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<p>X-ray computed tomography (CT) imaging constitutes one of the most widely used
diagnostic tools in radiology today with nearly 85 million CT examinations performed
in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient
compared to other x-ray imaging modalities and as a result of this fact, coupled with
its popularity, CT is currently the single largest source of medical radiation exposure
to the U.S. population. For this reason, there is a critical need to optimize CT examinations
such that the dose is minimized while the quality of the CT images is not degraded.
This optimization can be difficult to achieve due to the relationship between dose
and image quality. All things being held equal, reducing the dose degrades image quality
and can impact the diagnostic value of the CT examination. </p><p>A recent push from
the medical and scientific community towards using lower doses has spawned new dose
reduction technologies such as automatic exposure control (i.e., tube current modulation)
and iterative reconstruction algorithms. In theory, these technologies could allow
for scanning at reduced doses while maintaining the image quality of the exam at an
acceptable level. Therefore, there is a scientific need to establish the dose reduction
potential of these new technologies in an objective and rigorous manner. Establishing
these dose reduction potentials requires precise and clinically relevant metrics of
CT image quality, as well as practical and efficient methodologies to measure such
metrics on real CT systems. The currently established methodologies for assessing
CT image quality are not appropriate to assess modern CT scanners that have implemented
those aforementioned dose reduction technologies.</p><p>Thus the purpose of this doctoral
project was to develop, assess, and implement new phantoms, image quality metrics,
analysis techniques, and modeling tools that are appropriate for image quality assessment
of modern clinical CT systems. The project developed image quality assessment methods
in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms,
and (c) clinical images.</p><p>The work in this dissertation used the “task-based”
definition of image quality. That is, image quality was broadly defined as the effectiveness
by which an image can be used for its intended task. Under this definition, any assessment
of image quality requires three components: (1) A well defined imaging task (e.g.,
detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists
or a detection algorithm), and (3) a way to measure the observer’s performance in
completing the task at hand (e.g., detection sensitivity/specificity).</p><p>First,
this task-based image quality paradigm was implemented using a novel multi-sized phantom
platform (with uniform background) developed specifically to assess modern CT systems
(Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed
on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in
terms of noise, resolution, and detectability as a function of patient size, dose,
tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm
(i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction–
ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented
and used as the basis of image quality comparisons. It was found that image quality
increased with increasing dose and decreasing phantom size. The CT system exhibited
nonlinear noise and resolution properties, especially at very low-doses, large phantom
sizes, and for low-contrast objects. Objective image quality metrics generally increased
with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE
algorithm could offer comparable image quality at reduced doses or improved image
quality at the same dose (increase in detectability index by up to 163% depending
on iterative strength). The use of automatic exposure control resulted in more consistent
image quality with changing phantom size.</p><p>Based on those results, the dose reduction
potential of ADMIRE was further assessed specifically for the task of detecting small
(<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom
(with uniform background) was designed and fabricated using a multi-material 3D printer.
The phantom was imaged at multiple dose levels and images were reconstructed with
FBP and ADMIRE. Human perception experiments were performed to measure the detection
accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance
to FBP at 56% less dose.</p><p>Using the same image data as the previous study, a
number of different mathematical observer models were implemented to assess which
models would result in image quality metrics that best correlated with human detection
performance. The models included naïve simple metrics of image quality such as contrast-to-noise
ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched
filter observer model family and the channelized Hotelling observer model family.
It was found that non-prewhitening matched filter observers and the channelized Hotelling
observers both correlated strongly with human performance. Conversely, CNR was found
to not correlate strongly with human performance, especially when comparing different
reconstruction algorithms.</p><p>The uniform background phantoms used in the previous
studies provided a good first-order approximation of image quality. However, due to
their simplicity and due to the complexity of iterative reconstruction algorithms,
it is possible that such phantoms are not fully adequate to assess the clinical impact
of iterative algorithms because patient images obviously do not have smooth uniform
backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture
and fine texture) and a uniform phantom of similar size were built and imaged on a
SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and
a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction
technique, quantum noise was measured in all images of each phantom. It was found
that in FBP, the noise was independent of the background (textured vs uniform). However,
for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the
uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to
66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this
result, it clear that further investigation was needed into to understand the impact
that background texture has on image quality when iterative reconstruction algorithms
are used.</p><p>To further investigate this phenomenon with more realistic textures,
two anthropomorphic textured phantoms were designed to mimic lung vasculature and
fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom)
were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner.
Scans were repeated a total of 50 times in order to get ensemble statistics of the
noise. A novel method of estimating the noise power spectrum (NPS) from irregularly
shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary
noise patterns with pixels near edges having higher noise than pixels in more uniform
regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions
for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e.,
NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it
was concluded that quantum noise properties in the uniform phantoms are not representative
of those in patients for iterative reconstruction algorithms and texture should be
considered when assessing image quality of iterative algorithms.</p><p>The move beyond
just assessing noise properties in textured phantoms towards assessing detectability,
a series of new phantoms were designed specifically to measure low-contrast detectability
in the presence of background texture. The textures used were optimized to match the
texture in the liver regions actual patient CT images using a genetic algorithm. The
so called “Clustured Lumpy Background” texture synthesis framework was used to generate
the modeled texture. Three textured phantoms and a corresponding uniform phantom were
fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner.
Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized
Hotelling observer to measure detectability and the dose reduction potential of SAFIRE
based on the uniform and textured phantoms. It was found that at the same dose, the
improvement in detectability from SAFIRE (compared to FBP) was higher when measured
in a uniform phantom compared to textured phantoms.</p><p>The final trajectory of
this project aimed at developing methods to mathematically model lesions, as a means
to help assess image quality directly from patient images. The mathematical modeling
framework is first presented. The models describe a lesion’s morphology in terms of
size, shape, contrast, and edge profile as an analytical equation. The models can
be voxelized and inserted into patient images to create so-called “hybrid” images.
These hybrid images can then be used to assess detectability or estimability with
the advantage that the ground truth of the lesion morphology and location is known
exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney
stones were modeled based on images of real lesions. The lesion models were virtually
inserted into patient images to create a database of hybrid images to go along with
the original database of real lesion images. ROI images from each database were assessed
by radiologists in a blinded fashion to determine the realism of the hybrid images.
It was found that the radiologists could not readily distinguish between real and
virtual lesion images (area under the ROC curve was 0.55). This study provided evidence
that the proposed mathematical lesion modeling framework could produce reasonably
realistic lesion images.</p><p>Based on that result, two studies were conducted which
demonstrated the utility of the lesion models. The first study used the modeling framework
as a measurement tool to determine how dose and reconstruction algorithm affected
the quantitative analysis of liver lesions, lung nodules, and renal stones in terms
of their size, shape, attenuation, edge profile, and texture features. The same database
of real lesion images used in the previous study was used for this study. That database
contained images of the same patient at 2 dose levels (50% and 100%) along with 3
reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms
in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based
Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted
from the lesions under each condition. It was found that both dose and reconstruction
algorithm had a statistically significant effect on the feature measurements. In particular,
radiation dose affected five, three, and four of the 23 features (related to lesion
size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules,
and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23
features (including size, attenuation, and texture features) for liver lesions, lung
nodules, and renal stones, respectively. Lesion texture was not significantly affected
by radiation dose.</p><p>The second study demonstrating the utility of the lesion
modeling framework focused on assessing detectability of very low-contrast liver lesions
in abdominal imaging. Specifically, detectability was assessed as a function of dose
and reconstruction algorithm. As part of a parallel clinical trial, images from 21
patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle
liver lesion models (contrast = -15 HU) were inserted into the raw projection data
from the patient scans. The projections were then reconstructed with FBP and SAFIRE
(strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and
detectability index of an observer model (non-prewhitening matched filter) were assessed.
It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased
CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC
human perception experiment was performed to assess the dose reduction potential of
SAFIRE, which was found to be 22% compared to the standard of care dose. </p><p>In
conclusion, this dissertation provides to the scientific community a series of new
methodologies, phantoms, analysis techniques, and modeling tools that can be used
to rigorously assess image quality from modern CT systems. Specifically, methods to
properly evaluate iterative reconstruction have been developed and are expected to
aid in the safe clinical implementation of dose reduction technologies.</p>
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