Purpose: To evaluate the geometric distortion on magnetic resonance (MR) images obtained with a permanent magnet system and determine the usefulness of MR imaging-assisted x-ray simulation in radiation therapy treatment planning (RTTP).
Materials And Methods: The authors measured the distortion on MR images of grid-pattern phantoms. MR imaging-assisted x-ray simulation was performed with skin markers in 14 patients with bone tumors. Treatment planning had already been performed with a conventional system.
Results: On phantom images, most of the positional displacements within a 120-mm radius from the center of the static magnetic field were less than 2 mm; larger displacements were observed in the peripheral region of the images. MR imaging was useful in the RTTP of all patients. The original radiation field was modified after MR examination in six patients.
Conclusion: The amount of image distortion within the practical area is acceptable for RTTP. MR imaging-assisted x-ray simulation is useful for patients with bone tumors and warrants further investigation.
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http://dx.doi.org/10.1148/radiology.199.3.8638017 | DOI Listing |
Sci Rep
January 2025
Department of Pediatric Surgery, West China Hospital of Sichuan University, NO. 37 GUOXUE Lane, Chengdu, 610041, Sichuan Province, China.
Identification of lesion demarcation during thoracoscopic anatomical lesion resection is fundamental for treating children with congenital lung malformation. Existing lesion demarcations do not always meet the needs of clinical practice. This study aimed to explore the safety and efficacy of near-infrared fluorescence imaging with nebulized inhalation of indocyanine green for thoracoscopic anatomical lesion resection in children with congenital lung malformation.
View Article and Find Full Text PDFPLoS One
December 2024
School of Computing and Artificial Intelligence, Changzhou University, Changzhou, China.
In modern medical imaging-assisted therapies, manual annotation is commonly employed for liver and tumor segmentation in abdominal CT images. However, this approach suffers from low efficiency and poor accuracy. With the development of deep learning, automatic liver tumor segmentation algorithms based on neural networks have emerged, for the improvement of the work efficiency.
View Article and Find Full Text PDFACS Biomater Sci Eng
December 2024
Drug Discovery Research Center, Korea Research Institute of Chemical Technology (KRICT), Daejeon 34114, Republic of Korea.
Mechanical stiffness of liver organoid is a key indicator for the progress of hepatic steatosis. Probe indentation is a noninvasive methodology to measure Young's modulus (YM); however, the inhomogeneous nature of the liver organoid induces measurement uncertainty requiring a large number of indentations covering a wide scanning area. Here, we demonstrate that lipid-stained fluorescence imaging-assisted probe indentation significantly reduces the number of measurements by specifying the highly lipid-induced area.
View Article and Find Full Text PDFUltrasound
September 2024
Institute of Health and Social Care, School of Allied and Community Health, London South Bank University (LSBU), London, UK.
Musculoskeletal disorders are a significant global health concern, affecting over 1.71 billion individuals worldwide, with a considerable impact on quality of life and economic burden due to healthcare costs and productivity losses. In the United Kingdom, approximately one-third of the population suffers from musculoskeletal disorders, underscoring the need for effective diagnostic and management strategies.
View Article and Find Full Text PDFComput Biol Med
November 2024
School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, 127 West Youyi Road, Beilin District, Xi'an, 710072, Shaanxi, China; School of Computer Science, Northwestern Polytechnical University, 1 Dongxiang Road, Chang'an District, Xi'an, 710129, Shaanxi, China. Electronic address:
Automated computer-aided diagnosis (CAD) is becoming more significant in the field of medicine due to advancements in computer hardware performance and the progress of artificial intelligence. The knowledge graph is a structure for visually representing knowledge facts. In the last decade, a large body of work based on knowledge graphs has effectively improved the organization and interpretability of large-scale complex knowledge.
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