Thoracic Imaging 2003 - Society of Thoracic Radiology

WEDNESDAY
214
capable of studying the internal architecture of nodules through
texture analysis. McNitt-Gray et al used texture measures to
identify nodules with uniform attenuation from those with inhomogeneous
attenuation. This could be applied to non-contrast
and contrast CT studies of nodules. Integration of the results of
computer analysis with a database management system may not
only assist in daily clinical activities but also provide indispensable
data for research.
Volume Measurement
Precise measurement of nodule size and assessment for
growth is very important, particularly for the interpretation of
screening chest CT. Nodule size on CT has been traditionally
expressed as bi-dimensional perpendicular measurements (the
largest dimension and its perpendicular dimension) that are then
multiplied to obtain a bidimensional cross product, as recommended
by the World Health Organization criteria. Size has
also been recorded in terms of the largest dimension, as suggested
by the Response Evaluation Criteria in Solid Tumors
Guidelines. However, inter-observer error occurs when small
nodules are measured using manual calipers, in combination
with film scales, or electronic calipers.
Volume measurement can be quantified using two (2D) or
three-dimensional (3D) methods that can be manual, semi-automated-
or automated. 2D methods require an assumption of a
nodule’s shape. The largest nodule dimension is converted into
nodule volume by assuming a spherical shape, or the bi-dimensional
perpendicular measurements are used with the presumption
that a nodule is an ellipse. 3D measurement entails using
the entire CT data set in which the nodule is encoded to calculate
nodule volume. The superiority of 3D methods was
demonstrated by Yankelevitz et al, particularly for deformed
non-spherical nodules. 3D methods sum the volumes of the
nodule on each axial section to obtain total nodule volume and
therefore may more accurately measure irregularly shaped nodules.
Nodule volume quantification with automated methods can
potentially detect smaller differences in nodule size at earlier
intervals than simply relying on cross-sectional dimensions.
However, there are a number of impediments to performing
automated/ semi-automated volume quantification. The major
problem is the reproducibility of volume measurements. Partial
volume effects play a major role in generating errors in measurement.
Threshold-based methods are frequently used to separate
or segment nodules from the surrounding lung parenchyma.
If a nodule does not fill an entire voxel, the nodule’s attenuation
will be averaged with the surrounding lung parenchyma.
Hence, depending on the threshold, the voxel may or may not
be considered as part of the nodule. Validation of these methods
is therefore important. Using synthetic nodules imaged in air
and 2D and 3D quantitative methods for volume measurement,
Yankelevitz et al demonstrated that 0.5 mm axial sections were
associated with smaller errors as compared to nodule volume
measurements performed on 1.0 mm sections. It is important to
understand the error in measurement methods so that identification
of change in nodule volume can be interpreted with knowledge
of the limitations of a measurement system, whether automated
or semi-automated.
In patients, the volume measurement error is likely to be
higher secondary to a number of factors. These include lung
pathology such as emphysema, consolidation, and/ or infiltrative
lung disease in addition to adjacent normal parenchymal struc-
tures, such as bronchi and vessels. Automated segmentation of
nodules from vasculature has been addressed recently by Zhao
et al. 3D volume measurement methods may use 2D and 3D
criteria for segmentation. Automated segmentation techniques
are difficult to validate, as there is no gold standard for segmentation
accuracy.
Image Registration
The follow up of pulmonary nodules emphasizes the need
for image registration. Image registration entails “superimposing”
image data or determining the spatial alignment between
different images from the same modality at different points in
time (intramodality registration) or between different imaging
modalities such as CT, MR and positron emission tomography
(intermodality registration). To accurately correlate a nodule on
a given CT study with its matching counterpart on a subsequent
CT, global registration of the thorax and local registration of
nodules and smaller structures need to be performed.
A large number of reports concerning image registration
have been published, primarily in the brain and to a lesser
degree in other organ systems. Within the chest, primary interest
has focused on registering the metabolic information provided
by nuclear medicine studies and CT. Due to the recent attention
focusing on screening CT and the need to better measure
and compare nodule size, significant interest in comparing CT
studies has emerged.
Image registration techniques include rigid body, affine, and
elastic image methods. 112 On a CT scan, translational differences
may occur in the x, y, or z position without rotation or
distortion. Rotational differences occur when the torso is rotated
in the axial plane (x, y rotation) and rotated out of the axial
plane (z rotation). Rigid body transformation methods account
for these rotational and translational differences. Image distortion,
termed skewing, from non-uniform image reconstruction or
changes in perspective, affects 2D radiographic images more
than CT. However, global skewing can be introduced when different
gantry tilts are used and would be particularly relevant for
head CTs. Skewing is introduced as the patient exhales and the
thorax deforms. Global scaling factors are related to the FOV
and slice thickness on CT. Affine transformations address differences
in scaling and skewing in addition to rigid body parameters
and globally represent the differences between two data
sets.
The lung is a deformable structure that differs in shape and
volume related to the degree of patient inspiration. Each lobe
may have a distinct deformation or strain pattern in response to
varying inspiratory volumes that may translate to different
shapes and attenuation on CT. Deformable models may ultimately
compensate for global and local differences in thorax
shape; however, deformable models have been primarily studied
in the heart. In addition to problems created by different levels
of inspiration, pathology such as atelectasis, which can change
the shape of the thorax and shift anatomical structures and any
disease distorting the normal contours of structures within the
bony thorax could pose difficulties.
CAD in Clinical Workflow
The ultimate goal is an interactive system that enables easy
identification of corresponding structures on initial and subsequent
CT studies, documentation of nodules and their characteristics,
and storage of image results for future analysis and documentation.
This would prove vital to the follow up of a large