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Parameter-Optimization for DBT Imaging Systems using Tools from Machine-LearningMedical 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. | Project manager: Prof. Dr.-Ing. Joachim Hornegger
Project participants: Dipl.-Math. Frank Schebesch, Dr. Anna Jerebko, Prof. Dr.-Ing. habil. Andreas Maier, Dr. Thomas Mertelmeier
Keywords: medical image reconstruction; tomosynthesis; parameter optimization; model observer
Duration: 1.1.2014 - 31.12.2016
Sponsored by: Siemens AG, Healthcare Sector
| Publications |
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Hanif, Suneeza ; Schebesch, Frank ; Jerebko, Anna ; Ritschl, Ludwig ; Mertelmeier, Thomas ; Maier, Andreas: Lesion Ground Truth Estimation for a Physical Breast Phantom. In: K.H. Maier-Hein ; T.M. Deserno ; H. Handels ; T. Tolxdorff (Ed.) : Bildverarbeitung für die Medizin 2017 - Algorithmen, Systeme, Anwendungen (Workshop Bildverarbeitung für die Medizin 2017 Heidelberg 12.-14.03.2017). 2017, pp 243-248. |
Institution: Chair of Computer Science 5 (Pattern Recognition)
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