18.01.2013 Views

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 ...

SHOW MORE
SHOW LESS

Create successful ePaper yourself

Turn your PDF publications into a flip-book with our unique Google optimized e-Paper software.

13<br />

12<br />

14<br />

V<br />

V<br />

V<br />

11<br />

15<br />

V<br />

V<br />

V<br />

10<br />

16<br />

V<br />

9<br />

(a) First <strong>of</strong> 16 drive pairs<br />

1<br />

V<br />

8<br />

V<br />

2<br />

7<br />

V<br />

3<br />

V<br />

V<br />

V<br />

6<br />

4<br />

5<br />

13<br />

12<br />

14<br />

V<br />

V<br />

V<br />

11<br />

15<br />

V<br />

V<br />

V<br />

10<br />

16<br />

V<br />

V<br />

9<br />

(b) Second <strong>of</strong> 16 drive pairs<br />

Figure 6.1: 2D Adjacent drive patterns. In figure 6.1(a) current is injected through electrode<br />

pair (1,2) and the resulting boundary voltage differences are measured from electrode pairs<br />

(3,4),(4,5),...,(14, 15),(15,16). Voltages are not measured between pairs (16,1),(1,2), or<br />

(2,3). In figure 6.1(b) the current is injected between pair (2,3), and the voltage differences<br />

measured between pairs (4,5),(5,6),...,(15, 16),(16,1). Voltages are not measured between<br />

pairs (1,2),(2,3), or (3,4).<br />

applications, in which one wants to obtain more accurate tomographic slices through the<br />

chest.<br />

6.2 Methods<br />

We consider EIT difference imaging, which is widely understood to improve reconstructed<br />

image stability in the presence <strong>of</strong> problems such as unknown contact impedance, inaccurate<br />

electrode positions, nonlinearity, and in the 2D case, the use <strong>of</strong> 2D approximations <strong>for</strong><br />

<strong>3D</strong> electrical fields [17][87]. We address the class <strong>of</strong> normalized one-step linearized reconstruction<br />

algorithms that calculate the change in a finite element conductivity distribution,<br />

x = σ2 − σ1 due to a change in EIT difference signal, z = v2 − v1 over a time interval<br />

(t1,t2). By convention we consider the signal at t1 to be the reference frame and the signal<br />

at t2 to be the data frame. Since we do not know σ1, x is interpreted as the change in<br />

conductivity with respect to the unknown initial conductivity x = ∆σ = σ2 − σ1.<br />

For small changes around a background conductivity the relationship between x and z<br />

may be linearized as<br />

z = Hx + n (6.1)<br />

where H is the Jacobian or sensitivity matrix and n is the measurement system noise,<br />

assumed to be uncorrelated additive white Gaussian (AWGN). Each element i, j, <strong>of</strong> H is<br />

calculated as Hij = ∂zi<br />

�<br />

�<br />

and relates a small change in the ith difference measurement to<br />

� ∂xj σ0<br />

a small change in the conductivity <strong>of</strong> jth element. H is a function <strong>of</strong> the FEM, the current<br />

injection pattern, and the background conductivity. We use a homogenous background<br />

conductivity in which σ0 = 1 <strong>for</strong> each <strong>of</strong> the elements.<br />

79<br />

1<br />

V<br />

8<br />

V<br />

2<br />

7<br />

V<br />

3<br />

V<br />

V<br />

6<br />

4<br />

5

Hooray! Your file is uploaded and ready to be published.

Saved successfully!

Ooh no, something went wrong!