NAEOTOM Alpha Photon-Counting CT Scanner

Project Summary/Abstract This is a request to upgrade our current Siemens SOMATOM Force research energy integrating CT scanner to a newly released photon-counting CT scanner (Siemens NAEOTOM Alpha). While we have made great progress in the use of quantitative CT imaging to sub-phenotype lung disease there are limitations which this new scanner design will eliminate. Beam hardening is a

scanning artifact which makes the lung appear less dense than it actually is. Because the new scanner directly counts every photon passing through the body and bins them into energy ranges, by reconstructing the lungs with a narrower photon range (selected for the kV range which best maximizes tissue contrast) will essentiall eliminate this error. Additionally, because the detection

of photons is a digital process, the noise associated with analogue to digital conversion of light signals is eliminated, significantly reducing electronic noise. The spatial resolution is considerably higher and there is a choice of keeping similar dose as previous protocols (which have already been reduced nearly 10 fold) while taking advantage of the improved spatial resolution, or

significanlty reducing the dose further and keep the same resolution. Because the photons are captured along with their energy characteristics, the photon count at each location can be binned into energy ranges, allowing for the seperation of multiple materials such as krypton and gadolinium for the simultaneous assessment of ventilation and perfusion. Additional contrast

agents are under development to also, simultaneously, tag inflammation. Because of the improved contrast resolution, we will be able to further reduce the amount of contrast agent used by as much as 40%. We propose 9 major projects, all associated with either multi-center studies seeking new phenotypes of lung disease (COPD, Asthma, IPF, PASC (long COVID) etc. , or local

investigations into lung pathologies. The scanner promises to improve the ability to assess airway wall thickness further into the lung periphery and to make possible the identification and seperation of arteries and veins.with similar abilities to extend to the lung periphery. Through deep learning and transfer learning, we propose that these improvement will help advance utility

of existing scanner images as well. Because we are the radiology Center, the scanner not only allows us to take advantage of the advanced methodology locally, but we will be able to continue to disseminate newer protocols, keeping the lung community at imaging state-of-the-art. There are already 7 such scanners delivered to cinical centers within the US and this is expected to

rapidly expand. Thus, the opportunity for research translation.