A photo of Samuel Brady.

Samuel L. Brady, PhD

  • Clinical Medical Physicist, Department of Radiology and Medical Imaging

About

Biography

As a medical physicist, Samuel Brady, PhD's, primary experience lies in developing and maintaining protocols for imaging efficacy and safety. His particular interest lies in dose-minimization for CT imaging techniques. This is a particularly important concept in pediatric radiology, where tissues are still developing and are thus especially susceptible to radiation damage.

BS: Physics, Utah Valley University, Orem, UT.

MS: Medical Physics, Duke University, Durham, NC.

PhD: Medical Physics, Duke University, Durham, NC.

Services and Specialties

Radiology

Research Areas

Radiology

Publications

Application of reference air kerma alert levels for pediatric fluoroscopic examinations. Somasundaram, E; Brady, SL; Strauss, KJ. Journal of Applied Clinical Medical Physics. 2022.

Defining Normal Ranges of Skeletal Muscle Area and Skeletal Muscle Index in Children on CT Using an Automated Deep Learning Pipeline: Implications for Sarcopenia Diagnosis. Somasundaram, E; Castiglione, JA; Brady, SL; Trout, AT. American Journal of Roentgenology. 2022; 219:326-336.

Simulated Reduced-Count Whole-Body FDG PET: Evaluation in Children and Young Adults Imaged on a Digital PET Scanner. Alves, VD P V; Brady, S; Ata, NA; Li, Y; MacLean, J; Zhang, B; Sharp, SE; Trout, AT. American Journal of Roentgenology. 2022.

Reducing Pediatric Intraoral Radiography Radiation Dose Using Reduced-Power Dental X-Ray Units: A Randomized Trial. Cully, JL; Somasundaram, E; Campbell, R; Brady, SL; Gosnell, ES; Specht, S; Candon, LA; Strauss, KJ. Journal of Dentistry for Children. 2022; 89:95-103.

Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms. Thapaliya, S; Brady, SL; Somasundaram, E; Anton, CG; Coley, BD; Towbin, AJ; Zhang, B; Dillman, JR; Trout, AT. Abdominal Radiology. 2022; 47:265-271.

Comparison of 0.3-mSv CT to Standard-Dose CT for Detection of Lung Nodules in Children and Young Adults With Cancer. Thapaliya, S; Gilligan, LA; Brady, SL; Anton, CG; Crotty, EJ; Nasser, MP; Geller, JI; Pressey, JG; Zhang, B; Dillman, JR; et al. American Journal of Roentgenology. 2021; 217:1444-1451.

Current and emerging artificial intelligence applications for pediatric abdominal imaging. Dillman, JR; Somasundaram, E; Brady, SL; He, L. Pediatric Radiology. 2021.

Effect of Propranolol on 18F-Fluorodeoxyglucose Uptake in Brown Adipose Tissue in Children and Young Adults with Neoplastic Diseases. Brady, SL; Wong, KK; Doubrovin, M; Han, Y; Li, Y; Wu, S; Hossain, AK M M; Chism, CB; Naik, MH; Rossi, M; et al. Molecular Imaging and Biology. 2021; 23:260-269.

DeepLiverNet: a deep transfer learning model for classifying liver stiffness using clinical and T2-weighted magnetic resonance imaging data in children and young adults. Li, H; He, L; Dudley, JA; Maloney, TC; Somasundaram, E; Brady, SL; Parikh, NA; Dillman, JR. Pediatric Radiology. 2021; 51:392-402.

Automated Segmentation of Abdominal Skeletal Muscle on Pediatric CT Scans Using Deep Learning. Castiglione, J; Somasundaram, E; Gilligan, LA; Trout, AT; Brady, S. Radiology. Artificial intelligence.. 2021; 3.