A Uniﬁed Probabilistic Computational Framework for Cross-Domain Compensated Medical Imaging
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The quality of medical imaging is an important factor that can directly aﬀect on the results of diﬀerent medical imaging tests which routinely being used for various clinical studies and related research. The existence of any type of image imperfection such as diﬀerent noise and artifacts can make some diﬃculties for the correct analyzing and consequent interpretation of medical imaging test results and even may impact on the overall decision of the performed clinical procedure. Due to this issue, there is always a motivation for the quality enhancement of medical imaging that needs to be done by correcting for the diﬀerent image degradation that typically arise during medical image capture or reconstruction. Computational compensated imaging provides a useful, cheap and easy solution for the quality enhancement of diﬀerent types of medical imaging when it compares to the similar hardware-based compensation methods. A computational compensated medical imaging basically aims to recover a compensated (true) image from degraded medical measurement that is aﬀected by the diﬀerent types of acquisition/reconstruction image degradation. Compensating for each single image degradation issue can be useful for the quality improvement of medical imaging. Having a computational compensated imaging framework to jointly correct for multiple acquisition/reconstruction degradation issues can improve the functionality of current existing compensated imaging frameworks as they mostly account only for a single speciﬁc type of degradation issue. This work presents a novel probabilistic based computational compensated medical imaging which is able to jointly account for several acquisition/reconstruction degradation issues from the diﬀerent domains in a uniﬁed computational framework. The developed computational cross-domain compensated medical imaging speciﬁcally takes advantage of a stochastically fully connected conditional random ﬁeld (SFC-CRF) model in its frame- work which improves the performance of the proposed compensated medical imaging in producing of a desired compensated medical image. A compensated optical coherence tomography (C-OCT) imaging is developed within the framework of proposed computational cross-domain compensated medical imaging and with aiming to jointly compensate for the degradation due to the optical aberrations and speckle noise in OCT imaging. The developed C-OCT imaging is expanded to design a compensated super resolution OCT (C-SR-OCT) imaging framework which is able to generate a super resolution OCT (SR-OCT) image of higher quality from multiple OCT measurements. The proposed computational cross-domain compensated medical imaging is also used to develop a compensated magnetic resonance imaging (CMR) framework which aims to improve the quality of MR imaging from diﬀerent modalities by jointly correcting for the MR image degradation due to the intrinsic properties of MR scanner, bias ﬁeld inhomogeneities and inherent MR noise. The results of all three designed compensated medical imaging platforms for the OCT imaging and MR imaging elaborate the promising eﬃcacy of proposed probabilistic based computational compensated medical imaging framework for the quality enhancement of diﬀerent medical imaging techniques.
Cite this work
Ameneh Boroomand (2016). A Uniﬁed Probabilistic Computational Framework for Cross-Domain Compensated Medical Imaging. UWSpace. http://hdl.handle.net/10012/11143