Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.

Correction of gradient-induced phase errors in radial MRI

PURPOSE

To correct gradient-induced phase errors in radial MRI.

METHODS

Gradient-induced eddy currents affect the MRI data acquisition by gradient delays and phase errors that may lead to severe image artifacts for non-Cartesian imaging scenarios such as radial trajectories. While gradient delays are dealt with by respective shifts of the acquisition window during radial image acquisition, this work introduces a simple method for quantifying and correcting phase errors from the actual data prior to image reconstruction. For a given gradient system, the approach yields a specific phase error per gradient that can be used for correcting the raw data.

RESULTS

Phantom studies at 9.4 T demonstrated marked improvements in radial image quality. It could be shown that the phase correction is not compromised by data undersampling. Moreover, the selective correction of gradient-induced phase errors retained the phase information caused by different concentrations of a paramagnetic contrast agent.

CONCLUSION

The proposed method does not require additional reference measurements and separately corrects for phase errors induced by eddy currents, while retaining the residual phase of the object which may carry physiologic information.

Addressing phase errors in fat-water imaging using a mixed magnitude/complex fitting method

Accurate, noninvasive measurements of liver fat content are needed for the early diagnosis and quantitative staging of nonalcoholic fatty liver disease. Chemical shift-based fat quantification methods acquire images at multiple echo times using a multiecho spoiled gradient echo sequence, and provide fat fraction measurements through postprocessing. However, phase errors, such as those caused by eddy currents, can adversely affect fat quantification. These phase errors are typically most significant at the first echo of the echo train, and introduce bias in complex-based fat quantification techniques. These errors can be overcome using a magnitude-based technique (where the phase of all echoes is discarded), but at the cost of significantly degraded signal-to-noise ratio, particularly for certain choices of echo time combinations. In this work, we develop a reconstruction method that overcomes these phase errors without the signal-to-noise ratio penalty incurred by magnitude fitting. This method discards the phase of the first echo (which is often corrupted) while maintaining the phase of the remaining echoes (where phase is unaltered). We test the proposed method on 104 patient liver datasets (from 52 patients, each scanned twice), where the fat fraction measurements are compared to coregistered spectroscopy measurements. We demonstrate that mixed fitting is able to provide accurate fat fraction measurements with high signal-to-noise ratio and low bias over a wide choice of echo combinations.