Med Phys
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Pulmonary positron emission tomography (PET) imaging is confounded by blurring artifacts caused by respiratory motion. These artifacts degrade both image quality and quantitative accuracy. In this paper, the authors present a complete data acquisition and processing framework for respiratory motion compensated image reconstruction (MCIR) using simultaneous whole body PET/magnetic resonance (MR) and validate it through simulation and clinical patient studies. ⋯ Standalone MR is not the traditional choice for lung scans due to the low proton density, high magnetic susceptibility, and low T2 (∗) relaxation time in the lungs. By developing and validating this PET/MR pulmonary imaging framework, the authors show that simultaneous PET/MR, unique in its capability of combining structural information from MR with functional information from PET, shows promise in pulmonary imaging.
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The authors' purpose is to model the case of an implantable pulse generator (IPG) and the electrode of an active implantable medical device using lumped circuit elements in order to analyze their effect on radio frequency induced tissue heating problem during a magnetic resonance imaging (MRI) examination. ⋯ Electrical models for the IPG case and electrode are suggested, and the method is proposed to determine the parameter values. The concept of matching of the electrode to the lead is clarified using the defined electrode impedance and the lead Thevenin impedance. The effect of the IPG case and electrode on tip heating can be predicted using the proposed theory. With these models, understanding the tissue heating due to the implants becomes easier. Also, these models are beneficial for implant safety testers and designers. Using these models, worst case conditions can be determined and the corresponding implant test experiments can be planned.
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Volumetric modulated arc therapy (VMAT) optimization is a computationally challenging problem due to its large data size, high degrees of freedom, and many hardware constraints. High-performance graphics processing units (GPUs) have been used to speed up the computations. However, GPU's relatively small memory size cannot handle cases with a large dose-deposition coefficient (DDC) matrix in cases of, e.g., those with a large target size, multiple targets, multiple arcs, and/or small beamlet size. The main purpose of this paper is to report an implementation of a column-generation-based VMAT algorithm, previously developed in the authors' group, on a multi-GPU platform to solve the memory limitation problem. While the column-generation-based VMAT algorithm has been previously developed, the GPU implementation details have not been reported. Hence, another purpose is to present detailed techniques employed for GPU implementation. The authors also would like to utilize this particular problem as an example problem to study the feasibility of using a multi-GPU platform to solve large-scale problems in medical physics. ⋯ The results demonstrate that the multi-GPU implementation of the authors' column-generation-based VMAT optimization can handle the large-scale VMAT optimization problem efficiently without sacrificing plan quality. The authors' study may serve as an example to shed some light on other large-scale medical physics problems that require multi-GPU techniques.
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To check the accuracy of a gantry equipped with dual x-ray imagers and a robotic patient positioner for proton radiotherapy, and to evaluate the accuracy and feasibility of single-beam registration using the robotic positioner. ⋯ Results demonstrate that the gantry equipped with a robotic patient positioner and dual imaging panels satisfies treatment requirements for proton radiotherapy. The combined accuracy of the gantry, couch, and imagers allows a patient to be registered at one setup position and then moved precisely to another treatment position by commanding the robotic patient positioner and delivering treatment without requiring additional image registration.
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Comparative Study
Simultaneous segmentation and iterative registration method for computing ADC with reduced artifacts from DW-MRI.
Apparent diffusion coefficient (ADC), derived from diffusion-weighted magnetic resonance images (DW-MRI), measures the motion of water molecules in vivo and can be used to quantify tumor response to therapy. The accurate measurement of ADC can be adversely affected by organ motion and imaging artifacts. In this paper, the authors' goal was to develop an automated method for reducing artifacts and thereby improve the accuracy of ADC measurements in moving organs such as liver. ⋯ The authors developed a novel approach for reducing artifacts in ADC maps through simultaneous registration and segmentation of multiple b-value DW images. The authors' method explicitly employs a registration quality metric to align images. When compared to basic affine and no image registrations, the authors' approach produces registrations of greater accuracy with lowest artifact ratio and median standard deviation of the computed mean ADC values for a wide range of displacements.