Purpose: To develop a Monte Carlo (MC)-based computed tomography (CT) dose estimation method with a graphical user interface with options to define almost arbitrary simulation scenarios, to make calculations sufficiently fast for comfortable handling, and to make the software free of charge for general availability to the scientific community.
Methods: A framework called GMctdospp was developed to calculate phantom and patient doses with the MC method based on the EGSnrc system. A CT scanner was modeled for testing and was adapted to half-value layer, beam-shaping filter, z-profile, and tube-current modulation (TCM). To validate the implemented variance reduction techniques, depth-dose and cross-profile calculations of a static beam were compared against DOSXYZnrc/EGSnrc. Measurements for beam energies of 80 and 120 kVp at several positions of a CT dose-index (CTDI) standard phantom were compared against calculations of the created CT model. Finally, the efficiency of the adapted code was benchmarked against EGSnrc defaults.
Results: The CT scanner could be modeled accurately. The developed TCM scheme was confirmed by the dose measurement. A comparison of calculations to DOSXYZnrc showed no systematic differences. Measurements in a CTDI phantom could be reproduced within 2% average, with a maximal difference of about 6%. Efficiency improvements of about six orders of magnitude were observed for larger organ structures of a chest-examination protocol in a voxelized phantom. In these cases, simulations took 25 s to achieve a statistical uncertainty of ∼0.5%.
Conclusions: A fast dose-calculation system for phantoms and patients in a CT examination was developed, successfully validated, and benchmarked. Influences of scan protocols, protection method, and other issues can be easily examined with the developed framework.
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http://dx.doi.org/10.1118/1.4922391 | DOI Listing |
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Sci Data
January 2025
Department of Civil & Geotechnical Engineering and Geomechanics, AGH University of Krakow, al.Mickiewicza 30, 30-059, Krakow, Poland.
The presented dataset comes from an experimental study on the autogenous self-healing of high-strength concrete and the development of deep learning metasensor for crack width assessment and self-healing evaluation. Concrete specimens were prepared, matured, cracked, and exposed to self-healing. High-resolution scanning of the specimen surface and scale-invariant image processing were performed, multiple grid lines crossing cracks were established, and brightness degree profiles along grid lines were extracted.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Electrical and Computer Engineering, University of Massachusetts Lowell, Ball Hall, 1 University Ave, Lowell, Massachusetts, 01854, UNITED STATES.
Objective: X-ray photon-counting detectors (PCDs) have recently gained popularity due to their capabilities in energy discrimination power, noise suppression, and resolution refinement. The latest extremity photon-counting computed tomography (PCCT) scanner leverages these advantages for tissue characterization, material decomposition, beam hardening correction, and metal artifact reduction. However, technical challenges such as charge splitting and pulse pileup can distort the energy spectrum and compromise image quality.
View Article and Find Full Text PDFJ Dent Sci
January 2025
School of Dental Technology, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan.
Background/purpose: The performance of intraoral scanners (IOSs) relies on the operator's skills. However, whether operator experience influences IOS accuracy remains unclear. This study investigated the effect of operator experience on the trueness accuracy and time-based efficiency of IOSs.
View Article and Find Full Text PDFJ Dent Sci
January 2025
Department of Dentistry, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan.
Background/purpose: Different types of scanners are gradually used to produce digital dental casts in the current dental practice. This study tested the accuracy of the three desktop scanners and two intraoral scanners and evaluated whether the desktop scanners had higher precision than the intraoral scanners.
Materials And Methods: This study used the three desktop and two intraoral scanners to scan a standard dental cast 5 times.
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