Abstract
Purpose To synthesize current evidence on dose optimization using deep learning reconstruction (DLR) with emphasis on computed tomography (CT), pediatrics, contrast to noise ratio (CNR), signal to noise ratio (SNR), and radiation dosage.
Method The methodology was a systematic literature review, synthesizing peer-reviewed literature on the use of DLR in pediatric CT. Databases searched included Scopus, IEEE Xplore, and Web of Science. Keywords include combinations of deep learning reconstruction, DLR, CT, pediatric, dose, and optimization. Boolean operators were used to refine search results.
Results Three themes emerged: dosage reduction, noise reduction, and improved spatial resolution with the use of DLR in pediatric CT imaging.
Discussion The synthesized research suggested that DLR has the capabilities to improve diagnostic imaging in the sphere of pediatric imaging. Key implications such as reductions in overall dose, increased CNR and SNR resulting in high-quality scans, and improved spatial resolution resulting in improved differentiation of anatomical structures, especially when pediatric anatomy is already small. However, most research conducted has been on adult demographics resulting in fewer studies being examined on pediatrics alone.
Conclusion DLR was determined to reduce dosage and improve CNR when compared to current CT algorithms in pediatric imaging. Future research should be conducted into the improvements of DLR and standardization of algorithm training across all vendors and healthcare organizations.
DOI
https://doi.org/10.52519/00276
Graduation Date
Summer 8-7-2026
Document Type
Thesis
Degree Name
Master of Science in Radiologic Science
Program
The School of Health Professions
Faculty Advisor
Kevin R. Clark
Committee Member
Jessyca B. Wagner
Director, Graduate Program
William A. Undie
Dean
Kimberly Hoggatt Krumwiede
Recommended Citation
Wyatt, C. Deep Learning Reconstruction in Pediatric CT Imaging. [thesis]. University of Texas MD Anderson Cancer Center; 2026. https://doi.org/10.52519/00276
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