Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
Department of Radio Diagnosis, All India Institute of Medical Sciences, New Delhi, India.
Med Phys. 2017 Nov;44(11):5849-5858. doi: 10.1002/mp.12520. Epub 2017 Oct 11.
Quantitative analysis in intravoxel incoherent motion (IVIM) imaging commonly uses voxel-wise estimation of the bi-exponential model, which might not be reliable for clinical interpretation. Improving model fitting performance and qualitative and quantitative parametric estimation, two novel methodologies are proposed here.
Five IVIM analyses methodologies: (a) Bi-exponential (BE) model, (b) Segmented BE method with two-parameter fitting (BEseg-2), (c) Segmented BE method with one-parameter fitting (BEseg-1), (d) BE with adaptive Total Variation penalty function (BE+TV) and (e) BE with adaptive Huber penalty function (BE+HPF) were evaluated. Relative root-mean-square error (RRMSE), relative bias (RB) and relative parameters (Drel,Drel∗,&frel) were calculated to estimate the accuracy of methods in simulations. Empirical datasets from 14 patients with bone tumor were analyzed using these methodologies. Coefficient of variation (CV) were estimated for each IVIM parameter in tumor volume to measure the precision of the estimation methods in vivo.
Both BE+TV and BE+HPF showed consistently lower RRMSE (~10-42%) and lower RB (-4 to 8%) at all noise levels, compared to BE, BEseg-2 and BEseg-1 (RRMSE: ~15-120% and RB: -20 to 62%). Estimated Drel,Drel∗&frel for both BE+TV and BE+HPF methods were ~1 (0.96-1.08), whereas BE, BEseg-2 and BEseg-1 showed sub-optimal parameter estimation (0.80-1.62). For clinical data BE+TV and BE+HPF showed 30-50% improved CV in estimating D, D*, and f than BE and improved CV in estimating D* (7-23%) and f (26-30%) than BEseg-2 and BEseg-1.
Bi-exponential model with penalty function showed quantitatively and qualitatively improved IVIM parameter estimation for both simulated and clinical dataset of bone tumors, thus potentially making this approach suitable for clinical applications in future.
体素内不相干运动（IVIM）成像中的定量分析通常采用双指数模型的体素估计，但这可能不利于临床解释。为了提高模型拟合性能和定性及定量参数估计，本文提出了两种新的方法。
本研究评估了五种 IVIM 分析方法：（a）双指数（BE）模型，（b）具有双参数拟合的分段 BE 方法（BEseg-2），（c）具有单参数拟合的分段 BE 方法（BEseg-1），（d）具有自适应全变分惩罚函数的 BE 方法（BE+TV）和（e）具有自适应 Huber 惩罚函数的 BE 方法（BE+HPF）。在模拟中，通过计算相对均方根误差（RRMSE）、相对偏差（RB）和相对参数（Drel、Drel∗和 frel）来评估这些方法的准确性。采用这些方法对 14 例骨肿瘤患者的实证数据集进行了分析。通过测量每种 IVIM 参数在肿瘤体积中的变异系数（CV），来评估在体方法的估计精度。
与 BE、BEseg-2 和 BEseg-1 相比，BE+TV 和 BE+HPF 在所有噪声水平下均表现出更低的 RRMSE（10-42%）和 RB（-4 至 8%）（RRMSE：15-120%和 RB：-20 至 62%）。对于 BE+TV 和 BE+HPF 方法，估计的 Drel、Drel∗和 frel 值均接近 1（0.96-1.08），而 BE、BEseg-2 和 BEseg-1 则表现出次优的参数估计（0.80-1.62）。对于临床数据，BE+TV 和 BE+HPF 方法在估计 D、D和 f 方面的 CV 改善了 30-50%，在估计 D（7-23%）和 f（26-30%）方面的 CV 改善了比 BEseg-2 和 BEseg-1。
对于骨肿瘤的模拟和临床数据集，具有惩罚函数的双指数模型在 IVIM 参数估计方面表现出了定量和定性的改善，因此未来可能适用于临床应用。
