ABSTRACT

This special issue is the second part of a themed issue that focuses on synergistic tomographic image reconstruction and includes a range of contributions in multiple disciplines and application areas. The primary subject of study lies within inverse problems which are tackled with various methods including statistical and computational approaches. This volume covers algorithms and methods for a wide range of imaging techniques such as spectral X-ray computed tomography (CT), positron emission tomography combined with CT or magnetic resonance imaging, bioluminescence imaging and fluorescence-mediated imaging as well as diffuse optical tomography combined with ultrasound. Some of the articles demonstrate their utility on real-world challenges, either medical applications (e.g. motion compensation for imaging patients) or applications in material sciences (e.g. material decomposition and characterization). One of the desired outcomes of the special issues is to bring together different scientific communities which do not usually interact as they do not share the same platforms such as journals and conferences.

ARTICLES

Polycarpou I, Soultanidis G, Tsoumpas C. 2021 Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging. Phil. Trans. R. Soc. A 379, 20200207. (doi:10.1098/rsta.2020.0207)

Jørgensen JS et al. 2021 Core Imaging Library part I: a versatile Python framework for tomographic imaging. Phil. Trans. R. Soc. A 379, 20200192 (2021). (doi: 10.1098/rsta.2020.0192)

Papoutsellis E et al. 2021 Core Imaging Library – Part II: multichannel reconstruction for dynamic and spectral tomography. Phil. Trans. R. Soc. A 379, 20200193 (2021). (doi:10.1098/rsta.2020.0193)

Brown R et al. 2021 Motion estimation and correction for simultaneous PET/MR using SIRF and CIL. Phil. Trans. R. Soc. A 379, 20200208 (2021). (doi:10.1098/rsta.2020.0208)

Peter J. 2021 Musiré: multimodal simulation and reconstruction framework for the radiological imaging sciences. Phil. Trans. R. Soc. A 379, 20200190. (doi:10.1098/rsta.2020.0190)

Li S, Wang G. 2021Modified kernel MLAA using autoencoder for PET-enabled dual-energy CT. Phil. Trans. R. Soc. A 379, 20200204. (doi:10.1098/rsta.2020.0204)