In this paper, we propose a coupled level set (LS) framework for segmentation of bladder wall using T1-weighted magnetic resonance (MR) images with clinical applications to virtual cystoscopy (i.e., MR cystography). The framework uses two collaborative LS functions and a regional adaptive clustering algorithm to delineate the bladder wall for the wall thickness measurement on a voxel-by-voxel basis. It is significantly different from most of the pre-existing bladder segmentation work in four aspects. First of all, while most previous work only segments the inner border of the wall or at most manually segments the outer border, our framework extracts both the inner and outer borders automatically except that the initial seed point is given by manual selection. Secondly, it is adaptive to T1-weighted images with decreased intensities in urine, as opposed to enhanced intensities in T2-weighted scenario and computed tomography. Thirdly, by considering the image global intensity distribution and local intensity contrast, the defined image energy function in the framework is more immune to inhomogeneity effect, motion artifacts and image noise. Finally, the bladder wall thickness is measured by the length of integral path between the two borders which mimic the electric field line between two iso-potential surfaces. The framework was tested on six datasets with comparison to the well-known Chan-Vese (C-V) LS model. Five experts blindly scored the segmented inner and outer borders of the presented framework and the C-V model. The scores demonstrated statistically the improvement in detecting the inner and outer borders.

Fissioning nuclei produce with increasing excitation varying yields of fragments. For compound nuclei ranging from 232 Th to 242 Pu endowed with excitation energies typically between 0 and 10 MeV, we analyze these variations in terms of a fission-channel model. Most of the variations can be attributed to changing channel probabilities. We present a systematics of channel probabilities with respect to compound nuclei and their excitation energies, and we relate the systematics to potential energy which the nuclei experience when they float to scission. The trend, which is most difficult to explain, is a shift in the energy sensitivity of the standard channels, as one compares light with heavy compound nuclei. According to their behavior, we divide nuclei into standard I increasers and decreasers and attribute the difference to the standard secondary barriers. As a basic concept, bifurcation ratios are introduced, and a novel expression for transmission coefficients is proposed.

Ultrashort echo time bi-component analysis provides consistent bound and free water fractions at 1.5 T and 3 T, thereby allowing field-independent comparisons.

Providing a movie of the beating heart in a single prescribed plane, cine MRI has been widely used in clinical cardiac diagnosis, especially in the left ventricle (LV). Right ventricular (RV) morphology and function are also important for the diagnosis of cardiopulmonary diseases and serve as predictors for the long term outcome. The purpose of this study is to develop a self-gated free-breathing 3D imaging method for RV quantification and to evaluate its performance by comparing it with breath-hold 2D cine imaging in 7 healthy volunteers. Compared with 2D, the 3D RV functional measurements show a reduction of RV end-diastole volume (RVEDV) by 10%, increase of RV end-systole volume (RVESV) by 1.8%, reduction of RV systole volume (RVSV) by 21%, and reduction of RV ejection fraction (RVEF) by 12%. High correlations between the two techniques were found (RVEDV: 0.94; RVESV: 0.85; RVSV: 0.95; and RVEF: 0.89). Compared with 2D, the 3D image quality measurements show a small reduction in blood SNR, myocardium-blood CNR, myocardium contrast, and image sharpness. In conclusion, the proposed self-gated free-breathing 3D cardiac cine imaging technique provides comparable image quality and correlated functional measurements to those acquired with the multiple breath-hold 2D technique in RV.

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