Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of breast cancer patients has shown considerable promise in aiding diagnoses of breast lesions and characterizing treatment response. The challenge in DCE breast imaging is the need for both good temporal resolution to capture tracer kinetic properties and good spatial resolution for visualizing morphology. Traditional dynamic methods in MRI acquire incomplete k-space data at each time point, and use k-space temporal interpolation (or data sharing) to form "complete" k-space datasets prior to Fourier reconstruction. We propose to investigate model-based image reconstruction methods that avoid k-space interpolation by estimating the object model parameters that best fit the available k-space data. These reconstruction methods will incorporate parallel imaging techniques. They will also be extended to account for nonrigid deformations due to patient motion during the scan using novel methods for joint estimation of motion parameters and image intensity parameters. The methods will be evaluated using computer simulations, phantom studies, and human DCE-MRI scan data. The human data will be collected as part of Project 1 and will include DCE-MRI scans of breast cancer patients undergoing neoadjuvant chemotherapy, where early prediction of tumor response is of clinical importance. The proposed methods have the potential to improve image quality both in breast DCE-MRI as well as other dynamic MR applications.

Public Health Relevance

The relevance of this research to public health is that improving the quality of MR images through more sophisticated data processing may lead to more accurate diagnosis and treatment of patients with breast cancer and other diseases.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA087634-10
Application #
8445394
Study Section
Special Emphasis Panel (ZCA1-GRB-P)
Project Start
Project End
2015-02-28
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
10
Fiscal Year
2013
Total Cost
$209,071
Indirect Cost
$64,965
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
073133571
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109
Muckley, Matthew J; Noll, Douglas C; Fessler, Jeffrey A (2015) Fast parallel MR image reconstruction via B1-based, adaptive restart, iterative soft thresholding algorithms (BARISTA). IEEE Trans Med Imaging 34:578-88
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Allison, Michael J; Ramani, Sathish; Fessler, Jeffrey A (2013) Accelerated regularized estimation of MR coil sensitivities using augmented Lagrangian methods. IEEE Trans Med Imaging 32:556-64
Ramani, Sathish; Weller, Daniel S; Nielsen, Jon-Fredrik et al. (2013) Non-cartesian MRI reconstruction with automatic regularization Via Monte-Carlo SURE. IEEE Trans Med Imaging 32:1411-22
Sripada, Chandra Sekhar; Kessler, Daniel; Welsh, Robert et al. (2013) Distributed effects of methylphenidate on the network structure of the resting brain: a connectomic pattern classification analysis. Neuroimage 81:213-21
Chun, Se Young; Fessler, Jeffrey A (2013) Noise properties of motion-compensated tomographic image reconstruction methods. IEEE Trans Med Imaging 32:141-52
Matakos, Antonios; Ramani, Sathish; Fessler, Jeffrey A (2013) Accelerated edge-preserving image restoration without boundary artifacts. IEEE Trans Image Process 22:2019-29

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