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Abstract #2157

Accelerated 3DPCASL Using Compressed Sensing

Li Zhao1, Samuel W. Fielden1, Xiao Chen1, John P. Mugler, III2, Josef Pfeuffer3, Manal Nicolas-Jilwan2, Max Wintermark2, Craig H. Meyer1

1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States; 2Radiology, University of Virginia, Charlottesville, VA, United States; 3Siemens, Erlangen, Germany

Low SNR in ASL limits the achievable spatial resolution and the accuracy of perfusion maps. Dynamic ASL is time-consuming and also suffers from low SNR. Compressed sensing can improve image quality by enforcing spatial-domain sparsity. Compressed sensing can also enforce time-domain sparsity in dynamic ASL. Volunteer data are shown to demonstrate CS performance on single PLD PCASL images and multiple dynamic frames. The results show image SNR and image quality improvement. More importantly, the estimated CBF becomes more accurate and stable with compressed sensing image reconstruction.

Keywords

accelerated accuracy achieve acquiring acquisition amplifies arbitrary artifacts balanced biomedical blood bolus bottom brain combine combined compressed consistent constant constrained constraint constraints consuming controls covered density described design diagnostic domain dual duration dynamic efficiently enabling enforces enforcing engineering equal equation estimation except experiment exploiting fidelity flows general goal gradual helps important improve improved improvement improves includes inherent intensity interleaves iterative john knowledge known makes maps mask motion naturally next noise nonlinear observation parallel part patients perfusion permits preservation preserve prior produces proposed pulse pulses quality radiology rapid readout readouts real reconstruction reconstructions reduces reduction regularization relatively resolution rest rotation sampled sampling scanners self sensing sets settings slices space sparsity spatial spiral spirals spirit stack stroke structure structures suppress tagged target temporal tracking trajectory transform trio tumor unreliable variation visualization wavelet wavelets whole yield