Graduate Program in Biomedical Engineering, The University of Western Ontario, London, Canada.
Med Image Anal. 2013 Oct;17(7):805-15. doi: 10.1016/j.media.2013.04.008. Epub 2013 Apr 28.
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?
新型成像方式正在推动医学成像的可能性边界，但它们的信号特性并不总是被很好地理解。评估这些新型成像方式对于实现其研究和临床潜力至关重要。将新型模态图像与公认的参考标准模态图像进行配准是对模态进行特征描述和阐明潜在局灶性疾病对成像信号影响的重要组成部分。这些分析得出的结论的有效性受到统计能力的限制。基于这样一种观察，即在这种情况下，统计能力部分取决于来自配准误差的不确定性，我们推导出了一个与配准误差、受试者数量以及成像上正常和病理区域之间最小可检测差异相关的功效计算公式，用于一种可容纳图像区域内信号相关性的成像验证研究设计。通过蒙特卡罗模拟来评估推导模型并测试其假设的强度，结果表明，当满足推导假设时，该模型能够准确预测模拟实验的功效、受试者数量和最小可检测差异，最大误差为 1%，并对模型对假设违反的敏感性进行了特征描述。通过对我们机构目前正在进行的前列腺癌成像验证研究进行建模的案例研究的计算，说明了这些公式的使用。功效计算公式解决了成像验证研究设计中的三个核心问题：（1）可接受的最大配准误差是多少？（2）需要多少个受试者？（3）正常和病理图像区域之间的最小可检测差异是多少？
