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This study introduces a novel optimization framework for cranial three-dimensional rotational angiography (3DRA), combining the development of a brain equivalent in-house phantom with Figure of Merit (FOM) a quantitative evaluation method. The technical contribution involves the development of an in-house phantom constructed using iodine-infused epoxy and lycal resins, validated against clinical Hounsfield Units (HU). A customized head phantom was developed to simulate brain tissue and cranial vasculature for 3DRA optimization. The phantom was constructed using epoxy resin with 0.15-0.2% iodine to replicate brain tissue and lycal resin with iodine concentrations ranging from 0.65 to 0.7% to simulate blood vessels of varying diameters. The phantom materials validation was performed by comparing their HU values to clinical reference HU values from brain tissue and cranial vessels, ensuring accurate tissue simulation. The validated phantom was used to acquire images using cranial 3DRA protocols, specifically Prop-Scan and Roll-Scan. Image quality was assessed using Signal-Difference-to-Noise Ratio (SDNR), Dose-Area Product (DAP), and Modulation Transfer Function (MTF). Imaging efficiency was quantified using the Figure of Merit (FOM), calculated as SDNR/DAP, to objectively compare the performance of two cranial 3DRA protocols. The task-based optimization showed that Roll-Scan consistently outperformed Prop-Scan across all vessel sizes and regions. Roll-Scan yields FOM values ranging from 183 to 337, while Prop-Scan FOM values ranged from 96 to 189. Additionally, Roll-Scan (0.27 lp/pixel) delivered better spatial resolution, as indicated by higher MTF 10% value than Prop-Scan (0.23 lp/pixel). Most notably, Roll-Scan consistently detecting 2 mm vessel structures among all regions of the phantom. This capability is clinically important in cerebral angiography, which is accurate visualization of small vessels, i.e. the Anterior Cerebral Artery (ACA), Posterior Cerebral Artery (PCA), and Middle Cerebral Artery (MCA). These findings highlight Roll-Scan as the superior protocol for brain interventional imaging, underscoring the significance of FOM as a comprehensive parameter for optimizing imaging protocols in clinical practice. The experimental results support the use of the Roll-Scan protocol as the preferred acquisition method for cerebral angiography in clinical practice. The analysis using FOM provides substantial and quantifiable evidence in determining the acquisition methods. Furthermore, the customized in-house phantom is recommended as a candidate to optimization tools for clinical medical physicists.
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http://dx.doi.org/10.1007/s13246-025-01632-z | DOI Listing |
Phys Eng Sci Med
September 2025
Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, 16424, Indonesia.
This study introduces a novel optimization framework for cranial three-dimensional rotational angiography (3DRA), combining the development of a brain equivalent in-house phantom with Figure of Merit (FOM) a quantitative evaluation method. The technical contribution involves the development of an in-house phantom constructed using iodine-infused epoxy and lycal resins, validated against clinical Hounsfield Units (HU). A customized head phantom was developed to simulate brain tissue and cranial vasculature for 3DRA optimization.
View Article and Find Full Text PDFCancers (Basel)
August 2025
Department of Radiation Physics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA.
: We investigated the performance of a slow computed tomography (CT) protocol to reduce alignment errors arising from motion when using CT-on-rail (CTOR) for image guidance for patients receiving thoracic stereotactic body radiation therapy (SBRT). : A Quasar lung phantom with a moving tumor was programmed with three breathing rates and three motion amplitudes. MIP and average 4DCT images were used for contouring and alignment, respectively.
View Article and Find Full Text PDFPLoS One
August 2025
Radiation Oncology, University of California, San Francisco, California, United States of America.
Objectives: Computed tomography (CT) provides high spatial-resolution visualization of 3D structures for various applications. Traditional analytical/iterative CT reconstruction algorithms require hundreds of angular samplings, a condition may not be met practically for physical and mechanical limitations. Sparse view CT reconstruction has been proposed using constrained optimization and machine learning methods with varying success, less so for ultra-sparse view reconstruction.
View Article and Find Full Text PDFJ Appl Clin Med Phys
August 2025
Department of Physics and Astronomy, University of Manitoba, Winnipeg, Manitoba, Canada.
Purpose: The nonwater-equivalent energy response of electronic portal imaging devices (EPIDs) is a major obstacle to using them for linear accelerator (linac) beam parameter verification. In this study, we propose an EPID-based machine quality assurance (QA) application that uses a model-based radiation transport algorithm to convert EPID-measured images into water-equivalent dose distributions that can be used to assess beam flatness and symmetry.
Methods: An in-house developed, model-based radiation transport algorithm was used to estimate the incident beam fluence from measured EPID images and convert it into either 3D dose distributions in a virtual water tank or 2D water-equivalent dose distributions in a virtual ion chamber array.
Sci Rep
August 2025
Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea.
This study aimed to develop and publicly release an in-house software package that converts the binary file format of 2D cine magnetic resonance (MR) images acquired through the Treatment Session Manager (TSM) of MOSAIQ on the Elekta Unity (Elekta AB, Stockholm, Sweden) into standard readable data formats. The software was developed using MATLAB (MathWorks, Natick, MA, USA) and includes an automatic image-sorting algorithm to classify the images into coronal, sagittal, and axial planes. To verify the geometric accuracy of the converted images, they were acquired from an MRgRT motion management QA phantom, both with and without motion.
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