Fusco Roberta, Sansone Mario, Petrillo Antonella
Radiology Unit, Department of Diagnostic Imaging Radiant and Metabolic Therapy, Istituto Nazionale Tumori Fondazione Giovanni Pascale RCCS, Via Mariano Semmola, 80131, Naples, Italy.
Department of Electrical Engineering and Information Technologies, University 'Federico II' of Naples, Via Claudio, 21, Naples, Italy.
MAGMA. 2017 Apr;30(2):113-120. doi: 10.1007/s10334-016-0591-y. Epub 2016 Sep 26.
The objective of this study is to propose a modified VARiable PROjection (VARPRO) algorithm specifically tailored for fitting the intravoxel incoherent motion (IVIM) model to diffusion-weighted magnetic resonance imaging (DW-MRI) data from locally advanced rectal cancer (LARC).
The proposed algorithm is compared with classical non-linear least squares (NLLS) analysis using the Levenberg-Marquardt (LM) algorithm and with two recently proposed algorithms for 'segmented' analysis. These latter two comprise two consecutive steps: first, a subset of parameters is estimated using a portion of data; second, the remaining parameters are estimated using the whole data and the previous estimates. The comparison between the algorithms was based on the [Formula: see text] goodness-of-fit measure: performance analysis was carried out on real data obtained by DW-MRI on 40 LARC patients.
The performance of the proposed algorithm was higher than that of LM in 64 % of cases; 'segmented' methods were poorer than our algorithm in 100 % of cases.
The proposed modified VARPRO algorithm can lead to better fit of the IVIM model to LARC DW-MRI data compared to other techniques.
本研究的目的是提出一种改进的可变投影(VARPRO)算法,该算法专门用于将体素内不相干运动(IVIM)模型拟合到局部晚期直肠癌(LARC)的扩散加权磁共振成像(DW-MRI)数据。
将所提出的算法与使用列文伯格-马夸尔特(LM)算法的经典非线性最小二乘法(NLLS)分析以及最近提出的两种用于“分段”分析的算法进行比较。后两种算法包括两个连续步骤:首先,使用一部分数据估计参数子集;其次,使用全部数据和先前的估计值估计其余参数。算法之间的比较基于拟合优度度量:对40例LARC患者通过DW-MRI获得的真实数据进行性能分析。
在64%的病例中,所提出算法的性能高于LM算法;在100%的病例中,“分段”方法比我们的算法差。
与其他技术相比,所提出的改进VARPRO算法能使IVIM模型更好地拟合LARC的DW-MRI数据。