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Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

ABSTRACT: PURPOSE:The purpose of this work was to develop and evaluate a T1 -weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data. THEORY AND METHODS:The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed scheme is compared against an indirect method that creates intermediate images by parallel imaging and compressed sensing before to TK modeling. Thirteen fully sampled brain tumor DCE-MRI scans with 5-second temporal resolution are retrospectively undersampled at rates R?=?20, 40, 60, 80, and 100 for each dynamic frame. TK maps are quantitatively compared based on root mean-squared-error (rMSE) and Bland-Altman analysis. The approach is also applied to four prospectively R?=?30 undersampled whole-brain DCE-MRI data sets. RESULTS:In the retrospective study, the proposed method performed statistically better than indirect method at R???80 for all 13 cases. This approach provided restoration of TK parameter values with less errors in tumor regions of interest, an improvement compared to a state-of-the-art indirect method. Applied prospectively, the proposed method provided whole-brain, high-resolution TK maps with good image quality. CONCLUSION:Model-based direct estimation of TK maps from k,t-space DCE-MRI data is feasible and is compatible up to 100-fold undersampling. Magn Reson Med 78:1566-1578, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

SUBMITTER: Guo Y

PROVIDER: S-EPMC5435562 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

Guo Yi Y Lingala Sajan Goud SG Zhu Yinghua Y Lebel R Marc RM Nayak Krishna S KS

Magnetic resonance in medicine 20161117 4

<h4>Purpose</h4>The purpose of this work was to develop and evaluate a T<sub>1</sub> -weighted dynamic contrast enhanced (DCE) MRI methodology where tracer-kinetic (TK) parameter maps are directly estimated from undersampled (k,t)-space data.<h4>Theory and methods</h4>The proposed reconstruction involves solving a nonlinear least squares optimization problem that includes explicit use of a full forward model to convert parameter maps to (k,t)-space, utilizing the Patlak TK model. The proposed sc ...[more]

PMID: 27859563

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Project description:Pulmonary perfusion with dynamic contrast-enhanced (DCE-) MRI is typically assessed using a single-input tracer kinetic model. Preliminary studies based on perfusion CT are indicating that dual-input perfusion modeling of lung tumors may be clinically valuable as lung tumors have a dual blood supply from the pulmonary and aortic system. This study aimed to investigate the feasibility of fitting dual-input tracer kinetic models to DCE-MRI datasets of thoracic malignancies, including malignant pleural mesothelioma (MPM) and nonsmall cell lung cancer (NSCLC), by comparing them to single-input (pulmonary or systemic arterial input) tracer kinetic models for the voxel-level analysis within the tumor with respect to goodness-of-fit statistics. Fifteen patients (five MPM, ten NSCLC) underwent DCE-MRI prior to radiotherapy. DCE-MRI data were analyzed using five different single- or dual-input tracer kinetic models: Tofts-Kety (TK), extended TK (ETK), two compartment exchange (2CX), adiabatic approximation to the tissue homogeneity (AATH) and distributed parameter (DP) models. The pulmonary blood flow (BF), blood volume (BV), mean transit time (MTT), permeability-surface area product (PS), fractional interstitial volume (vI ), and volume transfer constant (KTrans ) were calculated for both single- and dual-input models. The pulmonary arterial flow fraction (?), pulmonary arterial blood flow (BFPA ) and systemic arterial blood flow (BFA ) were additionally calculated for only dual-input models. The competing models were ranked and their Akaike weights were calculated for each voxel according to corrected Akaike information criterion (cAIC). The optimal model was chosen based on the lowest cAIC value. In both types of tumors, all five dual-input models yielded lower cAIC values than their corresponding single-input models. The 2CX model was the best-fitted model and most optimal in describing tracer kinetic behavior to assess microvascular properties in both MPM and NSCLC. The dual-input 2CX-model-derived BFA was the most significant parameter in differentiating adenocarcinoma from squamous cell carcinoma histology for NSCLC patients.

Dataset Information

Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

Publications

Direct estimation of tracer-kinetic parameter maps from highly undersampled brain dynamic contrast enhanced MRI.

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