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Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients.

ABSTRACT: The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations. Three different measures of performance of the fitting algorithms were assessed: (i) uniqueness of fit parameters (Spearman's rho); (ii) precision (within-subject coefficient of variation, wCV); and (iii) contrast between tumour and normal-appearing pancreatic tissue. For the diffusivity D and perfusion fraction f, a Bayesian fit (IVIM-Bayesian-lin) offered the best trade-off between tumour contrast and precision. With the exception for IVIM-Bayesian-lin, all algorithms resulted in a very poor precision of the pseudo-diffusion coefficient D* with a wCV of more than 50%. The pseudo-diffusion coefficient D* of the Bayesian approaches were, however, significantly correlated with D and f. Therefore, the added value of fitting D* was considered limited in pancreatic cancer patients. The easier implemented least squares fit with fixed D* (IVIM-fixed) performed similar to IVIM-Bayesian-lin for f and D. In conclusion, the best performing IVIM fit algorithm was IVM-Bayesian-lin, but an easier to implement least squares fit with fixed D* performs similarly in pancreatic cancer patients.

SUBMITTER: Gurney-Champion OJ

PROVIDER: S-EPMC5884505 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients.

Gurney-Champion Oliver J OJ Klaassen Remy R Froeling Martijn M Barbieri Sebastiano S Stoker Jaap J Engelbrecht Marc R W MRW Wilmink Johanna W JW Besselink Marc G MG Bel Arjan A van Laarhoven Hanneke W M HWM Nederveen Aart J AJ

PloS one 20180404 4

The intravoxel incoherent motion (IVIM) model for diffusion-weighted imaging (DWI) MRI data bears much promise as a tool for visualizing tumours and monitoring treatment response. To improve the currently poor precision of IVIM, several fit algorithms have been suggested. In this work, we compared the performance of two Bayesian IVIM fit algorithms and four other IVIM fit algorithms for pancreatic cancer imaging. DWI data were acquired in 14 pancreatic cancer patients during two MRI examinations ...[more]

PMID: 29617445

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Project description:OBJECTIVES:Diffusion-weighted imaging (DWI) and the introduction of the intravoxel incoherent motion (IVIM) model have provided a unique method for evaluating perfusion and diffusion within a tissue without the need for a contrast agent. Despite its relevance, cardiac DWI has thus far been limited by low b values because of signal loss induced by physiological motion. The goal of this study was to develop a methodology for estimating IVIM parameters of in vivo cardiac magnetic resonance imaging using an efficient DWI acquisition framework. This was achieved by investigating various acquisition strategies (principal component analysis [PCA] filtering and temporal maximum intensity projection [PCATMIP] and single trigger delay [TD]) and fitting methods. MATERIAL AND METHODS:Simulations were performed on a synthetic dataset of diffusion-weighted signal intensity (SI) to determine the fitting method that would yield IVIM parameters with the greatest accuracy. The required number of b values to correctly estimate IVIM parameters was also investigated. Breath-hold DWI scans were performed for 12 volunteers to collect several TD values during diastole. Thirteen b values ranging from 0 to 550 s/mm were used. The IVIM parameters derived using the data from all the acquired TDs (PCATMIP technique) were compared with those derived using a single acquisition performed at an optimized diastolic time point (1TD). RESULTS:The main result of this study was that PCATMIP, when combined with a fitting model that accounted for T1 and T2 relaxation, provided IVIM parameters with less variability. However, an acquisition performed with 1 optimized diastolic TD provided results that were as good as those provided using PCATMIP if the R-R variability during the acquisition was sufficiently low (± 5%). Furthermore, the use of only 9 b values (that could be acquired in 2 breath-holds), instead of 13 b values (requiring 3 breath-holds), was sufficient to determine the IVIM parameters. CONCLUSIONS:This study demonstrates that IVIM is technically feasible in vivo and reports for the first time the perfusion fraction, f, and the diffusion coefficients, D and D*, for the cardiac DWI of healthy volunteers. Motion-induced signal loss, which is the main problem associated with cardiac DWI, could be avoided with the combined use of sliding acquisition during the cardiac cycle and image postprocessing with the PCATMIP algorithm. This study provides new perspectives for perfusion imaging without a contrast agent and demonstrates that IVIM parameters can act as promising tools to further characterize microvascular abnormalities or dysfunction.

Project description:Quantitative diffusion-weighted MR imaging (DW-MRI) of the body enables characterization of the tissue microenvironment by measuring variations in the mobility of water molecules. The diffusion signal decay model parameters are increasingly used to evaluate various diseases of abdominal organs such as the liver and spleen. However, previous signal decay models (i.e., mono-exponential, bi-exponential intra-voxel incoherent motion (IVIM) and stretched exponential models) only provide insight into the average of the distribution of the signal decay rather than explicitly describe the entire range of diffusion scales. In this work, we propose a probability distribution model of incoherent motion that uses a mixture of Gamma distributions to fully characterize the multi-scale nature of diffusion within a voxel. Further, we improve the robustness of the distribution parameter estimates by integrating spatial homogeneity prior into the probability distribution model of incoherent motion (SPIM) and by using the fusion bootstrap solver (FBM) to estimate the model parameters. We evaluated the improvement in quantitative DW-MRI analysis achieved with the SPIM model in terms of accuracy, precision and reproducibility of parameter estimation in both simulated data and in 68 abdominal in-vivo DW-MRIs. Our results show that the SPIM model not only substantially reduced parameter estimation errors by up to 26%; it also significantly improved the robustness of the parameter estimates (paired Student's t-test, p < 0.0001) by reducing the coefficient of variation (CV) of estimated parameters compared to those produced by previous models. In addition, the SPIM model improves the parameter estimates reproducibility for both intra- (up to 47%) and inter-session (up to 30%) estimates compared to those generated by previous models. Thus, the SPIM model has the potential to improve accuracy, precision and robustness of quantitative abdominal DW-MRI analysis for clinical applications.

Project description:BACKGROUND:Pediatric retroperitoneal tumors in the renal bed are often large and heterogeneous, and their diagnosis based on conventional imaging alone is not possible. More advanced imaging methods, such as diffusion-weighted (DW) MRI and the use of intravoxel incoherent motion (IVIM), have the potential to provide additional biomarkers that could facilitate their noninvasive diagnosis. PURPOSE:To assess the use of an IVIM model for diagnosis of childhood malignant abdominal tumors and discrimination of benign from malignant lesions. STUDY TYPE:Retrospective. POPULATION:Forty-two pediatric patients with abdominal lesions (n?=?32 malignant, n?=?10 benign), verified by histopathology. FIELD STRENGTH/SEQUENCE:1.5T MRI system and a DW-MRI sequence with six b-values (0, 50, 100, 150, 600, 1000 s/mm2 ). ASSESSMENT:Parameter maps of apparent diffusion coefficient (ADC), and IVIM maps of slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were computed using a segmented fitting model. Histograms were constructed for whole-tumor regions of each parameter. STATISTICAL TESTS:Comparison of histogram parameters of and their diagnostic performance was determined using Kruskal-Wallis, Mann-Whitney U, and receiver-operating characteristic (ROC) analysis. RESULTS:IVIM parameters D* and f were significantly higher in neuroblastoma compared to Wilms' tumors (P < 0.05). The ROC analysis showed that the best diagnostic performance was achieved with D* 90th percentile (area under the curve [AUC]?=?0.935; P?=?0.002; cutoff value?=?32,376 × 10-6 mm2 /s) and f mean values (AUC?=?1.00; P < 0.001; cutoff value?=?14.7) in discriminating between neuroblastoma (n?=?11) and Wilms' tumors (n?=?8). Discrimination between tumor types was not possible with IVIM D or ADC parameters. Malignant tumors revealed significantly lower ADC, D, and higher D* values than in benign lesions (all P < 0.05). DATA CONCLUSION:IVIM perfusion parameters could distinguish between malignant childhood tumor types, providing potential imaging biomarkers for their diagnosis. LEVEL OF EVIDENCE:4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1475-1486.

Dataset Information

Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients.

Publications

Comparison of six fit algorithms for the intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging data of pancreatic cancer patients.

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