Advancements in biocomputing and MRI acquisition technologies make it possible to consider applying image reconstruction methods that are more complex than the standard inverse Fourier transforms to MRI data. This will have a profound impact on dynamic MRI. Temporal and spatial models are proposed to be incorporated into the reconstruction of dynamic contrast enhanced (DCE) MRI data within an inverse problem framework.
Specific aims are (1) Develop and incorporate low level (constraints on changes of intensity over time) and higher level (parameterized) temporal models within reconstruction methods for sparsely sampled dynamic MRI datasets. These models will allow for a large increase in volume coverage without concomitant SNR reductions. (2) Develop spatial model-based reconstruction methods for sparsely sampled dynamic MRI datasets. Low level spatial models will realize spatial constraints, and higher level spatial models will be created from patient-specific spatial reference data. (3) Extend the model-based spatio-temporal acquisition and reconstruction methods to accommodate patient motion. (4) Extend the model-based methods to combine with multi-coil speedup (parallel imaging) methods. (5) Validate the proposed computational methods for the clinical application of myocardial perfusion MR imaging and provide data and software tools to the broader research community. Methods: Our multi-disciplinary team will develop software tools as a collaborative process combining biocomputing and MRI expertise with clinical cardiac imaging expertise. Both Cartesian and radial reduced k-space acquisitions of cardiac perfusion data will be reconstructed with the model-based multi-coil methods and compared. Respiratory motion will be identified and compensated using either software approaches or a respiratory strap and pre-scan calibrations. The resulting software tools will be integrated into ITK and provided for use to the research community. The relevance to public health is that heart disease is the leading cause of death. This proposal offers new reconstruction methods that will advance the field of dynamic MRI and improve the non-invasive assessment of myocardial blood flow. Such improvements will allow better and more timely treatments and monitoring of heart disease. The proposed approach can be extended to improve non-cardiac dynamic MRI applications such as studies of the response of tumors to therapy and the response of the brain to stimuli.
|Welsh, Christopher L; Dibella, Edward V R; Adluru, Ganesh et al. (2013) Model-based reconstruction of undersampled diffusion tensor k-space data. Magn Reson Med 70:429-40|
|Kamesh Iyer, Srikant; Tasdizen, Tolga; Dibella, Edward V R (2012) Edge-enhanced spatiotemporal constrained reconstruction of undersampled dynamic contrast-enhanced radial MRI. Magn Reson Imaging 30:610-9|
|Chen, Liyong; Adluru, Ganesh; Schabel, Matthias C et al. (2012) Myocardial perfusion MRI with an undersampled 3D stack-of-stars sequence. Med Phys 39:5204-11|
|Liang, Dong; DiBella, Edward V R; Chen, Rong-Rong et al. (2012) k-t ISD: dynamic cardiac MR imaging using compressed sensing with iterative support detection. Magn Reson Med 68:41-53|
|Chen, Liyong; Samsonov, Alexey; DiBella, Edward V R (2011) A framework for generalized reference image reconstruction methods including HYPR-LR, PR-FOCUSS, and k-t FOCUSS. J Magn Reson Imaging 34:403-12|
|Lingala, Sajan Goud; Hu, Yue; DiBella, Edward et al. (2011) Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans Med Imaging 30:1042-54|
|Chen, Liyong; Schabel, Matthias C; DiBella, Edward V R (2010) Reconstruction of dynamic contrast enhanced magnetic resonance imaging of the breast with temporal constraints. Magn Reson Imaging 28:637-45|
|Todd, Nick; Adluru, Ganesh; Payne, Allison et al. (2009) Temporally constrained reconstruction applied to MRI temperature data. Magn Reson Med 62:406-19|
|Adluru, Ganesh; McGann, Chris; Speier, Peter et al. (2009) Acquisition and reconstruction of undersampled radial data for myocardial perfusion magnetic resonance imaging. J Magn Reson Imaging 29:466-73|