Aspects of Green Hospital Approaches with a Focus on Developing ...

Figure 10: Example scan <strong>with</strong>out (A) and <strong>with</strong> (B) usage <strong>of</strong> a s<strong>of</strong>tware noise reduction filter [48]
nonlinear reconstruction filter based on a statistical model. Thereby, these filters enhance
the signal-dependent noise in the reconstruction data up to 30-60% and minimize the
loss <strong>of</strong> image quality and resolution to less than 5% [49]. However, it is more promising
to work directly in the raw data domain than on the reconstructed image such as spatialdata
based filters. Thus, if a s<strong>of</strong>tware noise filter is used in developing nations it should
be a raw-data based filter.
b) Iterative Image Reconstruction Techniques
The reason why the currently clinical widely used filtered back projection (FBP) algorithm
is dose inefficient is that it assumes a perfect signal. FBP presents an overly
simplistic view <strong>of</strong> reality, where ideal system optics feature a point source, point voxels
and point detector elements, all linked together by an infinitely small pencil beam. This
beam is assumed to expose the detector array in a square angle. It also assumes perfect
projection samples, ignoring the noise inherent to X-ray attenuation and detection electronics
[50].
On contrary, the peculiarity <strong>of</strong> iterative reconstruction (IR) algorithms is that they are
able to correct image data by incorporating an assortment <strong>of</strong> physical models <strong>of</strong> the CT
system into the reconstruction process that can accurately characterize the data acquisition
process including noise, beam hardening, scatter, etc. Even so, a current limitation
<strong>of</strong> IR is the long computing time. Therefore, modified and computationally faster
Figure 11: Filtered back projection (A) and iterative reconstruction (ASIR) (B), both scans <strong>with</strong>
same dose <strong>of</strong> CDTI vol
= 9 mGy [54]
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