Enhancement-constrained acceleration: A robust reconstruction framework in breast DCE-MRI

Abstract

In patients with dense breasts or at high risk of breast cancer, dynamic contrast enhanced MRI (DCE-MRI) is a highly sensitive diagnostic tool. However, its specificity is highly variable and sometimes low; quantitative measurements of contrast uptake parameters may improve specificity and mitigate this issue. To improve diagnostic accuracy, data need to be captured at high spatial and temporal resolution. While many methods exist to accelerate MRI temporal resolution, not all are optimized to capture breast DCE-MRI dynamics. We propose a novel, flexible, and powerful framework for the reconstruction of highly-undersampled DCE-MRI data: enhancement-constrained acceleration (ECA). Enhancement-constrained acceleration uses an assumption of smooth enhancement at small time-scale to estimate points of smooth enhancement curves in small time intervals at each voxel. This method is tested in silico with physiologically realistic virtual phantoms, simulating state-of-the-art ultrafast acquisitions at 3.5s temporal resolution reconstructed at 0.25s temporal resolution (demo code available here). Virtual phantoms were developed from real patient data and parametrized in continuous time with arterial input function (AIF) models and lesion enhancement functions. Enhancement-constrained acceleration was compared to standard ultrafast reconstruction in estimating the bolus arrival time and initial slope of enhancement from reconstructed images. We found that the ECA method reconstructed images at 0.25s temporal resolution with no significant loss in image fidelity, a 4x reduction in the error of bolus arrival time estimation in lesions (p < 0.01) and 11x error reduction in blood vessels (p < 0.01). Our results suggest that ECA is a powerful and versatile tool for breast DCE-MRI.

Pharmacokinetic Analysis of Enhancement-Constrained Acceleration (ECA) reconstruction-based high temporal resolution breast DCE-MRI

“…Intuitively speaking, ECA searches for the smoothest set of enhancement curves consistent with the highly under-sampled k-space data measured during each of the reconstructed time intervals. A positive semidefinite smoothness penalty matrix is used to penalize the discretized second derivative in temporal dimension [ 32 ]. The ill-posed inverse problem is then solved by conjugate gradient method.…”

“…The ill-posed inverse problem is then solved by conjugate gradient method. The details of the ECA reconstruction are provided in [ 32 ].…”

“…Our previous study proposed an enhancement-constrained acceleration (ECA) reconstruction from highly under-sampled k-space data with Cartesian sampling trajectory [ 32 ]. The strategy of ECA reconstruction is to recover missing k-space points by exploiting correlations, based on two assumptions: (I) each k-space point contains some information about other k-space points and a properly selected subset of k-space points contains sufficient information to recover the rest of k-space; and (II) signal in each voxel enhances smoothly within short-time intervals.…”

New Approaches and Recommendations for Risk‐Adapted Breast Cancer Screening

“…Other accelerating techniques have been proposed, using parallel imaging, spatial compressed sensing, and multiplanar reconstruction 143,144 . Ongoing research on simulating state‐of‐the‐art ultrafast acquisitions using an enhancement‐constrained acceleration (ECA) technique, also demonstrates promising results 145 …”

Systematic evaluation of MRI-based characterization of tumor-associated vascular morphology and hemodynamics via a dynamic digital phantom

“…31 For example, we have recently developed an in silico validation framework to virtually generate DCE-MRI data. 32 This framework consists of (1) a digital phantom that provides detailed, realistic vascular structure, tissue properties, perfusion, and time-resolved contrast agent delivery of a murine kidney, and (2) a virtual MR scanner 33 to produce realistic DCE-MRI data based on the digital phantom under various (user-defined) acquisition settings including spatial resolution, temporal resolution, and signal-to-noise ratio (SNR). This digital-phantom-based validation framework provides a unique opportunity to validate methods employing DCE-MRI data to characterize both tumor-associated vascular morphology and hemodynamics.…”