Volume 16 Supplement 1
Motion-corrected compressed-sensing enables robust spiral first-pass perfusion imaging with whole heart coverage
© Yang et al.; licensee BioMed Central Ltd. 2014
Published: 16 January 2014
First-pass perfusion imaging using CMR is an important tool for diagnosing coronary artery disease (CAD), but most clinical techniques are limited in their spatial coverage. While compressed-sensing (CS) holds promise for highly accelerated perfusion spiral imaging, CS techniques suffer from blurring artifacts in the setting of respiratory motion. Spiral pulse sequences have multiple advantages for myocardial perfusion imaging including high acquisition efficiency, high signal to noise (SNR) and robustness to motion and for CS including a relatively incoherent aliasing pattern. Thus, we develop a whole heart spiral first-pass perfusion sequence combined with robust motion-correction.
Eight subjects undergoing clinical scans were recruited for rest perfusion studies using an accelerated 4× spiral perfusion sequence with whole heart coverage. The sequence parameters included: 2 variable density interleaves, 6.1 ms readout per interleaf, TE 1.0 ms, TR 9 ms, TI 80 ms, FA 45°, FOV 340 mm2, in-plane resolution of ~2 mm, 6 ~ 10 slices to cover the whole heart. All perfusion images were acquired on a 1.5T Siemens Avanto scanner during infusion of 0.1 mmol/kg of Gd-DTPA. The images were reconstructed by direct reconstruction with zero padding (DC), L1-SPIRiT using finite time difference as the sparsity transform, and Block LOw-rank Sparsity with Motion guidance compressed sensing (BLOSM). The images were evaluated for quality and blurring by two experienced cardiologists.
We demonstrate the feasibility of whole heart spiral first pass perfusion imaging using BLOSM to perform robust CS even in the setting of significant respiratory motion. Further validation will be required in patients undergoing vasodilator stress CMR.
K23 HL112910, R01 HL079110.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.