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Surface Guidance for Breast
Published in Jeremy D. P. Hoisak, Adam B. Paxton, Benjamin Waghorn, Todd Pawlicki, Surface Guided Radiation Therapy, 2020
The heart should be segmented on the CT typically following the Radiation Therapy Oncology Group atlas,10 extending superiorly from an axial level just inferior to the pulmonary artery to the cardiac apex inferiorly. Whole-breast radiation therapy patients are typically treated with tangent fields. Techniques such as mixed-energy, field in field (FinF), and irregular surface compensators can be used to improve dose homogeneity. Different institutions might have slightly different dose/volume constraints for the heart and other critical organs. During treatment planning, the physician defines the borders of the treatment field while balancing the competing goals of target coverage and normal tissue sparing, which are patient specific. For most cases, the heart is totally excluded from the primary treatment beam unless this compromises coverage of the glandular breast tissue within several centimeters of the tumor bed (usually, marked by clips).
Electromagnetic-thermal dosimetry
Published in Riadh Habash, BioElectroMagnetics, 2020
As a comprehensive process, treatment planning includes: (1) methods for the determination of the target volume (target definition); (2) segmenting medical image data, generating 3D models of the target and normal tissue structures; (3) calculating the absorbed power distribution; (4) assigning tissue thermal properties; (5) virtually placing heat sources into the 3D structure; (6) measuring SAR patterns; (7) calculating heat transfer from the solution of bioheat equations during treatment from the power deposition to provide temperature distribution as a function of time; and (8) finally estimating 3D dose calculation [17,119,128]. An important feature of a thermal model must be its capability to describe the complex heat transfer related to the vasculature [128,131].
Machine learning for radiation oncology
Published in Jun Deng, Lei Xing, Big Data in Radiation Oncology, 2019
The developed BN is not only able to predict LC but can also be used to explore an understanding of underlying LC radiobiology, which is essential for understanding molecular mediators of response and the development of targeted interventions as discussed below. Clinically, this would allow one to conduct personalized treatment planning based on an individual’s characteristics. Following the arrows entering node “Tumor_gEUD” in Figures 4.6 and 4.9, the appropriate radiation dose to increase the probability of LC could be predicted by the patient’s characteristics including SNPs, miRNAs, cytokines, and PET information.
Optimization of sliding windows IMRT treatment planning
Published in IISE Transactions on Healthcare Systems Engineering, 2022
Rafiq R. Habib, Jessie Yeung, Johnson Darko, Ernest Osei, Houra Mahmoudzadeh
Cancer is one of the leading causes of death globally, causing about 1 in every 6 deaths and creating an estimated annual economic cost of US $1.6 trillion (World Health Organization, 2018). Consequently, there is a large academic contingent that has been researching, developing, and implementing improvements in cancer treatments for decades (Skarsgard, 1998). Radiation therapy has been a common and very effective cancer treatment method since its discovery. It has evolved over the last few decades from 3D conformal radiation therapy (CRT) through intensity-modulated radiation therapy (IMRT) to volumetric modulated arc therapy (VMAT) (Abshire & Lang, 2018). In this paper, we focus on IMRT treatment planning which is currently one of the most widely used types of radiation therapy treatment.
An inertial method for solving generalized split feasibility problems over the solution set of monotone variational inclusions
Published in Optimization, 2022
C. Izuchukwu, G. N. Ogwo, O. T. Mewomo
Let and be two vector spaces, be a linear operator, and be two inverse problems in and respectively, a Split Inverse Problem (SIP) (see [1,2]) is formulated as follows: such that The first known model of SIP is the following Split Feasibility Problem (SFP) introduced and studied by Censor and Elfving [3] from the modelling of medical image reconstruction: where C and Q are nonempty closed and convex subsets of and respectively, and is a real matrix. The SFP has been used in practice as a model in Intensity-Modulated Radiation Therapy (IMRT) treatment planning. The problem is also known to have wide applications in phase retrieval, image and signal processing, data compression, among others (see [1,4–9] and the references therein).
Optimization of three-dimensional modeling for geometric precision and efficiency for healthy and diseased aortas
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2018
Christopher P. Cheng, Yufei D. Zhu, Ga-Young Suh
Three-dimensional (3D) medical imaging is useful for vascular anatomic characterization for disease diagnosis, treatment planning, and evaluation of treatment efficacy. While much is gained from qualitative and quantitative characterization from the images, accurate and detailed 3D geometric modeling is necessary for deep understanding of disease processes, subject specific treatment planning, and boundary condition development for device design and evaluation (Taylor et al. 1998; Hua and Mower 2001; Kuhl et al. 2007; Beller et al. 2008; Choi et al. 2009; Raut et al. 2013; Lee, D’Ancona, et al. 2014; Lee, Lee, et al. 2014; Suh et al. 2014; Ullery et al. 2015) For example, in the case of aortic pathology, precise geometric modeling of tortuosity, aneurysms, and dissections can be used for aortic endograft device development.