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Calibration‐free beam hardening reduction in x‐ray CBCT using the epipolar consistency condition and physical constraints
Author(s) -
Würfl Tobias,
Hoffmann Mathis,
Aichert André,
Maier Andreas K.,
Maaß Nicole,
Dennerlein Frank
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13625
Subject(s) - algorithm , regularization (linguistics) , computer science , calibration , hardening (computing) , mathematics , mathematical optimization , optics , artificial intelligence , physics , materials science , statistics , layer (electronics) , composite material
Background The beam hardening effect is a typical source of artifacts in x‐ray cone beam computed tomography (CBCT). It causes streaks in reconstructions and corrupted Hounsfield units toward the center of objects, widely known as cupping artifacts. Purpose We present a novel efficient projection data‐based method for reduction of beam‐hardening artifacts and incorporate physical constraints on the shape of the compensation functions. The method is calibration‐free and requires no additional knowledge of the scanning setup. Method The mathematical model of the beam hardening effect caused by a single material is analyzed. We show that the effect of beam hardening on the resulting functions on the line integral measurements are monotonous and concave functions of the ideal data. This holds irrespective of any limiting assumptions on the energy dependency of the material, the detector response or properties of the x‐ray source. A regression model for the beam hardening effect respecting these theoretical restrictions is proposed. Subsequently, we present an efficient method to estimate the parameters of this model directly in projection domain using an epipolar consistency condition. Computational efficiency is achieved by exploiting the linearity of an intermediate function in the formulation of our optimization problem. Results Our evaluation shows that the proposed physically constrained ECC2 algorithm is effective even in challenging measured data scenarios with additional sources of inconsistency. Conclusions The combination of mathematical consistency condition and a compensation model that is based on the properties of x‐ray physics enables us to improve image quality of measured data retrospectively and to decrease the need for calibration in a data‐driven manner.