# Active Contrast Encoding MRI for Breast Cancer

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2020 · $550,173

## Abstract

PROJECT SUMMARY
Despite recent development of various new approaches to therapy, breast cancer remains the second leading
cause of cancer death in women. Treatment with anti-angiogenic drugs combined with conventional cytotoxic
drugs or immunotherapy is a promising means of treating aggressive cancer. Anti-angiogenic drugs are
thought to temporarily normalize abnormal vasculature and paradoxically increase blood flow and hence
delivery of drug and effector immune cells to tumors. However, a substantial proportion of patients do not
respond to this combination therapy and it is unclear whether the failure is due to failure of the anti-angiogenic
to normalize the vasculature or failure of the cytotoxic drugs or immune cells to kill cancer cells. It is therefore
necessary to assess both blood flow and cell death to elucidate the mechanism and to optimize the
combination treatments. In this proposal we investigate a single MRI acquisition and analysis that will allow
assessment of both.
 Dynamic contrast enhanced (DCE) MRI has been widely used as an important part of most clinical MRI
exams for diagnosis of cancer, and it holds high potential as a single MRI method to estimate both perfusion
parameters (such as, flow, F, vascular volume fraction, vp, and vascular permeability-surface area product, PS)
and cellular parameters (such as, interstitial volume fraction, ve, and intracellular water life time, τi). Recently,
we developed a novel data acquisition method, namely active contrast encoding (ACE)-MRI, which measures
dynamic data together with pre-contrast T1 and B1 that are critical for measurement of perfusion and cellular
parameters. ACE-MRI is also implemented with a fast 3D imaging method to acquire high-spatial and high-
temporal resolution data using a 3D golden-angle ultra-short echo-time (UTE) sequence and an image
reconstruction method to combine both compressed sensing and parallel imaging, also known as GRASP
(Golden-angle RAdial Sparsity and Parallel).
 In this study, we plan to further develop ACE-MRI for accurate estimation of contrast agent concentration in
vascular plasma and tissue using a direct blood sampling method (Aim 1), and to assess the association of τi
with tumor metabolic rate and treatment response in comparison with 18F-FDG-PET and pathology (Aim 2).
Overall, the ACE-MRI parameters will be used to assess treatment response and metastatic potential (Aim 3).
This study will be conducted with murine and human breast cancer models at a 7T small animal MRI scanner.
However, the methods developed in this study can be easily translated to clinical applications as it is used on a
UTE sequence readily available on most clinical scanners.

## Key facts

- **NIH application ID:** 9867684
- **Project number:** 5R01CA160620-07
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Sungheon Gene Kim
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $550,173
- **Award type:** 5
- **Project period:** 2012-03-01 → 2020-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9867684

## Citation

> US National Institutes of Health, RePORTER application 9867684, Active Contrast Encoding MRI for Breast Cancer (5R01CA160620-07). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9867684. Licensed CC0.

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