Autocorrection in MR imaging: adaptive motion correction without navigator echoes.
ABSTRACT A technique for automatic retrospective correction of motion artifacts on magnetic resonance (MR) images was developed that uses only the raw (complex) data from the MR imager and requires no knowledge of patient motion during the acquisition. The algorithm was tested on coronal images of the rotator cuff in a series of 144 patients, and the improvements in image quality were similar to those achieved with navigator echoes. The results demonstrate that autocorrection can significantly reduce motion artifacts in a technically demanding MR imaging application.
- SourceAvailable from: Shang-Hong Lai
Conference Paper: Compensation of motion artifacts in MRI via graph-based optimization.[Show abstract] [Hide abstract]
ABSTRACT: In two-dimensional Fourier transform magnetic resonance imaging (2DFT-MRI), patient/object motion during the image acquisition results in ghosting and blurring. These motion artifacts are commonly considered as a major limitation in the MRI community. To correct these artifacts without resorting to additional navigator echoes, most existing methods perform image quality measure to estimate motion; but they may easily fail when the motion is large. Viewed as a blind image restoration problem where the motion point spread function (PSF) is unknown, state-of-the-art restoration algorithms can not be easily applied because they cannot handle a complex PSF kernel that has the same size as the image. To overcome these challenges, we propose a novel approach that exploits the image structure to segment the kernel into several fragments. Based on this kernel representation, determining a kernel fragment can be formulated as a binary optimization problem, where each binary variable represents whether a segment in MR signals is corrupted by a certain motion or not. We establish a graphical model for these variables and estimate the kernel by minimizing an energy functional associated with the model. Experimental results show that the proposed method can provide satisfactory compensation of motion artifacts even when large motions are involved in the MR images.2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA; 06/2009
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ABSTRACT: A technique is proposed for correcting both translational and rotational motion artifacts in magnetic resonance imaging without the need to collect additional navigator data or to perform intensive postprocessing. The method is based on measuring the point spread function (PSF) by attaching one or two point-sized markers to the main imaging object. Following the isolation of a PSF marker from the acquired image, translational motion could be corrected directly from the modulation transfer function, without the need to determine the object's positions during the scan, although the shifts could be extracted if desired. Rotation is detected by analyzing the relative displacements of two such markers. The technique was evaluated with simulations, phantom and in vivo experiments.IEEE Transactions on Medical Imaging 10/2005; 24(9):1170-6. DOI:10.1109/TMI.2005.853235 · 3.80 Impact Factor
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ABSTRACT: Tissue stiffness is a strong biomarker of the state of tissue health. Accurate assessment of tissue stiffness provides significant clinical values for early detection, diagnosis and prognosis of diseases such as cancer and fibrosis. In the last two decades, ultrasound shear wave elastography (SWE) has emerged as a promising imaging tool that is capable of noninvasively, quantitatively and directly estimating tissue stiffness. Ultrasound SWE has showed great promises in numerous clinical applications such as early detection of breast cancer and accurate noninvasive staging of liver fibrosis. However, ultrasound SWE also suffers from technical challenges that undermine its diagnostic value and limit its clinical applications. The overall goal of the research reported in this thesis, therefore, is to overcome these technical challenges and make ultrasound SWE a faster and better approach. This thesis research identified the key challenges that reside in the shear wave generation, shear elasticity map reconstruction, shear wave detection and SWE implementation of ultrasound SWE, and proposed four novel techniques including Comb-push Ultrasound Shear Elastography (CUSE), fast shear compounding, shear wave detection with harmonic imaging, and Time Aligned Sequential Tracking (TAST) to address these challenges, respectively. These novel techniques developed in this thesis research have great potentials to substantially improve the performance of ultrasound SWE and widen its spectrum of clinical applications.03/2014, Degree: PhD, Supervisor: James F. Greenleaf and Shigao Chen