ALGORITHM DEVELOPMENT:
The image reconstruction problem in SCT can be divided in to two separated steps: a nonlinear material decomposition step, which decomposes the energy-based data into material-based data, and a linear tomographic reconstruction step. In this work, we investigated robust regularization methods for the material decomposition problem. In [1], we proposed a material-dependent spatial regularization method for material decomposition and evaluated it using a realistic numerical thorax phantom. The proposed method, named regularized weighted least squares Gauss–Newton algorithm (RWLS-GN), improved image quality and contrast-to-noise ratio of the gadolinium image with respect to reference maximum likelihood Nelder–Mead (ML-NM) algorithm, which was very sensitive to noise. In addition, RWLS-GN was 70 times faster than ML-NM. RWLS-GN allowed material decomposition with a number of incident photons equal or larger than 10^5 and with a marker concentration equal or larger than 0.03 gcm^-3.
In [2], we studied nonconvexity and proposed an iterative scheme based on the Bregman distance. We first proved the existence of a convex set where the usual data fidelity term is convex, which provides a guideline for selecting a good initial guess when using convex optimization methods. Using numerical simulations, we showed that the proposed Bregman scheme is robust to the selection of the initial guess. The improvement in global convergence of Bregman iterative scheme combined with other interesting properties of the Bregman distance appears as a compelling strategy for solving nonlinear inverse problems. Fig. 1 shows decomposed images by the proposed method for different number of incident photons. Preliminary results were presented at “IMA Conference on Inverse Problems from Theory to Application 2017” [3] and at “Recherche en Imagerie et Technologies pour la Santé (RITS) 2017”, [4].
On-going work focus on validating these algorithms using experimental data from a clinical spectral CT scanner.
This work has also contributed to other publications for the development of sparse reconstruction methods and multi-dimensional regularization for X-ray imaging [5], [6] and for other imaging modalities [7]-[11]. The work [5] was presented at “SPIE - Developments in X-Ray Tomography XI, 2017”.
VALIDATION OF SCT WITH MONOCHROMATIC SYNCHROTRON RADIATION AND FEASIBILITY OF SCT FOR EARLY DETECTION OF OSTEOARTHRITIS:
We carried out the first experimental validation of SCT using monochromatic synchrotron radiation data. This is part of a competitive national project (MD-1045) that aimed to acquire monochromatic X-ray radiation data at the European Synchrotron Radiation Facility (ESRF), Grenoble. Tissue characterization phantoms and biological samples were acquired both at ESRF beamline ID17, Grenoble, and at CERMEP, Lyon, which holds a Philips spectral CT scanner. Phantom studies show a linear relationship between the two modalities. Processing and analysis is in progress. Preliminary analysis of biological data show the unique, great potential of X-ray energy-based imaging for imaging osteoarthritis (images from ESRF experiment MD-1045 in Fig. 2).
[1] N Ducros et al, Med Phys: 44(9), e174-e187, 2017
[2] JFPJ Abascal et al, hal-01621265 (preprint), 2018
[3] JFPJ Abascal et al, IMA Conference on Inverse Problems from Theory to Application, Cambridge, UK, 2017
[4] JFPJ Abascal et al, Recherche en Imagerie et Technologies pour la Santé (RITS) 2017, Lyon, France. hal-01505326
[5] JFPJ Abascal et al, Proc. SPIE 10391, Developments in X-Ray Tomography XI, San Diego, USA, 2017 (invited speaker)
[6] C Goubet et al, Proc. ISBI, IEEE International Symposium on Biomedical Imaging, Washington, DC, USA, 2018
[7] JFPJ Abascal et al, IEEE Trans Med Imaging, 37(2): 547 – 556, 2017
[8] F Li et al, IEEE Sensors Journal, 17 (4): 976-985, 2016
[9] E Al Hosani et al, Measurement Science and Technology, 27(11): 115402, 2016
[10] B Chen et al, Sensors, 18: 1704, 2018
[11] C Chittenden et al, IEEE Sensors Journal (in press), 2018
