Photon-counting CT scanners, a recent generational leap in CT technology, offer immense potential in reducing electron noise and elevating image quality. Photon-counting computed tomography (PCCT) stands apart from traditional CT scans through its revolutionary scanning process. Unlike conventional CT scans, which rely on a two-step process to convert X-rays into visible light, photon-counting CT scanners directly transform X-ray photons into electrical signals, effectively minimizing electronic noise. The intrinsic features of PCCT, relying on semiconductor technologies, bring about a significant reduction in electron noise and an enhancement in spatial resolution. This, in turn, yields high-quality images while concurrently lowering radiation doses compared to traditional CT scans. The application of photon-counting detectors in pediatric imaging has been the subject of extensive study, with promising results showcased in initial clinical experiences, especially in pediatric chest imaging. This development is particularly critical given the heightened radiation sensitivity of children when compared to adults. In the United States alone, where over 5 million pediatric CT scans are performed each year, the need to lower radiation exposure is a serious concern. In conclusion, photon-counting CT scanners represent a promising technological advancement with the capacity to improve image quality and reduce radiation exposure in pediatric patients. The ongoing research and development in this field hold the promise of even more substantial enhancements in CT technology, contributing to safer radiation exposure and improved imaging outcomes. This exhibit will review the current PCCT literature and provide case examples of how this technology can implemented in a pediatric setting. Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Khanna Praneet, Park Brandon, Randhawa Hari, Jain Amit, Lynn Mitchell

Keywords: PCCT

MRI is crucial to the diagnosis, treatment planning, and monitoring of pediatric posterior fossa tumors; recent advances in machine and deep learning offer the prospect of increased diagnostic accuracy and minimally invasive management strategies. Machine learning and deep learning represent two artificial intelligence (AI) applications of radiomics that show promising results in the identification and classification of pediatric posterior fossa tumors, such as medulloblastoma, diffuse intrinsic pontine glioma, ependymoma, and pilocytic astrocytoma, based on MRI data. Many AI models demonstrate comparable, or in some cases, better, performance than human radiologists. Beyond diagnosis, machine and deep learning algorithms have been developed for tumor histology determination, prognostication, and molecular subtyping, to name just a few examples. In its early stages, however, the technology has several barriers to overcome before mainstream use. Studies of AI algorithms are often conducted with small sample sizes due to the relative rarity of pediatric posterior fossa tumors; many are also conducted at a single institution. Algorithms are susceptible to class imbalance as well, making it more difficult to identify tumors with lower prevalence. Multi-institution studies are important in addressing those limitations, though they have their own cumbersome requirements, such as data harmonization. Deep learning training strategies, such as federated learning, data augmentation, and transfer learning, help to address the problems introduced by working with small data sets. The purpose of this exhibit is to review the methods, results, and limitations identified in studies testing the efficacy of machine and deep learning models for the identification and classification of pediatric posterior fossa tumors and to discuss their potential for use in the clinical setting. Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Randhawa Hari, Park Brandon, Khanna Praneet, Jung Daniel, Das Ayushman, Ginn Kevin, Mitchell Grace

Keywords: Artificial Intelligence, Deep Learning, Machine Learning