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The study employs a generalizable ideal observer methodology for the quantification of tumor tracking performance.
Automated lung tumor tracking algorithms often leverage the differences in benign and malignant lung tissue textures.
To better treat the target, tumor tracking strategies involving beam's-eye-view (BEV) have been explored.
Real-time tumor tracking performance using current digital megavolt (MV) imagers is hindered by poor image quality.
Dual-energy (DE) imaging using an on-board imager (OBI) is being considered for real-time tumor tracking purposes.
Optimized DE imaging increases soft tissue visibility and may enhance automated lung tumor tracking allowing for real-time adaptive radiotherapy.
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Therefore, tumor-tracking techniques will continue to play an integral role in these techniques.
We present a preliminary design and experimental results of tumor objects tracking method for magnetic resonance imaging (MRI) brain images (some stock images) that utilizes color-converted segmentation algorithm with K-means clustering technique.
Therefore, the C-dots are non-toxic to mice under any dose and have potential use in fluorescence imaging in vivo, tumor cell tracking, and others.
The development of the present work in combination with the magnetoliposomes as tumor cell tracking and drug delivery system for liver specific target detection has been accepted and will be presented in RSNA2012.
As illustrated by Alberto Astolfo (Synchrotron Trieste, Trieste, Italy), tumor cell tracking using bright synchrotron sources and phase contrast microtomography are feasible techniques producing high-resolution 3D images.
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