Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast‐enhanced MRI using a model consistency constraint

Guo, Yi; Lingala, Sajan Goud; Bliesener, Yannick; Lebel, R. Marc; Zhu, Yinghua; Nayak, Krishna S.

doi:10.1002/mrm.26904

Cited by 21 publications

(27 citation statements)

References 44 publications

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“…The readout dimension was fully sampled while the phase encode dimensions were acquired along rasterized spiral-in trajectories. The B 1 + maps were estimated by techniques (9)(10)(11)(12), a similar characterization has not yet been performed for high-spatiotemporal-resolution whole-brain DCE MRI systems with sparse sampling. Characterization of measurement uncertainty relies on reproducibility studies in controlled test-retest settings.…”

Section: Sparse Dce Mri Data Acquisitionmentioning

confidence: 99%

“…Sparse DCE-MRI Reconstruction MethodsLebel et al (10) demonstrated reconstruction of high-spatial-resolution whole-brain DCE MRI time series from highly undersampled raw k-space data for tracer-kinetic parameter estimation. Guo et al(12) demonstrated joint estimation of tracer-kinetic parameter maps and patientspecific VIF. In the current study, we extended the sparse SENSE (SPSENSE) framework by Lebel et al(10) and the model consistency constrained (MOCCO) method by Guo et al(12) with an automated delineation of brain vessels based on common image/time series features in the literature…”

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confidence: 99%

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Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma

Bliesener¹,

Lebel²,

Acharya³

et al. 2021

Radiology

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D ynamic contrast-enhanced (DCE) MRI enables assessment of neurovascular parameters by monitoring enhancement patterns in tissue after injection of contrast agent. Such parameters could provide markers for tumor grading in patients with high-grade glioma (1,2) and for early brain tumor response to antiangiogenic therapy (3). Brain lesions commonly exhibit large regions of spatial heterogeneity or thinly enhancing rims around necrotic cores; in addition, multifocal metastases may be visible throughout the entire brain (4). Effective markers therefore require high-spatiotemporal-resolution whole-brain DCE MRI protocols, as well as accurate and reproducible tracer kinetic parameters (3,5,6).Many attempts have been made to achieve desired spatial resolution and coverage while preserving rapid sampling of the temporal evolution of contrast agent dynamics. Although direct estimation of tracer-kinetic parameters from undersampled MRI raw data exists (7,8), more commonly, constrained reconstruction algorithms reconstruct the image time series in an intermediate step (9-11) or simultaneously to tracer-kinetic estimation (12). This can be advantageous because it offers great flexibility in the estimation of nuisance parameters, such as the vascular input function (VIF), which is commonly done on the basis of image data.Extensive efforts by the Radiological Society of North America Quantitative Imaging Biomarkers Alliance DCE MRI task force and other groups have resulted in standardization and characterization of Nyquist-sampled DCE MRI (13). Although much research has been devoted to obtaining 20-to 50-fold undersampled parallel imaging Background: Advances in sub-Nyquist-sampled dynamic contrast-enhanced (DCE) MRI enable monitoring of brain tumors with millimeter resolution and whole-brain coverage. Such undersampled quantitative methods need careful characterization regarding achievable test-retest reproducibility.Purpose: To demonstrate a fully automated high-resolution whole-brain DCE MRI pipeline with 30-fold sparse undersampling and estimate its reproducibility on the basis of reference regions of stable tissue types during multiple posttreatment time points by using longitudinal clinical images of high-grade glioma. Materials and Methods:Two methods for sub-Nyquist-sampled DCE MRI were extended with automatic estimation of vascular input functions. Continuously acquired three-dimensional k-space data with ramped-up flip angles were partitioned to yield high-resolution, whole-brain tracer kinetic parameter maps with matched precontrast-agent T1 and M 0 maps. Reproducibility was estimated in a retrospective study in participants with high-grade glioma, who underwent three consecutive standard-of-care examinations between December 2016 and April 2019. Coefficients of variation and reproducibility coefficients were reported for histogram statistics of the tracer kinetic parameters plasma volume fraction and volume transfer constant (K trans ) on five healthy tissue types. Results:The images from 13 participants (mean ag...

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Section: Sparse Dce Mri Data Acquisitionmentioning

confidence: 99%

mentioning

confidence: 99%

Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma

Bliesener¹,

Lebel²,

Acharya³

et al. 2021

Radiology

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“…The optimization process is as given below: Eqs. (20) and (21), where Eq. (21) was solved using conjugate gradient (CG) and Eq.…”

Section: D Modlmentioning

confidence: 99%

“…Contrary to the indirect methods, Guo et al 19,20 and Dikaois et al 21 estimated the TK parameters directly from undersampled k-t space data without going into image domain. Their experiments have shown that direct reconstruction methods, perform better than the indirect methods.…”

Section: Introductionmentioning

confidence: 99%

“…[21] Bayesian inference on prostate cancer dataset was utilized to achieve an undersampling rate of 49. More recent works ( 19,20 ) validated direct estimation methods for higher undersampling rate of 1009 and gave the flexibility to incorporate any prior. However, both indirect and direct methods mentioned above are solved iteratively and involve regularization.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of iterative parametric and indirect deep learning‐based reconstruction methods in highly undersampled DCE‐MR Imaging of the breast

Rastogi

Yalavarthy

2020

Medical Physics

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To compare the performance of iterative direct and indirect parametric reconstruction methods with indirect deep learning-based reconstruction methods in estimating tracer-kinetic parameters from highly undersampled DCE-MR Imaging breast data and provide a systematic comparison of the same. Methods: Estimation of tracer-kinetic parameters using indirect methods from undersampled data requires to reconstruct the anatomical images initially by solving an inverse problem. This reconstructed images gets utilized in turn to estimate the tracer-kinetic parameters. In direct estimation, the parameters are estimated without reconstructing the anatomical images. Both problems are ill-posed and are typically solved using prior-based regularization or using deep learning. In this study, for indirect estimation, two deep learning-based reconstruction frameworks namely, ISTA-Net + and MODL, were utilized. For direct and indirect parametric estimation, sparsity inducing priors (L1 and Total-Variation) and limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm as solver was deployed. The performance of these techniques were compared systematically in estimation of vascular permeability (K trans) from undersampled DCE-MRI breast data using Patlak as pharmaco-kinetic model. The experiments involved retrospective undersampling of the data 209, 509, and 1009 and compared the results using PSNR, nRMSE, SSIM, and Xydeas metrics. The K trans maps estimated from fully sampled data were utilized as ground truth. The developed code was made available as https://github.com/Medical-Imaging-Group/DCE-MRI-Compare open-source for enthusiastic users. Results: The reconstruction methods performance was evaluated using ten patients breast data (five patients each for training and testing). Consistent with other studies, the results indicate that direct parametric reconstruction methods provide improved performance compared to the indirect parameteric reconstruction methods. The results also indicate that for 209 undersampling, deep learningbased methods performs better or at par with direct estimation in terms of PSNR, SSIM, and nRMSE. However, for higher undersampling rates (509 and 1009) direct estimation performs better in all metrics. For all undersampling rates, direct reconstruction performed better in terms of Xydeas metric, which indicated fidelity in magnitude and orientation of edges. Conclusion: Deep learning-based indirect techniques perform at par with direct estimation techniques for lower undersampling rates in the breast DCE-MR imaging. At higher undersampling rates, they are not able to provide much needed generalization. Direct estimation techniques are able to provide more accurate results than both deep learning-and parametric-based indirect methods in these high undersampling scenarios.

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Accelerated brain tumor dynamic contrast‐enhanced MRI using Adaptive Pharmaco‐Kinetic Model Constrained method

Liu

Jin

et al. 2021

Int J Imaging Syst Tech

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In brain tumor, dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) spatiotemporally resolved high‐quality reconstruction which is required for quantitative analysis of some physiological characteristics of brain tissue. By exploiting some kind of sparsity priori, compressed sensing methods can achieve high spatiotemporal DCE‐MRI image reconstruction from undersampled k‐space data. Recently, as a kind of priori information about the contrast agent (CA) concentration dynamics, Pharmacokinetic (PK) models have been explored for undersampled DCE‐MRI reconstruction. This paper presents a novel dictionary learning‐based reconstruction method with Adaptive Pharmaco‐Kinetic Model Constraints (APKMC). In APKMC, the priori knowledge about CA dynamics is incorporated into a novel dictionary, which consists of PK model‐based atoms and adaptive atoms. The PK atoms are constructed based on Patlak model and K‐SVD dimension reduction algorithm, and the adaptive ones are used to resolve PK model inconsistencies. To solve APKMC, an optimization algorithm based on variable splitting and alternating iterative optimization is presented. The proposed method has been validated on three brain tumor DCE‐MRI data sets by comparing with two state‐of‐the‐art methods. As demonstrated by the quantitative and qualitative analysis of results, APKMC achieved substantially better quality in the reconstruction of brain DCE‐MRI images, as well as in the reconstruction of PK model parameter maps.

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Joint arterial input function and tracer kinetic parameter estimation from undersampled dynamic contrast‐enhanced MRI using a model consistency constraint

Cited by 21 publications

References 44 publications

Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma

Pseudo Test-Retest Evaluation of Millimeter-Resolution Whole-Brain Dynamic Contrast-enhanced MRI in Patients with High-Grade Glioma

Comparison of iterative parametric and indirect deep learning‐based reconstruction methods in highly undersampled DCE‐MR Imaging of the breast

Accelerated brain tumor dynamic contrast‐enhanced MRI using Adaptive Pharmaco‐Kinetic Model Constrained method

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