To develop a free-breathing cardiac self-gated technique that provides cine images and B + 1 slice profile-corrected T 1 maps from a single acquisition. Methods: Without breath-holding or electrocardiogram gating, data were acquired continuously on a 3T scanner using a golden-angle gradient-echo spiral pulse sequence, with an inversion RF pulse applied every 4 seconds. Flip angles of 3° and 15° were used for readouts after the first four and second four inversions. Self-gating cardiac triggers were extracted from heart image navigators, and respiratory motion was corrected by rigid registration on each heartbeat. Cine images were reconstructed from the steady-state portion of 15° readouts using a low-rank plus sparse reconstruction. The T 1 maps were fit using a projection onto convex sets approach from images reconstructed using slice profile-corrected dictionary learning. This strategy was evaluated in a phantom and 14 human subjects. Results: The self-gated signal demonstrated close agreement with the acquired electrocardiogram signal. The image quality for the proposed cine images and standard clinical balanced SSFP images were 4.31 (±0.50) and 4.65 (±0.30), respectively. The slice profile-corrected T 1 values were similar to those of the inversion-recovery spinecho technique in phantom, and had a higher global T 1 value than that of MOLLI in human subjects. Conclusions: Cine and T 1 mapping using spiral acquisition with respiratory and cardiac self-gating successfully acquired T 1 maps and cine images in a single acquisition without the need for electrocardiogram gating or breath-holding. This dual-excitation flip-angle approach provides a novel approach for measuring T 1 while accounting for B + 1 and slice profile effect on the apparent T * 1. K E Y W O R D S cardiac MRI, cine, dictionary learning, self-gating, spiral trajectory, T 1 mapping | 83 ZHOU et al.
Abstract-Conventional imaging uses steady-state illumination and light sensing with focusing optics; variations of the light field with time are not exploited. We develop a signal processing framework for estimating the reflectance of a Lambertian planar surface in a known position using omnidirectional, time-varying illumination and unfocused, time-resolved sensing in place of traditional optical elements such as lenses and mirrors. Our model associates time sampling of the intensity of light incident at each sensor with a linear functional of . The discrete-time samples are processed to obtain -regularized estimates of . Improving on previous work, using nonimpulsive, bandlimited light sources instead of impulsive illumination significantly improves signal-tonoise ratio (SNR) and reconstruction quality. Our simulations suggest that practical diffuse imaging applications may be realized with commercially-available temporal light intensity modulators and sensors used in standard optical communication systems.
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