Jack Tsao

Large random thin arrays provide a high angular resolution microwave images but are plagued with artifacts (false targets and target breakup or speckle) caused by high sidelobe levels. The CLEAN algorithm for reducing the sidelobe-induced artifacts is extended to the coherent radiation field and the theory placed on a quantitative basis. The CLEAN technique decomposes the received echoes of a coherent multiple-target scene by iterative cancellation of the largest target found. At each step, cancellation information is used to create a target image. The image includes target intensities, phases, and directions. The process is designed for an imaging instrument consisting of a random thinned array. A condition is derived which, when satisfied, guarantees that all proper targets will be preserved in the cleaned image and all false targets discarded. An algorithm involving moving thresholds is derived to extract the target coordinates. It is shown that targets much weaker than the sidelobe level can be detected and displayed without the hazard of artifacts. The target dynamic range and the image contrast can be increased by up to twice the signal-to-noise ratio per element.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Mutual information (MI) is an increasingly popular match metric for multimodality image registration. However, its value is affected by interpolation, which may limit registration accuracy. The purpose of this study was to characterize the artifacts from eight interpolators and to investigate efficient strategies to overcome these artifacts. The interpolators were: 1) nearest neighbor; 2) linear; 3) cubic Catmull-Rom; 4) Hamming-windowed sinc; 5) partial volume; 6) NN with jittered sampling (JIT); 7) NN with histogram blurring (BLUR); and 8) NN with JIT and BLUR. The impact of interpolation on MI was evaluated in two dimensions over different translational and rotational misregistration. Interpolation caused spurious fluctuations in MI whenever the voxel grids had coinciding periodicities and were nearly aligned. The artifacts did not lessen by using intensity interpolators with wider support (e.g., cubic Catmull-Rom, Hamming-windowed sinc). PV could lead to either arch artifacts or inverted-arch artifacts, depending on the relative voxel sizes. Several strategies reduced artifacts and improved registration robustness: JIT, BLUR, avoiding an extreme number of intensity bins, and resampling the images in a rotated orientation with different relative voxel sizes (e.g., pi/3). These findings also apply to related methods, including normalized MI, joint entropy, and Hill's third moment.

In magnetic resonance (MR) imaging, limited data sampling in k-space leads to the well-known Fourier truncation artifact, which includes ringing and blurring. This problem is particularly severe for MR spectroscopic imaging, where only 16-24 points are typically acquired along each spatial dimension. Several methods have been proposed to overcome this problem by incorporating prior information in the image reconstruction. These include the generalized series (GS) model and the finite-support extrapolation method. This paper shows the connection between finite-support extrapolation and the GS model. In particular, finite-support extrapolation is a limiting case of the GS model, when the only available prior information is the support region. The support region refers to those image portions with nonzero intensities, and it can be estimated in practice as the nonbackground region of an image. By itself, the support region constitutes a rather weak constraint that may not lead to considerable resolution gain. This situation can be improved by using additional prior information, which can be incorporated systematically with the GS model. Examples of such additional prior information include intensity estimates of anatomical structures inside the support region.