用于从标准Tofts模型快速计算生理参数的解耦迭代算法。

Decoupling iterative algorithm for rapid calculation of physiological parameters from the standard Tofts model.

作者信息

Chang Shu, Fan Xiaobing, Ma Ying, Huang Guantian, Qi Shouliang, Qian Wei, Shi Xin, He Dianning

机构信息

College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.

Department of Radiology, University of Chicago, Chicago, Illinois, USA.

出版信息

Med Phys. 2025 Aug;52(8):e18043. doi: 10.1002/mp.18043.

DOI:10.1002/mp.18043

PMID:40804784

Abstract

BACKGROUND

The standard Tofts model (STM) is an important pharmacokinetic model for analyzing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. However, it is very time-consuming using the STM to perform pixel-by-pixel analysis for 3D DCE-MRI data.

PURPOSE

We developed a decoupling iterative algorithm, prediction-correction method (PCM), for rapid calculation of physiological parameters using the STM.

METHODS

The idea behind PCM is to eliminate the need to fit the entire contrast agent concentration (C(t)) as function of time (t) curve to calculate the volume transfer constant (K) and the volume fraction of the extravascular extracellular space (v) using the STM. The early portion of C(t) was used to obtain K with prediced v value, and the late portion of C(t) was used to obtain v with predicted K. This procedure was iteratively performed until the changes of K and v were less than the given tolerance errors. The method was first validated using the quantitative imaging biomarker alliance (QIBA) data. Then the public prostate DCE-MRI dataset that was scanned twice and the breast DCE-MRI dataset were used as applications of the PCM and compared with the conventional way of fitting the STM. The repeatability coefficients (RC) of the calculated parameters were also determined.

RESULTS

For QIBA data, there was an excellent agreement for calculating physiological parameters between the PCM and the conventional STM. For clinical data, there was a small percentage error (<10%) in the calculations of K and v between the two methods and between two scans. Overall, the PCM was about 10 times faster than the STM for each pixel. The repeatability of calculating K and v was similar between the PCM and STM.

CONCLUSION

The PCM significantly accelerated the calculations of K and v with an accuracy close to the STM. By using the PCM, the physiological parameters can be calculated rapidly for 3D DCE-MRI data to aid cancer diagnosis.

摘要

背景

标准Tofts模型（STM）是分析动态对比增强磁共振成像（DCE-MRI）数据的重要药代动力学模型。然而，使用STM对三维DCE-MRI数据进行逐像素分析非常耗时。

目的

我们开发了一种去耦迭代算法——预测校正方法（PCM），用于使用STM快速计算生理参数。

方法

PCM背后的理念是，无需将整个造影剂浓度（C(t)）作为时间（t）曲线的函数进行拟合，即可使用STM计算容积转运常数（K）和血管外细胞外间隙容积分数（v）。利用C(t)的早期部分，结合预测的v值来获得K，利用C(t)的晚期部分，结合预测的K值来获得v。该过程反复进行，直到K和v的变化小于给定的容许误差。该方法首先使用定量成像生物标志物联盟（QIBA）数据进行验证。然后，将扫描两次的公共前列腺DCE-MRI数据集和乳腺DCE-MRI数据集作为PCM的应用实例，并与拟合STM的传统方法进行比较。还确定了计算参数的重复性系数（RC）。