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The impact of registration accuracy on imaging validation study design: A novel statistical power calculation

机译：配准精度对成像验证研究设计的影响：一种新颖的统计功效计算

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Novel imaging modalities are pushing the boundaries of what is possible in medical imaging, but their signal properties are not always well understood. The evaluation of these novel imaging modalities is critical to achieving their research and clinical potential.Image registration of novel modalities to accepted reference standard modalities is an important part of characterizing the modalities and elucidating the effect of underlying focal disease on the imaging signal. The strengths of the conclusions drawn from these analyses are limited by statistical power. Based on the observation that in this context, statistical power depends in part on uncertainty arising from registration error, we derive a power calculation formula relating registration error, number of subjects, and the minimum detectable difference between normal and pathologic regions on imaging, for an imaging validation study design that accommodates signal correlations within image regions.Monte Carlo simulations were used to evaluate the derived models and test the strength of their assumptions, showing that the model yielded predictions of the power, the number of subjects, and the minimum detectable difference of simulated experiments accurate to within a maximum error of 1% when the assumptions of the derivation were met, and characterizing sensitivities of the model to violations of the assumptions. The use of these formulae is illustrated through a calculation of the number of subjects required for a case study, modeled closely after a prostate cancer imaging validation study currently taking place at our institution.The power calculation formulae address three central questions in the design of imaging validation studies: (1) What is the maximum acceptable registration error? (2) How many subjects are needed? (3) What is the minimum detectable difference between normal and pathologic image regions?.
机译：新颖的成像方式正在推动医学成像领域的发展，但是它们的信号特性并不总是被很好地理解。这些新颖的成像方式的评估对于实现其研究和临床潜力至关重要。将新方式的图像配准到公认的参考标准方式是表征这些方式并阐明潜在的局灶性疾病对成像信号的影响的重要部分。这些分析得出的结论的强度受到统计能力的限制。基于在此情况下的观察结果，统计功效部分取决于配准误差引起的不确定性，我们得出了配准误差，对象数以及成像正常区域和病理区域之间的最小可检测差异之间的幂计算公式。成像验证研究设计，可适应图像区域内的信号相关性。蒙特卡洛模拟用于评估衍生模型并测试其假设的强度，表明该模型可预测功率，被摄对象数量和可检测的最小差异满足推导假设时，模拟实验的精确度最大误差在1％以内，并描述了模型对违反假设的敏感性。通过计算案例研究所需的受试者数量来说明这些公式的使用，该案例是在我们机构目前正在进行的前列腺癌成像验证研究之后紧密建模的。功效计算公式解决了成像设计中的三个核心问题验证研究：（1）最大可接受注册误差是多少？ （2）需要多少科目？ （3）正常和病理图像区域之间的最小可检测差异是多少？

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