Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia.
Department of Imaging Science and Innovation, Geisinger, Danville, Pennsylvania.
Magn Reson Med. 2019 Mar;81(3):1806-1817. doi: 10.1002/mrm.27541. Epub 2018 Nov 13.
Images acquired with spiral k-space trajectories can suffer from off-resonance image blur. Previous work showed that averaging 2 images acquired with a retraced, in/out (RIO) trajectory self-corrects image blur so long as off-resonant spins accrue less than 1 half-cycle of relative phase over the readout. Practical scenarios frequently exceed this threshold. Here, we derive and characterize a more-robust off-resonance image blur correction method for RIO acquisitions.
Phantom and human volunteer data were acquired using a RIO trajectory with readout durations ranging from 4 to 60 ms. The resulting images were deblurred using 3 candidate methods: conventional linear correction of the component images; semiautomatic deblurring of the component images using an established minimized phase objective function; and semiautomatic deblurring of the average of the component images using a maximized energy objective function, derived below. Deblurring errors were estimated relative to images acquired with 4 ms readouts.
All 3 methods converged to similar solutions in cases where less than 2 and 4 cycles of phase accrued over the readout in in vivo and phantom images, respectively (<13 ms readout at 3T). Above this threshold, the linear and minimized phase methods introduced several errors. The maximized energy function provided accurate deblurring so long as less than 6 and 10 cycles of phase accrued over the readout in in vivo and phantom images, respectively (<34 ms readout at 3T).
The maximized energy objective function can accurately deblur RIO acquisitions over a wide spectrum of off resonance frequencies.
采用螺旋 k 空间轨迹采集的图像可能会出现离频图像模糊。先前的研究表明,只要在读取过程中离频自旋积累的相对相位不超过 1 个半周期,对采用重绕、内外(RIO)轨迹采集的 2 幅图像进行平均,就可以自我校正图像模糊。然而,实际情况经常会超过这个阈值。在此,我们为 RIO 采集推导并描述了一种更稳健的离频图像模糊校正方法。
采用 RIO 轨迹采集了包括体模和志愿者在内的数据,其读取时间从 4 毫秒到 60 毫秒不等。使用 3 种候选方法对所采集的图像进行去模糊处理:对分量图像进行常规线性校正;使用既定的最小相位目标函数对分量图像进行半自动去模糊;以及使用下面推导得出的最大化能量目标函数对分量图像的平均值进行半自动去模糊。与采集 4 毫秒读取时间的图像相比,对图像进行了去模糊误差的评估。
在体内和体模图像中,分别当读取时间小于 2 个和 4 个周期的相位时(3T 下小于 13 毫秒的读取时间),所有 3 种方法都收敛到相似的解。当超过这个阈值时,线性和最小相位方法会引入几个误差。只要在体内和体模图像中,读取时间分别小于 6 个和 10 个周期的相位(3T 下小于 34 毫秒的读取时间),最大化能量函数就可以提供准确的去模糊处理。
最大化能量目标函数可以在广泛的离频频率范围内准确地对 RIO 采集进行去模糊处理。