@article{TEXTUAL,
      recid = {5897},
      author = {Easley, Ty O. and Ren, Zhen and Kim, Byol and Karczmar,  Gregory S. and Barber, Rina F. and Pineda, Federico D.},
      title = {Enhancement-constrained acceleration: A robust  reconstruction framework in breast DCE-MRI},
      journal = {PLOS ONE},
      address = {2021-10-28},
      number = {TEXTUAL},
      abstract = {<p>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.</p>},
      url = {http://knowledge.uchicago.edu/record/5897},
}