The goal of this proposal is to develop and validate novel compressed sensing (CS) approaches to dramatically improve the spatial and temporal resolution of quantitative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). CS exploits prior information (assumptions) about MR images to infer missing data and produce high-quality images from significantly less data than previously thought possible. CS has already proven extremely successful in MR angiography and cardiac MRI, where it has accelerated some acquisitions by up to 10- to 100-fold, but is relatively unexplored in cancer imaging. DCE-MRI involves the serial acquisition of heavily T1-weighted images before and after the injection of a contrast agent to increase water relaxation rates in tissues. The resulting data can then be analyzed with appropriate pharmacokinetic models to extract quantitative parameters reporting on, for example, vessel perfusion and permeability, and tissue volume fractions. DCE-MRI has been applied to predict the early response to neoadjuvant chemotherapy in breast cancer, but the technique is not yet robust and accurate enough for the clinic. A fundamental practical limitation of DCE-MRI is the necessity to simultaneously acquire high temporal resolution, to adequately sample the contrast time course, and high spatial resolution, which is required for clinical morphological assessment and accurate tumor delineation. In traditional Cartesian MRI acquisitions, one must choose between high spatial or high temporal resolution before the scan. With a golden ratio acquisition, the tradeoff between spatial and temporal resolution is eliminated. A single DCE-MRI scan may then be used for both accurate kinetic modeling by slicing the data at high temporal cadence, while also allowing a high spatial resolution image to be formed by taking the data as a whole. Thus, a golden ratio acquisition coupled with CS has great potential to enable a clinically relevant DCE-MRI protocol that provides adequate temporal resolution for kinetic modeling without sacrificing the spatial resolution required for morphological evaluation. This project has three aims: (1) to develop a compressed sensing based high temporal resolution protocol for quantitative DCE-MRI, (2) to develop a compressed sensing based high spatial resolution T1-weighted anatomical imaging protocol for morphological evaluation, and (3) to apply the developed CS-based protocols in vivo for validation and evaluation. If this project is successful, it will significantly improve the ability to predict response to neoadjuvant chemotherapy, provide new CS methods for the community to apply to other in vivo applications, and validate CS in an important cancer imaging application.

Public Health Relevance

Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) has shown early promise as a non- invasive tool for monitoring the response to chemotherapy in breast cancer. This project seeks to employ novel MRI data collection and processing techniques to improve the accuracy and reliability of DCE-MRI. With these improvements, DCE-MRI would have a greater potential to inform clinical decisions and help more patients avoid unnecessary chemotherapy and its concomitant harmful side effects. The written critiques of individual reviewers are provided in essentially unedited form in this section. Please note that these critiques and criteria scores were prepared prior to the meeting and may not have been revised subsequent to any discussions at the review meeting. The Resume and Summary of Discussion section above summarizes the final opinions of the committee.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Mentored Quantitative Research Career Development Award (K25)
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Subcommittee B - Comprehensiveness (NCI)
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Jakowlew, Sonia B
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Vanderbilt University Medical Center
Schools of Medicine
United States
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Smith, David S; Sengupta, Saikat; Smith, Seth A et al. (2018) Trajectory optimized NUFFT: Faster non-Cartesian MRI reconstruction through prior knowledge and parallel architectures. Magn Reson Med :
Sengupta, Saikat; Smith, David S; Smith, Alex K et al. (2017) Dynamic Imaging of the Eye, Optic Nerve, and Extraocular Muscles With Golden Angle Radial MRI. Invest Ophthalmol Vis Sci 58:4390–4398
Wang, Dong; Arlinghaus, Lori R; Yankeelov, Thomas E et al. (2017) Quantitative Evaluation of Temporal Regularizers in Compressed Sensing Dynamic Contrast Enhanced MRI of the Breast. Int J Biomed Imaging 2017:7835749
Knight, Silvin P; Browne, Jacinta E; Meaney, James F et al. (2016) A novel anthropomorphic flow phantom for the quantitative evaluation of prostate DCE-MRI acquisition techniques. Phys Med Biol 61:7466-7483
Sengupta, Saikat; Smith, David S; Gifford, Aliya et al. (2016) Whole-body continuously moving table fat-water MRI with dynamic B0 shimming at 3 Tesla. Magn Reson Med 76:183-90
Sengupta, Saikat; Smith, David S; Welch, E Brian (2015) Continuously moving table MRI with golden angle radial sampling. Magn Reson Med 74:1690-7
Smith, David S; Li, Xia; Arlinghaus, Lori R et al. (2015) DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis. PeerJ 3:e909