Centre for Medical Imaging, University College London, 250 Euston Road, NW1 2PG London, UK; Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT London, UK.
Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT London, UK.
Med Image Anal. 2014 Oct;18(7):989-1001. doi: 10.1016/j.media.2014.05.001. Epub 2014 May 24.
The Magnetic Resonance Imaging (MRI) signal can be made sensitive to functional parameters that provide information about tissues. In dynamic contrast enhanced (DCE) MRI these functional parameters are related to the microvasculature environment and the concentration changes that occur rapidly after the injection of a contrast agent. Typically DCE images are reconstructed individually and kinetic parameters are estimated by fitting a pharmacokinetic model to the time-enhancement response; these methods can be denoted as "indirect". If undersampling is present to accelerate the acquisition, techniques such as kt-FOCUSS can be employed in the reconstruction step to avoid image degradation. This paper suggests a Bayesian inference framework to estimate functional parameters directly from the measurements at high temporal resolution. The current implementation estimates pharmacokinetic parameters (related to the extended Tofts model) from undersampled (k, t)-space DCE MRI. The proposed scheme is evaluated on a simulated abdominal DCE phantom and prostate DCE data, for fully sampled, 4 and 8-fold undersampled (k, t)-space data. Direct kinetic parameters demonstrate better correspondence (up to 70% higher mutual information) to the ground truth kinetic parameters (of the simulated abdominal DCE phantom) than the ones derived from the indirect methods. For the prostate DCE data, direct kinetic parameters depict the morphology of the tumour better. To examine the impact on cancer diagnosis, a peripheral zone prostate cancer diagnostic model was employed to calculate a probability map for each method.
磁共振成像（MRI）信号可以对提供组织信息的功能参数变得敏感。在动态对比增强（DCE）MRI 中，这些功能参数与微血管环境和造影剂注射后迅速发生的浓度变化有关。通常，DCE 图像是单独重建的，通过将药代动力学模型拟合到时间增强响应来估计动力学参数；这些方法可以表示为“间接”。如果存在欠采样以加速采集，则可以在重建步骤中使用 kt-FOCUSS 等技术来避免图像降级。本文提出了一种贝叶斯推理框架，可从高时间分辨率的测量值直接估计功能参数。当前的实现从欠采样（k，t）空间 DCE MRI 中估计药代动力学参数（与扩展 Tofts 模型相关）。在所提出的方案中，对模拟腹部 DCE 体模和前列腺 DCE 数据进行了评估，包括完全采样、4 倍和 8 倍欠采样（k，t）空间数据。直接动力学参数与模拟腹部 DCE 体模的真实动力学参数（ground truth kinetic parameters）具有更好的一致性（高达 70%的互信息），比间接方法得出的参数更好。对于前列腺 DCE 数据，直接动力学参数更好地描绘了肿瘤的形态。为了检查对癌症诊断的影响，使用前列腺外周带癌诊断模型计算了每种方法的概率图。
