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.

/pdf/marching-cubes-a-high-resolution-3d-surface-construction-3ealswkmtb.pdf

Marching cubes: A high resolution 3D surface construction algorithm

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

https://users.fmrib.ox.ac.uk/~jesper/papers/readgroup_071009/Ashburner07.pdf

A fast diffeomorphic image registration algorithm

http://midag.cs.unc.edu/pubs/papers/Neuroimage06_Yushkevich.pdf

User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability

A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present. The method has the advantage that it can be applied at an early stage in an automated data analysis, before a tissue model is available. Described as nonparametric nonuniform intensity normalization (N3), the method is independent of pulse sequence and insensitive to pathological data that might otherwise violate model assumptions. To eliminate the dependence of the field estimate on anatomy, an iterative approach is employed to estimate both the multiplicative bias field and the distribution of the true tissue intensities. The performance of this method is evaluated using both real and simulated MR data.

/pdf/a-nonparametric-method-for-automatic-correction-of-intensity-4smqm7oz20.pdf

A nonparametric method for automatic correction of intensity nonuniformity in MRI data

An algorithm is described to compute continuous, transmural distributions of finite strains from tagged magnetic resonance (MR) images of the heart. Validation is performed by constructing a finite element (FE) model from the undeformed image and simulating complex deformations of the undeformed tags and intersections. Strains are calculated from these deformations by the algorithm and are compared with the FE-predicted strains. >

Calculation of regional ventricular wall strains using magnetic resonance imaging tissue-tagging

/pdf/nodule-size-and-overdiagnosis-in-lung-cancer-ct-screening-yl5iyei3s5.pdf

Nodule Size and Overdiagnosis in Lung Cancer CT Screening

Imaging modalities across spatial scales.

In a recent study by Ginther et al., the probability of receiving a U.S. National Institutes of Health (NIH) RO1 award was related to the applicant's race/ethnicity. The results indicate black/African-American applicants were 10% less likely than white peers to receive an award, after controlling for background and qualifications. It has generated a widespread debate regarding the unfairness of the NIH grant review process and its correction. In this paper, the work by Ginther et al. was augmented by pairing analysis, axiomatically-individualized productivity and normalized funding success measurement. Although there are racial differences in R01 grant success rates, normalized figures of merit for funding success explain the discrepancy. The suggested "leverage points for policy intervention" are in question and require deeper and more thorough investigations. Further adjustments in policies to remove racial disparity should be made more systematically for equal opportunity, rather than being limited to the NIH review process.

Michael W. Vannier

Papers

Calculation of regional ventricular wall strains using magnetic resonance imaging tissue-tagging

Nodule Size and Overdiagnosis in Lung Cancer CT Screening

Imaging modalities across spatial scales.

Scientific Productivity, Research Funding, Race and Ethnicity

Open-Source Inline Generative AI for Fast Cardiac MRI.