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.
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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