DCEMRI.jl : a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis
Year: 2015
Type: article
Abstract: We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10-20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N (1.9)), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.
Source: PeerJ
DOI: 10.7717/peerj.909
Authors: David S Smith, Xia Li, Lori R Arlinghaus, Thomas E Yankeelov, E. Brian Welch
Institution: Vanderbilt University
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,395
Awards: Quantitative MRI for Predicting Response of Breast Cancer to Neoadjuvant Therapy, Multidisciplinary Training in Basic and Translational Imaging of Cancer, Evaluation of MRI Biomarkers of Breast Cancer Response, Applying Compressed Sensing to Dynamic Contrast Enhanced MRI of Breast Cancer, Postdoctoral Training in Biomedical MRI and MRS
OpenAlex ID: W2007422086