Spectral rho Z-Projection Method for Characterization of Body Fluids in Computed Tomography: Ex Vivo Experiments

Department of Diagnostic Radiology and Applied Medical Engineering, Helmholtz Institute, RWTH Aachen University, Pauwelsstrasse 30, D 52074, Aachen, Germany.
Academic radiology (Impact Factor: 1.75). 07/2009; 16(6):763-9. DOI: 10.1016/j.acra.2009.01.002
Source: PubMed


The identification of body fluids in computed tomography poses a major diagnostic challenge. The chemical composition of body fluids deviates only slightly from water with very similar computed tomographic (CT) values, which typically range from 0 to 100 HU. The aim of this study was to assess physical and chemical properties of different body fluids in an ex vivo setting.
A total of 44 samples of blood, blood mixed with pus, pus, bile, and urine obtained during diagnostic and therapeutic punctures were scanned at 80 and 140 kV. Data was quantitatively assessed using the spectral rhoZ-projection algorithm, which converts dual-energy CT scans into mass density (rho) and effective atomic number (Z(eff.)) information.
Attenuation values measured at 80 and 140 kV were largely overlapping. CT values allowed, to some degree, for the differentiation of bile or pus from blood or the blood/pus mixture. By applying the rhoZ-projection, most substances, except for urine, were distinguishable with only small standard deviations ranging between 0.003 and 0.007 g/cm(3) for mass density and between 0.020 and 0.043 for Z(eff.).
The rhoZ-projection method is suited to quantitatively assess mass density and effective atomic number of ex vivo body fluid samples. In clinical routine, this technique might be useful for identifying unclear fluid collections even in unenhanced computed tomography.

13 Reads
  • Source
    • "The correction reduces errors on Z cor to ±1%, except for lung tissue (Z eff = 7.60). This level of accuracy is within the 0.1 to 0.2 (in units of Z) requirement suggested by Mahnken et al (2009) for soft tissue identification. Similar agreement is found for the relative electron density. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This work compares Monte Carlo (MC) dose calculations for (125)I and (103)Pd low-dose rate (LDR) brachytherapy sources performed in virtual phantoms containing a series of human soft tissues of interest for brachytherapy. The geometries are segmented (tissue type and density assignment) based on simulated single energy computed tomography (SECT) and dual energy (DECT) images, as well as the all-water TG-43 approach. Accuracy is evaluated by comparison to a reference MC dose calculation performed in the same phantoms, where each voxel's material properties are assigned with exactly known values. The objective is to assess potential dose calculation accuracy gains from DECT. A CT imaging simulation package, ImaSim, is used to generate CT images of calibration and dose calculation phantoms at 80, 120, and 140 kVp. From the high and low energy images electron density ρ(e) and atomic number Z are obtained using a DECT algorithm. Following a correction derived from scans of the calibration phantom, accuracy on Z and ρ(e) of ±1% is obtained for all soft tissues with atomic number Z ∊ [6,8] except lung. GEANT4 MC dose calculations based on DECT segmentation agreed with the reference within ±4% for (103)Pd, the most sensitive source to tissue misassignments. SECT segmentation with three tissue bins as well as the TG-43 approach showed inferior accuracy with errors of up to 20%. Using seven tissue bins in our SECT segmentation brought errors within ±10% for (103)Pd. In general (125)I dose calculations showed higher accuracy than (103)Pd. Simulated image noise was found to decrease DECT accuracy by 3-4%. Our findings suggest that DECT-based segmentation yields improved accuracy when compared to SECT segmentation with seven tissue bins in LDR brachytherapy dose calculation for the specific case of our non-anthropomorphic phantom. The validity of our conclusions for clinical geometry as well as the importance of image noise in the tissue segmentation procedure deserves further experimental investigation.
    Physics in Medicine and Biology 09/2011; 56(19):6257-78. DOI:10.1088/0031-9155/56/19/007 · 2.76 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The development of atherosclerotic plaques occurs slowly over decades. This provides an opportunity for diagnostic imaging to identify patients before clinical events occur. Computed tomography (CT) is an important imaging technique that is rountinely used for the noninvasive imaging of the arteries throughout the body. One of the most recent innovations in CT is the use of two tubes with different energy, which is called dual-energy CT. This technique has the potential to improve the abilities to differentiate various body tissues with CT, and to increase the inherently low contrast of single-energy CT. This chapter reviews the current status and potential future role of dual-energy CT to detect, characterize and differentiate atherosclerotic plaques.
    01/2011: pages 73-79;
  • [Show abstract] [Hide abstract]
    ABSTRACT: To evaluate the feasibility and accuracy of a model for tissue characterization with dual source computed tomography (DSCT). A model for tissue characterization in CT was used with a parameterization of linear attenuation coefficients. Sixteen chemical substances with effective atomic numbers between 5.21 and 13.08 and electron densities between 2.20 and 4.12 x10(23) electrons/cm(3) were scanned at energies of 80 and 140 kV on a DSCT. From the reconstructed dual energy data sets, effective atomic numbers and electron densities of the substances were calculated. Our presented model using DSCT approximated the effective atomic numbers and effective electron densities of 16 substances very well. The measured effective atomic numbers deviated 3.4 ± 6.8% (R(2) = 0.994) from theoretical effective atomic numbers. In addition, measured effective electron densities deviated -0.6 ± 2.2% (R(2) = 0.999) from theoretical effective electron densities. Effective atomic numbers and effective electron densities can be determined with a high accuracy with DSCT. Therefore the model can be of potential benefit for clinical applications of quantitative tissue characterization with DSCT.
    Physica Medica 02/2011; 28(1):25-32. DOI:10.1016/j.ejmp.2011.01.004 · 2.40 Impact Factor
Show more