Evaluation of Hepatic Tumors Using Intravoxel Incoherent Motion Diffusion-Weighted MRI

Abstract

BackgroundThis study aimed to evaluate the diagnostic value of the D value, D* value, and f magnitude for identifying benign and malignant hepatic tumors using intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI).Material/MethodsData of 89 cases (123 lesions) with hepatic tumor confirmed by surgical pathology and postoperative follow-up were retrospectively collected. Among these cases, 40 cases were benign hepatic tumors (57 lesions) and 49 cases were malignant hepatic tumors (66 lesions). All subjects underwent conventional MRI with T1WI, T2WI, multi-b-value DWI, and dynamic enhanced LAVA scan. Diffusion-weighted images with 11 b values (0, 10, 20, 30, 50, 80, 100, 200, 400, 800, and 1000 s/mm2) were obtained to calculate true molecular diffusion (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (f). The diagnostic performance in differentiating between malignant and benign hepatic lesions was analyzed.ResultsMalignant lesions had a significantly lower D value ([1.04±0.34]×10−3 mm2/s) and D* value ([16.5±7.7]×10−3 mm2/s) compared to benign lesions (D value: [1.70±0.55]×10−3 mm2/s, P<0.01; D* value: [21.7±9.9]×10−3 mm2/s, P<0.01). There was no statistically significant difference in f values between malignant (23.3±9.5) and benign lesions (33.5±14.9, P=0.13). In addition, D exhibited a better diagnostic performance than D* in terms of the area under the curve, sensitivity, and specificity when identifying malignancies from benign lesions.ConclusionsD and D* are significant parameters for diagnosing hepatic tumors. Moreover, the D value is a more reliable parameter in distinguishing benign and malignant hepatic tumors.

Quantitative Analysis of Intravoxel Incoherent Motion (IVIM) Diffusion MRI using Total Variation and Huber Penalty Function

“…, (b) Segmented BE method with two‐parameter fitting (BEseg‐2), (c) Segmented BE method with one‐parameter fitting (BEseg‐1), (d) BE model with Total Variation penalty function (BE+TV), and (e) BE model with Huber penalty function (BE+HPF). In both the segmented methods (BEseg‐2 and BEseg‐1), Diffusion coefficient (D) is evaluated using mono‐exponential fit at b values ≥ 200 s/mm 2 as:assuming perfusion effect is negligible at higher b values (b ≥ 200 s/mm 2 ). In BEseg‐2 method, Perfusion coefficient ( D* ) and Perfusion fraction ( f ) are evaluated by fitting the BE model; with two free variables ( D*, f ); using Eq.…”

“…In both the segmented methods (BEseg‐2 and BEseg‐1), Diffusion coefficient (D) is evaluated using mono‐exponential fit at b values ≥ 200 s/mm 2 as:assuming perfusion effect is negligible at higher b values (b ≥ 200 s/mm 2 ). In BEseg‐2 method, Perfusion coefficient ( D* ) and Perfusion fraction ( f ) are evaluated by fitting the BE model; with two free variables ( D*, f ); using Eq. over all the 11 b values keeping D fixed as estimated from Eq.…”

“…The simulated and empirical datasets were analyzed using five analyses methodologies: (a) Bi-exponential (BE) model with simultaneous three-parameter fitting using Eq. (1), 6,7,[16][17][18] (b) Segmented BE method with two-parameter fitting (BEseg-2), 8,9,[13][14][15][19][20][21] (c) Segmented BE method with oneparameter fitting (BEseg-1), 5,12,19,[21][22][23] (d) BE model with Total Variation penalty function (BE+TV), and (e) BE model with Huber penalty function (BE+HPF). In both the segmented methods (BEseg-2 and BEseg-1), Diffusion coefficient (D) is evaluated using mono-exponential fit at b values ≥ 200 s/mm 2 as 5,12-15,19-22 :…”

“…assuming perfusion effect is negligible at higher b values (b ≥ 200 s/mm 2 ). In BEseg-2 method, 8,9,[13][14][15][19][20][21]…”

“…Reliable parameter estimation of IVIM has been challenging due to: (a) nonlinear characteristics of the model, (b) low signal to noise ratio (SNR) in DWI, (c) high sensitivity of signal to the SNR variation at low b values, 1,4 and (d) limited number of b values used in acquisition. 2,10 Moreover, commonly used methodologies employ BE model, 6,7,[16][17][18] segmented techniques with two-parameter fitting, 8,9,[13][14][15][19][20][21] one-parameter fitting, 5,12,19,[21][22][23] and Bayesian-based approaches. 11,24,25 These methodologies optimize the signal fitting at each voxel independently; overlooking the spatial context of the signal resulting in high noise in estimated parameters, 7 especially observed in perfusion values.…”