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

as opposed to anatomical imaging. With functional applications images can be considered
intermediate data for some technique such as the determination of the change in lung tidal
volume.
1.2 Current state and problems
A detailed review of the current state of EIT is presented in chapters 2 and 3. Chapter 3
concludes with a discussion of the best practices used in EIT lung imaging at the start of
this work.
The field of impedance imaging of lungs is immature as an engineering endeavour. Despite
20 years of research and the availability of inexpensive medical grade EIT scanners,
EIT has yet to make the transition from the lab to the clinic [48]. The adoption of EIT
imaging for clinical use will require algorithms that provide 3D information, improve resolution,
and be robust and reliable enough that clinicians will routinely and confidently
employ the equipment for diagnostics.
Although there are many papers (the bibliography lists over 80) and even a few books
(for example [69]) dealing with EIT, there is a lack of rigorous evaluation and comparison
of competing methods and techniques. Moreover, many algorithms (perhaps the majority)
are ad hoc and require tweaking of parameters that prevents repeatability in experimentation.
Finally, 2D algorithms continue to be used: the bulk of research in EIT has revolved
around 2D finite element models. Improvements in computational power has permitted the
recent development of 3D algorithms, however the use of 2D continues to be routine in lung
imaging, perhaps due to the ready availability of equipment designed for 2D applications.
Among other limitations, 2D imaging cannot provide vertical location information of offplane
contrasts. Although one can predict theoretically how a known off-plane contrast will
affect the resulting 2D image, one cannot infer the location of the source of an artefact in a
2D image, caused by an off-plane contrast, from the 2D image. This is a severe limitation
to 2D imaging that should encourage the development of 3D algorithms. The practical use
of 3D imaging has been pioneered by the Industrial Process Monitoring (IPM) community.
The main difference between IPM applications and lung applications is that IPM has the
advantage of well known and stable electrode position information thus reconstruction models
can match the tank geometry to high precision. Contrarily, lung imaging suffers from
unknown a priori electrode position which is exacerbated by continuous electrode movement
throughout data collection. These problems make difference imaging difficult and
make absolute imaging practically impossible unless one can track the electrode positions.
EIT remains a promising medical imaging modality. However, to move the modality
into routine use for lung imaging will, as a minimum, require the development of rigorous,
repeatable and rapid 3D image reconstruction techniques. Thus the aim of this thesis is to
develop Enhancements in EIT Image Reconstruction for 3D Lung Imaging. In other words,
to remove some of the limitations that continue to prevent the routine use of 3D models.
Although it is expected that these enhancements will be useful in other applications the
specific interest in this thesis is 3D lung imaging.
1.3 Objectives
The aim of this thesis will be addressed in terms of the following four major objectives:
O1: Improve the method of hyperparameter selection in order to eliminate case by case
2