Peter B. Noël | Searcharxiv

arXiv2026

Integration of Spectral CT with PET and SPECT: Bringing Tissue Composition Information to Molecular Imaging

Molecular imaging has been transformed by integrating positron emission tomography (PET) or single-photon emission computed tomography (SPECT) with x-ray computed tomography (CT). However, x-ray CT in hybrid imaging is used primarily for anatomical localization and for attenuation and scatter correction of emission data. Its ability to characterize tissue composition remains underutilized. Spectral CT, including dual-energy CT (DECT) and photon-counting CT (PCCT), can provide material-specific information, such as images of iodine concentration, bone or calcium fractions, electron-density, effective atomic number and other spatially-varying material properties. These capabilities create an opportunity to bring tissue-composition information into molecular imaging. In addition, spectral CT can also reduce CT artifacts and provide more accurate corrections for emission data. In this review, we discuss the technical basis, integration pathways, and translational opportunities for combining spectral CT with molecular imaging. We review sequential and integrated PET/DECT approaches, emerging PET/PCCT concepts, PET-enabled spectral CT, and extensions to SPECT/CT. We then examine how spectral CT information may improve attenuation correction, scatter correction, tissue-fraction correction, positron range correction, reconstruction priors, and dosimetry. Clinical and translational applications include contrast-enhanced molecular imaging, vascular and perfusion imaging, quantitative bone marrow imaging, musculoskeletal imaging, and theranostics. Overall, spectral CT integration may move hybrid molecular imaging toward tissue-composition-informed molecular imaging, in which radiotracer signals are interpreted within their material context.

physics.med-ph↗

arXiv2023

Convolutional neural network-based single-shot speckle tracking for x-ray phase-contrast imaging

X-ray phase-contrast imaging offers enhanced sensitivity for weakly-attenuating materials, such as breast and brain tissue, but has yet to be widely implemented clinically due to high coherence requirements and expensive x-ray optics. Speckle-based phase contrast imaging has been proposed as an affordable and simple alternative; however, obtaining high-quality phase-contrast images requires accurate tracking of sample-induced speckle pattern modulations. This study introduced a convolutional neural network to accurately retrieve sub-pixel displacement fields from pairs of reference (i.e., without sample) and sample images for speckle tracking. Speckle patterns were generated utilizing an in-house wave-optical simulation tool. These images were then randomly deformed and attenuated to generate training and testing datasets. The performance of the model was evaluated and compared against conventional speckle tracking algorithms: zero-normalized cross-correlation and unified modulated pattern analysis. We demonstrate improved accuracy (1.7 times better than conventional speckle tracking), bias (2.6 times), and spatial resolution (2.3 times), as well as noise robustness, window size independence, and computational efficiency. In addition, the model was validated with a simulated geometric phantom. Thus, in this study, we propose a novel convolutional-neural-network-based speckle-tracking method with enhanced performance and robustness that offers improved alternative tracking while further expanding the potential applications of speckle-based phase contrast imaging.

physics.med-ph↗