60
seconds of non-stop scanning. Single breathhold scanning of the thorax
is therefore possible and reconstruction, with minimal loss of spatial
resolution, of sagittal and coronal planes is feasible.

Axial reconstruction in helical scanning is illustrated in the following
figure. It is seen that no axial slices are actually irradiated during
the scan and that any transaxial image data needs to be estimated from
the acquired helical record. One estimation technique is referred to as
z-interpolation, because it applies data interpolation techniques along
the z-dimension of the acquired data, i.e. the patient\'s head-to-foot
axis.

!Illustration of transaxial reconstruction in helical
scanning.

The filtered back projection reconstruction process is therefore
modified to include an z-interpolation step prior to Fourier filtration,
as illustrated in the following flow diagram:

!The image reconstruction process in helical
scanning.

## Interpolation & Extrapolation

Before proceeding, let\'s briefly review the topic of interpolation and
extrapolation to clarify our basic understanding and to put the
subsequent discussion in context. Let\'s start with a simple
one-dimensional case, as illustrated in the following graph. Suppose
we\'ve taken measurements A and B and plotted them on the graph, and
wish to estimate the value at an in-between point, C. Such an estimation
process is called **interpolation**, and if we assume that there\'s a
linear relationship between variables X and Y as illustrated in our
graph, the process of estimating point C is called **linear
interpolation**. Other functions can also be used as interpolatants for
more complex data than we\'re considering here.

!Illustration of interpolation and
extrapolation.

If we also require knowledge about point D on our graph, i.e. a point
outside the measured range, we refer to the estimation process as
extrapolation, and linear extrapolation is illustrated in our graph
above.

Mathematically we can write that given:

```{=html}
<div class="center">
```
$A \Rightarrow (x_1, y_1)\ \ B \Rightarrow (x_2, y_2)\ \ C \Rightarrow (x_3, y_3)$

```{=html}
</div>
```
then the Y-value of point C is given by:

```{=html}
<div class="center">
```
$y_3 = y_1 + \frac{(x_3 - x_1)(y_2 - y_1)}{(x_2 - x_1)}$

```{=html}
</div>
```
using linear interpolation. You might like to develop a similar equation
as an exercise which can be used to determine the Y-value of point, D.

Since helical scanning is a three-dimensional phenomenon, we need to
develop some two-dimensional perspectives on the situation before
proceeding with our discussion -- as illustrated in the following
figure:

!Three perspectives of the helical scan in the top left
corner.

We\'ll use the side view in the following discussion.

## Interpolation in Helical CT

A large number of computational methods have been developed for helical
scanning. The **360^o^ linear interpolation algorithm** is illustrated
in the following figure as an example:

!A side view of a helical scan and parameters used in the 360^o^ linear
interpolation algorithm (360LI), where Z~ref~ indicates the position of
the axial slice to be
interpolated., where Zref indicates the position of the axial slice to be interpolated.")

The following equation can be developed on the basis of our earlier
discussion for linear interpolation of the transaxial data of interest:

```{=html}
<div class="center">
```
$P_z(i, \alpha) = (1-w)P_j(i, \alpha) + wP_{j+1}(i, \alpha)\,\!$

```{=html}
</div>
```
where:

:   P~z~(i, α) is the interpolated projection for an angle, α, at
    position Z~ref~;
:   P~j~(i, α) is the helical projection measured in rotation j at
    position Z~j~ and projection angle α;
:   P~j+1~(i, α) is the helical projection measured in rotation j+1 at
    position Z~j+1~ and projection angle α; and

```{=html}
<div class="center">
```
$w = \frac{Z_{\text{ref}} - Z_j}{d}$

```{=html}
</div>
```
This interpolation process is applied throughout all data points in the
helix to generate estimates of the axial data, prior to filtered
backprojection. Higher order interpolation is also possible where data
points from, say, four rays are used in the estimation. In addition,
sophisticated z-axis filters can be applied for these estimations in
multi-slice CT.

## Volume Imaging

It is possible to produce a number of slices per rotation when the fan
beam of a CT scanner is broadened along the z-axis and two-dimensional
detectors are used. The fan beam can be formed into the shape of a cone
on this basis and concepts such as **volume imaging** and **cone beam
CT** can be applied. Terms such as **multi-slice** and
**multi-detector** CT can also be used in this context, although as you
will see the second of these two terms is probably the more appropriate.

The 2-D detectors are solid-state devices and the simplest example is
the **Matrix Array**, as illustrated below:

!Illustration of four modes of multi-slice acquisition for a matrix
array CT
detector.{width="280"}

The array can consist of (say) 912 columns by 16 rows of identical
detectors, each 1.25 mm square, curved to fit an arc of the x-ray tube
rotation. Eight of these columns are shown in panel (a) above being
irradiated by an X-ray beam of width 6 mm, with the implication that
data for four 1.25 mm axial slices can be acquired simultaneously. More
rows, up to sixteen in this case, can be irradiated simultaneously with
this arrangement when the x-ray beam is broadened to encompass the width
of the detector array.

Flexibility can be built into this design by coupling the outputs of
adjacent detector rows, as illustrated in panels (b), (c) and (d), where
the outputs of 2, 3 and 4 rows, respectively, are summed to
simultaneously generate four 2.5 mm thick slices, or four 3.75 mm
slices, or (you guessed it!) four 5 mm slices.

A more flexible design is provided by the **Adaptive Array** detector,
as illustrated in the next figure:

!Illustration of an adaptive array CT
detector.{width="280"}

Rather than columns of square detectors, this array uses columns of
detectors of variable width, such that the width in the two central
columns is relatively narrow, e.g. 1 mm as in the figure above, with the
column width increasing towards the periphery, e.g. from 1.5 mm, through
2.5 mm to 5 mm as in our figure. The reason for this pattern is
explained below.

Four modes of operation of this detector array are shown in the
following figure:

!Illustration of four modes of multi-slice acquisition for an adaptive
array CT
detector.{width="280"}

We can see in panel (a) that two 0.5 mm slices can be acquired when a
1 mm thick fan beam is aligned with the central columns of the adaptive
array. In panel (b) we can see that four 1 mm slices can be acquired
using a fan beam of thickness 4 mm. Detector coupling is illustrated in
panels (c) and (d), where the outputs of detectors in the four central
columns are coupled so as to simulate two 2.5 mm wide detection columns
and hence acquire data for four 2.5 mm slices. How do you think this
coupling process is applied in panel (d) to generate four 5 mm slices?

A second advantage of the adaptive array is that the number of
individual detector elements on each row can be considerably reduced,
from 16 to 8 in the case we\'ve just discussed. This adds greatly to the
speed with which data can be generated by the array and reduces the
number of computations necessary for uniformity and other corrections
that must also be applied to the measured data.

The interpolation process in multi-slice helical CT is illustrated in
our final figure where the situation in a 4-slice system using a
**360^o^ multi-slice filtered interpolation algorithm** is shown:

!Illustration of the 360MFI
algorithm.{width="280"}

The number of detector rows in Multi-Detector CT (MDCT) in 2010 is
typically 8, 16 or 64, or greater -- even a 320-slice scanner has been
developed.

## CT Image Display

After the computer calculates the linear attenuation coefficient for
each pixel via filtered back projection, the values are normalised to
the value for water as a reference, scaled and presented as a Hounsfield
or CT-number which is defined as:

```{=html}
<div class="center">
```
$H = 1000 \frac{\mu_m - \mu_{\text{water}}}{\mu_{\text{water}}}$

```{=html}
</div>
```
where μ~m~ and μ~water~ are the linear attenuation coefficients for the
tissue material and for water, respectively. The CT-number of water is
therefore zero. CT-numbers for a number of tissues are shown in the
following table:

  Tissue                CT-Number (H)
  --------------------- ---------------
  **Lung**              -300
  **Fat**               -90
  **White Matter**      30
  **Gray Matter**       40
  **Muscle**            50
  **Trabecular Bone**   300-500
  **Cortical Bone**     600-3,000

The image is usually presented on a computer monitor using a grey scale.
The grey scale is chosen to encompass all or some part of the entire
range of CT-numbers by selecting a suitable window level and window
width. The window width is the range of CT-numbers we select for display
and the window level is, usually but not always, the central CT-number
about which the window is chosen. Typically, the highest number is
assigned to white and the lowest number to black with all intervening
numbers assigned intensities on a linear scale. Thus, air will be
displayed as black and cortical bone will appear relatively bright.

Examples of image display manipulation are shown in the following two
figures. In the first, the same image of a slice through a patient\'s
liver is displayed using a relatively narrow window (high contrast) and
also with a wide window. The image with the narrower window appears
noisier, but this is merely a reflection of the fact that the gray scale
spreads over a narrow range of CT-numbers.

!Effect of window width and level on CT image display. (a) Level = 50;
Width = 200. (b) Level = 50; Width = 400. The image in (a) is displayed
with greater contrast and appears noisier than that in
(b). Level = 50; Width = 200. (b) Level = 50; Width = 400. The image in (a) is displayed with greater contrast and appears noisier than that in (b)."){width="380"}

In the second figure, a relatively narrow window has been used to
highlight pathology:

!Effect of window width and level on CT image display. (a) Level =
-600; Width = 1700. (b) Level = -60; Width = 400. The image in (a)
displays the lung tissue more clearly, whereas the image in (b)
highlights pulmonary
metastases. Level = -600; Width = 1700. (b) Level = -60; Width = 400. The image in (a) displays the lung tissue more clearly, whereas the image in (b) highlights pulmonary metastases."){width="380"}

## Scanned Projection Radiography

Most CT systems use scanned projection radiography (**SPR**), also known
as a *scout view* and *scanogram*, to establish a consistent set of
anatomical markers for all subsequent procedures. In SPR, the fan beam
is not rotated about the patient but is fixed, and the patient couch is
moved slowly through the beam. Each detector element measures a varying
amount of radiation intensity as the anatomy changes between the focus
and the detector. These discrete detector signals are digitised but not
processed to any great extent. The computer stores the signals from all
the detectors as a series of line images. The width of each line is
small, typically 1.5 -- 2 mm, corresponding to the finite width of the
fan beam. Subsequently, a projection radiograph is synthesized by making
a composite of these line scan images with the grey scale suitably
chosen to display the anatomy with an adequate contrast -- see an
example image below:

!Lateral view SPR image of a patient\'s head acquired prior to CT
scanning.{width="280"}

The volume of tissue to be examined in CT mode can be defined once the
SPR image has been generated. The CT procedure can then be automatically
controlled by the computer once the appropriate scanning factors have
been selected.

