Mitchell A Cooper1,2, Thanh D Nguyen2, Bo Xu1,2, Martin R Prince2, Michael Elad3, Yi Wang1,2, Pascal Spincemaille2. 1. Department of Biomedical Engineering, Cornell University, Ithaca, New York, USA. 2. Department of Radiology, Weill Cornell Medical College, New York, New York, USA. 3. Division of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel.
Abstract
PURPOSE: High spatial-temporal four-dimensional imaging with large volume coverage is necessary to accurately capture and characterize liver lesions. Traditionally, parallel imaging and adapted sampling are used toward this goal, but they typically result in a loss of signal to noise. Furthermore, residual under-sampling artifacts can be temporally varying and complicate the quantitative analysis of contrast enhancement curves needed for pharmacokinetic modeling. We propose to overcome these problems using a novel patch-based regularization approach called Patch-based Reconstruction Of Under-sampled Data (PROUD). THEORY AND METHODS: PROUD produces high frame rate image reconstructions by exploiting the strong similarities in spatial patches between successive time frames to overcome the severe k-space under-sampling. To validate PROUD, a numerical liver perfusion phantom was developed to characterize contrast-to-noise ratio (CNR) performance compared with a previously proposed method, TRACER. A second numerical phantom was constructed to evaluate the temporal footprint and lag of PROUD and TRACER reconstructions. Finally, PROUD and TRACER were evaluated in a cohort of five liver donors. RESULTS: In the CNR phantom, PROUD, compared with TRACER, improved peak CNR by 3.66 times while maintaining or improving temporal fidelity. In vivo, PROUD demonstrated an average increase in CNR of 60% compared with TRACER. CONCLUSION: The results presented in this work demonstrate the feasibility of using a combination of patch based image constraints with temporal regularization to provide high SNR, high temporal frame rate and spatial resolution four dimensional imaging.
PURPOSE: High spatial-temporal four-dimensional imaging with large volume coverage is necessary to accurately capture and characterize liver lesions. Traditionally, parallel imaging and adapted sampling are used toward this goal, but they typically result in a loss of signal to noise. Furthermore, residual under-sampling artifacts can be temporally varying and complicate the quantitative analysis of contrast enhancement curves needed for pharmacokinetic modeling. We propose to overcome these problems using a novel patch-based regularization approach called Patch-based Reconstruction Of Under-sampled Data (PROUD). THEORY AND METHODS: PROUD produces high frame rate image reconstructions by exploiting the strong similarities in spatial patches between successive time frames to overcome the severe k-space under-sampling. To validate PROUD, a numerical liver perfusion phantom was developed to characterize contrast-to-noise ratio (CNR) performance compared with a previously proposed method, TRACER. A second numerical phantom was constructed to evaluate the temporal footprint and lag of PROUD and TRACER reconstructions. Finally, PROUD and TRACER were evaluated in a cohort of five liver donors. RESULTS: In the CNR phantom, PROUD, compared with TRACER, improved peak CNR by 3.66 times while maintaining or improving temporal fidelity. In vivo, PROUD demonstrated an average increase in CNR of 60% compared with TRACER. CONCLUSION: The results presented in this work demonstrate the feasibility of using a combination of patch based image constraints with temporal regularization to provide high SNR, high temporal frame rate and spatial resolution four dimensional imaging.