Correction of distortion of histologic sections of arteries.

Histologic sections of arteries can be used to generate three-dimensional (3D) geometric models and identify structural constituents. However, geometric distortions are introduced by fixation, embedding and sectioning; distortions which can, for example, lead to errors in stresses predicted by finite element models. We developed a method to measure and correct for distortions caused by acrylic processing and applied it to intact, healthy porcine coronary arteries. Micro-computed tomography was used to image arteries in the fresh and embedded states. Tissue blocks were sectioned, stained and imaged using a light microscope. Each section contained four registration marks used to determine strains introduced by sectioning and staining. Using these three image sets, 3D geometric models were generated and distortions were measured. Fixation, processing, and embedding resulted in shrinkage of 6.4+/-2.3% axially and 35.4+/-5.0% in mean cross-sectional area (n=5). Shrinkage in a cross section was well characterized by a uniform, equibiaxial strain. Sectioning and staining resulted in additional compressive strains in the sectioning direction of 0.067+/-0.011 and, in the direction perpendicular to sectioning, of 0.023+/-0.005 (n=5). These strains are assumed uniform and form the basis for correcting section geometry. Reconstructions using corrections for sectioning and shrinkage-related distortions had errors of 1.6+/-0.5% (n=5) and 4.0+/-1.7% (n=5), respectively.

A practical method for three-dimensional reconstruction of joints using a C-arm system and shift-and-add algorithm.

Currently, radiography with C-arm systems is playing a major role in the assessment of arthritis. However, the radiographic two-dimensional projection images of joints often interfere with physicians' efforts to better understand and measure the structure changes of joints due to the overlap of bone structures at different depths. An accurate, low-cost, and practical three-dimensional (3D) reconstruction approach of joints will be beneficial in diagnosing arthritis. Toward this end, a novel method is developed in this paper based on a C-arm system. The idea is to apply the shift-and-add algorithm (commonly used in digital tomosynthesis) on the segmented projection images at multiple angles, which results in accurate reconstruction of the 3D structures of joints. The method provides a new solution to precisely distinguish objects from blurring background. The proposed method has been tested and evaluated on simulated cylinders, a chicken bone phantom with known structure, and an in vivo human index finger. The results are demonstrated and discussed.

Comparison and evaluation of methods for liver segmentation from CT datasets.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.

Two-dimensional bioluminescence tomography: numerical simulations and phantom experiments.

The reconstruction of internal light sources in bioluminescence tomography (BLT) is a challenging inverse problem because of the limited amount of information available compared with that for other kinds of tomography such as fluorescence tomography in which external illumination sources are used. We demonstrated previously, using phantom experiments, that a target containing luciferases could be detected tomographically when the target was located relatively close to the imaging boundary. Here we describe an improved BLT reconstruction method that can detect luciferase-containing targets located anywhere within an imaging domain. The method is tested with numerical simulations and further confirmed with several phantom experiments.