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Medical Imaging Technology: Techniques and Applications
Amy Schnelle
Computer Science
University of Wisconsin-Platteville
[email protected]
Abstract
Since the advent of the x-ray more than one hundred years ago, medical imaging has
progressed from accidental discovery to a mainstay of modern medicine. This progress
has led to easier, earlier, and less invasive diagnosis as well as a better understanding of
the human body. The technology that makes medical imaging possible is continuing to
develop at a rapid place, making it faster, cheaper and more user-friendly. This
technology includes more advanced hardware, image capturing techniques, and
visualization of captured images. Some of the most commonly used medical imaging
techniques are CT (computed tomography), MRI (magnetic resonance imaging),
radiography, ultrasound, and nuclear imaging. Approaches to image rendering include
real-time volume rendering, finite-element modeling, deformable models, and tensormass models. In recent years a greater number of applications for medical imaging have
arisen, including computer-aided diagnosis, surgery simulation, and drug development.
As medical imaging continues to advance, so will the quality of health care for people
around the world.
Why Medical Imaging is an Important Field of Study
Medical imaging is a large and diverse field composed of technologies related to
image capture, visual rendering, and applications such as computer-aided diagnosis,
surgery simulation and drug development. It is a field that requires the input of medical
professionals, physicists, electrical engineers and computer scientists. With a
collaborative effort, medical imaging has the potential to continually revolutionize the
medical field.
Easier diagnosis is one of the many benefits of medical imaging. Before many of
the current imaging systems, doctors had to rely on the patient’s description of symptoms
and external cues. Most disease and disorder symptoms can vary from patient to patient.
Moreover, each patient’s description of the same symptoms is different. External
symptoms are also not a very easy way to diagnosis a condition. They can also vary from
person to person and in many cases no external symptoms are visible.
Modern medical imaging systems can detect disease before any symptoms are
noticed by physician or patient. A good example of this is cancer, which is much more
easily treated if caught early. If cancer goes undiagnosed until symptoms are noticed, it
has often metastasized and can be beyond treatment. As the technology continues to
improve, even earlier diagnosis will be the reality.
Less invasive diagnosis and procedures are great benefits to patients. Exploratory
surgery, which would be necessary without imaging to see inside the body, is much
riskier than imaging due to destruction of tissues, various levels of anesthesia, possibility
of infection, and surgeon error. Less invasive procedures are also commonplace today.
Arthroscopic surgeries, for example, greatly increase recovery time and decrease tissue
damage. Medical imaging also plays an important role in surgery planning by giving the
surgeon a glimpse into what the surgery will entail.
The applications of medical imaging also provide great benefits to society.
Imaging can help prove safety and efficacy of new drugs. Surgeons can gain more
training and practice with surgery simulation. Physicians can use computer-aided
diagnosis techniques to assist them in diagnosis problems. With computer-aided
diagnosis, doctors with little or no experience in a rare problem will be able to increase
their diagnostic abilities. For all the above reasons, and many more, medical imaging has
helped to and will continue to improve health care for everyone [11], [16].
The History of Medical Imaging
Medical imaging began in 1895, when Professor Roentgen accidentally
discovered x-rays while experimenting with Crookes tubes. He went on to win the first
Nobel Prize in Physics in 1901 for his discovery. X-ray technology continued to slowly
progress up until the Second World War. Afterwards, breakthroughs in X-ray
technology, such as image intensifiers, contrast media, and fluorescent screens improved
the quality and safety of X-rays [13].
Nuclear imaging and magnetic resonance both began in the 1950’s. Anger
developed the gamma camera in 1958, which led to exploration in the field of nuclear
imaging. In 1946, Felix Bloch and Edward Purcell discovered the presence of magnetic
resonance in solids and liquids. Nuclear magnetic resonance took shape from that time
through the 1970’s, but was mainly used for chemical and physical molecular analysis.
Magnetic resonance was not used for medical purposes until a physician named
Raymonde Damadian discovered that malignant body tissue had a different spectrum
than normal tissue. This discovery, as well as recent advances in electronics and
computing, started the exploration of MRI for medical purposes. This new technology
was first realized in 1974, when Damadian took an MRI of a tumor in a rat [9].
The ultrasound was first developed in the 1960’s thanks to sonar development
during World War II. Ultrasound was unique at the time, because it provided an imaging
device without radiation. This made it ideal for monitoring fetal development. The
ultrasound also had the additional feature of being able to produce real-time images.
Another medical imaging breakthrough made possible by digital computers was
the CT, or computed tomography, scan. The CT scan was introduced in 1972, by
Godfrey Hounsfield who later received the Nobel Prize for his work. One problem with
the original CT scan was its speed. To take a single slice and convert it to digital image,
took days. Since that time, there have been many advances in CT technology, and
modern scans take only seconds [13].
Image Capturing Technologies
Radiography
Radiography is the process of creating an image by passing x-rays through a
patient to a receptor. Because x-rays penetrate solid objects, but are slightly attenuated,
or weakened, by them, the receptor receives different amounts of x-rays at different
locations. The picture formed by the pattern of x-rays is known as a radiograph and
allows physicians to see the structure of bones and solid foreign objects within the body.
The principles of radiography are also used in other imaging processes such as CT.
In traditional radiography, the image is imprinted onto a film which can be
viewed by the physician against a light or later converted to a digital image. Since the
image is in analog form, it must be converted to digital form. This takes place in a three
step process. The first step involves scanning the image, which is typically accomplished
by a laser beam. During the scanning process, the image is divided into a certain number
of lines. The number of lines the image is divided into, will determine the number of
pixels the image has horizontally. The next step is sampling, where the continuous
analog signal created during scanning is divided at certain intervals. The intervals mark
the bounadry between one pixel of the final image and the next. Therefore, the number
of scan lines and sample intervals determine the number of pixels in the entire image.
More scan lines and sample intervals translate into longer scan time and a larger file but a
better image. The last step is conversion where an analog-to-digital converter translates
the value of the analog signal at a specific sample interval. The result of this conversion
is a binary number that represensts a specific pixel in the digital image [22].
With digital radiography, the detector contains a thin active matrix which creates
the image digitally. This eliminates the need for analog-to-digital conversion which is
time consuming. In addition, the image does not lose some of the quality that can happen
when converting to digital.
Fluoroscopy is a specialization of radiography. Unlike radiography, that produces
a single static image, fluoroscopy allows for real-time, continuous x-ray image
production. Originally, the fluoroscopy recptor was just a fluorescent screen, but the
advent of the image intensifier tube allowed for much brighter images without increased
exposure. The image is viewed by passing it from the image intensifier tube to a video
camera and onto a monitor. Because of its real-time capabilities, fluoroscopy is
especially useful for guiding a procedure, searching through a patient, or observing a
dynamic function such as joint movement [19].
Magnetic Resonance Imaging
Magnetic Resonance Imaging (MRI) uses magnetic energy and radio waves that
have varying sensitivity to the presence of water within tissues. Although this may not
seem very useful, it is in fact very useful because the properties and amount of water in
tissues can vary greatly with injury or illness. MRI is also distinctive, because it can
selectively image several different tissue characteristics [13].
The magnet itself is one of the most important components of the MRI. It
produces the magnetic field that the patient is placed into during an MRI procedure. The
magnet can be superconducting, resistive, or permanent. The superconducting magnet is
the most common in modern MRI devices, and is capable of producing the strongest
magnetic field. This field is produced by a wire that has no resistance to electrical flow.
The magnet is made of many loops of this wire which carry a large current. Along with
producing a strong magnetic field, the magnet also requires constant cooling with liquid
helium. The strength of these magnetic fields are measured in Tesla units and range from
0.5 to 1.5 Tesla. For comparison, a 1.5 Tesla MRI system has a magnetic field 30,000
times stronger than the pull of gravity on the earth's surface [19].
The magnetic field produced by the magnet results in tissue magnetization and
tissue resonance. Inside the MRI, the patient’s tissues become temporarily magnetized.
Different tissues become magnetized to different levels and at different rates. This
applies to both normal and pathological tissues. During the MRI procedure, the patient’s
tissues react to the strong magnetic field and resonate in the radio frequency range. This
results in radio communication between the patient’s tissues, the radio frequency
transmitter, and the radio frequency receiver throughout the procedure.
Image capture during an MRI is accomplished by a computer. The capturing
takes place in two distinct steps: acquisition and reconstruction. During Acquisition, a
series of radio frequency pulses are transmitted to the patient inside the MRI. The radio
frequency signals resonating from the body are collected and then another series is
completed until sufficient data is collected to create an image. The number of series
completed is indicated by the TR number. The higher the TR number, the more series
completed and the better the image quality. To make the MRI procedure easier to
complete, many preset protocols are stored for many clinical procedures [19] [22].
After acquisition, the image must be reconstructed. This is done by a process
known as Fourier transform. The Fourier transform (FT) is a mathematical technique for
converting time domain data to frequency domain data, and vice versa.
Computed Tomography
Computed Tomography (CT) imaging involves taking a series of x-ray images
and combining them to form a 2D or 3D cross-sectional image. Unlike conventional
radiography, CT allows a physician to see the entire structure of soft tissue structures
such as organs and fat, boney structures, and blood vessels. This makes CT especially
useful for detecting tumors, blood clots, and problems with muscles. Because of its 3D
capabilities, CT is also highly useful for the reconstruction of facial trauma, or
diagnosing problems with complex joints [8].
A CT scanner consists of an x-ray tube and a CT detector that sit opposite of each
other on a large ring. This ring rotates around the patient in a spiral pattern and the
detector takes around 1000 snapshots of the x-ray beam per 360 degrees. Each snapshot
in a rotation provides one specific “shadow” just as in an x-ray image. All the snapshots
for one rotation are processed by a dedicated processor that lines all the images up around
a central axis and then stretches the image so they overlap to create a single 2D slice in
the entire CT scan [15, 22].
The thickness of each slice can be adjusted between 1mm and 10mm depending
on the amount of detail desired and the available storage. Since this process is costly, a
dedicated processor is used to create the slices and then paste them back together after the
scan completes. A modern CT scanner also has computers that control the movement of
the scanner and allow the physician to control and monitor the scan [8].
Ultrasound
Ultrasound uses high frequency sound waves and their corresponding echoes to
create images of the internal structures of patients. During an ultrasound, the transducer
sends out millions of sound waves per second in the 1 to 5 megahertz range. When the
sound waves encounter a boundary between tissues, it is echoed back. These echoes are
received by the transducer and are processed to form the image. The process of
ultrasound works similar to sonar and echolocation.
The light and dark areas in an ultrasound image are determined by the amplitude
of the returning sound wave. In order to compensate for tissue absorption, a technique
called time-gain compensation is applied to the results. Time-gain compensation is
preformed by the electronic pulse amplifier. It works by amplifying the electrical pulse
of echoes received by the transducer in relationship to the amount of time it takes to
transmit and receive the signal. The gain of the amplifier, which can be set to a particular
level or adjusted by the operator, determines how much the electrical pulses need to be
adjusted to properly compensate for distance. The result of time-gain compensation is an
image with brighter areas regardless if distance from transducer [19].
Because there are no x-rays involved in ultrasound, it has become very popular
for checking fetal development. However, ultrasound is also used for evaluating many
internal organs. Since ultrasound produces images in real-time, it is ideal for determining
organ function and guiding surgical procedures [17].
Nuclear Imaging
Nuclear Imaging involves detecting radiopharmaceuticals given to a patient for
diagnostic purposes and then creating images of the collected data. Nuclear imaging is
especially useful for finding cancer, because radioactive substances that attach to tumors
can be given and traced throughout the body to find very small metastases that may be
difficult to otherwise detect. The two main types of nuclear imaging are positron
emission tomography (PET) and single photon emission computed tomography (SPECT).
A PET scanning system works by detecting the location of radioactive particles
that have accumulated in a certain area. When a positron from an unstable radionuclide,
such as Carbon-11 or Fluorine-18, collides with an electron, both particles are annihilated
and two photons are emitted. When annihilation occurs, each photon is emitted in
exactly the opposite direction as the other. These photons are then detected by the PET
scanners circular array of radiation detectors. If a detector pair (two detectors exactly
opposite of each other) both detect a photon within a given time frame, then the location
of the line between the two detectors is recorded. Multiple lines are then overlaid to
produce the multi-dimensional location of the annihilation event, and ultimately the
location of the radionuclide concentration [19, 22].
The radioactive substances used in SPECT are Xenon-133, Technetium-99, and
Iodine-123. The process of SPECT imaging is similar to CT, but uses a gamma camera
to take images of the gamma rays emitted by the particles. The path of the radioactive
substances can be traced throughout the body, shedding light on the flow of blood and
metabolisms. SPECT has the downside of producing less detailed images with lower
sensitivity, but is more easily accessible for most patients [14, 19].
In attempts to further PET and CT technology, new hybrid devices are gaining in
popularity. They are able to combine the benefits of PET and CT scanning devices to
create images that clearly show both structure and function. The combination of these
two imaging technologies creates a powerful new tool and also makes both technologies
more affordable, and therefore more accessible [6].
Techniques for Visual Rendering of Images
Real-time Volume Rendering
Volumetric rendering allows for the visualization of the internal structure of
objects, making it a highly important contribution to medicine. Although a great tool,
volumetric rendering is very computationally expensive. For example, “rendering a
dataset of 2563 16-bit voxels at 30 Hz requires 32 MBytes of storage, a memory transfer
rate of 1 GByte per second, and approximately 5 billion instructions per second. This
problem is aggravated by the continuing trend towards larger datasets. High-resolution
sampling devices, faster supercomputers, and more accurate modeling techniques will
make 10243 and larger datasets the norm” [11].
Volume rendering is the process of creating 2D images from a 3D collection of
voxels. Voxels, or volume pixels, are the smallest distinguishable cubes that combine to
form a 3D image. This 3D image is captured through one of the many image capturing
techniques and is then processed using one of the many volume rendering techniques.
These processes can be either indirect, where a geometrical model is first built from the
data, or direct, where an image is created directly from the acquired data [12].
Indirect techniques, such as the marching cube algorithm, require preprocessing,
which is computationally expensive. This preprocessing consists of creating a
geometrical representation of the isosurfaces in the volume data. This is done according
to the intensity threshold, which determines what should be visible by a predetermined
value. The geometrical model forms a polygonal mesh that is easily rendered with
graphics cards. Another problem with the marching cube algorithm is that it can only
model the surface of 3D objects. Therefore, it is not useful for the modeling of semitransparent tissues [12].
Direct volume rendering techniques can be further segmented into image-order
algorithms such as ray casting, object-order algorithms such as splatting, and hybrid
algorithms such as shear-warp.
Image-order techniques generate an image pixel-by-pixel by using rays. Each ray
is cast from the view point through the image to the volume. These rays intersect the 3D
image at different voxels. The values of these voxels along one ray are then examined to
form appropriate shading. By examining multiple values along a ray, image-order
algorithms allow for the display of transparencies [1, 12].
On the other hand, object-order techniques project the image voxel-by-voxel, onto
the screen. Traditionally the image is traversed either front-to-back or back-to-front and
each voxel is examined. If the new voxel is in front of another voxel at the same
coordinate, then the old value is replaced with the value from the new voxel. This
method, however, does not allow for transparencies. One way around this is to use
splatting with a radially symmetric interpolation kernel, but this method results in a
blurring. One way around this is to use a post-classified footprint for the projected voxels
instead of a pre-calculated footprint [1, 12, and 21].
The hybrid shear-warp algorithm works to simplify the process of projection. The
data is first broken down into slices parallel with the front of the image. The slices are
then sheared and projected onto the image plane. The image is then transformed into the
final image by warping. Although this algorithm is very fast, the image quality can be
low, if the resolution of the volume is not high enough for the resolution of the final
image [21].
Since volume rendering and especially real-time volume rendering techniques are
quite expensive, it has often required the use of specialized hardware acceleration.
Recently, the VolumePro hardware acceleration card has brought this ability to PC-class
machines. VolumePro is based on cube-4 architecture which “uses a distributed
interleaved memory, several parallel processing pipelines, and an innovative parallel
dataflow scheme that requires no global communication, except at the pixel level.” [10]
Using ray casting, VolumePro provides up to 30 fps in parallel projection. This allows
visualization of data from CTs and MRIs to be much more accessible [20].
Deformable Models with Finite-element Modeling
+In order to realistically model moving tissues, deformable models are needed.
These models are necessary when studying the heart, an organ that requires analysis of its
movement, or the movement of complex joints. Deformable models rely on image
capturing techniques such as MRI and CT, as well sound rendering techniques.
+Unlike previous surface deformation models, finite-element modeling allows for
a quick and accurate representation of the volume as well as the surface of soft tissues. In
the finite-element method, the model is made up of layers of nodes which each have
specific properties. The nodes are overlaid by a mesh that represents the surface of a
specific layer. Depending on the properties of the nodes and the mesh, when one node is
deformed, a number of the surrounding nodes will also be deformed. Since the
surrounding nodes react to forces on one node, a finite element model will react much
like a real tissue to outside force [2].
Specialized Applications of Medical Imaging Technology
Surgery Simulation
One special application of medical imaging is surgery simulation. Surgery
simulation has the potential to make surgical operations safer and more cost effective.
They can train a surgeon on a new procedure, give a surgeon an extra tool in surgery
planning, and help tailor a surgical procedure to a specific patient. In order for surgery
simulation to be useful, the simulator must provide accurate deformable models that
interact with surgical tools and provide feedback for the surgeon.
Since creating a realistic surgery simulator would be very computationally
demanding, existing imaging, UI, and force feedback techniques will all need to be
improved. One way to reduce the processor needs for the modeling, would be to
precompute a set of deformations for a specific tissue. Another method could involve
limiting the areas where the surgeon could interact with the model. For example, a liver
in surgery simulation could be comprised mostly of precomputed regions, with
deformable 3D bands represented by tensor-mass models [4].
Computer-aided Diagnosis
Computer-aided diagnosis is another way medical imaging can be used. In
studies, computer-aided diagnosis has been shown to greatly improve diagnostic accuracy
of doctors [3], [5], [7]. This becomes especially apparent when doctors with little or no
experience in diagnosing a certain ailment use a computer-aided diagnosis system.
Furthermore, computer-aided diagnosis systems can help eliminate human error and
subjectivity.
One technique that has been widely explored for computer-aided diagnosis is
neural networks. A neural network is a network of nodes, which is modeled after the
human brain. Each node has a specific weight and depending on the input it receives, it
either fires or does not. The neural network receives a set of inputs for the input nodes.
Depending on the inputs, the input nodes then either fire or do not fire the message onto
the next layer. This continues until an output is reached.
To use a neural network, an initial set of weights is determined. The network is
then run on a training set of data where the correct output is known. If the correct output
is not achieved, then the weights of the nodes are adjusted. This continues until the
correct results are received for the training cases. The network can then be given new
data that the correct output is not known. In this way, the neural network is able to
determine, for example, whether or not a lesion is cancerous depending on a known set of
attributes (size, shape, etc.)
Drug Development
Although it may seem odd to think of drug development as benefiting from
medical imaging, it is in fact a very useful application. As medical imaging advances,
quantitative, as well as spatial properties of organs are being realized. If medical testing
was able to be accomplished through computerized models, many new medications
would be able to reach the market more quickly and with fewer side effects. Medical
imaging can enable drug developments in other, more immediate ways. A medical image
is much easier and cheaper to share with physician across the world through networks. In
addition, medical imaging gives physicians a clearer picture of how drugs are affecting
the patient internally [18].
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