We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.

We present a new algorithm, called marching cubes, that creates triangle models of constant density surfaces from 3D medical data. Using a divide-and-conquer approach to generate inter-slice connectivity, we create a case table that defines triangle topology. The algorithm processes the 3D medical data in scan-line order and calculates triangle vertices using linear interpolation. We find the gradient of the original data, normalize it, and use it as a basis for shading the models. The detail in images produced from the generated surface models is the result of maintaining the inter-slice connectivity, surface data, and gradient information present in the original 3D data. Results from computed tomography (CT), magnetic resonance (MR), and single-photon emission computed tomography (SPECT) illustrate the quality and functionality of marching cubes. We also discuss improvements that decrease processing time and add solid modeling capabilities.

MR-guided interventional procedures can be performed with full patient access with use of an open-configuration, superconducting MR magnet with near real-time imaging and interactive image plane control.

The parameters of a spoiled gradient-echo (SPGR) pulse sequence have been optimized for in vivo localization of a focused ultrasound beam. Temperature elevation was measured by using the proton resonance frequency shift technique, and the phase difference signal-to-noise ratio (SNR delta phi) was estimated in skeletal muscle and kidney cortex in 10 rabbits. Optimized parameters included the echo time equivalent to T2* of the tissue, the longest repetition time possible with a 20-s sonication, and the flip angle equivalent to the Ernst angle. Optimal SPGR phase imaging can detect a sonication beam with a peak phase difference of 0.55 radian, which corresponds to a temperature elevation of 7.3 degrees C. The sonication beam can be localized within one voxel (0.6 x 0.6 x 5 mm3) at power levels that are below the threshold for thermal damage of the tissue.

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