Pontificia Universidad Católica de Chile Pontificia Universidad Católica de Chile
Prieto C., Andia M., von Bary C., Onthank D., Schaeffter T. and Botnar R. (2012)

Accelerating 3D molecular cardiovascular MR imaging using compressed sensing. http://dx.doi.org/10.1002/jmri.23763

Revista : Journal of Magnetic Resonance Imaging
Volumen : 36
Número : 6
Páginas : 1362-1371
Tipo de publicación : ISI Ir a publicación


Purpose: To accelerate the acquisition of 3D high-resolution cardiovascular molecular MRI by using Compressed Sensing (CS) reconstruction.Methods: Molecular MRI is an emerging technique for the early assessment of cardiovascular disease. This technique provides excellent soft tissue differentiation at a molecular and cellular level using target-specific contrast agents (CAs). However, long scan times are required for 3D molecular MRI. Parallel imaging can be used to speed-up these acquisitions, but hardware considerations limit the maximum acceleration factor. This limitation is important in small-animal studies, where single-coils are commonly used. Here we exploit the sparse nature of molecular MR images, which are characterized by localized and high-contrast biological target-enhancement, to accelerate data acquisition. CS was applied to detect: a) venous thromboembolism and b) coronary injury and aortic vessel wall in single- and multiple-coils acquisitions, respectively. Results: Retrospective undersampling showed good overall image quality with accelerations up to four for thrombus and aortic images, and up to three for coronary artery images. For higher acceleration factors, features with high CA uptake were still well recovered while low affinity targets were less preserved with increased noise-like artifacts. Prospective undersampling was performed in an aortic image with acceleration of two, showing good contrast and well-defined tissue boundaries in the contrast-enhanced regions. Conclusion: We demonstrate the successful application of CS to preclinical molecular MR with target specific gadolinium-based CAs using retrospective (accelerations up to four) and prospective (acceleration of two) undersampling.