Image Reconstruction for 3D Lung Imaging - Department of Systems ...

Chapter 8
Conclusion and Future Work
At the start of this work the prevailing algorithms in use for lung/chest imaging were limited
to 2D models that relied on ad hoc tweaking to produce reconstructions. The aim of this
thesis was to develop enhancements in EIT image reconstruction for 3D lung imaging; in
other words, to remove some of the limitations that continue to impede the routine use of 3D
models for lung imaging. Due to our desire to validate theoretical and simulated models with
lab data we constrained our field of investigation to models that could be realized with a 16
electrode scanner designed for tetrapolar (ostensibly adjacent drive protocol) measurements
in a 2D configuration. This required the application of engineering principles to develop
and analyze reconstruction algorithms and protocols suitable for use with the 16 electrode
EIT systems such as the Goe-MF Type II scanner.
The aim was attained through the systematic achievement of the four main objectives:
1. The development of the BestRes objective hyperparameter selection method provides
a calibration based method of calculating a hyperparameter once for a specific configuration
of mesh and equipment. Using this algorithm eliminates the necessity of
ad hoc tweaking by users during reconstruction. Disparate researchers can now more
easily repeat the work of others. Moreover, by calculating the hyperparameter off-line,
a good image can be obtained from a single matrix inversion. Contrarily, methods
such as L-curve and expert selection require multiple inversions to be calculated for
each useful solution. Moreover the L-Curve method is shown to be unreliable for EIT.
2. The development of the Nodal Jacobian Inverse Solver algorithm enables the solution
of large dense 3D finite element models that, previous to this work, were not easily
solvable using linear algebra systems based on 32 bit pointers. This solver allows
one to model and solve complex, accurate geometries containing a priori structures
with linear algebra software that could not solve the same model using the traditional
elemental Jacobian.
3. The evaluation of an admittedly small set of potential 3D EP configurations, nevertheless
provides a sound basis for recommending a specific method to collect 3D
lung data. Moreover it provides a firm basis to discontinue further evaluation of
configurations that perform poorly, such as the proposed opposite configurations.
4. The convergence improvements and subsequent evaluation of the PD-IPM algorithm
for TV regularization provide a defensible argument for when and when not to use TV
regularization. Moreover the promising 2D results provide justification and incentive
for further research into this algorithm aimed at increasing the size of 3D models that
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