This review paper proposes a new framework for intraoperative cone-beam computed tomography (CBCT) that shifts focus from "data completeness" to "data sufficiency." The authors argue that achieving mathematically complete data is impossible with current CBCT geometries and that excessive sampling can negatively impact image quality, imaging time, and radiation dose (Q-T-D trade-offs). Instead, the paper advocates for a task-driven approach that prioritizes meeting minimum image-quality thresholds necessary for clinical decision-making, allowing for acceptable approximation errors to achieve a better Q-T-D balance. AI
IMPACT This research could lead to more efficient and safer intraoperative imaging by optimizing the balance between image quality, scan time, and radiation dose.
RANK_REASON The cluster contains a research paper published on arXiv. [lever_c_demoted from research: ic=2 ai=0.4]
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