Image Reconstruction for 3D Lung Imaging - Department of Systems ...
Image Reconstruction for 3D Lung Imaging - Department of Systems ...
Image Reconstruction for 3D Lung Imaging - Department of Systems ...
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1. Heuristic selections <strong>of</strong> hyperparameter are inconsistent among experts and unrepeatable.<br />
This suggests that there is no single preferred value <strong>of</strong> λ, rather there is a<br />
preferred region <strong>of</strong> λ over which reconstructions are not subjectively distinguishable.<br />
Moreover, it was not possible <strong>for</strong> observers to differentiate reconstructions based on<br />
heuristic hyperparameter selections from those produced from the objective methods.<br />
2. The GCV method is unreliable <strong>for</strong> the class <strong>of</strong> algorithms used in this work.<br />
3. The L-Curve is, in general, shallow <strong>for</strong> EIT applications and is not reliable <strong>for</strong> all<br />
configurations (doesn’t indicate a hyperparameter). When the method does work it<br />
provides a lower hyperparameter value than the Fixed NF and BestRes methods.<br />
4. With NF = 1 the Fixed NF Method calculates a hyperparameter that falls in the<br />
minimum region <strong>of</strong> the Resolution Curve. At low noise levels λNF=1 is very close to<br />
λBestRes. As AWGN is added to the simulated data λBestRes increases while λNF=1<br />
remains constant. However at the noise level found in our EIT equipment a NF <strong>of</strong> 1<br />
produces good reconstructions that are close to the optimal reconstructions achieved<br />
with BestRes method.<br />
5. The Fixed NF Method provides a configuration independent method to select λ that<br />
is repeatable and is more consistent than expert selection. One could use the Fixed<br />
NF method with NF = 1 to calculate a minimum hyperparameter value <strong>for</strong> any<br />
configuration. This method is repeatable and in applications with realistic noise levels<br />
will produce consistent stable reconstructions that are as good as heuristic selection.<br />
6. Hyperparameters taken from the minimum region <strong>of</strong> the Resolution Curve (BestRes<br />
method) always produce good solutions that are comparable to, but more consistent<br />
than expert selections. Moreover λBestRes is optimal in terms <strong>of</strong> our figure <strong>of</strong> merit.<br />
For the class <strong>of</strong> regularized reconstruction algorithms used in this work both the Fixed NF<br />
and BestRes methods provide objective methods to select a good value <strong>for</strong> λ. The values<br />
were indistinguishable from those selected by human experts. Both methods were developed<br />
using simulated data but shown to be applicable (validated) using Tank data.<br />
Although these methods do not completely solve the problem <strong>of</strong> obtaining an optimal<br />
hyperparameter value, they do provide a rationale and method <strong>for</strong> objectively and automatically<br />
selecting λ. Using the BestRes method with imaging equipment provides a sound<br />
engineering method <strong>for</strong> manufacturers or researchers to obtain a configuration-dependent<br />
hyperparameter that is optimal in terms <strong>of</strong> resolution. This allows end users to per<strong>for</strong>m<br />
impedance imaging without the necessity <strong>of</strong> having to manually tweak parameters.<br />
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