Angle resolved imaging of polymer blend systems: From images to a 3D volume of material morphology

High Resolution Computed Tomography (HRCT) images of the lungs contain a huge amount of geometrical information that could be effectively extracted to construct a mesh surface of the underlying geometry to a high level of detail. In this paper, we consider this as a problem of constructing 3D surfaces from contours of axial slices. While many reconstruction algorithms adopt volumetric approaches and ray casting methods, we propose a novel algorithm for automatic segmentation of large volume sets, and a contour-based construction of a three-dimensional mesh representation that may be easily used in any rendering application or combined with larger meshes of anatomical parts. Several acceleration structures to reduce the complexity of the algorithm are also presented. Experimental results show that the proposed method provides a high level of detail and quality of rendering that may find useful applications in the field of visualization and graphics.

Reconstruction of High Resolution 3D Meshes of Lung Geometry from HRCT Contours

In the medical field, 3D volumes are reconstructed mainly by tomographic techniques. Transmission or emission projection data sets are acquired and processed to reconstruct slices across the volume of interest, e.g., the patient's body. Although initially based on 2D acquisitions, current reconstruction techniques for emission modalities (e.g. PET) use more sensitive 3D acquisition data sets processed through modified or entirely original algorithms. Beyond the simple 3D imaging that single tomographies permit, multimodality approaches and equipment actually help reconstruction algorithms to perform better. Mixing these technical developments with complex clinical imaging protocols provides the foundation for a refined and open-ended multidimentional and multisensor approach to either diagnosis or therapy planning and follow-up.

3D image reconstruction in medicine and beyond

In﻿this﻿paper,﻿we﻿consider﻿the﻿problem﻿of﻿extracting﻿shape﻿contours﻿from﻿High﻿Resolution﻿Computed﻿ Tomography﻿(HRCT)﻿and﻿Magnetic﻿Resonance﻿Imaging﻿(MRI)﻿stacks﻿and﻿using﻿them﻿to﻿construct﻿ a﻿three-dimensional﻿mesh﻿surface﻿of﻿the﻿underlying﻿geometry﻿to﻿a﻿high﻿level﻿of﻿detail.﻿While﻿many﻿ reconstruction﻿algorithms﻿adopt﻿volumetric﻿approaches﻿and﻿ray﻿casting﻿methods,﻿we﻿propose﻿a﻿novel﻿ algorithm﻿for﻿automatic﻿segmentation﻿of﻿large﻿volume﻿sets,﻿and﻿a﻿contour-based﻿construction﻿of﻿a﻿ mesh﻿representation﻿that﻿could﻿be﻿used﻿in﻿any﻿rendering﻿application﻿or﻿combined﻿with﻿larger﻿meshes﻿ of﻿anatomical﻿parts.﻿Several﻿acceleration﻿structures﻿to﻿reduce﻿the﻿complexity﻿of﻿the﻿algorithm﻿are﻿ also﻿presented.﻿Experimental﻿results﻿show﻿that﻿the﻿proposed﻿method﻿provides﻿a﻿high﻿level﻿of﻿detail﻿ and﻿quality﻿of﻿rendering﻿that﻿may﻿find﻿useful﻿applications﻿in﻿the﻿field﻿of﻿visualization﻿and﻿graphics. KEywoRDS 3D Reconstruction, Axial HRCT Scans, Biomedical Image Processing, Contour Extraction, Geometrical Meshes, Mesh Construction and Rendering, MRI Scans

Contour Based High Resolution 3D Mesh Construction Using HRCT and MRI Stacks

2000 In de x IEEE Trans ac tions on Med i cal Imagin g

I N FULLY three-dimensional (3-D) image acquisition, information about multiple slices through the object volume is acquired simultaneously, and each projection element generally contains information about multiple trans-axial slices through the object. This is in contrast with two-dimensional (2-D) tomographic acquisition, where information about each slice is acquired independently. Over the past years, the number of imaging systems capable to acquire fully 3-D tomographic projection data has increased dramatically. Examples include multiring positron emission tomography (PET) systems with retracted septa, gamma-camera-based PET systems, single photon emission computed tomography (SPECT) systems with a wide variety of collimation geometries, and more recently multirow X-ray computed tomography (CT) systems. The main advantage of a 3-D acquisition is that it allows to collect more photons per second; this can be exploited either to define faster scanning protocols or to obtain higher quality images. In addition, the availability of 3-D reconstruction methods increases the number of degrees of freedom available to optimize the design of tomographic imaging devices. It also allows to better model tomographic systems which have been traditionally handled as 2-D, slice-by-slice systems. For instance, in SPECT imaging with parallel-hole or fan-beam collimators, a close-to-optimal compensation for the crosstalk between transaxial slices caused by detector blurring and photon scattering can only be achieved using 3-D modeling and volumetric reconstruction. Three-dimensional acquisition considerably complicates the reconstruction problem, since the reconstructions of the individual slices are not independent of each other. However, faster computers and more efficient algorithms have made it possible now to reconstruct medical images based on such acquisitions, in clinically acceptable computation times and with an ever improving accuracy. These algorithms are referred to as fully three-dimensionalor volumetric reconstruction algorithms because they estimate an entire 3-D object distribution from projection data elements containing information about multiple slices. Volumetric reconstruction should not be confused with the sequential reconstruction of individual slices (slice-by-slice reconstruction) to obtain a volumetric image, which is a reconstruction problem of much smaller numerical and mathematical complexity. Additional complications arise in the development of reconstruction algorithms for time sequences of volumetric images (“four–dimensional (4-D) reconstruction”), for example in dynamic imaging of the beating heart, a subject which is also covered by this issue. Solving such 3-D or 4-D reconstruction problems while meeting the high image quality and speed standards required for clinical practice is a considerable challenge. As a result,

F.J. Beekman

Papers

Volumetric reconstruction of medical images