Objective: This study aimed to evaluate the computed tomography number and the variation of dose distribution based on 12-bit, 16-bit, and revised 16-bit images while the metal bars were inserted.
Methods: The phantoms containing stainless steel, titanium alloy, and aluminum bar were scanned with computed tomography. These images were reconstructed with 12-bit and 16-bit imaging technologies. The "cupping artifacts" computed tomography value of the metal object revised by Matlab software was called the revised 16-bit image. The computed tomography values of these metal materials were analyzed. Two radiotherapy treatment plans were designed using the treatment plan system: (1) gantry was of 0° irradiation field and (2) gantry was of 90° and 270° for 2 opposed irradiation fields. The dose profile and dose-volume histogram of a structure of interest were analyzed in various images. The analysis was based on the radiotherapy plan differences between 3 different imaging techniques (12-bit imaging, 16-bit imaging, and revised 16-bit imaging technologies).
Results: For low-density metal object (computed tomography value <3071 Hounsfield unit, HU), the radiotherapy plan results were consistent based on 3 different imaging techniques. For high-density metal object (computed tomography value >3071 HU), the difference in radiotherapy plan results was obvious. The dose of 12-bit was 15.9% higher than revised 16-bit on average for the downstream of titanium rod. For stainless steel, this number reached up to 42.7%.
Conclusion: A 16-bit imaging technology of metal implants can distinguish the computed tomography value of different metal materials. Furthermore, the revised 16-bit imaging technology can improve the dose computational accuracy of radiotherapy plan with high-density metal implants.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5616029 | PMC |
http://dx.doi.org/10.1177/1533034616649530 | DOI Listing |
HardwareX
June 2024
Photon Science Institute, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom.
Various applications require multi-channel high-voltage sources for their control, e.g. electrostatic adhesion, electrophoresis and artificial muscles such as piezoelectric, hydraulically amplified self-healing electrostatic(HASEL) and dielectric elastomer actuators(DEAs).
View Article and Find Full Text PDFData Brief
December 2024
Czech Technical University in Prague, Faculty of Civil Engineering, Prague 166 29, Czech Republic.
The dataset represents micro computed tomography (µCT) images of undisturbed samples of constructed Technosol, obtained by sampling from the top layer of the biofilter in two bioretention cells. A bioretention cell is a stormwater management system designed to collect and temporarily retain stormwater runoff and treat it by filtering it through a soil media called a biofilter. Soil samples were collected at 7, 12, 18, 23, and 31 months after the establishment of bioretention cells.
View Article and Find Full Text PDFComput Biol Med
December 2024
Department of Dermatology, The First Affiliated Hospital of Ningbo University, Ningbo, 315000, China.
Skin cancer is recognized as one of the most perilous diseases globally. In the field of medical image classification, precise identification of early-stage skin lesions is imperative for accurate diagnosis. However, deploying these algorithms on low-cost devices and attaining high-efficiency operation with minimal energy consumption poses a formidable challenge due to their intricate computational demands.
View Article and Find Full Text PDFBiomed Opt Express
September 2024
Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
Radiol Med
October 2024
Dipartimento di Diagnostica per Immagini Policlinico, Università degli Studi di Palermo, Via del Vespro 127, 90127, Palermo, Italy.
Purpose: Evaluate the agreement between bone age assessments conducted by two distinct machine learning system and standard Greulich and Pyle method.
Materials And Methods: Carpal radiographs of 225 patients (mean age 8 years and 10 months, SD = 3 years and 1 month) were retrospectively analysed at two separate institutions (October 2018 and May 2022) by both expert radiologists and radiologists in training as well as by two distinct AI software programmes, 16-bit AI and BoneXpert® in a blinded manner.
Results: The bone age range estimated by the 16-bit AI system in our sample varied between 1 year and 1 month and 15 years and 8 months (mean bone age 9 years and 5 months SD = 3 years and 3 months).
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!