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N3 Bias Field Correction Explained as a Bayesian Modeling Method

机译:N3偏置场更正被解释为贝叶斯建模方法

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摘要

Although N3 is perhaps the most widely used method for MRI bias field correction, its underlying mechanism is in fact not well understood. Specifically, the method relies on a relatively heuristic recipe of alternating iterative steps that does not optimize any particular objective function. In this paper we explain the successful bias field correction properties of N3 by showing that it implicitly uses the same generative models and computational strategies as expectation maximization (EM) based bias field correction methods. We demonstrate experimentally that purely EM-based methods are capable of producing bias field correction results comparable to those of N3 in less computation time.
机译:尽管N3可能是MRI偏置场校正的最广泛使用的方法,但其潜在的机制实际上尚未理解。具体地,该方法依赖于不优化任何特定目标函数的交替迭代步骤的相对启发式的配方。在本文中,我们通过表示它隐含地使用与基于期望的偏置字段校正方法的预期最大化(EM)相同的生成模型和计算策略来解释N3的成功偏置字段校正特性。我们通过实验证明,纯基于EM的方法能够在较少的计算时间中产生与N3的偏差场校正结果相当。

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