Medical image reconstruction is an important tool for
clinical diagnostics in
order to provide 2D and 3D views of patients' inner organs.
The image quality of the reconstructed volumes depends
(often heavily) on both modifiable
and intrinsic parameters of the imaging system and process.
Additionally,
image quality itself has to be understood in terms of image
properties that
are required for diagnosis which again is influenced by the
personal sensation
of the human observer.In digital breast tomosynthesis (DBT) data acquisition is
subject to several
restrictions (limited angle, low dose) which makes optimal
setting of the reconstruction parameters mandatory to
achieve competitive image qualities.
Mammographic images mainly serve as an instrument for early
detection of
breast cancer - the top cancer in women according to WHO
(2014) [1]. Therefore the demand of optimal image quality
in DBT necessitates bringing in
line preservation and best possible detectability of lesions
like microcalcifications, masses or spiculations with
reduction of noise induced by the imaging
system.
The goal of this project is to provide tools that can deal
with multidimensional parameterspaces to estimate optimal
reconstruction settings with respect to pre-defined observer
requirements. Combination of techniques from
machine learning with a parameterized reconstruction
algorithm will be used
to improve the diagnostic value of tomosynthesis images.
[1] World Cancer Report 2014, IARC, Lyon 2014.